id	sid	tid	token	lemma	pos
cana-1628	1	1	communications	communication	NOUN
cana-1628	1	2	on	on	ADP
cana-1628	1	3	applied	apply	VERB
cana-1628	1	4	nonlinear	nonlinear	ADJ
cana-1628	1	5	analysis	analysis	NOUN
cana-1628	1	6	issn	issn	NOUN
cana-1628	1	7	:	:	PUNCT
cana-1628	1	8	1074	1074	NUM
cana-1628	1	9	-	-	PUNCT
cana-1628	1	10	133x	133x	NUM
cana-1628	1	11	vol	vol	NOUN
cana-1628	1	12	32	32	NUM
cana-1628	1	13	no	no	NOUN
cana-1628	1	14	.	.	NOUN
cana-1628	1	15	1	1	NUM
cana-1628	1	16	(	(	PUNCT
cana-1628	1	17	2025	2025	NUM
cana-1628	1	18	)	)	PUNCT
cana-1628	1	19	155	155	NUM
cana-1628	1	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	1	21	nonlinear	nonlinear	NOUN
cana-1628	1	22	ensemble	ensemble	ADJ
cana-1628	1	23	deep	deep	ADJ
cana-1628	1	24	learning	learning	NOUN
cana-1628	1	25	model	model	NOUN
cana-1628	1	26	for	for	ADP
cana-1628	1	27	energy	energy	NOUN
cana-1628	1	28	consumption	consumption	NOUN
cana-1628	1	29	prediction	prediction	NOUN
cana-1628	1	30	with	with	ADP
cana-1628	1	31	bayesian	bayesian	NOUN
cana-1628	1	32	optimization	optimization	NOUN
cana-1628	1	33	ejigu	ejigu	ADJ
cana-1628	1	34	tefera1	tefera1	PROPN
cana-1628	1	35	,	,	PUNCT
cana-1628	1	36	kula	kula	PROPN
cana-1628	1	37	kekeba1	kekeba1	PROPN
cana-1628	1	38	,	,	PUNCT
cana-1628	1	39	ravindra	ravindra	PROPN
cana-1628	1	40	babu.b2	babu.b2	PROPN
cana-1628	1	41	,	,	PUNCT
cana-1628	1	42	and	and	CCONJ
cana-1628	1	43	m.	m.	NOUN
cana-1628	1	44	mart´ınez	mart´ınez	PROPN
cana-1628	1	45	-	-	PUNCT
cana-1628	1	46	ballesteros3	ballesteros3	ADV
cana-1628	1	47	1big	1big	NUM
cana-1628	1	48	data	datum	NOUN
cana-1628	1	49	and	and	CCONJ
cana-1628	1	50	hpc	hpc	PROPN
cana-1628	1	51	center	center	NOUN
cana-1628	1	52	of	of	ADP
cana-1628	1	53	excellence	excellence	PROPN
cana-1628	1	54	,	,	PUNCT
cana-1628	1	55	department	department	NOUN
cana-1628	1	56	of	of	ADP
cana-1628	1	57	software	software	PROPN
cana-1628	1	58	engineering	engineering	PROPN
cana-1628	1	59	,	,	PUNCT
cana-1628	1	60	addis	addis	PROPN
cana-1628	1	61	ababa	ababa	PROPN
cana-1628	1	62	science	science	PROPN
cana-1628	1	63	&	&	CCONJ
cana-1628	1	64	technology	technology	PROPN
cana-1628	1	65	university	university	PROPN
cana-1628	1	66	,	,	PUNCT
cana-1628	1	67	addis	addis	PROPN
cana-1628	1	68	ababa	ababa	PROPN
cana-1628	1	69	p.o	p.o	PROPN
cana-1628	1	70	.	.	PROPN
cana-1628	1	71	box	box	PROPN
cana-1628	1	72	16417	16417	NUM
cana-1628	1	73	,	,	PUNCT
cana-1628	1	74	ethiopia	ethiopia	PROPN
cana-1628	1	75	,	,	PUNCT
cana-1628	1	76	ejigu.tefera@aastustudent.edu.et	ejigu.tefera@aastustudent.edu.et	VERB
cana-1628	1	77	,	,	PUNCT
cana-1628	1	78	kuulla@gmail.com	kuulla@gmail.com	X
cana-1628	2	1	2distributed	2distributed	NUM
cana-1628	2	2	systems	systems	PROPN
cana-1628	2	3	research	research	NOUN
cana-1628	2	4	group	group	NOUN
cana-1628	2	5	(	(	PUNCT
cana-1628	2	6	sig	sig	NOUN
cana-1628	2	7	)	)	PUNCT
cana-1628	2	8	,	,	PUNCT
cana-1628	2	9	adama	adama	PROPN
cana-1628	2	10	science	science	NOUN
cana-1628	2	11	and	and	CCONJ
cana-1628	2	12	technology	technology	PROPN
cana-1628	2	13	university	university	PROPN
cana-1628	2	14	,	,	PUNCT
cana-1628	2	15	p.o	p.o	PROPN
cana-1628	2	16	.	.	PROPN
cana-1628	2	17	box	box	PROPN
cana-1628	2	18	1888	1888	NUM
cana-1628	2	19	,	,	PUNCT
cana-1628	2	20	adama	adama	PROPN
cana-1628	2	21	,	,	PUNCT
cana-1628	2	22	ethiopia	ethiopia	PROPN
cana-1628	2	23	,	,	PUNCT
cana-1628	2	24	ravindrababu4u@yahoo.com	ravindrababu4u@yahoo.com	PROPN
cana-1628	2	25	3department	3department	NUM
cana-1628	2	26	of	of	ADP
cana-1628	2	27	computer	computer	NOUN
cana-1628	2	28	science	science	NOUN
cana-1628	2	29	,	,	PUNCT
cana-1628	2	30	university	university	NOUN
cana-1628	2	31	of	of	ADP
cana-1628	2	32	seville	seville	PROPN
cana-1628	2	33	,	,	PUNCT
cana-1628	2	34	es-41012	es-41012	PROPN
cana-1628	2	35	seville	seville	PROPN
cana-1628	2	36	,	,	PUNCT
cana-1628	2	37	spain	spain	PROPN
cana-1628	2	38	,	,	PUNCT
cana-1628	2	39	mariamartinez@us.es	mariamartinez@us.es	PROPN
cana-1628	2	40	article	article	NOUN
cana-1628	2	41	history	history	NOUN
cana-1628	2	42	:	:	PUNCT
cana-1628	2	43	received	receive	VERB
cana-1628	2	44	:	:	PUNCT
cana-1628	2	45	10	10	NUM
cana-1628	2	46	-	-	SYM
cana-1628	2	47	07	07	NUM
cana-1628	2	48	-	-	PUNCT
cana-1628	2	49	2024	2024	NUM
cana-1628	2	50	revised	revise	VERB
cana-1628	2	51	:	:	PUNCT
cana-1628	2	52	23	23	NUM
cana-1628	2	53	-	-	SYM
cana-1628	2	54	08	08	NUM
cana-1628	2	55	-	-	PUNCT
cana-1628	2	56	2024	2024	NUM
cana-1628	2	57	accepted	accept	VERB
cana-1628	2	58	:	:	PUNCT
cana-1628	2	59	06	06	NUM
cana-1628	2	60	-	-	SYM
cana-1628	2	61	09	09	NUM
cana-1628	2	62	-	-	PUNCT
cana-1628	2	63	2024	2024	NUM
cana-1628	2	64	abstract	abstract	NOUN
cana-1628	2	65	:	:	PUNCT
cana-1628	2	66	accurate	accurate	ADJ
cana-1628	2	67	prediction	prediction	NOUN
cana-1628	2	68	of	of	ADP
cana-1628	2	69	electric	electric	ADJ
cana-1628	2	70	energy	energy	NOUN
cana-1628	2	71	consumption	consumption	NOUN
cana-1628	2	72	is	be	AUX
cana-1628	2	73	crucial	crucial	ADJ
cana-1628	2	74	for	for	ADP
cana-1628	2	75	efficient	efficient	ADJ
cana-1628	2	76	load	load	NOUN
cana-1628	2	77	dispatching	dispatching	NOUN
cana-1628	2	78	,	,	PUNCT
cana-1628	2	79	energy	energy	NOUN
cana-1628	2	80	utilization	utilization	NOUN
cana-1628	2	81	,	,	PUNCT
cana-1628	2	82	and	and	CCONJ
cana-1628	2	83	grid	grid	NOUN
cana-1628	2	84	operation	operation	NOUN
cana-1628	2	85	.	.	PUNCT
cana-1628	3	1	traditional	traditional	ADJ
cana-1628	3	2	statistical	statistical	ADJ
cana-1628	3	3	and	and	CCONJ
cana-1628	3	4	classical	classical	ADJ
cana-1628	3	5	machine	machine	NOUN
cana-1628	3	6	learning	learn	VERB
cana-1628	3	7	methods	method	NOUN
cana-1628	3	8	struggle	struggle	VERB
cana-1628	3	9	with	with	ADP
cana-1628	3	10	the	the	DET
cana-1628	3	11	nonlinear	nonlinear	ADJ
cana-1628	3	12	nature	nature	NOUN
cana-1628	3	13	of	of	ADP
cana-1628	3	14	energy	energy	NOUN
cana-1628	3	15	consumption	consumption	NOUN
cana-1628	3	16	data	datum	NOUN
cana-1628	3	17	,	,	PUNCT
cana-1628	3	18	often	often	ADV
cana-1628	3	19	leading	lead	VERB
cana-1628	3	20	to	to	ADP
cana-1628	3	21	higher	high	ADJ
cana-1628	3	22	prediction	prediction	NOUN
cana-1628	3	23	errors	error	NOUN
cana-1628	3	24	.	.	PUNCT
cana-1628	4	1	additionally	additionally	ADV
cana-1628	4	2	,	,	PUNCT
cana-1628	4	3	deep	deep	ADJ
cana-1628	4	4	learning	learning	NOUN
cana-1628	4	5	models	model	NOUN
cana-1628	4	6	using	use	VERB
cana-1628	4	7	a	a	DET
cana-1628	4	8	single	single	ADJ
cana-1628	4	9	approach	approach	NOUN
cana-1628	4	10	face	face	NOUN
cana-1628	4	11	challenges	challenge	NOUN
cana-1628	4	12	such	such	ADJ
cana-1628	4	13	as	as	ADP
cana-1628	4	14	convergence	convergence	NOUN
cana-1628	4	15	to	to	ADP
cana-1628	4	16	local	local	ADJ
cana-1628	4	17	minima	minima	NOUN
cana-1628	4	18	and	and	CCONJ
cana-1628	4	19	poor	poor	ADJ
cana-1628	4	20	generalization	generalization	NOUN
cana-1628	4	21	.	.	PUNCT
cana-1628	5	1	this	this	DET
cana-1628	5	2	paper	paper	NOUN
cana-1628	5	3	proposes	propose	VERB
cana-1628	5	4	a	a	DET
cana-1628	5	5	nonlinear	nonlinear	ADJ
cana-1628	5	6	ensemble	ensemble	ADJ
cana-1628	5	7	deep	deep	ADJ
cana-1628	5	8	learning	learning	NOUN
cana-1628	5	9	model	model	NOUN
cana-1628	5	10	for	for	ADP
cana-1628	5	11	residential	residential	ADJ
cana-1628	5	12	energy	energy	NOUN
cana-1628	5	13	consumption	consumption	NOUN
cana-1628	5	14	prediction	prediction	NOUN
cana-1628	5	15	,	,	PUNCT
cana-1628	5	16	incorporating	incorporate	VERB
cana-1628	5	17	bayesian	bayesian	NOUN
cana-1628	5	18	optimization	optimization	NOUN
cana-1628	5	19	for	for	ADP
cana-1628	5	20	hyperparameter	hyperparameter	NOUN
cana-1628	5	21	tuning	tune	VERB
cana-1628	5	22	.	.	PUNCT
cana-1628	6	1	the	the	DET
cana-1628	6	2	model	model	NOUN
cana-1628	6	3	combines	combine	VERB
cana-1628	6	4	long	long	ADJ
cana-1628	6	5	short	short	ADJ
cana-1628	6	6	-	-	PUNCT
cana-1628	6	7	term	term	NOUN
cana-1628	6	8	memory	memory	NOUN
cana-1628	6	9	(	(	PUNCT
cana-1628	6	10	lstm	lstm	NOUN
cana-1628	6	11	)	)	PUNCT
cana-1628	6	12	,	,	PUNCT
cana-1628	6	13	bidirectional	bidirectional	ADJ
cana-1628	6	14	lstm	lstm	NOUN
cana-1628	6	15	(	(	PUNCT
cana-1628	6	16	bilstm	bilstm	NOUN
cana-1628	6	17	)	)	PUNCT
cana-1628	6	18	,	,	PUNCT
cana-1628	6	19	and	and	CCONJ
cana-1628	6	20	1d	1d	NUM
cana-1628	6	21	convolutional	convolutional	ADJ
cana-1628	6	22	neural	neural	ADJ
cana-1628	6	23	networks	network	NOUN
cana-1628	6	24	(	(	PUNCT
cana-1628	6	25	1d	1d	NOUN
cana-1628	6	26	-	-	PUNCT
cana-1628	6	27	cnn	cnn	NOUN
cana-1628	6	28	)	)	PUNCT
cana-1628	6	29	,	,	PUNCT
cana-1628	6	30	leveraging	leverage	VERB
cana-1628	6	31	their	their	PRON
cana-1628	6	32	powerful	powerful	ADJ
cana-1628	6	33	nonlinear	nonlinear	ADJ
cana-1628	6	34	feature	feature	NOUN
cana-1628	6	35	learning	learn	VERB
cana-1628	6	36	capabilities	capability	NOUN
cana-1628	6	37	.	.	PUNCT
cana-1628	7	1	a	a	DET
cana-1628	7	2	k	k	NOUN
cana-1628	7	3	-	-	PUNCT
cana-1628	7	4	means	means	NOUN
cana-1628	7	5	clustering	clustering	ADJ
cana-1628	7	6	approach	approach	NOUN
cana-1628	7	7	is	be	AUX
cana-1628	7	8	used	use	VERB
cana-1628	7	9	to	to	PART
cana-1628	7	10	preprocess	preprocess	NOUN
cana-1628	7	11	and	and	CCONJ
cana-1628	7	12	reduce	reduce	VERB
cana-1628	7	13	variability	variability	NOUN
cana-1628	7	14	in	in	ADP
cana-1628	7	15	the	the	DET
cana-1628	7	16	data	datum	NOUN
cana-1628	7	17	,	,	PUNCT
cana-1628	7	18	enhancing	enhance	VERB
cana-1628	7	19	the	the	DET
cana-1628	7	20	ensemble	ensemble	ADJ
cana-1628	7	21	model	model	NOUN
cana-1628	7	22	's	's	PART
cana-1628	7	23	performance	performance	NOUN
cana-1628	7	24	.	.	PUNCT
cana-1628	8	1	the	the	DET
cana-1628	8	2	ensemble	ensemble	ADJ
cana-1628	8	3	model	model	NOUN
cana-1628	8	4	was	be	AUX
cana-1628	8	5	tested	test	VERB
cana-1628	8	6	on	on	ADP
cana-1628	8	7	real	real	ADJ
cana-1628	8	8	energy	energy	NOUN
cana-1628	8	9	consumption	consumption	NOUN
cana-1628	8	10	data	datum	NOUN
cana-1628	8	11	from	from	ADP
cana-1628	8	12	two	two	NUM
cana-1628	8	13	districts	district	NOUN
cana-1628	8	14	in	in	ADP
cana-1628	8	15	addis	addis	PROPN
cana-1628	8	16	ababa	ababa	PROPN
cana-1628	8	17	,	,	PUNCT
cana-1628	8	18	showing	show	VERB
cana-1628	8	19	significant	significant	ADJ
cana-1628	8	20	improvements	improvement	NOUN
cana-1628	8	21	in	in	ADP
cana-1628	8	22	prediction	prediction	NOUN
cana-1628	8	23	accuracy	accuracy	NOUN
cana-1628	8	24	with	with	ADP
cana-1628	8	25	lower	low	ADJ
cana-1628	8	26	mae	mae	PROPN
cana-1628	8	27	,	,	PUNCT
cana-1628	8	28	rmse	rmse	NOUN
cana-1628	8	29	,	,	PUNCT
cana-1628	8	30	and	and	CCONJ
cana-1628	8	31	mape	mape	NOUN
cana-1628	8	32	values	value	NOUN
cana-1628	8	33	compared	compare	VERB
cana-1628	8	34	to	to	ADP
cana-1628	8	35	single	single	ADJ
cana-1628	8	36	models	model	NOUN
cana-1628	8	37	and	and	CCONJ
cana-1628	8	38	unclustered	unclustered	ADJ
cana-1628	8	39	data	datum	NOUN
cana-1628	8	40	.	.	PUNCT
cana-1628	9	1	the	the	DET
cana-1628	9	2	integration	integration	NOUN
cana-1628	9	3	of	of	ADP
cana-1628	9	4	clustering	clustering	NOUN
cana-1628	9	5	and	and	CCONJ
cana-1628	9	6	bayesian	bayesian	NOUN
cana-1628	9	7	optimization	optimization	NOUN
cana-1628	9	8	further	far	ADV
cana-1628	9	9	enhanced	enhance	VERB
cana-1628	9	10	model	model	NOUN
cana-1628	9	11	generalizability	generalizability	NOUN
cana-1628	9	12	and	and	CCONJ
cana-1628	9	13	minimized	minimized	ADJ
cana-1628	9	14	overfitting	overfitte	VERB
cana-1628	9	15	,	,	PUNCT
cana-1628	9	16	demonstrating	demonstrate	VERB
cana-1628	9	17	the	the	DET
cana-1628	9	18	effectiveness	effectiveness	NOUN
cana-1628	9	19	of	of	ADP
cana-1628	9	20	a	a	DET
cana-1628	9	21	nonlinear	nonlinear	ADJ
cana-1628	9	22	approach	approach	NOUN
cana-1628	9	23	in	in	ADP
cana-1628	9	24	capturing	capture	VERB
cana-1628	9	25	complex	complex	ADJ
cana-1628	9	26	energy	energy	NOUN
cana-1628	9	27	consumption	consumption	NOUN
cana-1628	9	28	patterns	pattern	NOUN
cana-1628	9	29	.	.	PUNCT
cana-1628	10	1	keywords	keyword	NOUN
cana-1628	10	2	:	:	PUNCT
cana-1628	10	3	bayesian	bayesian	NOUN
cana-1628	10	4	optimization	optimization	NOUN
cana-1628	10	5	,	,	PUNCT
cana-1628	10	6	deep	deep	ADJ
cana-1628	10	7	learning	learning	NOUN
cana-1628	10	8	,	,	PUNCT
cana-1628	10	9	ensemble	ensemble	ADJ
cana-1628	10	10	learning	learning	NOUN
cana-1628	10	11	,	,	PUNCT
cana-1628	10	12	hyperparameter	hyperparameter	NOUN
cana-1628	10	13	tuning	tuning	NOUN
cana-1628	10	14	,	,	PUNCT
cana-1628	10	15	k	k	ADJ
cana-1628	10	16	-	-	PUNCT
cana-1628	10	17	means	means	NOUN
cana-1628	10	18	clustering	clustering	NOUN
cana-1628	10	19	.	.	PUNCT
cana-1628	11	1	1	1	X
cana-1628	11	2	.	.	X
cana-1628	11	3	introduction	introduction	NOUN
cana-1628	11	4	nowadays	nowadays	ADV
cana-1628	11	5	,	,	PUNCT
cana-1628	11	6	people	people	NOUN
cana-1628	11	7	are	be	AUX
cana-1628	11	8	highly	highly	ADV
cana-1628	11	9	dependent	dependent	ADJ
cana-1628	11	10	on	on	ADP
cana-1628	11	11	the	the	DET
cana-1628	11	12	supply	supply	NOUN
cana-1628	11	13	of	of	ADP
cana-1628	11	14	sufficient	sufficient	ADJ
cana-1628	11	15	and	and	CCONJ
cana-1628	11	16	stable	stable	ADJ
cana-1628	11	17	eclectic	eclectic	ADJ
cana-1628	11	18	energy	energy	NOUN
cana-1628	11	19	to	to	PART
cana-1628	11	20	live	live	VERB
cana-1628	11	21	comfortably	comfortably	ADV
cana-1628	11	22	[	[	X
cana-1628	11	23	31	31	NUM
cana-1628	11	24	]	]	PUNCT
cana-1628	11	25	.	.	PUNCT
cana-1628	12	1	consequently	consequently	ADV
cana-1628	12	2	,	,	PUNCT
cana-1628	12	3	electricity	electricity	NOUN
cana-1628	12	4	consumption	consumption	NOUN
cana-1628	12	5	demand	demand	NOUN
cana-1628	12	6	has	have	AUX
cana-1628	12	7	been	be	AUX
cana-1628	12	8	rising	rise	VERB
cana-1628	12	9	due	due	ADP
cana-1628	12	10	to	to	ADP
cana-1628	12	11	the	the	DET
cana-1628	12	12	growth	growth	NOUN
cana-1628	12	13	of	of	ADP
cana-1628	12	14	urbanization	urbanization	NOUN
cana-1628	12	15	along	along	ADP
cana-1628	12	16	with	with	ADP
cana-1628	12	17	the	the	DET
cana-1628	12	18	rapid	rapid	ADJ
cana-1628	12	19	growth	growth	NOUN
cana-1628	12	20	of	of	ADP
cana-1628	12	21	the	the	DET
cana-1628	12	22	human	human	ADJ
cana-1628	12	23	population	population	NOUN
cana-1628	12	24	throughout	throughout	ADP
cana-1628	12	25	the	the	DET
cana-1628	12	26	world	world	NOUN
cana-1628	12	27	[	[	X
cana-1628	12	28	32	32	NUM
cana-1628	12	29	]	]	PUNCT
cana-1628	12	30	.	.	PUNCT
cana-1628	13	1	more	more	ADV
cana-1628	13	2	importantly	importantly	ADV
cana-1628	13	3	,	,	PUNCT
cana-1628	13	4	energy	energy	NOUN
cana-1628	13	5	demand	demand	NOUN
cana-1628	13	6	in	in	ADP
cana-1628	13	7	africa	africa	PROPN
cana-1628	13	8	keeps	keeps	AUX
cana-1628	13	9	growing	grow	VERB
cana-1628	13	10	annually	annually	ADV
cana-1628	13	11	at	at	ADP
cana-1628	13	12	an	an	DET
cana-1628	13	13	average	average	ADJ
cana-1628	13	14	rate	rate	NOUN
cana-1628	13	15	of	of	ADP
cana-1628	13	16	4	4	NUM
cana-1628	13	17	%	%	NOUN
cana-1628	13	18	,	,	PUNCT
cana-1628	13	19	the	the	DET
cana-1628	13	20	highest	high	ADJ
cana-1628	13	21	in	in	ADP
cana-1628	13	22	the	the	DET
cana-1628	13	23	world	world	NOUN
cana-1628	13	24	[	[	X
cana-1628	13	25	13	13	NUM
cana-1628	13	26	]	]	PUNCT
cana-1628	13	27	.	.	PUNCT
cana-1628	14	1	similar	similar	ADJ
cana-1628	14	2	to	to	ADP
cana-1628	14	3	africa	africa	PROPN
cana-1628	14	4	,	,	PUNCT
cana-1628	14	5	energy	energy	NOUN
cana-1628	14	6	consumption	consumption	NOUN
cana-1628	14	7	demand	demand	NOUN
cana-1628	14	8	steadily	steadily	ADV
cana-1628	14	9	growing	grow	VERB
cana-1628	14	10	in	in	ADP
cana-1628	14	11	ethiopia	ethiopia	PROPN
cana-1628	14	12	.	.	PUNCT
cana-1628	15	1	from	from	ADP
cana-1628	15	2	the	the	DET
cana-1628	15	3	ethiopian	ethiopian	ADJ
cana-1628	15	4	context	context	NOUN
cana-1628	15	5	,	,	PUNCT
cana-1628	15	6	the	the	DET
cana-1628	15	7	residential	residential	ADJ
cana-1628	15	8	consumption	consumption	NOUN
cana-1628	15	9	demand	demand	NOUN
cana-1628	15	10	accounts	account	VERB
cana-1628	15	11	for	for	ADP
cana-1628	15	12	39	39	NUM
cana-1628	15	13	%	%	NOUN
cana-1628	15	14	which	which	PRON
cana-1628	15	15	is	be	AUX
cana-1628	15	16	the	the	DET
cana-1628	15	17	largest	large	ADJ
cana-1628	15	18	followed	follow	VERB
cana-1628	15	19	by	by	ADP
cana-1628	15	20	the	the	DET
cana-1628	15	21	industrial	industrial	ADJ
cana-1628	15	22	(	(	PUNCT
cana-1628	15	23	34	34	NUM
cana-1628	15	24	%	%	NOUN
cana-1628	15	25	)	)	PUNCT
cana-1628	15	26	and	and	CCONJ
cana-1628	15	27	commercial	commercial	ADJ
cana-1628	15	28	(	(	PUNCT
cana-1628	15	29	27	27	NUM
cana-1628	15	30	%	%	NOUN
cana-1628	15	31	)	)	PUNCT
cana-1628	15	32	sectors	sector	NOUN
cana-1628	15	33	.	.	PUNCT
cana-1628	16	1	specifically	specifically	ADV
cana-1628	16	2	,	,	PUNCT
cana-1628	16	3	household	household	NOUN
cana-1628	16	4	energy	energy	NOUN
cana-1628	16	5	demand	demand	NOUN
cana-1628	16	6	is	be	AUX
cana-1628	16	7	expected	expect	VERB
cana-1628	16	8	to	to	PART
cana-1628	16	9	exceed	exceed	VERB
cana-1628	16	10	population	population	NOUN
cana-1628	16	11	growth	growth	NOUN
cana-1628	16	12	because	because	SCONJ
cana-1628	16	13	economic	economic	ADJ
cana-1628	16	14	improvements	improvement	NOUN
cana-1628	16	15	have	have	AUX
cana-1628	16	16	driven	drive	VERB
cana-1628	16	17	households	household	NOUN
cana-1628	16	18	to	to	PART
cana-1628	16	19	have	have	VERB
cana-1628	16	20	appliances	appliance	NOUN
cana-1628	16	21	and	and	CCONJ
cana-1628	16	22	become	become	VERB
cana-1628	16	23	owners	owner	NOUN
cana-1628	16	24	of	of	ADP
cana-1628	16	25	energy	energy	NOUN
cana-1628	16	26	dependent	dependent	ADJ
cana-1628	16	27	technological	technological	ADJ
cana-1628	16	28	devices	device	NOUN
cana-1628	16	29	that	that	PRON
cana-1628	16	30	use	use	VERB
cana-1628	16	31	energy	energy	NOUN
cana-1628	16	32	continuously	continuously	ADV
cana-1628	16	33	.	.	PUNCT
cana-1628	17	1	however	however	ADV
cana-1628	17	2	,	,	PUNCT
cana-1628	17	3	energy	energy	NOUN
cana-1628	17	4	supply	supply	NOUN
cana-1628	17	5	and	and	CCONJ
cana-1628	17	6	distribution	distribution	NOUN
cana-1628	17	7	are	be	AUX
cana-1628	17	8	characterized	characterize	VERB
cana-1628	17	9	by	by	ADP
cana-1628	17	10	frequent	frequent	ADJ
cana-1628	17	11	power	power	NOUN
cana-1628	17	12	interruption	interruption	NOUN
cana-1628	17	13	,	,	PUNCT
cana-1628	17	14	inefficient	inefficient	ADJ
cana-1628	17	15	utilization	utilization	NOUN
cana-1628	17	16	,	,	PUNCT
cana-1628	17	17	and	and	CCONJ
cana-1628	17	18	substantial	substantial	ADJ
cana-1628	17	19	waste	waste	NOUN
cana-1628	18	1	[	[	X
cana-1628	18	2	6	6	NUM
cana-1628	18	3	]	]	PUNCT
cana-1628	18	4	.	.	PUNCT
cana-1628	19	1	apart	apart	ADV
cana-1628	19	2	from	from	ADP
cana-1628	19	3	the	the	DET
cana-1628	19	4	high	high	ADJ
cana-1628	19	5	demand	demand	NOUN
cana-1628	19	6	frequent	frequent	ADJ
cana-1628	19	7	power	power	NOUN
cana-1628	19	8	outages	outage	NOUN
cana-1628	19	9	problems	problem	NOUN
cana-1628	19	10	,	,	PUNCT
cana-1628	19	11	and	and	CCONJ
cana-1628	19	12	the	the	DET
cana-1628	19	13	high	high	ADJ
cana-1628	19	14	level	level	NOUN
cana-1628	19	15	of	of	ADP
cana-1628	19	16	energy	energy	NOUN
cana-1628	19	17	demand	demand	NOUN
cana-1628	19	18	especially	especially	ADV
cana-1628	19	19	in	in	ADP
cana-1628	19	20	ethiopia	ethiopia	PROPN
cana-1628	19	21	,	,	PUNCT
cana-1628	19	22	once	once	SCONJ
cana-1628	19	23	it	it	PRON
cana-1628	19	24	is	be	AUX
cana-1628	19	25	mailto:ejigu.tefera@aastustudent.edu.et	mailto:ejigu.tefera@aastustudent.edu.et	PROPN
cana-1628	19	26	mailto:kuulla@gmail.com	mailto:kuulla@gmail.com	NOUN
cana-1628	19	27	mailto:ravindrababu4u@yahoo.com	mailto:ravindrababu4u@yahoo.com	NOUN
cana-1628	19	28	mailto:mariamartinez@us.es	mailto:mariamartinez@us.es	PROPN
cana-1628	19	29	communications	communication	NOUN
cana-1628	19	30	on	on	ADP
cana-1628	19	31	applied	apply	VERB
cana-1628	19	32	nonlinear	nonlinear	ADJ
cana-1628	19	33	analysis	analysis	NOUN
cana-1628	19	34	issn	issn	NOUN
cana-1628	19	35	:	:	PUNCT
cana-1628	19	36	1074	1074	NUM
cana-1628	19	37	-	-	PUNCT
cana-1628	19	38	133x	133x	NUM
cana-1628	19	39	vol	vol	NOUN
cana-1628	19	40	32	32	NUM
cana-1628	19	41	no	no	NOUN
cana-1628	19	42	.	.	NOUN
cana-1628	19	43	1	1	NUM
cana-1628	19	44	(	(	PUNCT
cana-1628	19	45	2025	2025	NUM
cana-1628	19	46	)	)	PUNCT
cana-1628	19	47	156	156	NUM
cana-1628	19	48	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	19	49	generated	generate	VERB
cana-1628	19	50	,	,	PUNCT
cana-1628	19	51	storing	store	VERB
cana-1628	19	52	and	and	CCONJ
cana-1628	19	53	preserving	preserve	VERB
cana-1628	19	54	the	the	DET
cana-1628	19	55	produced	produce	VERB
cana-1628	19	56	electricity	electricity	NOUN
cana-1628	19	57	energy	energy	NOUN
cana-1628	19	58	sufficiently	sufficiently	ADV
cana-1628	19	59	using	use	VERB
cana-1628	19	60	the	the	DET
cana-1628	19	61	current	current	ADJ
cana-1628	19	62	energy	energy	NOUN
cana-1628	19	63	storage	storage	NOUN
cana-1628	19	64	technology	technology	NOUN
cana-1628	19	65	[	[	X
cana-1628	19	66	1	1	X
cana-1628	19	67	]	]	PUNCT
cana-1628	19	68	is	be	AUX
cana-1628	19	69	difficult	difficult	ADJ
cana-1628	19	70	.	.	PUNCT
cana-1628	20	1	in	in	ADP
cana-1628	20	2	other	other	ADJ
cana-1628	20	3	words	word	NOUN
cana-1628	20	4	,	,	PUNCT
cana-1628	20	5	energy	energy	NOUN
cana-1628	20	6	waste	waste	NOUN
cana-1628	20	7	will	will	AUX
cana-1628	20	8	occur	occur	VERB
cana-1628	20	9	if	if	SCONJ
cana-1628	20	10	the	the	DET
cana-1628	20	11	electricity	electricity	NOUN
cana-1628	20	12	is	be	AUX
cana-1628	20	13	adequately	adequately	ADV
cana-1628	20	14	distributed	distribute	VERB
cana-1628	20	15	and	and	CCONJ
cana-1628	20	16	consumed	consume	VERB
cana-1628	20	17	as	as	ADV
cana-1628	20	18	soon	soon	ADV
cana-1628	20	19	as	as	SCONJ
cana-1628	20	20	it	it	PRON
cana-1628	20	21	is	be	AUX
cana-1628	20	22	produced	produce	VERB
cana-1628	20	23	following	follow	VERB
cana-1628	20	24	the	the	DET
cana-1628	20	25	consumption	consumption	NOUN
cana-1628	20	26	demand	demand	NOUN
cana-1628	20	27	in	in	ADP
cana-1628	20	28	each	each	DET
cana-1628	20	29	district	district	NOUN
cana-1628	20	30	.	.	PUNCT
cana-1628	21	1	to	to	ADP
cana-1628	21	2	this	this	DET
cana-1628	21	3	end	end	NOUN
cana-1628	21	4	,	,	PUNCT
cana-1628	21	5	given	give	VERB
cana-1628	21	6	the	the	DET
cana-1628	21	7	large	large	ADJ
cana-1628	21	8	contribution	contribution	NOUN
cana-1628	21	9	of	of	ADP
cana-1628	21	10	the	the	DET
cana-1628	21	11	residential	residential	ADJ
cana-1628	21	12	sector	sector	NOUN
cana-1628	21	13	to	to	PART
cana-1628	21	14	total	total	VERB
cana-1628	21	15	energy	energy	NOUN
cana-1628	21	16	demand	demand	NOUN
cana-1628	21	17	,	,	PUNCT
cana-1628	21	18	it	it	PRON
cana-1628	21	19	is	be	AUX
cana-1628	21	20	feasible	feasible	ADJ
cana-1628	21	21	to	to	PART
cana-1628	21	22	study	study	VERB
cana-1628	21	23	consumption	consumption	NOUN
cana-1628	21	24	trends	trend	NOUN
cana-1628	21	25	and	and	CCONJ
cana-1628	21	26	develop	develop	VERB
cana-1628	21	27	accurate	accurate	ADJ
cana-1628	21	28	models	model	NOUN
cana-1628	21	29	using	use	VERB
cana-1628	21	30	state	state	NOUN
cana-1628	21	31	-	-	PUNCT
cana-1628	21	32	of	of	ADP
cana-1628	21	33	-	-	PUNCT
cana-1628	21	34	the	the	DET
cana-1628	21	35	-	-	PUNCT
cana-1628	21	36	art	art	NOUN
cana-1628	21	37	data	data	NOUN
cana-1628	21	38	-	-	PUNCT
cana-1628	21	39	driven	drive	VERB
cana-1628	21	40	algorithms	algorithm	NOUN
cana-1628	21	41	for	for	ADP
cana-1628	21	42	effective	effective	ADJ
cana-1628	21	43	planning	planning	NOUN
cana-1628	21	44	and	and	CCONJ
cana-1628	21	45	demand	demand	NOUN
cana-1628	21	46	-	-	PUNCT
cana-1628	21	47	supply	supply	NOUN
cana-1628	21	48	management	management	NOUN
cana-1628	21	49	.	.	PUNCT
cana-1628	22	1	moreover	moreover	ADV
cana-1628	22	2	,	,	PUNCT
cana-1628	22	3	for	for	ADP
cana-1628	22	4	reliable	reliable	ADJ
cana-1628	22	5	and	and	CCONJ
cana-1628	22	6	efficient	efficient	ADJ
cana-1628	22	7	grid	grid	NOUN
cana-1628	22	8	systems	system	NOUN
cana-1628	22	9	,	,	PUNCT
cana-1628	22	10	effective	effective	ADJ
cana-1628	22	11	load	load	NOUN
cana-1628	22	12	dispatching	dispatch	VERB
cana-1628	22	13	,	,	PUNCT
cana-1628	22	14	and	and	CCONJ
cana-1628	22	15	efficient	efficient	ADJ
cana-1628	22	16	energy	energy	NOUN
cana-1628	22	17	utilization	utilization	NOUN
cana-1628	22	18	,	,	PUNCT
cana-1628	22	19	accurate	accurate	ADJ
cana-1628	22	20	electricity	electricity	NOUN
cana-1628	22	21	consumption	consumption	NOUN
cana-1628	22	22	forecasting	forecasting	NOUN
cana-1628	22	23	has	have	AUX
cana-1628	22	24	become	become	VERB
cana-1628	22	25	indispensable	indispensable	ADJ
cana-1628	22	26	for	for	ADP
cana-1628	22	27	energy	energy	NOUN
cana-1628	22	28	companies	company	NOUN
cana-1628	22	29	.	.	PUNCT
cana-1628	23	1	in	in	ADP
cana-1628	23	2	this	this	DET
cana-1628	23	3	regard	regard	NOUN
cana-1628	23	4	,	,	PUNCT
cana-1628	23	5	machine	machine	NOUN
cana-1628	23	6	learning	learning	NOUN
cana-1628	23	7	and	and	CCONJ
cana-1628	23	8	deep	deep	ADJ
cana-1628	23	9	learning	learning	NOUN
cana-1628	23	10	models	model	NOUN
cana-1628	23	11	have	have	AUX
cana-1628	23	12	been	be	AUX
cana-1628	23	13	widely	widely	ADV
cana-1628	23	14	used	use	VERB
cana-1628	23	15	for	for	ADP
cana-1628	23	16	electric	electric	ADJ
cana-1628	23	17	load	load	NOUN
cana-1628	23	18	forecasting	forecasting	NOUN
cana-1628	23	19	,	,	PUNCT
cana-1628	23	20	power	power	NOUN
cana-1628	23	21	system	system	NOUN
cana-1628	23	22	monitoring	monitoring	NOUN
cana-1628	23	23	,	,	PUNCT
cana-1628	23	24	and	and	CCONJ
cana-1628	23	25	anomalous	anomalous	ADJ
cana-1628	23	26	energy	energy	NOUN
cana-1628	23	27	usage	usage	NOUN
cana-1628	23	28	detection	detection	NOUN
cana-1628	23	29	[	[	X
cana-1628	23	30	21	21	NUM
cana-1628	23	31	]	]	PUNCT
cana-1628	23	32	.	.	PUNCT
cana-1628	24	1	however	however	ADV
cana-1628	24	2	,	,	PUNCT
cana-1628	24	3	the	the	DET
cana-1628	24	4	existing	exist	VERB
cana-1628	24	5	machine	machine	NOUN
cana-1628	24	6	learning	learning	NOUN
cana-1628	24	7	methods	method	NOUN
cana-1628	24	8	are	be	AUX
cana-1628	24	9	incapable	incapable	ADJ
cana-1628	24	10	of	of	ADP
cana-1628	24	11	capturing	capture	VERB
cana-1628	24	12	nonlinear	nonlinear	ADJ
cana-1628	24	13	energy	energy	NOUN
cana-1628	24	14	consumption	consumption	NOUN
cana-1628	24	15	data	datum	NOUN
cana-1628	24	16	and	and	CCONJ
cana-1628	24	17	can	can	AUX
cana-1628	24	18	not	not	PART
cana-1628	24	19	yield	yield	VERB
cana-1628	24	20	accurate	accurate	ADJ
cana-1628	24	21	prediction	prediction	NOUN
cana-1628	24	22	results	result	NOUN
cana-1628	24	23	[	[	X
cana-1628	24	24	5	5	NUM
cana-1628	24	25	,	,	PUNCT
cana-1628	24	26	15	15	NUM
cana-1628	24	27	]	]	PUNCT
cana-1628	24	28	.	.	PUNCT
cana-1628	25	1	moreover	moreover	ADV
cana-1628	25	2	,	,	PUNCT
cana-1628	25	3	deep	deep	ADJ
cana-1628	25	4	learning	learning	NOUN
cana-1628	25	5	methods	method	NOUN
cana-1628	25	6	with	with	ADP
cana-1628	25	7	a	a	DET
cana-1628	25	8	single	single	ADJ
cana-1628	25	9	model	model	NOUN
cana-1628	25	10	have	have	AUX
cana-1628	25	11	been	be	AUX
cana-1628	25	12	plagued	plague	VERB
cana-1628	25	13	by	by	ADP
cana-1628	25	14	a	a	DET
cana-1628	25	15	poor	poor	ADJ
cana-1628	25	16	capacity	capacity	NOUN
cana-1628	25	17	for	for	ADP
cana-1628	25	18	generalization	generalization	NOUN
cana-1628	25	19	and	and	CCONJ
cana-1628	25	20	a	a	DET
cana-1628	25	21	tendency	tendency	NOUN
cana-1628	25	22	to	to	PART
cana-1628	25	23	converge	converge	VERB
cana-1628	25	24	to	to	ADP
cana-1628	25	25	local	local	ADJ
cana-1628	25	26	minima	minima	NOUN
cana-1628	26	1	[	[	X
cana-1628	26	2	11	11	NUM
cana-1628	26	3	,	,	PUNCT
cana-1628	26	4	29	29	NUM
cana-1628	26	5	]	]	PUNCT
cana-1628	26	6	.	.	PUNCT
cana-1628	27	1	in	in	ADP
cana-1628	27	2	existing	exist	VERB
cana-1628	27	3	methods	method	NOUN
cana-1628	27	4	,	,	PUNCT
cana-1628	27	5	little	little	ADJ
cana-1628	27	6	attention	attention	NOUN
cana-1628	27	7	is	be	AUX
cana-1628	27	8	given	give	VERB
cana-1628	27	9	to	to	ADP
cana-1628	27	10	the	the	DET
cana-1628	27	11	fine	fine	ADJ
cana-1628	27	12	graining	graining	NOUN
cana-1628	27	13	of	of	ADP
cana-1628	27	14	the	the	DET
cana-1628	27	15	input	input	NOUN
cana-1628	27	16	data	datum	NOUN
cana-1628	27	17	,	,	PUNCT
cana-1628	27	18	which	which	PRON
cana-1628	27	19	accounts	account	VERB
cana-1628	27	20	for	for	ADP
cana-1628	27	21	model	model	NOUN
cana-1628	27	22	complexity	complexity	NOUN
cana-1628	27	23	and	and	CCONJ
cana-1628	27	24	larger	large	ADJ
cana-1628	27	25	prediction	prediction	NOUN
cana-1628	27	26	errors	error	NOUN
cana-1628	27	27	[	[	X
cana-1628	27	28	35	35	NUM
cana-1628	27	29	]	]	PUNCT
cana-1628	27	30	.	.	PUNCT
cana-1628	28	1	in	in	ADP
cana-1628	28	2	general	general	ADJ
cana-1628	28	3	,	,	PUNCT
cana-1628	28	4	despite	despite	SCONJ
cana-1628	28	5	several	several	ADJ
cana-1628	28	6	studies	study	NOUN
cana-1628	28	7	have	have	AUX
cana-1628	28	8	been	be	AUX
cana-1628	28	9	conducted	conduct	VERB
cana-1628	28	10	for	for	ADP
cana-1628	28	11	electric	electric	ADJ
cana-1628	28	12	load	load	NOUN
cana-1628	28	13	forecasting	forecasting	NOUN
cana-1628	28	14	based	base	VERB
cana-1628	28	15	on	on	ADP
cana-1628	28	16	deep	deep	ADJ
cana-1628	28	17	learning	learning	NOUN
cana-1628	28	18	and	and	CCONJ
cana-1628	28	19	ensemble	ensemble	ADJ
cana-1628	28	20	methods	method	NOUN
cana-1628	28	21	,	,	PUNCT
cana-1628	28	22	enhancement	enhancement	NOUN
cana-1628	28	23	is	be	AUX
cana-1628	28	24	required	require	VERB
cana-1628	28	25	to	to	PART
cana-1628	28	26	get	get	VERB
cana-1628	28	27	optimal	optimal	ADJ
cana-1628	28	28	prediction	prediction	NOUN
cana-1628	28	29	performance	performance	NOUN
cana-1628	28	30	by	by	ADP
cana-1628	28	31	ensembling	ensemble	VERB
cana-1628	28	32	multiple	multiple	ADJ
cana-1628	28	33	deep	deep	ADJ
cana-1628	28	34	learning	learning	NOUN
cana-1628	28	35	algorithms	algorithm	NOUN
cana-1628	28	36	with	with	ADP
cana-1628	28	37	clustering	cluster	VERB
cana-1628	28	38	and	and	CCONJ
cana-1628	28	39	fine	fine	ADJ
cana-1628	28	40	graining	graining	NOUN
cana-1628	28	41	of	of	ADP
cana-1628	28	42	the	the	DET
cana-1628	28	43	input	input	NOUN
cana-1628	28	44	data	datum	NOUN
cana-1628	28	45	to	to	PART
cana-1628	28	46	learn	learn	VERB
cana-1628	28	47	nonlinear	nonlinear	ADJ
cana-1628	28	48	and	and	CCONJ
cana-1628	28	49	complex	complex	ADJ
cana-1628	28	50	energy	energy	NOUN
cana-1628	28	51	data	datum	NOUN
cana-1628	28	52	effectively	effectively	ADV
cana-1628	29	1	[	[	X
cana-1628	29	2	29	29	NUM
cana-1628	29	3	,	,	PUNCT
cana-1628	29	4	25	25	NUM
cana-1628	29	5	]	]	PUNCT
cana-1628	29	6	.	.	PUNCT
cana-1628	30	1	in	in	ADP
cana-1628	30	2	this	this	DET
cana-1628	30	3	regard	regard	NOUN
cana-1628	30	4	,	,	PUNCT
cana-1628	30	5	the	the	DET
cana-1628	30	6	electricity	electricity	NOUN
cana-1628	30	7	consumption	consumption	NOUN
cana-1628	30	8	prediction	prediction	NOUN
cana-1628	30	9	method	method	NOUN
cana-1628	30	10	is	be	AUX
cana-1628	30	11	imperative	imperative	ADJ
cana-1628	30	12	to	to	PART
cana-1628	30	13	ensure	ensure	VERB
cana-1628	30	14	efficient	efficient	ADJ
cana-1628	30	15	load	load	NOUN
cana-1628	30	16	dispatching	dispatching	NOUN
cana-1628	30	17	,	,	PUNCT
cana-1628	30	18	scheduling	scheduling	NOUN
cana-1628	30	19	,	,	PUNCT
cana-1628	30	20	and	and	CCONJ
cana-1628	30	21	efficient	efficient	ADJ
cana-1628	30	22	energy	energy	NOUN
cana-1628	30	23	utilization	utilization	NOUN
cana-1628	30	24	[	[	X
cana-1628	30	25	19	19	NUM
cana-1628	30	26	]	]	PUNCT
cana-1628	30	27	.	.	PUNCT
cana-1628	31	1	this	this	DET
cana-1628	31	2	paper	paper	NOUN
cana-1628	31	3	aims	aim	VERB
cana-1628	31	4	to	to	PART
cana-1628	31	5	investigate	investigate	VERB
cana-1628	31	6	the	the	DET
cana-1628	31	7	effectiveness	effectiveness	NOUN
cana-1628	31	8	of	of	ADP
cana-1628	31	9	an	an	DET
cana-1628	31	10	ensemble	ensemble	ADJ
cana-1628	31	11	deep	deep	ADJ
cana-1628	31	12	learning	learning	NOUN
cana-1628	31	13	model	model	NOUN
cana-1628	31	14	for	for	ADP
cana-1628	31	15	energy	energy	NOUN
cana-1628	31	16	consumption	consumption	NOUN
cana-1628	31	17	prediction	prediction	NOUN
cana-1628	31	18	with	with	ADP
cana-1628	31	19	fin	fin	NOUN
cana-1628	31	20	-	-	PUNCT
cana-1628	31	21	graining	graining	NOUN
cana-1628	31	22	of	of	ADP
cana-1628	31	23	input	input	NOUN
cana-1628	31	24	data	datum	NOUN
cana-1628	31	25	including	include	VERB
cana-1628	31	26	identifying	identify	VERB
cana-1628	31	27	optimal	optimal	ADJ
cana-1628	31	28	clusters	cluster	NOUN
cana-1628	31	29	of	of	ADP
cana-1628	31	30	residential	residential	ADJ
cana-1628	31	31	energy	energy	NOUN
cana-1628	31	32	consumption	consumption	NOUN
cana-1628	31	33	profiles	profile	NOUN
cana-1628	31	34	.	.	PUNCT
cana-1628	32	1	the	the	DET
cana-1628	32	2	contributions	contribution	NOUN
cana-1628	32	3	of	of	ADP
cana-1628	32	4	this	this	DET
cana-1628	32	5	paper	paper	NOUN
cana-1628	32	6	can	can	AUX
cana-1628	32	7	be	be	AUX
cana-1628	32	8	summarized	summarize	VERB
cana-1628	32	9	as	as	SCONJ
cana-1628	32	10	follows	follow	VERB
cana-1628	32	11	:	:	PUNCT
cana-1628	33	1	1	1	X
cana-1628	33	2	.	.	X
cana-1628	33	3	k	k	X
cana-1628	33	4	-	-	PUNCT
cana-1628	33	5	means	mean	VERB
cana-1628	33	6	clustering	clustering	NOUN
cana-1628	33	7	was	be	AUX
cana-1628	33	8	applied	apply	VERB
cana-1628	33	9	for	for	ADP
cana-1628	33	10	energy	energy	NOUN
cana-1628	33	11	consumption	consumption	NOUN
cana-1628	33	12	profile	profile	NOUN
cana-1628	33	13	characterization	characterization	NOUN
cana-1628	33	14	to	to	PART
cana-1628	33	15	acquire	acquire	VERB
cana-1628	33	16	a	a	DET
cana-1628	33	17	more	more	ADV
cana-1628	33	18	thorough	thorough	ADJ
cana-1628	33	19	understanding	understanding	NOUN
cana-1628	33	20	of	of	ADP
cana-1628	33	21	how	how	SCONJ
cana-1628	33	22	power	power	NOUN
cana-1628	33	23	consumption	consumption	NOUN
cana-1628	33	24	patterns	pattern	NOUN
cana-1628	33	25	of	of	ADP
cana-1628	33	26	users	user	NOUN
cana-1628	33	27	behave	behave	VERB
cana-1628	33	28	.	.	PUNCT
cana-1628	34	1	moreover	moreover	ADV
cana-1628	34	2	,	,	PUNCT
cana-1628	34	3	optimal	optimal	ADJ
cana-1628	34	4	clusters	cluster	NOUN
cana-1628	34	5	were	be	AUX
cana-1628	34	6	identified	identify	VERB
cana-1628	34	7	that	that	PRON
cana-1628	34	8	will	will	AUX
cana-1628	34	9	lead	lead	VERB
cana-1628	34	10	the	the	DET
cana-1628	34	11	subsequent	subsequent	ADJ
cana-1628	34	12	ensemble	ensemble	ADJ
cana-1628	34	13	model	model	NOUN
cana-1628	34	14	to	to	PART
cana-1628	34	15	learn	learn	VERB
cana-1628	34	16	the	the	DET
cana-1628	34	17	detail	detail	NOUN
cana-1628	34	18	features	feature	NOUN
cana-1628	34	19	and	and	CCONJ
cana-1628	34	20	intrinsic	intrinsic	ADJ
cana-1628	34	21	behaviors	behavior	NOUN
cana-1628	34	22	of	of	ADP
cana-1628	34	23	energy	energy	NOUN
cana-1628	34	24	consumption	consumption	NOUN
cana-1628	34	25	data	datum	NOUN
cana-1628	34	26	.	.	PUNCT
cana-1628	35	1	2	2	X
cana-1628	35	2	.	.	X
cana-1628	35	3	optimal	optimal	ADJ
cana-1628	35	4	hyperparameter	hyperparameter	NOUN
cana-1628	35	5	combination	combination	NOUN
cana-1628	35	6	is	be	AUX
cana-1628	35	7	searched	search	VERB
cana-1628	35	8	using	use	VERB
cana-1628	35	9	a	a	DET
cana-1628	35	10	bayesian	bayesian	NOUN
cana-1628	35	11	optimization	optimization	NOUN
cana-1628	35	12	algorithm	algorithm	NOUN
cana-1628	35	13	to	to	PART
cana-1628	35	14	get	get	VERB
cana-1628	35	15	an	an	DET
cana-1628	35	16	improved	improved	ADJ
cana-1628	35	17	prediction	prediction	NOUN
cana-1628	35	18	model	model	NOUN
cana-1628	35	19	.	.	PUNCT
cana-1628	36	1	3	3	X
cana-1628	36	2	.	.	X
cana-1628	36	3	the	the	DET
cana-1628	36	4	robust	robust	ADJ
cana-1628	36	5	ensemble	ensemble	ADJ
cana-1628	36	6	model	model	NOUN
cana-1628	36	7	has	have	AUX
cana-1628	36	8	been	be	AUX
cana-1628	36	9	developed	develop	VERB
cana-1628	36	10	based	base	VERB
cana-1628	36	11	on	on	ADP
cana-1628	36	12	optimal	optimal	ADJ
cana-1628	36	13	cluster	cluster	NOUN
cana-1628	36	14	-	-	PUNCT
cana-1628	36	15	generated	generate	VERB
cana-1628	36	16	energy	energy	NOUN
cana-1628	36	17	consumption	consumption	NOUN
cana-1628	36	18	data	datum	NOUN
cana-1628	36	19	.	.	PUNCT
cana-1628	37	1	4	4	X
cana-1628	37	2	.	.	X
cana-1628	37	3	the	the	DET
cana-1628	37	4	ensemble	ensemble	ADJ
cana-1628	37	5	deep	deep	ADJ
cana-1628	37	6	learning	learning	NOUN
cana-1628	37	7	model	model	NOUN
cana-1628	37	8	’s	’s	PART
cana-1628	37	9	effectiveness	effectiveness	NOUN
cana-1628	37	10	in	in	ADP
cana-1628	37	11	predicting	predict	VERB
cana-1628	37	12	the	the	DET
cana-1628	37	13	monthly	monthly	ADJ
cana-1628	37	14	aggregate	aggregate	ADJ
cana-1628	37	15	residential	residential	ADJ
cana-1628	37	16	energy	energy	NOUN
cana-1628	37	17	consumption	consumption	NOUN
cana-1628	37	18	is	be	AUX
cana-1628	37	19	evaluated	evaluate	VERB
cana-1628	37	20	and	and	CCONJ
cana-1628	37	21	verified	verify	VERB
cana-1628	37	22	against	against	ADP
cana-1628	37	23	the	the	DET
cana-1628	37	24	base	base	NOUN
cana-1628	37	25	models	model	NOUN
cana-1628	37	26	using	use	VERB
cana-1628	37	27	mae	mae	PROPN
cana-1628	37	28	,	,	PUNCT
cana-1628	37	29	rmse	rmse	NOUN
cana-1628	37	30	,	,	PUNCT
cana-1628	37	31	and	and	CCONJ
cana-1628	37	32	mape	mape	NOUN
cana-1628	37	33	.	.	PUNCT
cana-1628	38	1	2	2	NUM
cana-1628	38	2	related	relate	VERB
cana-1628	38	3	works	work	NOUN
cana-1628	38	4	accurate	accurate	ADJ
cana-1628	38	5	electric	electric	ADJ
cana-1628	38	6	energy	energy	NOUN
cana-1628	38	7	consumption	consumption	NOUN
cana-1628	38	8	forecasting	forecasting	NOUN
cana-1628	38	9	at	at	ADP
cana-1628	38	10	both	both	CCONJ
cana-1628	38	11	long	long	ADJ
cana-1628	38	12	-	-	PUNCT
cana-1628	38	13	term	term	NOUN
cana-1628	38	14	and	and	CCONJ
cana-1628	38	15	short	short	ADJ
cana-1628	38	16	-	-	PUNCT
cana-1628	38	17	term	term	NOUN
cana-1628	38	18	horizons	horizon	NOUN
cana-1628	38	19	is	be	AUX
cana-1628	38	20	necessary	necessary	ADJ
cana-1628	38	21	to	to	PART
cana-1628	38	22	establish	establish	VERB
cana-1628	38	23	a	a	DET
cana-1628	38	24	more	more	ADV
cana-1628	38	25	stable	stable	ADJ
cana-1628	38	26	supply	supply	NOUN
cana-1628	38	27	-	-	PUNCT
cana-1628	38	28	and	and	CCONJ
cana-1628	38	29	-	-	PUNCT
cana-1628	38	30	demand	demand	NOUN
cana-1628	38	31	equilibrium	equilibrium	NOUN
cana-1628	39	1	[	[	X
cana-1628	39	2	30	30	NUM
cana-1628	39	3	]	]	PUNCT
cana-1628	39	4	.	.	PUNCT
cana-1628	40	1	to	to	ADP
cana-1628	40	2	this	this	DET
cana-1628	40	3	end	end	NOUN
cana-1628	40	4	,	,	PUNCT
cana-1628	40	5	several	several	ADJ
cana-1628	40	6	studies	study	NOUN
cana-1628	40	7	have	have	AUX
cana-1628	40	8	been	be	AUX
cana-1628	40	9	conducted	conduct	VERB
cana-1628	40	10	on	on	ADP
cana-1628	40	11	energy	energy	NOUN
cana-1628	40	12	consumption	consumption	NOUN
cana-1628	40	13	forecasting	forecasting	NOUN
cana-1628	40	14	problems	problem	NOUN
cana-1628	40	15	.	.	PUNCT
cana-1628	41	1	wen	wen	PROPN
cana-1628	41	2	et	et	PROPN
cana-1628	41	3	al	al	PROPN
cana-1628	41	4	.	.	PUNCT
cana-1628	42	1	[	[	X
cana-1628	42	2	35	35	NUM
cana-1628	42	3	]	]	PUNCT
cana-1628	42	4	proposed	propose	VERB
cana-1628	42	5	a	a	DET
cana-1628	42	6	deepcommunications	deepcommunication	NOUN
cana-1628	42	7	on	on	ADP
cana-1628	42	8	applied	apply	VERB
cana-1628	42	9	nonlinear	nonlinear	ADJ
cana-1628	42	10	analysis	analysis	NOUN
cana-1628	42	11	issn	issn	NOUN
cana-1628	42	12	:	:	PUNCT
cana-1628	42	13	1074	1074	NUM
cana-1628	42	14	-	-	PUNCT
cana-1628	42	15	133x	133x	NUM
cana-1628	42	16	vol	vol	NOUN
cana-1628	42	17	32	32	NUM
cana-1628	42	18	no	no	NOUN
cana-1628	42	19	.	.	NOUN
cana-1628	42	20	1	1	NUM
cana-1628	42	21	(	(	PUNCT
cana-1628	42	22	2025	2025	NUM
cana-1628	42	23	)	)	PUNCT
cana-1628	42	24	157	157	NUM
cana-1628	42	25	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	42	26	learning	learn	VERB
cana-1628	42	27	model	model	NOUN
cana-1628	42	28	to	to	PART
cana-1628	42	29	forecast	forecast	VERB
cana-1628	42	30	the	the	DET
cana-1628	42	31	load	load	NOUN
cana-1628	42	32	demand	demand	NOUN
cana-1628	42	33	for	for	ADP
cana-1628	42	34	residential	residential	ADJ
cana-1628	42	35	buildings	building	NOUN
cana-1628	42	36	with	with	ADP
cana-1628	42	37	a	a	DET
cana-1628	42	38	one	one	NUM
cana-1628	42	39	-	-	PUNCT
cana-1628	42	40	hour	hour	NOUN
cana-1628	42	41	resolution	resolution	NOUN
cana-1628	42	42	.	.	PUNCT
cana-1628	43	1	hyperopt	hyperopt	PROPN
cana-1628	43	2	hyperparameter	hyperparameter	NOUN
cana-1628	43	3	tuning	tuning	NOUN
cana-1628	43	4	was	be	AUX
cana-1628	43	5	employed	employ	VERB
cana-1628	43	6	to	to	PART
cana-1628	43	7	find	find	VERB
cana-1628	43	8	the	the	DET
cana-1628	43	9	optimal	optimal	ADJ
cana-1628	43	10	hyperparameter	hyperparameter	NOUN
cana-1628	43	11	combination	combination	NOUN
cana-1628	43	12	.	.	PUNCT
cana-1628	44	1	moreover	moreover	ADV
cana-1628	44	2	,	,	PUNCT
cana-1628	44	3	accurate	accurate	ADJ
cana-1628	44	4	forecasting	forecasting	NOUN
cana-1628	44	5	of	of	ADP
cana-1628	44	6	electricity	electricity	NOUN
cana-1628	44	7	consumption	consumption	NOUN
cana-1628	44	8	is	be	AUX
cana-1628	44	9	a	a	DET
cana-1628	44	10	very	very	ADV
cana-1628	44	11	challenging	challenging	ADJ
cana-1628	44	12	task	task	NOUN
cana-1628	44	13	due	due	ADP
cana-1628	44	14	to	to	ADP
cana-1628	44	15	the	the	DET
cana-1628	44	16	high	high	ADJ
cana-1628	44	17	volatility	volatility	NOUN
cana-1628	44	18	of	of	ADP
cana-1628	44	19	energy	energy	NOUN
cana-1628	44	20	consumption	consumption	NOUN
cana-1628	44	21	.	.	PUNCT
cana-1628	45	1	a.	a.	NOUN
cana-1628	45	2	salam	salam	PROPN
cana-1628	45	3	and	and	CCONJ
cana-1628	45	4	a.	a.	PROPN
cana-1628	45	5	el	el	PROPN
cana-1628	45	6	hibaoui	hibaoui	VERB
cana-1628	45	7	in	in	ADP
cana-1628	45	8	[	[	X
cana-1628	45	9	24	24	NUM
cana-1628	45	10	]	]	PUNCT
cana-1628	45	11	introduced	introduce	VERB
cana-1628	45	12	an	an	DET
cana-1628	45	13	improved	improve	VERB
cana-1628	45	14	intelligent	intelligent	ADJ
cana-1628	45	15	energy	energy	NOUN
cana-1628	45	16	prediction	prediction	NOUN
cana-1628	45	17	model	model	NOUN
cana-1628	45	18	based	base	VERB
cana-1628	45	19	on	on	ADP
cana-1628	45	20	deep	deep	ADJ
cana-1628	45	21	feedforward	feedforward	NOUN
cana-1628	45	22	neural	neural	ADJ
cana-1628	45	23	networks	network	NOUN
cana-1628	45	24	and	and	CCONJ
cana-1628	45	25	long	long	ADJ
cana-1628	45	26	short	short	ADJ
cana-1628	45	27	-	-	PUNCT
cana-1628	45	28	term	term	NOUN
cana-1628	45	29	memory	memory	NOUN
cana-1628	45	30	.	.	PUNCT
cana-1628	46	1	m.	m.	NOUN
cana-1628	46	2	cai	cai	PROPN
cana-1628	46	3	et	et	PROPN
cana-1628	46	4	al	al	PROPN
cana-1628	46	5	.	.	PUNCT
cana-1628	47	1	[	[	X
cana-1628	47	2	2	2	X
cana-1628	47	3	]	]	PUNCT
cana-1628	47	4	proposed	propose	VERB
cana-1628	47	5	deep	deep	ADJ
cana-1628	47	6	neural	neural	ADJ
cana-1628	47	7	network	network	NOUN
cana-1628	47	8	models	model	NOUN
cana-1628	47	9	,	,	PUNCT
cana-1628	47	10	namely	namely	ADV
cana-1628	47	11	recurrent	recurrent	ADJ
cana-1628	47	12	neural	neural	ADJ
cana-1628	47	13	networks	network	NOUN
cana-1628	47	14	(	(	PUNCT
cana-1628	47	15	rnn	rnn	PROPN
cana-1628	47	16	)	)	PUNCT
cana-1628	47	17	and	and	CCONJ
cana-1628	47	18	convolutional	convolutional	ADJ
cana-1628	47	19	neural	neural	ADJ
cana-1628	47	20	networks	network	NOUN
cana-1628	47	21	(	(	PUNCT
cana-1628	47	22	cnns	cnns	PROPN
cana-1628	47	23	)	)	PUNCT
cana-1628	47	24	.	.	PUNCT
cana-1628	48	1	the	the	DET
cana-1628	48	2	proposed	propose	VERB
cana-1628	48	3	model	model	NOUN
cana-1628	48	4	is	be	AUX
cana-1628	48	5	compared	compare	VERB
cana-1628	48	6	with	with	ADP
cana-1628	48	7	the	the	DET
cana-1628	48	8	seasonal	seasonal	ADJ
cana-1628	48	9	arimax	arimax	PROPN
cana-1628	48	10	model	model	NOUN
cana-1628	48	11	’s	’s	PART
cana-1628	48	12	accuracy	accuracy	NOUN
cana-1628	48	13	,	,	PUNCT
cana-1628	48	14	computational	computational	ADJ
cana-1628	48	15	efficiency	efficiency	NOUN
cana-1628	48	16	,	,	PUNCT
cana-1628	48	17	generalizability	generalizability	NOUN
cana-1628	48	18	,	,	PUNCT
cana-1628	48	19	and	and	CCONJ
cana-1628	48	20	robustness	robustness	NOUN
cana-1628	48	21	.	.	PUNCT
cana-1628	49	1	among	among	ADP
cana-1628	49	2	all	all	DET
cana-1628	49	3	the	the	DET
cana-1628	49	4	investigated	investigate	VERB
cana-1628	49	5	deep	deep	ADJ
cana-1628	49	6	learning	learning	NOUN
cana-1628	49	7	techniques	technique	NOUN
cana-1628	49	8	,	,	PUNCT
cana-1628	49	9	the	the	DET
cana-1628	49	10	gated	gate	VERB
cana-1628	49	11	24h	24h	NOUN
cana-1628	49	12	cnn	cnn	PROPN
cana-1628	49	13	model	model	NOUN
cana-1628	49	14	achieved	achieve	VERB
cana-1628	49	15	the	the	DET
cana-1628	49	16	best	good	ADJ
cana-1628	49	17	performance	performance	NOUN
cana-1628	49	18	,	,	PUNCT
cana-1628	49	19	improving	improve	VERB
cana-1628	49	20	the	the	DET
cana-1628	49	21	forecasting	forecasting	NOUN
cana-1628	49	22	accuracy	accuracy	NOUN
cana-1628	49	23	by	by	ADP
cana-1628	49	24	22.6	22.6	NUM
cana-1628	49	25	%	%	NOUN
cana-1628	49	26	compared	compare	VERB
cana-1628	49	27	to	to	ADP
cana-1628	49	28	the	the	DET
cana-1628	49	29	seasonal	seasonal	ADJ
cana-1628	49	30	arimax	arimax	NOUN
cana-1628	49	31	.	.	PUNCT
cana-1628	50	1	n.	n.	NOUN
cana-1628	50	2	somu	somu	NOUN
cana-1628	50	3	,	,	PUNCT
cana-1628	50	4	et	et	PROPN
cana-1628	50	5	al	al	PROPN
cana-1628	50	6	.	.	PUNCT
cana-1628	51	1	in	in	ADP
cana-1628	51	2	[	[	X
cana-1628	51	3	27	27	NUM
cana-1628	51	4	]	]	PUNCT
cana-1628	51	5	proposed	propose	VERB
cana-1628	51	6	a	a	DET
cana-1628	51	7	hybrid	hybrid	ADJ
cana-1628	51	8	model	model	NOUN
cana-1628	51	9	for	for	ADP
cana-1628	51	10	building	build	VERB
cana-1628	51	11	energy	energy	NOUN
cana-1628	51	12	consumption	consumption	NOUN
cana-1628	51	13	forecasting	forecasting	NOUN
cana-1628	51	14	using	use	VERB
cana-1628	51	15	long	long	ADJ
cana-1628	51	16	short	short	ADJ
cana-1628	51	17	-	-	PUNCT
cana-1628	51	18	term	term	NOUN
cana-1628	51	19	memory	memory	NOUN
cana-1628	51	20	networks	network	NOUN
cana-1628	51	21	.	.	PUNCT
cana-1628	52	1	in	in	ADP
cana-1628	52	2	this	this	DET
cana-1628	52	3	work	work	NOUN
cana-1628	52	4	,	,	PUNCT
cana-1628	52	5	a	a	DET
cana-1628	52	6	novel	novel	NOUN
cana-1628	52	7	haar	haar	X
cana-1628	52	8	wavelet	wavelet	NOUN
cana-1628	52	9	-	-	PUNCT
cana-1628	52	10	based	base	VERB
cana-1628	52	11	mutation	mutation	NOUN
cana-1628	52	12	operator	operator	NOUN
cana-1628	52	13	was	be	AUX
cana-1628	52	14	introduced	introduce	VERB
cana-1628	52	15	to	to	PART
cana-1628	52	16	improve	improve	VERB
cana-1628	52	17	the	the	DET
cana-1628	52	18	divergence	divergence	ADJ
cana-1628	52	19	nature	nature	NOUN
cana-1628	52	20	of	of	ADP
cana-1628	52	21	the	the	DET
cana-1628	52	22	sine	sine	ADJ
cana-1628	52	23	cosine	cosine	NOUN
cana-1628	52	24	optimization	optimization	NOUN
cana-1628	52	25	algorithm	algorithm	NOUN
cana-1628	52	26	while	while	SCONJ
cana-1628	52	27	dealing	deal	VERB
cana-1628	52	28	with	with	ADP
cana-1628	52	29	hyperparameter	hyperparameter	NOUN
cana-1628	52	30	tuning	tune	VERB
cana-1628	52	31	using	use	VERB
cana-1628	52	32	the	the	DET
cana-1628	52	33	sine	sine	ADJ
cana-1628	52	34	cosine	cosine	NOUN
cana-1628	52	35	optimization	optimization	NOUN
cana-1628	52	36	algorithm	algorithm	NOUN
cana-1628	52	37	.	.	PUNCT
cana-1628	53	1	on	on	ADP
cana-1628	53	2	the	the	DET
cana-1628	53	3	other	other	ADJ
cana-1628	53	4	hand	hand	NOUN
cana-1628	53	5	,	,	PUNCT
cana-1628	53	6	a	a	DET
cana-1628	53	7	hybrid	hybrid	NOUN
cana-1628	53	8	of	of	ADP
cana-1628	53	9	wavelet	wavelet	NOUN
cana-1628	53	10	transform	transform	NOUN
cana-1628	53	11	and	and	CCONJ
cana-1628	53	12	machine	machine	NOUN
cana-1628	53	13	learning	learning	NOUN
cana-1628	53	14	model	model	NOUN
cana-1628	53	15	is	be	AUX
cana-1628	53	16	proposed	propose	VERB
cana-1628	53	17	in	in	ADP
cana-1628	53	18	[	[	X
cana-1628	53	19	26	26	NUM
cana-1628	53	20	]	]	PUNCT
cana-1628	53	21	to	to	PART
cana-1628	53	22	estimate	estimate	VERB
cana-1628	53	23	electrical	electrical	ADJ
cana-1628	53	24	load	load	NOUN
cana-1628	53	25	consumption	consumption	NOUN
cana-1628	53	26	using	use	VERB
cana-1628	53	27	the	the	DET
cana-1628	53	28	historical	historical	ADJ
cana-1628	53	29	time	time	NOUN
cana-1628	53	30	-	-	PUNCT
cana-1628	53	31	series	series	NOUN
cana-1628	53	32	information	information	NOUN
cana-1628	53	33	of	of	ADP
cana-1628	53	34	energy	energy	NOUN
cana-1628	53	35	usage	usage	NOUN
cana-1628	53	36	.	.	PUNCT
cana-1628	54	1	to	to	PART
cana-1628	54	2	investigate	investigate	VERB
cana-1628	54	3	the	the	DET
cana-1628	54	4	effectiveness	effectiveness	NOUN
cana-1628	54	5	of	of	ADP
cana-1628	54	6	combining	combine	VERB
cana-1628	54	7	different	different	ADJ
cana-1628	54	8	deep	deep	ADJ
cana-1628	54	9	learning	learning	NOUN
cana-1628	54	10	algorithms	algorithm	NOUN
cana-1628	54	11	for	for	ADP
cana-1628	54	12	estimating	estimate	VERB
cana-1628	54	13	residential	residential	ADJ
cana-1628	54	14	household	household	NOUN
cana-1628	54	15	energy	energy	NOUN
cana-1628	54	16	consumption	consumption	NOUN
cana-1628	54	17	,	,	PUNCT
cana-1628	54	18	authors	author	NOUN
cana-1628	54	19	in	in	ADP
cana-1628	54	20	[	[	X
cana-1628	54	21	14	14	NUM
cana-1628	54	22	]	]	PUNCT
cana-1628	54	23	employed	employ	VERB
cana-1628	54	24	a	a	DET
cana-1628	54	25	hybrid	hybrid	ADJ
cana-1628	54	26	ensemble	ensemble	ADJ
cana-1628	54	27	model	model	NOUN
cana-1628	54	28	consisting	consist	VERB
cana-1628	54	29	of	of	ADP
cana-1628	54	30	cnn	cnn	PROPN
cana-1628	54	31	,	,	PUNCT
cana-1628	54	32	multilayer	multilayer	ADJ
cana-1628	54	33	lstm	lstm	NOUN
cana-1628	54	34	,	,	PUNCT
cana-1628	54	35	and	and	CCONJ
cana-1628	54	36	bilstm	bilstm	NOUN
cana-1628	54	37	algorithms	algorithm	NOUN
cana-1628	54	38	,	,	PUNCT
cana-1628	54	39	by	by	ADP
cana-1628	54	40	which	which	PRON
cana-1628	54	41	the	the	DET
cana-1628	54	42	cnn	cnn	PROPN
cana-1628	54	43	framework	framework	NOUN
cana-1628	54	44	can	can	AUX
cana-1628	54	45	extract	extract	VERB
cana-1628	54	46	spatial	spatial	ADJ
cana-1628	54	47	and	and	CCONJ
cana-1628	54	48	nonlinear	nonlinear	ADJ
cana-1628	54	49	patterns	pattern	NOUN
cana-1628	54	50	of	of	ADP
cana-1628	54	51	the	the	DET
cana-1628	54	52	energy	energy	NOUN
cana-1628	54	53	data	datum	NOUN
cana-1628	54	54	and	and	CCONJ
cana-1628	54	55	multilayer	multilayer	ADJ
cana-1628	54	56	lstm	lstm	NOUN
cana-1628	54	57	used	use	VERB
cana-1628	54	58	to	to	PART
cana-1628	54	59	learn	learn	VERB
cana-1628	54	60	temporal	temporal	ADJ
cana-1628	54	61	dependencies	dependency	NOUN
cana-1628	54	62	.	.	PUNCT
cana-1628	55	1	an	an	DET
cana-1628	55	2	ensemble	ensemble	ADJ
cana-1628	55	3	method	method	NOUN
cana-1628	55	4	[	[	X
cana-1628	55	5	36	36	NUM
cana-1628	55	6	]	]	PUNCT
cana-1628	55	7	is	be	AUX
cana-1628	55	8	developed	develop	VERB
cana-1628	55	9	to	to	PART
cana-1628	55	10	forecast	forecast	VERB
cana-1628	55	11	the	the	DET
cana-1628	55	12	residential	residential	ADJ
cana-1628	55	13	short	short	ADJ
cana-1628	55	14	-	-	PUNCT
cana-1628	55	15	term	term	NOUN
cana-1628	55	16	energy	energy	NOUN
cana-1628	55	17	consumption	consumption	NOUN
cana-1628	55	18	.	.	PUNCT
cana-1628	56	1	vector	vector	NOUN
cana-1628	56	2	auto	auto	NOUN
cana-1628	56	3	-	-	PUNCT
cana-1628	56	4	regression	regression	NOUN
cana-1628	56	5	,	,	PUNCT
cana-1628	56	6	gaussian	gaussian	ADJ
cana-1628	56	7	process	process	NOUN
cana-1628	56	8	regression	regression	NOUN
cana-1628	56	9	,	,	PUNCT
cana-1628	56	10	and	and	CCONJ
cana-1628	56	11	the	the	DET
cana-1628	56	12	long	long	ADJ
cana-1628	56	13	short	short	ADJ
cana-1628	56	14	-	-	PUNCT
cana-1628	56	15	term	term	NOUN
cana-1628	56	16	memory	memory	NOUN
cana-1628	56	17	neural	neural	ADJ
cana-1628	56	18	network	network	NOUN
cana-1628	56	19	model	model	NOUN
cana-1628	56	20	were	be	AUX
cana-1628	56	21	trained	train	VERB
cana-1628	56	22	as	as	ADP
cana-1628	56	23	base	base	NOUN
cana-1628	56	24	learners	learner	NOUN
cana-1628	56	25	.	.	PUNCT
cana-1628	57	1	another	another	DET
cana-1628	57	2	ensemble	ensemble	ADJ
cana-1628	57	3	method	method	NOUN
cana-1628	57	4	is	be	AUX
cana-1628	57	5	proposed	propose	VERB
cana-1628	57	6	in	in	ADP
cana-1628	57	7	[	[	X
cana-1628	57	8	23	23	NUM
cana-1628	57	9	]	]	PUNCT
cana-1628	57	10	by	by	ADP
cana-1628	57	11	combining	combine	VERB
cana-1628	57	12	the	the	DET
cana-1628	57	13	deep	deep	ADJ
cana-1628	57	14	lstm	lstm	NOUN
cana-1628	57	15	and	and	CCONJ
cana-1628	57	16	auto	auto	NOUN
cana-1628	57	17	-	-	PUNCT
cana-1628	57	18	regressive	regressive	ADJ
cana-1628	57	19	integrated	integrated	ADJ
cana-1628	57	20	moving	move	VERB
cana-1628	57	21	average	average	ADJ
cana-1628	57	22	(	(	PUNCT
cana-1628	57	23	arima	arima	NOUN
cana-1628	57	24	)	)	PUNCT
cana-1628	57	25	models	model	NOUN
cana-1628	57	26	.	.	PUNCT
cana-1628	58	1	in	in	ADP
cana-1628	58	2	this	this	DET
cana-1628	58	3	work	work	NOUN
cana-1628	58	4	,	,	PUNCT
cana-1628	58	5	the	the	DET
cana-1628	58	6	arima	arima	PROPN
cana-1628	58	7	was	be	AUX
cana-1628	58	8	used	use	VERB
cana-1628	58	9	to	to	PART
cana-1628	58	10	capture	capture	VERB
cana-1628	58	11	the	the	DET
cana-1628	58	12	stationary	stationary	ADJ
cana-1628	58	13	pattern	pattern	NOUN
cana-1628	58	14	of	of	ADP
cana-1628	58	15	load	load	NOUN
cana-1628	58	16	data	datum	NOUN
cana-1628	58	17	,	,	PUNCT
cana-1628	58	18	and	and	CCONJ
cana-1628	58	19	the	the	DET
cana-1628	58	20	nonlinearity	nonlinearity	NOUN
cana-1628	58	21	of	of	ADP
cana-1628	58	22	the	the	DET
cana-1628	58	23	complex	complex	ADJ
cana-1628	58	24	energy	energy	NOUN
cana-1628	58	25	consumption	consumption	NOUN
cana-1628	58	26	data	datum	NOUN
cana-1628	58	27	was	be	AUX
cana-1628	58	28	tackled	tackle	VERB
cana-1628	58	29	using	use	VERB
cana-1628	58	30	lstm	lstm	ADJ
cana-1628	58	31	architecture	architecture	NOUN
cana-1628	58	32	.	.	PUNCT
cana-1628	59	1	the	the	DET
cana-1628	59	2	performance	performance	NOUN
cana-1628	59	3	of	of	ADP
cana-1628	59	4	the	the	DET
cana-1628	59	5	proposed	propose	VERB
cana-1628	59	6	model	model	NOUN
cana-1628	59	7	surpasses	surpass	VERB
cana-1628	59	8	existing	exist	VERB
cana-1628	59	9	short	short	ADJ
cana-1628	59	10	-	-	PUNCT
cana-1628	59	11	term	term	NOUN
cana-1628	59	12	load	load	NOUN
cana-1628	59	13	forecasting	forecasting	NOUN
cana-1628	59	14	models	model	NOUN
cana-1628	59	15	with	with	ADP
cana-1628	59	16	less	less	ADJ
cana-1628	59	17	computation	computation	NOUN
cana-1628	59	18	complexity	complexity	NOUN
cana-1628	59	19	.	.	PUNCT
cana-1628	60	1	moreover	moreover	ADV
cana-1628	60	2	,	,	PUNCT
cana-1628	60	3	ensemble	ensemble	ADJ
cana-1628	60	4	learning	learning	NOUN
cana-1628	60	5	methods	method	NOUN
cana-1628	60	6	provide	provide	VERB
cana-1628	60	7	a	a	DET
cana-1628	60	8	powerful	powerful	ADJ
cana-1628	60	9	tool	tool	NOUN
cana-1628	60	10	for	for	ADP
cana-1628	60	11	improving	improve	VERB
cana-1628	60	12	accuracy	accuracy	NOUN
cana-1628	60	13	and	and	CCONJ
cana-1628	60	14	stability	stability	NOUN
cana-1628	60	15	in	in	ADP
cana-1628	60	16	power	power	NOUN
cana-1628	60	17	load	load	NOUN
cana-1628	60	18	forecasting	forecasting	NOUN
cana-1628	60	19	by	by	ADP
cana-1628	60	20	leveraging	leverage	VERB
cana-1628	60	21	the	the	DET
cana-1628	60	22	strengths	strength	NOUN
cana-1628	60	23	of	of	ADP
cana-1628	60	24	multiple	multiple	ADJ
cana-1628	60	25	predictor	predictor	NOUN
cana-1628	60	26	techniques	technique	NOUN
cana-1628	60	27	[	[	X
cana-1628	60	28	30	30	NUM
cana-1628	60	29	,	,	PUNCT
cana-1628	60	30	11	11	NUM
cana-1628	60	31	,	,	PUNCT
cana-1628	60	32	29	29	NUM
cana-1628	60	33	]	]	PUNCT
cana-1628	60	34	.	.	PUNCT
cana-1628	61	1	hadjout	hadjout	NOUN
cana-1628	61	2	et	et	PROPN
cana-1628	61	3	al	al	PROPN
cana-1628	61	4	.	.	PUNCT
cana-1628	62	1	[	[	X
cana-1628	62	2	10	10	NUM
cana-1628	62	3	]	]	PUNCT
cana-1628	62	4	introduced	introduce	VERB
cana-1628	62	5	an	an	DET
cana-1628	62	6	ensemble	ensemble	ADJ
cana-1628	62	7	model	model	NOUN
cana-1628	62	8	for	for	ADP
cana-1628	62	9	monthly	monthly	ADJ
cana-1628	62	10	industrial	industrial	ADJ
cana-1628	62	11	energy	energy	NOUN
cana-1628	62	12	consumption	consumption	NOUN
cana-1628	62	13	forecasting	forecasting	NOUN
cana-1628	62	14	.	.	PUNCT
cana-1628	63	1	the	the	DET
cana-1628	63	2	proposed	propose	VERB
cana-1628	63	3	model	model	NOUN
cana-1628	63	4	combines	combine	VERB
cana-1628	63	5	lstm	lstm	PROPN
cana-1628	63	6	,	,	PUNCT
cana-1628	63	7	gru	gru	PROPN
cana-1628	63	8	,	,	PUNCT
cana-1628	63	9	and	and	CCONJ
cana-1628	63	10	tcn	tcn	PROPN
cana-1628	63	11	based	base	VERB
cana-1628	63	12	on	on	ADP
cana-1628	63	13	weighted	weight	VERB
cana-1628	63	14	averages	average	NOUN
cana-1628	63	15	.	.	PUNCT
cana-1628	64	1	similarly	similarly	ADV
cana-1628	64	2	,	,	PUNCT
cana-1628	64	3	w.	w.	PROPN
cana-1628	64	4	khan	khan	PROPN
cana-1628	64	5	et	et	PROPN
cana-1628	64	6	al	al	PROPN
cana-1628	64	7	.	.	PUNCT
cana-1628	65	1	[	[	X
cana-1628	65	2	16	16	NUM
cana-1628	65	3	]	]	PUNCT
cana-1628	65	4	developed	develop	VERB
cana-1628	65	5	an	an	DET
cana-1628	65	6	effective	effective	ADJ
cana-1628	65	7	ensemble	ensemble	ADJ
cana-1628	65	8	model	model	NOUN
cana-1628	65	9	but	but	CCONJ
cana-1628	65	10	at	at	ADP
cana-1628	65	11	this	this	DET
cana-1628	65	12	time	time	NOUN
cana-1628	65	13	the	the	DET
cana-1628	65	14	authors	author	NOUN
cana-1628	65	15	employed	employ	VERB
cana-1628	65	16	a	a	DET
cana-1628	65	17	stacking	stacking	NOUN
cana-1628	65	18	-	-	PUNCT
cana-1628	65	19	based	base	VERB
cana-1628	65	20	ensemble	ensemble	ADJ
cana-1628	65	21	approach	approach	NOUN
cana-1628	65	22	using	use	VERB
cana-1628	65	23	simple	simple	ADJ
cana-1628	65	24	neural	neural	ADJ
cana-1628	65	25	networks	network	NOUN
cana-1628	65	26	(	(	PUNCT
cana-1628	65	27	ann	ann	PROPN
cana-1628	65	28	)	)	PUNCT
cana-1628	65	29	and	and	CCONJ
cana-1628	65	30	lstm	lstm	NOUN
cana-1628	65	31	as	as	ADP
cana-1628	65	32	base	base	NOUN
cana-1628	65	33	learners	learner	NOUN
cana-1628	65	34	for	for	ADP
cana-1628	65	35	solar	solar	ADJ
cana-1628	65	36	energy	energy	NOUN
cana-1628	65	37	forecasting	forecasting	NOUN
cana-1628	65	38	.	.	PUNCT
cana-1628	66	1	xgboost	xgboost	PROPN
cana-1628	67	1	algorithm	algorithm	PROPN
cana-1628	67	2	was	be	AUX
cana-1628	67	3	used	use	VERB
cana-1628	67	4	as	as	ADP
cana-1628	67	5	a	a	DET
cana-1628	67	6	meta	meta	ADJ
cana-1628	67	7	-	-	PUNCT
cana-1628	67	8	learner	learner	NOUN
cana-1628	67	9	to	to	PART
cana-1628	67	10	combine	combine	VERB
cana-1628	67	11	the	the	DET
cana-1628	67	12	base	base	NOUN
cana-1628	67	13	models	model	NOUN
cana-1628	67	14	and	and	CCONJ
cana-1628	67	15	the	the	DET
cana-1628	67	16	proposed	propose	VERB
cana-1628	67	17	model	model	NOUN
cana-1628	67	18	exhibited	exhibit	VERB
cana-1628	67	19	better	well	ADJ
cana-1628	67	20	consistency	consistency	NOUN
cana-1628	67	21	and	and	CCONJ
cana-1628	67	22	stability	stability	NOUN
cana-1628	67	23	in	in	ADP
cana-1628	67	24	different	different	ADJ
cana-1628	67	25	cases	case	NOUN
cana-1628	67	26	.	.	PUNCT
cana-1628	68	1	in	in	ADP
cana-1628	68	2	general	general	ADJ
cana-1628	68	3	,	,	PUNCT
cana-1628	68	4	most	most	ADJ
cana-1628	68	5	of	of	ADP
cana-1628	68	6	the	the	DET
cana-1628	68	7	related	relate	VERB
cana-1628	68	8	works	work	NOUN
cana-1628	68	9	[	[	X
cana-1628	68	10	16	16	NUM
cana-1628	68	11	,	,	PUNCT
cana-1628	68	12	23	23	NUM
cana-1628	68	13	,	,	PUNCT
cana-1628	68	14	24	24	NUM
cana-1628	68	15	]	]	PUNCT
cana-1628	68	16	have	have	AUX
cana-1628	68	17	employed	employ	VERB
cana-1628	68	18	grid	grid	NOUN
cana-1628	68	19	search	search	NOUN
cana-1628	68	20	for	for	ADP
cana-1628	68	21	optimization	optimization	NOUN
cana-1628	68	22	tasks	task	NOUN
cana-1628	68	23	to	to	PART
cana-1628	68	24	improve	improve	VERB
cana-1628	68	25	deep	deep	ADJ
cana-1628	68	26	learning	learning	NOUN
cana-1628	68	27	and	and	CCONJ
cana-1628	68	28	ensemble	ensemble	ADJ
cana-1628	68	29	model	model	NOUN
cana-1628	68	30	performance	performance	NOUN
cana-1628	68	31	.	.	PUNCT
cana-1628	69	1	however	however	ADV
cana-1628	69	2	,	,	PUNCT
cana-1628	69	3	this	this	DET
cana-1628	69	4	optimization	optimization	NOUN
cana-1628	69	5	approach	approach	NOUN
cana-1628	69	6	is	be	AUX
cana-1628	69	7	highly	highly	ADV
cana-1628	69	8	criticized	criticize	VERB
cana-1628	69	9	for	for	ADP
cana-1628	69	10	high	high	ADJ
cana-1628	69	11	computation	computation	NOUN
cana-1628	69	12	time	time	NOUN
cana-1628	69	13	requirements	requirement	NOUN
cana-1628	69	14	and	and	CCONJ
cana-1628	69	15	is	be	AUX
cana-1628	69	16	ineffective	ineffective	ADJ
cana-1628	69	17	when	when	SCONJ
cana-1628	69	18	the	the	DET
cana-1628	69	19	number	number	NOUN
cana-1628	69	20	of	of	ADP
cana-1628	69	21	hyperparameter	hyperparameter	NOUN
cana-1628	69	22	spaces	space	NOUN
cana-1628	69	23	and	and	CCONJ
cana-1628	69	24	the	the	DET
cana-1628	69	25	type	type	NOUN
cana-1628	69	26	of	of	ADP
cana-1628	69	27	hyperparameters	hyperparameter	NOUN
cana-1628	69	28	have	have	AUX
cana-1628	69	29	been	be	AUX
cana-1628	69	30	increased	increase	VERB
cana-1628	69	31	.	.	PUNCT
cana-1628	70	1	in	in	ADP
cana-1628	70	2	addition	addition	NOUN
cana-1628	70	3	,	,	PUNCT
cana-1628	70	4	the	the	DET
cana-1628	70	5	presence	presence	NOUN
cana-1628	70	6	of	of	ADP
cana-1628	70	7	outliers	outlier	NOUN
cana-1628	70	8	in	in	ADP
cana-1628	70	9	a	a	DET
cana-1628	70	10	dataset	dataset	NOUN
cana-1628	70	11	degrades	degrade	VERB
cana-1628	70	12	model	model	NOUN
cana-1628	70	13	prediction	prediction	NOUN
cana-1628	70	14	performance	performance	NOUN
cana-1628	70	15	and	and	CCONJ
cana-1628	70	16	reduces	reduce	VERB
cana-1628	70	17	model	model	NOUN
cana-1628	70	18	generalization	generalization	NOUN
cana-1628	70	19	abilities	ability	NOUN
cana-1628	70	20	.	.	PUNCT
cana-1628	71	1	this	this	DET
cana-1628	71	2	problem	problem	NOUN
cana-1628	71	3	has	have	AUX
cana-1628	71	4	been	be	AUX
cana-1628	71	5	observed	observe	VERB
cana-1628	71	6	in	in	ADP
cana-1628	71	7	the	the	DET
cana-1628	71	8	above	above	ADV
cana-1628	71	9	-	-	PUNCT
cana-1628	71	10	mentioned	mention	VERB
cana-1628	71	11	related	relate	VERB
cana-1628	71	12	works	work	NOUN
cana-1628	71	13	that	that	PRON
cana-1628	71	14	little	little	ADJ
cana-1628	71	15	attention	attention	NOUN
cana-1628	71	16	is	be	AUX
cana-1628	71	17	communications	communication	NOUN
cana-1628	71	18	on	on	ADP
cana-1628	71	19	applied	apply	VERB
cana-1628	71	20	nonlinear	nonlinear	ADJ
cana-1628	71	21	analysis	analysis	NOUN
cana-1628	71	22	issn	issn	NOUN
cana-1628	71	23	:	:	PUNCT
cana-1628	71	24	1074	1074	NUM
cana-1628	71	25	-	-	PUNCT
cana-1628	71	26	133x	133x	NUM
cana-1628	71	27	vol	vol	NOUN
cana-1628	71	28	32	32	NUM
cana-1628	71	29	no	no	NOUN
cana-1628	71	30	.	.	NOUN
cana-1628	71	31	1	1	NUM
cana-1628	71	32	(	(	PUNCT
cana-1628	71	33	2025	2025	NUM
cana-1628	71	34	)	)	PUNCT
cana-1628	71	35	158	158	NUM
cana-1628	71	36	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	71	37	given	give	VERB
cana-1628	71	38	to	to	ADP
cana-1628	71	39	the	the	DET
cana-1628	71	40	fine	fine	ADV
cana-1628	71	41	-	-	PUNCT
cana-1628	71	42	graining	graining	NOUN
cana-1628	71	43	of	of	ADP
cana-1628	71	44	the	the	DET
cana-1628	71	45	input	input	NOUN
cana-1628	71	46	data	datum	NOUN
cana-1628	71	47	,	,	PUNCT
cana-1628	71	48	which	which	PRON
cana-1628	71	49	accounts	account	VERB
cana-1628	71	50	for	for	ADP
cana-1628	71	51	model	model	NOUN
cana-1628	71	52	complexity	complexity	NOUN
cana-1628	71	53	and	and	CCONJ
cana-1628	71	54	larger	large	ADJ
cana-1628	71	55	prediction	prediction	NOUN
cana-1628	71	56	errors	error	NOUN
cana-1628	71	57	[	[	X
cana-1628	71	58	35	35	NUM
cana-1628	71	59	]	]	SYM
cana-1628	71	60	.	.	PUNCT
cana-1628	72	1	3	3	NUM
cana-1628	72	2	methodology	methodology	NOUN
cana-1628	72	3	the	the	DET
cana-1628	72	4	proposed	propose	VERB
cana-1628	72	5	method	method	NOUN
cana-1628	72	6	comprises	comprise	VERB
cana-1628	72	7	three	three	NUM
cana-1628	72	8	main	main	ADJ
cana-1628	72	9	phases	phase	NOUN
cana-1628	72	10	:	:	PUNCT
cana-1628	72	11	(	(	PUNCT
cana-1628	72	12	1	1	X
cana-1628	72	13	)	)	PUNCT
cana-1628	72	14	data	datum	NOUN
cana-1628	72	15	preprocessing	preprocessing	NOUN
cana-1628	72	16	and	and	CCONJ
cana-1628	72	17	clustering	cluster	VERB
cana-1628	72	18	for	for	ADP
cana-1628	72	19	energy	energy	NOUN
cana-1628	72	20	consumption	consumption	NOUN
cana-1628	72	21	pattern	pattern	NOUN
cana-1628	72	22	identification	identification	NOUN
cana-1628	72	23	;	;	PUNCT
cana-1628	72	24	(	(	PUNCT
cana-1628	72	25	2	2	X
cana-1628	72	26	)	)	PUNCT
cana-1628	72	27	deep	deep	ADJ
cana-1628	72	28	learning	learning	NOUN
cana-1628	72	29	model	model	NOUN
cana-1628	72	30	hyperparameter	hyperparameter	NOUN
cana-1628	72	31	tuning	tune	VERB
cana-1628	72	32	using	use	VERB
cana-1628	72	33	a	a	DET
cana-1628	72	34	bayesian	bayesian	NOUN
cana-1628	72	35	optimization	optimization	NOUN
cana-1628	72	36	algorithm	algorithm	NOUN
cana-1628	72	37	and	and	CCONJ
cana-1628	72	38	(	(	PUNCT
cana-1628	72	39	3	3	X
cana-1628	72	40	)	)	PUNCT
cana-1628	72	41	individual	individual	ADJ
cana-1628	72	42	base	base	NOUN
cana-1628	72	43	model	model	NOUN
cana-1628	72	44	training	training	NOUN
cana-1628	72	45	and	and	CCONJ
cana-1628	72	46	model	model	NOUN
cana-1628	72	47	fusion	fusion	NOUN
cana-1628	72	48	to	to	PART
cana-1628	72	49	develop	develop	VERB
cana-1628	72	50	ensemble	ensemble	ADJ
cana-1628	72	51	model	model	NOUN
cana-1628	72	52	and	and	CCONJ
cana-1628	72	53	performance	performance	NOUN
cana-1628	72	54	evaluation	evaluation	NOUN
cana-1628	72	55	.	.	PUNCT
cana-1628	73	1	in	in	ADP
cana-1628	73	2	general	general	ADJ
cana-1628	73	3	,	,	PUNCT
cana-1628	73	4	figure	figure	NOUN
cana-1628	73	5	11	11	NUM
cana-1628	73	6	shows	show	VERB
cana-1628	73	7	the	the	DET
cana-1628	73	8	details	detail	NOUN
cana-1628	73	9	of	of	ADP
cana-1628	73	10	the	the	DET
cana-1628	73	11	proposed	propose	VERB
cana-1628	73	12	model	model	NOUN
cana-1628	73	13	.	.	PUNCT
cana-1628	74	1	3.1	3.1	NUM
cana-1628	74	2	description	description	NOUN
cana-1628	74	3	of	of	ADP
cana-1628	74	4	data	datum	NOUN
cana-1628	74	5	and	and	CCONJ
cana-1628	74	6	data	datum	NOUN
cana-1628	74	7	preprocessing	preprocesse	VERB
cana-1628	74	8	the	the	DET
cana-1628	74	9	data	datum	NOUN
cana-1628	74	10	for	for	ADP
cana-1628	74	11	this	this	DET
cana-1628	74	12	study	study	NOUN
cana-1628	74	13	was	be	AUX
cana-1628	74	14	collected	collect	VERB
cana-1628	74	15	from	from	ADP
cana-1628	74	16	the	the	DET
cana-1628	74	17	ethiopian	ethiopian	ADJ
cana-1628	74	18	electric	electric	ADJ
cana-1628	74	19	utility	utility	NOUN
cana-1628	74	20	.	.	PUNCT
cana-1628	75	1	the	the	DET
cana-1628	75	2	collected	collect	VERB
cana-1628	75	3	data	data	NOUN
cana-1628	75	4	is	be	AUX
cana-1628	75	5	about	about	ADV
cana-1628	75	6	two	two	NUM
cana-1628	75	7	districts	district	NOUN
cana-1628	75	8	of	of	ADP
cana-1628	75	9	addis	addis	PROPN
cana-1628	75	10	ababa	ababa	PROPN
cana-1628	75	11	city	city	PROPN
cana-1628	75	12	,	,	PUNCT
cana-1628	75	13	south	south	NOUN
cana-1628	75	14	and	and	CCONJ
cana-1628	75	15	west	west	PROPN
cana-1628	75	16	addis	addis	PROPN
cana-1628	75	17	ababa	ababa	PROPN
cana-1628	75	18	(	(	PUNCT
cana-1628	75	19	hereafter	hereafter	ADV
cana-1628	75	20	south	south	PROPN
cana-1628	75	21	aa	aa	NOUN
cana-1628	75	22	and	and	CCONJ
cana-1628	75	23	west	west	PROPN
cana-1628	75	24	aa	aa	PROPN
cana-1628	75	25	)	)	PUNCT
cana-1628	75	26	district	district	NOUN
cana-1628	75	27	’s	’s	PART
cana-1628	75	28	monthly	monthly	ADJ
cana-1628	75	29	residential	residential	ADJ
cana-1628	75	30	energy	energy	NOUN
cana-1628	75	31	consumption	consumption	NOUN
cana-1628	75	32	data	datum	NOUN
cana-1628	75	33	ranging	range	VERB
cana-1628	75	34	from	from	ADP
cana-1628	75	35	may	may	PROPN
cana-1628	75	36	2019	2019	NUM
cana-1628	75	37	to	to	ADP
cana-1628	75	38	january	january	PROPN
cana-1628	75	39	2021	2021	NUM
cana-1628	75	40	.	.	PUNCT
cana-1628	76	1	since	since	SCONJ
cana-1628	76	2	each	each	DET
cana-1628	76	3	month	month	NOUN
cana-1628	76	4	’s	’s	PART
cana-1628	76	5	consumption	consumption	NOUN
cana-1628	76	6	data	datum	NOUN
cana-1628	76	7	was	be	AUX
cana-1628	76	8	obtained	obtain	VERB
cana-1628	76	9	from	from	ADP
cana-1628	76	10	a	a	DET
cana-1628	76	11	different	different	ADJ
cana-1628	76	12	monthly	monthly	ADJ
cana-1628	76	13	bill	bill	NOUN
cana-1628	76	14	report	report	NOUN
cana-1628	76	15	in	in	ADP
cana-1628	76	16	separate	separate	ADJ
cana-1628	76	17	excel	excel	NOUN
cana-1628	76	18	files	file	NOUN
cana-1628	76	19	for	for	ADP
cana-1628	76	20	each	each	DET
cana-1628	76	21	month	month	NOUN
cana-1628	76	22	,	,	PUNCT
cana-1628	76	23	it	it	PRON
cana-1628	76	24	is	be	AUX
cana-1628	76	25	necessary	necessary	ADJ
cana-1628	76	26	to	to	PART
cana-1628	76	27	combine	combine	VERB
cana-1628	76	28	the	the	DET
cana-1628	76	29	individual	individual	ADJ
cana-1628	76	30	monthly	monthly	ADJ
cana-1628	76	31	file	file	NOUN
cana-1628	76	32	into	into	ADP
cana-1628	76	33	a	a	DET
cana-1628	76	34	single	single	ADJ
cana-1628	76	35	,	,	PUNCT
cana-1628	76	36	sequentially	sequentially	ADV
cana-1628	76	37	arranged	arrange	VERB
cana-1628	76	38	dataset	dataset	NOUN
cana-1628	76	39	for	for	ADP
cana-1628	76	40	each	each	DET
cana-1628	76	41	district	district	NOUN
cana-1628	76	42	as	as	ADP
cana-1628	76	43	a	a	DET
cana-1628	76	44	figure	figure	NOUN
cana-1628	76	45	1	1	NUM
cana-1628	76	46	shows	show	NOUN
cana-1628	76	47	.	.	PUNCT
cana-1628	77	1	figure	figure	VERB
cana-1628	77	2	1	1	NUM
cana-1628	77	3	:	:	PUNCT
cana-1628	77	4	data	data	NOUN
cana-1628	77	5	aggregation	aggregation	NOUN
cana-1628	77	6	there	there	PRON
cana-1628	77	7	were	be	VERB
cana-1628	77	8	many	many	ADJ
cana-1628	77	9	missing	missing	ADJ
cana-1628	77	10	values	value	NOUN
cana-1628	77	11	in	in	ADP
cana-1628	77	12	each	each	DET
cana-1628	77	13	month	month	NOUN
cana-1628	77	14	’s	’s	PART
cana-1628	77	15	data	datum	NOUN
cana-1628	77	16	because	because	SCONJ
cana-1628	77	17	some	some	DET
cana-1628	77	18	customers	customer	NOUN
cana-1628	77	19	may	may	AUX
cana-1628	77	20	not	not	PART
cana-1628	77	21	have	have	AUX
cana-1628	77	22	paid	pay	VERB
cana-1628	77	23	their	their	PRON
cana-1628	77	24	consumption	consumption	NOUN
cana-1628	77	25	charge	charge	NOUN
cana-1628	77	26	within	within	ADP
cana-1628	77	27	the	the	DET
cana-1628	77	28	specified	specified	ADJ
cana-1628	77	29	billing	billing	NOUN
cana-1628	77	30	period	period	NOUN
cana-1628	77	31	.	.	PUNCT
cana-1628	78	1	consequently	consequently	ADV
cana-1628	78	2	,	,	PUNCT
cana-1628	78	3	users	user	NOUN
cana-1628	78	4	who	who	PRON
cana-1628	78	5	have	have	VERB
cana-1628	78	6	zero	zero	NUM
cana-1628	78	7	electric	electric	ADJ
cana-1628	78	8	consumption	consumption	NOUN
cana-1628	78	9	for	for	ADP
cana-1628	78	10	at	at	ADV
cana-1628	78	11	least	least	ADV
cana-1628	78	12	one	one	NUM
cana-1628	78	13	month	month	NOUN
cana-1628	78	14	out	out	ADP
cana-1628	78	15	of	of	ADP
cana-1628	78	16	the	the	DET
cana-1628	78	17	20	20	NUM
cana-1628	78	18	months	month	NOUN
cana-1628	78	19	or	or	CCONJ
cana-1628	78	20	users	user	NOUN
cana-1628	78	21	who	who	PRON
cana-1628	78	22	are	be	AUX
cana-1628	78	23	absent	absent	ADJ
cana-1628	78	24	for	for	ADP
cana-1628	78	25	at	at	ADV
cana-1628	78	26	least	least	ADV
cana-1628	78	27	one	one	NUM
cana-1628	78	28	month	month	NOUN
cana-1628	78	29	out	out	ADP
cana-1628	78	30	of	of	ADP
cana-1628	78	31	20	20	NUM
cana-1628	78	32	months	month	NOUN
cana-1628	78	33	have	have	AUX
cana-1628	78	34	been	be	AUX
cana-1628	78	35	removed	remove	VERB
cana-1628	78	36	using	use	VERB
cana-1628	78	37	filtering	filter	VERB
cana-1628	78	38	techniques	technique	NOUN
cana-1628	78	39	because	because	SCONJ
cana-1628	78	40	they	they	PRON
cana-1628	78	41	are	be	AUX
cana-1628	78	42	not	not	PART
cana-1628	78	43	good	good	ADJ
cana-1628	78	44	representatives	representative	NOUN
cana-1628	78	45	of	of	ADP
cana-1628	78	46	the	the	DET
cana-1628	78	47	samples	sample	NOUN
cana-1628	78	48	.	.	PUNCT
cana-1628	79	1	after	after	ADP
cana-1628	79	2	filtering	filter	VERB
cana-1628	79	3	out	out	ADP
cana-1628	79	4	the	the	DET
cana-1628	79	5	missing	miss	VERB
cana-1628	79	6	values	value	NOUN
cana-1628	79	7	,	,	PUNCT
cana-1628	79	8	table	table	NOUN
cana-1628	79	9	1	1	NUM
cana-1628	79	10	shows	show	VERB
cana-1628	79	11	the	the	DET
cana-1628	79	12	size	size	NOUN
cana-1628	79	13	of	of	ADP
cana-1628	79	14	the	the	DET
cana-1628	79	15	input	input	NOUN
cana-1628	79	16	observation	observation	NOUN
cana-1628	79	17	.	.	PUNCT
cana-1628	80	1	therefore	therefore	ADV
cana-1628	80	2	,	,	PUNCT
cana-1628	80	3	given	give	VERB
cana-1628	80	4	the	the	DET
cana-1628	80	5	number	number	NOUN
cana-1628	80	6	of	of	ADP
cana-1628	80	7	customers	customer	NOUN
cana-1628	80	8	,	,	PUNCT
cana-1628	80	9	c	c	PROPN
cana-1628	80	10	in	in	ADP
cana-1628	80	11	each	each	DET
cana-1628	80	12	month	month	NOUN
cana-1628	80	13	for	for	ADP
cana-1628	80	14	each	each	DET
cana-1628	80	15	selected	select	VERB
cana-1628	80	16	district	district	NOUN
cana-1628	80	17	,	,	PUNCT
cana-1628	80	18	and	and	CCONJ
cana-1628	80	19	the	the	DET
cana-1628	80	20	number	number	NOUN
cana-1628	80	21	of	of	ADP
cana-1628	80	22	months	month	NOUN
cana-1628	80	23	,	,	PUNCT
cana-1628	80	24	m	m	PRON
cana-1628	80	25	,	,	PUNCT
cana-1628	80	26	the	the	DET
cana-1628	80	27	input	input	NOUN
cana-1628	80	28	observations	observation	NOUN
cana-1628	80	29	or	or	CCONJ
cana-1628	80	30	dataset	dataset	VERB
cana-1628	80	31	d	d	NOUN
cana-1628	80	32	for	for	ADP
cana-1628	80	33	each	each	DET
cana-1628	80	34	case	case	NOUN
cana-1628	80	35	study	study	NOUN
cana-1628	80	36	data	datum	NOUN
cana-1628	80	37	is	be	AUX
cana-1628	80	38	:	:	PUNCT
cana-1628	80	39	dataset	dataset	ADJ
cana-1628	80	40	,	,	PUNCT
cana-1628	80	41	d	d	X
cana-1628	80	42	=	=	PUNCT
cana-1628	80	43	c	c	X
cana-1628	80	44	*	*	PUNCT
cana-1628	80	45	m	m	PROPN
cana-1628	80	46	(	(	PUNCT
cana-1628	80	47	1	1	NUM
cana-1628	80	48	)	)	PUNCT
cana-1628	80	49	where	where	SCONJ
cana-1628	80	50	c	c	NOUN
cana-1628	80	51	is	be	AUX
cana-1628	80	52	the	the	DET
cana-1628	80	53	number	number	NOUN
cana-1628	80	54	of	of	ADP
cana-1628	80	55	customers	customer	NOUN
cana-1628	80	56	in	in	ADP
cana-1628	80	57	each	each	DET
cana-1628	80	58	month	month	NOUN
cana-1628	80	59	and	and	CCONJ
cana-1628	80	60	m	m	NOUN
cana-1628	80	61	is	be	AUX
cana-1628	80	62	the	the	DET
cana-1628	80	63	number	number	NOUN
cana-1628	80	64	of	of	ADP
cana-1628	80	65	months	month	NOUN
cana-1628	80	66	considered	consider	VERB
cana-1628	80	67	in	in	ADP
cana-1628	80	68	each	each	DET
cana-1628	80	69	district	district	NOUN
cana-1628	80	70	.	.	PUNCT
cana-1628	81	1	furthermore	furthermore	ADV
cana-1628	81	2	,	,	PUNCT
cana-1628	81	3	table	table	NOUN
cana-1628	81	4	1	1	NUM
cana-1628	81	5	summarizes	summarize	NOUN
cana-1628	81	6	the	the	DET
cana-1628	81	7	descriptive	descriptive	ADJ
cana-1628	81	8	statistics	statistic	NOUN
cana-1628	81	9	of	of	ADP
cana-1628	81	10	each	each	DET
cana-1628	81	11	district	district	NOUN
cana-1628	81	12	dataset	dataset	VERB
cana-1628	81	13	.	.	PUNCT
cana-1628	82	1	table	table	NOUN
cana-1628	82	2	1	1	NUM
cana-1628	82	3	:	:	PUNCT
cana-1628	82	4	descriptive	descriptive	ADJ
cana-1628	82	5	statistics	statistic	NOUN
cana-1628	82	6	of	of	ADP
cana-1628	82	7	the	the	DET
cana-1628	82	8	load	load	NOUN
cana-1628	82	9	consumption	consumption	NOUN
cana-1628	82	10	dataset	dataset	VERB
cana-1628	82	11	dataset	dataset	NOUN
cana-1628	82	12	count	count	NOUN
cana-1628	83	1	mean	mean	PROPN
cana-1628	83	2	max	max	PROPN
cana-1628	83	3	.	.	PUNCT
cana-1628	83	4	min	min	PROPN
cana-1628	83	5	.	.	PROPN
cana-1628	84	1	std	std	PROPN
cana-1628	84	2	.	.	PUNCT
cana-1628	85	1	skewness	skewness	NOUN
cana-1628	85	2	kurtosis	kurtosis	VERB
cana-1628	85	3	south	south	ADJ
cana-1628	85	4	aa	aa	ADJ
cana-1628	85	5	309303	309303	NUM
cana-1628	85	6	304.00	304.00	NUM
cana-1628	85	7	919	919	NUM
cana-1628	85	8	0.100	0.100	NUM
cana-1628	85	9	182.74	182.74	NUM
cana-1628	85	10	0.849	0.849	NUM
cana-1628	85	11	0.29	0.29	NUM
cana-1628	85	12	west	west	NOUN
cana-1628	85	13	aa	aa	PROPN
cana-1628	85	14	313491	313491	NUM
cana-1628	85	15	279.72	279.72	NUM
cana-1628	85	16	9120	9120	NUM
cana-1628	85	17	0.020	0.020	NUM
cana-1628	85	18	215.87	215.87	NUM
cana-1628	85	19	11.24	11.24	NUM
cana-1628	85	20	7.93	7.93	NUM
cana-1628	85	21	communications	communication	NOUN
cana-1628	85	22	on	on	ADP
cana-1628	85	23	applied	apply	VERB
cana-1628	85	24	nonlinear	nonlinear	ADJ
cana-1628	85	25	analysis	analysis	NOUN
cana-1628	85	26	issn	issn	NOUN
cana-1628	85	27	:	:	PUNCT
cana-1628	85	28	1074	1074	NUM
cana-1628	85	29	-	-	PUNCT
cana-1628	85	30	133x	133x	NUM
cana-1628	85	31	vol	vol	NOUN
cana-1628	85	32	32	32	NUM
cana-1628	85	33	no	no	NOUN
cana-1628	85	34	.	.	NOUN
cana-1628	85	35	1	1	NUM
cana-1628	85	36	(	(	PUNCT
cana-1628	85	37	2025	2025	NUM
cana-1628	85	38	)	)	PUNCT
cana-1628	85	39	159	159	NUM
cana-1628	85	40	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	85	41	moreover	moreover	ADV
cana-1628	85	42	,	,	PUNCT
cana-1628	85	43	figure	figure	NOUN
cana-1628	85	44	2	2	NUM
cana-1628	85	45	shows	show	VERB
cana-1628	85	46	the	the	DET
cana-1628	85	47	average	average	ADJ
cana-1628	85	48	monthly	monthly	ADJ
cana-1628	85	49	electricity	electricity	NOUN
cana-1628	85	50	consumption	consumption	NOUN
cana-1628	85	51	over	over	ADP
cana-1628	85	52	20	20	NUM
cana-1628	85	53	months	month	NOUN
cana-1628	85	54	for	for	ADP
cana-1628	85	55	the	the	DET
cana-1628	85	56	south	south	ADJ
cana-1628	85	57	aa	aa	PROPN
cana-1628	85	58	district	district	NOUN
cana-1628	85	59	on	on	ADP
cana-1628	85	60	the	the	DET
cana-1628	85	61	left	left	NOUN
cana-1628	85	62	and	and	CCONJ
cana-1628	85	63	west	west	ADJ
cana-1628	85	64	aa	aa	PROPN
cana-1628	85	65	district	district	PROPN
cana-1628	85	66	on	on	ADP
cana-1628	85	67	the	the	DET
cana-1628	85	68	right	right	NOUN
cana-1628	85	69	.	.	PUNCT
cana-1628	86	1	figure	figure	NOUN
cana-1628	86	2	2	2	NUM
cana-1628	86	3	:	:	PUNCT
cana-1628	86	4	aggregate	aggregate	VERB
cana-1628	86	5	monthly	monthly	ADJ
cana-1628	86	6	energy	energy	NOUN
cana-1628	86	7	consumption	consumption	NOUN
cana-1628	86	8	of	of	ADP
cana-1628	86	9	west	west	NOUN
cana-1628	86	10	and	and	CCONJ
cana-1628	86	11	west	west	PROPN
cana-1628	86	12	aa	aa	PROPN
cana-1628	86	13	the	the	DET
cana-1628	86	14	data	data	NOUN
cana-1628	86	15	indicates	indicate	VERB
cana-1628	86	16	that	that	SCONJ
cana-1628	86	17	there	there	PRON
cana-1628	86	18	is	be	VERB
cana-1628	86	19	significant	significant	ADJ
cana-1628	86	20	variation	variation	NOUN
cana-1628	86	21	in	in	ADP
cana-1628	86	22	electricity	electricity	NOUN
cana-1628	86	23	consumption	consumption	NOUN
cana-1628	86	24	habits	habit	NOUN
cana-1628	86	25	from	from	ADP
cana-1628	86	26	month	month	NOUN
cana-1628	86	27	to	to	ADP
cana-1628	86	28	month	month	NOUN
cana-1628	86	29	in	in	ADP
cana-1628	86	30	both	both	DET
cana-1628	86	31	districts	district	NOUN
cana-1628	86	32	.	.	PUNCT
cana-1628	87	1	furthermore	furthermore	ADV
cana-1628	87	2	,	,	PUNCT
cana-1628	87	3	the	the	DET
cana-1628	87	4	pattern	pattern	NOUN
cana-1628	87	5	of	of	ADP
cana-1628	87	6	electricity	electricity	NOUN
cana-1628	87	7	usage	usage	NOUN
cana-1628	87	8	in	in	ADP
cana-1628	87	9	each	each	DET
cana-1628	87	10	district	district	NOUN
cana-1628	87	11	is	be	AUX
cana-1628	87	12	non	non	ADJ
cana-1628	87	13	-	-	ADJ
cana-1628	87	14	linear	linear	ADJ
cana-1628	87	15	and	and	CCONJ
cana-1628	87	16	irregular	irregular	ADJ
cana-1628	87	17	making	make	VERB
cana-1628	87	18	difficult	difficult	ADJ
cana-1628	87	19	accurate	accurate	ADJ
cana-1628	87	20	predictions	prediction	NOUN
cana-1628	87	21	of	of	ADP
cana-1628	87	22	this	this	DET
cana-1628	87	23	data	datum	NOUN
cana-1628	87	24	using	use	VERB
cana-1628	87	25	classical	classical	ADJ
cana-1628	87	26	machine	machine	NOUN
cana-1628	87	27	learning	learning	NOUN
cana-1628	87	28	models	model	NOUN
cana-1628	87	29	and	and	CCONJ
cana-1628	87	30	traditional	traditional	ADJ
cana-1628	87	31	statistical	statistical	ADJ
cana-1628	87	32	techniques	technique	NOUN
cana-1628	87	33	[	[	X
cana-1628	87	34	7	7	NUM
cana-1628	87	35	]	]	PUNCT
cana-1628	87	36	.	.	PUNCT
cana-1628	88	1	in	in	ADP
cana-1628	88	2	other	other	ADJ
cana-1628	88	3	words	word	NOUN
cana-1628	88	4	,	,	PUNCT
cana-1628	88	5	this	this	DET
cana-1628	88	6	kind	kind	NOUN
cana-1628	88	7	of	of	ADP
cana-1628	88	8	data	datum	NOUN
cana-1628	88	9	requires	require	VERB
cana-1628	88	10	an	an	DET
cana-1628	88	11	effective	effective	ADJ
cana-1628	88	12	preprocessing	preprocessing	NOUN
cana-1628	88	13	method	method	NOUN
cana-1628	88	14	such	such	ADJ
cana-1628	88	15	as	as	ADP
cana-1628	88	16	a	a	DET
cana-1628	88	17	k	k	NOUN
cana-1628	88	18	-	-	PUNCT
cana-1628	88	19	means	means	NOUN
cana-1628	88	20	clustering	cluster	VERB
cana-1628	88	21	algorithm	algorithm	NOUN
cana-1628	88	22	to	to	PART
cana-1628	88	23	discover	discover	VERB
cana-1628	88	24	the	the	DET
cana-1628	88	25	optimal	optimal	ADJ
cana-1628	88	26	clusters	cluster	NOUN
cana-1628	88	27	comprising	comprise	VERB
cana-1628	88	28	more	more	ADV
cana-1628	88	29	stable	stable	ADJ
cana-1628	88	30	and	and	CCONJ
cana-1628	88	31	similar	similar	ADJ
cana-1628	88	32	consumption	consumption	NOUN
cana-1628	88	33	profiles	profile	NOUN
cana-1628	88	34	.	.	PUNCT
cana-1628	89	1	moreover	moreover	ADV
cana-1628	89	2	,	,	PUNCT
cana-1628	89	3	integrating	integrate	VERB
cana-1628	89	4	advanced	advanced	ADJ
cana-1628	89	5	data	datum	NOUN
cana-1628	89	6	preprocessing	preprocesse	VERB
cana-1628	89	7	techniques	technique	NOUN
cana-1628	89	8	such	such	ADJ
cana-1628	89	9	as	as	ADP
cana-1628	89	10	k	k	NOUN
cana-1628	89	11	-	-	PUNCT
cana-1628	89	12	means	means	NOUN
cana-1628	89	13	clustering	clustering	NOUN
cana-1628	89	14	will	will	AUX
cana-1628	89	15	enable	enable	VERB
cana-1628	89	16	the	the	DET
cana-1628	89	17	subsequent	subsequent	ADJ
cana-1628	89	18	ensemble	ensemble	ADJ
cana-1628	89	19	deep	deep	ADJ
cana-1628	89	20	learning	learning	NOUN
cana-1628	89	21	model	model	NOUN
cana-1628	89	22	[	[	X
cana-1628	89	23	5	5	NUM
cana-1628	89	24	,	,	PUNCT
cana-1628	89	25	17	17	NUM
cana-1628	89	26	,	,	PUNCT
cana-1628	89	27	22	22	NUM
cana-1628	89	28	]	]	PUNCT
cana-1628	89	29	to	to	PART
cana-1628	89	30	learn	learn	VERB
cana-1628	89	31	the	the	DET
cana-1628	89	32	nonlinear	nonlinear	ADJ
cana-1628	89	33	complex	complex	PROPN
cana-1628	89	34	association	association	NOUN
cana-1628	89	35	between	between	ADP
cana-1628	89	36	energy	energy	NOUN
cana-1628	89	37	consumption	consumption	NOUN
cana-1628	89	38	features	feature	NOUN
cana-1628	89	39	and	and	CCONJ
cana-1628	89	40	make	make	VERB
cana-1628	89	41	accurate	accurate	ADJ
cana-1628	89	42	predictions	prediction	NOUN
cana-1628	89	43	.	.	PUNCT
cana-1628	90	1	3.2	3.2	NUM
cana-1628	90	2	experiment	experiment	NOUN
cana-1628	90	3	setup	setup	NOUN
cana-1628	90	4	this	this	DET
cana-1628	90	5	section	section	NOUN
cana-1628	90	6	tried	try	VERB
cana-1628	90	7	to	to	PART
cana-1628	90	8	discuss	discuss	VERB
cana-1628	90	9	experimentation	experimentation	NOUN
cana-1628	90	10	phases	phase	NOUN
cana-1628	90	11	of	of	ADP
cana-1628	90	12	our	our	PRON
cana-1628	90	13	study	study	NOUN
cana-1628	90	14	which	which	PRON
cana-1628	90	15	include	include	VERB
cana-1628	90	16	data	datum	NOUN
cana-1628	90	17	processing	processing	NOUN
cana-1628	90	18	and	and	CCONJ
cana-1628	90	19	ensemble	ensemble	ADJ
cana-1628	90	20	deep	deep	ADJ
cana-1628	90	21	learning	learning	NOUN
cana-1628	90	22	model	model	NOUN
cana-1628	90	23	development	development	NOUN
cana-1628	90	24	based	base	VERB
cana-1628	90	25	on	on	ADP
cana-1628	90	26	the	the	DET
cana-1628	90	27	cluster	cluster	NOUN
cana-1628	90	28	-	-	PUNCT
cana-1628	90	29	generated	generate	VERB
cana-1628	90	30	data	datum	NOUN
cana-1628	90	31	.	.	PUNCT
cana-1628	91	1	in	in	ADP
cana-1628	91	2	this	this	DET
cana-1628	91	3	phase	phase	NOUN
cana-1628	91	4	,	,	PUNCT
cana-1628	91	5	the	the	DET
cana-1628	91	6	keras	keras	PROPN
cana-1628	91	7	framework	framework	NOUN
cana-1628	91	8	on	on	ADP
cana-1628	91	9	top	top	NOUN
cana-1628	91	10	of	of	ADP
cana-1628	91	11	tensorflow	tensorflow	NOUN
cana-1628	91	12	was	be	AUX
cana-1628	91	13	selected	select	VERB
cana-1628	91	14	to	to	PART
cana-1628	91	15	utilize	utilize	VERB
cana-1628	91	16	a	a	DET
cana-1628	91	17	deep	deep	ADJ
cana-1628	91	18	learning	learning	NOUN
cana-1628	91	19	framework	framework	NOUN
cana-1628	91	20	,	,	PUNCT
cana-1628	91	21	hyperparameters	hyperparameter	NOUN
cana-1628	91	22	,	,	PUNCT
cana-1628	91	23	and	and	CCONJ
cana-1628	91	24	a	a	DET
cana-1628	91	25	bayesian	bayesian	ADJ
cana-1628	91	26	optimization	optimization	NOUN
cana-1628	91	27	algorithm	algorithm	NOUN
cana-1628	91	28	based	base	VERB
cana-1628	91	29	on	on	ADP
cana-1628	91	30	the	the	DET
cana-1628	91	31	bayessearchcv	bayessearchcv	NOUN
cana-1628	91	32	interface	interface	NOUN
cana-1628	91	33	.	.	PUNCT
cana-1628	92	1	3.2.1	3.2.1	NUM
cana-1628	92	2	energy	energy	NOUN
cana-1628	92	3	consumption	consumption	NOUN
cana-1628	92	4	clustering	clustering	NOUN
cana-1628	92	5	and	and	CCONJ
cana-1628	92	6	analysis	analysis	NOUN
cana-1628	92	7	in	in	ADP
cana-1628	92	8	this	this	DET
cana-1628	92	9	study	study	NOUN
cana-1628	92	10	,	,	PUNCT
cana-1628	92	11	k	k	PROPN
cana-1628	92	12	-	-	ADJ
cana-1628	92	13	means++	means++	ADJ
cana-1628	92	14	clustering	clustering	NOUN
cana-1628	92	15	was	be	AUX
cana-1628	92	16	employed	employ	VERB
cana-1628	92	17	to	to	PART
cana-1628	92	18	discover	discover	VERB
cana-1628	92	19	the	the	DET
cana-1628	92	20	optimal	optimal	ADJ
cana-1628	92	21	clusters	cluster	NOUN
cana-1628	92	22	because	because	SCONJ
cana-1628	92	23	kmeans++	kmeans++	PROPN
cana-1628	92	24	is	be	AUX
cana-1628	92	25	developed	develop	VERB
cana-1628	92	26	as	as	ADP
cana-1628	92	27	an	an	DET
cana-1628	92	28	enhanced	enhanced	ADJ
cana-1628	92	29	version	version	NOUN
cana-1628	92	30	of	of	ADP
cana-1628	92	31	k	k	NOUN
cana-1628	92	32	-	-	PUNCT
cana-1628	92	33	means	means	NOUN
cana-1628	92	34	clustering	clustering	NOUN
cana-1628	92	35	in	in	ADP
cana-1628	92	36	initial	initial	ADJ
cana-1628	92	37	cluster	cluster	NOUN
cana-1628	92	38	center	center	NOUN
cana-1628	92	39	identification	identification	NOUN
cana-1628	92	40	and	and	CCONJ
cana-1628	92	41	can	can	AUX
cana-1628	92	42	give	give	VERB
cana-1628	92	43	faster	fast	ADJ
cana-1628	92	44	computation	computation	NOUN
cana-1628	92	45	advantages	advantage	NOUN
cana-1628	92	46	[	[	X
cana-1628	92	47	33	33	NUM
cana-1628	92	48	]	]	PUNCT
cana-1628	92	49	.	.	PUNCT
cana-1628	93	1	in	in	ADP
cana-1628	93	2	this	this	DET
cana-1628	93	3	study	study	NOUN
cana-1628	93	4	,	,	PUNCT
cana-1628	93	5	k	k	PROPN
cana-1628	93	6	-	-	ADJ
cana-1628	93	7	means++	means++	ADJ
cana-1628	93	8	clustering	clustering	NOUN
cana-1628	93	9	was	be	AUX
cana-1628	93	10	employed	employ	VERB
cana-1628	93	11	to	to	PART
cana-1628	93	12	discover	discover	VERB
cana-1628	93	13	the	the	DET
cana-1628	93	14	optimal	optimal	ADJ
cana-1628	93	15	clusters	cluster	NOUN
cana-1628	93	16	because	because	SCONJ
cana-1628	93	17	k	k	PROPN
cana-1628	93	18	means++	means++	PROPN
cana-1628	93	19	is	be	AUX
cana-1628	93	20	developed	develop	VERB
cana-1628	93	21	as	as	ADP
cana-1628	93	22	an	an	DET
cana-1628	93	23	enhanced	enhanced	ADJ
cana-1628	93	24	version	version	NOUN
cana-1628	93	25	of	of	ADP
cana-1628	93	26	k	k	X
cana-1628	93	27	-means	-mean	NOUN
cana-1628	93	28	clustering	cluster	VERB
cana-1628	93	29	in	in	ADP
cana-1628	93	30	initial	initial	ADJ
cana-1628	93	31	cluster	cluster	NOUN
cana-1628	93	32	center	center	NOUN
cana-1628	93	33	identification	identification	NOUN
cana-1628	93	34	and	and	CCONJ
cana-1628	93	35	can	can	AUX
cana-1628	93	36	give	give	VERB
cana-1628	93	37	faster	fast	ADJ
cana-1628	93	38	computation	computation	NOUN
cana-1628	93	39	advantages	advantage	NOUN
cana-1628	93	40	[	[	X
cana-1628	93	41	5	5	NUM
cana-1628	93	42	]	]	PUNCT
cana-1628	93	43	.	.	PUNCT
cana-1628	94	1	the	the	DET
cana-1628	94	2	silhouette	silhouette	NOUN
cana-1628	94	3	coefficient	coefficient	NOUN
cana-1628	94	4	is	be	AUX
cana-1628	94	5	used	use	VERB
cana-1628	94	6	to	to	PART
cana-1628	94	7	evaluate	evaluate	VERB
cana-1628	94	8	how	how	SCONJ
cana-1628	94	9	effective	effective	ADJ
cana-1628	94	10	a	a	DET
cana-1628	94	11	clustering	cluster	VERB
cana-1628	94	12	method	method	NOUN
cana-1628	94	13	is	be	AUX
cana-1628	94	14	.	.	PUNCT
cana-1628	95	1	it	it	PRON
cana-1628	95	2	has	have	VERB
cana-1628	95	3	a	a	DET
cana-1628	95	4	value	value	NOUN
cana-1628	95	5	between	between	ADP
cana-1628	95	6	-1	-1	PUNCT
cana-1628	95	7	and	and	CCONJ
cana-1628	95	8	1	1	NUM
cana-1628	95	9	.	.	PUNCT
cana-1628	96	1	from	from	ADP
cana-1628	96	2	this	this	DET
cana-1628	96	3	𝑎(𝑖	𝑎(𝑖	NOUN
cana-1628	96	4	)	)	PUNCT
cana-1628	96	5	represents	represent	VERB
cana-1628	96	6	the	the	DET
cana-1628	96	7	average	average	ADJ
cana-1628	96	8	distance	distance	NOUN
cana-1628	96	9	from	from	ADP
cana-1628	96	10	an	an	DET
cana-1628	96	11	item	item	NOUN
cana-1628	96	12	𝑖	𝑖	NOUN
cana-1628	96	13	in	in	ADP
cana-1628	96	14	the	the	DET
cana-1628	96	15	cluster	cluster	NOUN
cana-1628	96	16	𝐴	𝐴	NOUN
cana-1628	96	17	to	to	ADP
cana-1628	96	18	all	all	DET
cana-1628	96	19	other	other	ADJ
cana-1628	96	20	objects	object	NOUN
cana-1628	96	21	in	in	ADP
cana-1628	96	22	𝐴	𝐴	PROPN
cana-1628	96	23	,	,	PUNCT
cana-1628	96	24	and	and	CCONJ
cana-1628	96	25	𝑑(𝑖	𝑑(𝑖	PROPN
cana-1628	96	26	,	,	PUNCT
cana-1628	96	27	𝐶	𝐶	PROPN
cana-1628	96	28	)	)	PUNCT
cana-1628	96	29	represents	represent	VERB
cana-1628	96	30	the	the	DET
cana-1628	96	31	average	average	ADJ
cana-1628	96	32	distance	distance	NOUN
cana-1628	96	33	from	from	ADP
cana-1628	96	34	an	an	DET
cana-1628	96	35	object	object	NOUN
cana-1628	96	36	𝑖	𝑖	X
cana-1628	96	37	to	to	ADP
cana-1628	96	38	all	all	DET
cana-1628	96	39	objects	object	NOUN
cana-1628	96	40	in	in	ADP
cana-1628	96	41	the	the	DET
cana-1628	96	42	cluster	cluster	NOUN
cana-1628	96	43	𝐶	𝐶	PROPN
cana-1628	96	44	≠	≠	PROPN
cana-1628	96	45	𝐴.	𝐴.	PROPN
cana-1628	96	46	after	after	ADP
cana-1628	96	47	computing	compute	VERB
cana-1628	96	48	𝑑(𝑖	𝑑(𝑖	PROPN
cana-1628	96	49	,	,	PUNCT
cana-1628	96	50	𝐶	𝐶	PROPN
cana-1628	96	51	)	)	PUNCT
cana-1628	96	52	and	and	CCONJ
cana-1628	96	53	𝐶	𝐶	PROPN
cana-1628	96	54	≠	≠	PROPN
cana-1628	96	55	𝐴	𝐴	PROPN
cana-1628	96	56	for	for	ADP
cana-1628	96	57	each	each	DET
cana-1628	96	58	cluster	cluster	NOUN
cana-1628	96	59	,	,	PUNCT
cana-1628	96	60	the	the	DET
cana-1628	96	61	smallest	small	ADJ
cana-1628	96	62	cluster	cluster	NOUN
cana-1628	96	63	is	be	AUX
cana-1628	96	64	chosen	choose	VERB
cana-1628	96	65	as	as	ADP
cana-1628	96	66	described	describe	VERB
cana-1628	96	67	below	below	ADV
cana-1628	96	68	.	.	PUNCT
cana-1628	97	1	𝑏(𝑖	𝑏(𝑖	PROPN
cana-1628	97	2	)	)	PUNCT
cana-1628	98	1	=	=	SYM
cana-1628	98	2	min	min	PROPN
cana-1628	98	3	𝐶≠𝐴	𝐶≠𝐴	PROPN
cana-1628	98	4	 	 	SPACE
cana-1628	98	5	𝑑(𝑖	𝑑(𝑖	PROPN
cana-1628	98	6	,	,	PUNCT
cana-1628	98	7	𝐶	𝐶	PROPN
cana-1628	98	8	)	)	PUNCT
cana-1628	98	9	with	with	ADP
cana-1628	98	10	𝑖	𝑖	PROPN
cana-1628	98	11	∈	∈	PROPN
cana-1628	98	12	𝐴	𝐴	PROPN
cana-1628	98	13	(	(	PUNCT
cana-1628	98	14	2	2	NUM
cana-1628	98	15	)	)	PUNCT
cana-1628	98	16	communications	communication	NOUN
cana-1628	98	17	on	on	ADP
cana-1628	98	18	applied	apply	VERB
cana-1628	98	19	nonlinear	nonlinear	ADJ
cana-1628	98	20	analysis	analysis	NOUN
cana-1628	98	21	issn	issn	NOUN
cana-1628	98	22	:	:	PUNCT
cana-1628	98	23	1074	1074	NUM
cana-1628	98	24	-	-	PUNCT
cana-1628	98	25	133x	133x	NUM
cana-1628	98	26	vol	vol	NOUN
cana-1628	98	27	32	32	NUM
cana-1628	98	28	no	no	NOUN
cana-1628	98	29	.	.	NOUN
cana-1628	98	30	1	1	NUM
cana-1628	98	31	(	(	PUNCT
cana-1628	98	32	2025	2025	NUM
cana-1628	98	33	)	)	PUNCT
cana-1628	98	34	160	160	NUM
cana-1628	98	35	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	98	36	the	the	DET
cana-1628	98	37	value	value	NOUN
cana-1628	98	38	𝑏(𝑖	𝑏(𝑖	PROPN
cana-1628	98	39	)	)	PUNCT
cana-1628	98	40	denotes	denote	NOUN
cana-1628	98	41	to	to	ADP
cana-1628	98	42	what	what	DET
cana-1628	98	43	extent	extent	NOUN
cana-1628	98	44	a	a	DET
cana-1628	98	45	data	data	NOUN
cana-1628	98	46	point	point	NOUN
cana-1628	98	47	𝑖	𝑖	VERB
cana-1628	98	48	is	be	AUX
cana-1628	98	49	dissimilar	dissimilar	ADJ
cana-1628	98	50	to	to	ADP
cana-1628	98	51	its	its	PRON
cana-1628	98	52	nearest	near	ADJ
cana-1628	98	53	neighbor	neighbor	NOUN
cana-1628	98	54	cluster	cluster	NOUN
cana-1628	98	55	.	.	PUNCT
cana-1628	99	1	thus	thus	ADV
cana-1628	99	2	,	,	PUNCT
cana-1628	99	3	the	the	DET
cana-1628	99	4	silhouette	silhouette	NOUN
cana-1628	99	5	values	value	NOUN
cana-1628	99	6	,	,	PUNCT
cana-1628	99	7	silh(i	silh(i	ADJ
cana-1628	99	8	)	)	PUNCT
cana-1628	99	9	are	be	AUX
cana-1628	99	10	given	give	VERB
cana-1628	99	11	in	in	ADP
cana-1628	99	12	equation	equation	NOUN
cana-1628	99	13	(	(	PUNCT
cana-1628	99	14	3	3	NUM
cana-1628	99	15	):	):	PUNCT
cana-1628	99	16	silh⁡(𝑖	silh⁡(𝑖	NUM
cana-1628	99	17	)	)	PUNCT
cana-1628	99	18	=	=	SYM
cana-1628	99	19	𝑎(𝑖	𝑎(𝑖	PROPN
cana-1628	99	20	)	)	PUNCT
cana-1628	99	21	−	−	PROPN
cana-1628	99	22	𝑏(𝑖	𝑏(𝑖	PROPN
cana-1628	99	23	)	)	PUNCT
cana-1628	99	24	max{𝑎(𝑖	max{𝑎(𝑖	PROPN
cana-1628	99	25	)	)	PUNCT
cana-1628	99	26	,	,	PUNCT
cana-1628	99	27	𝑏(𝑖	𝑏(𝑖	PROPN
cana-1628	99	28	)	)	PUNCT
cana-1628	99	29	}	}	PUNCT
cana-1628	99	30	(	(	PUNCT
cana-1628	99	31	3	3	X
cana-1628	99	32	)	)	PUNCT
cana-1628	99	33	another	another	DET
cana-1628	99	34	cluster	cluster	NOUN
cana-1628	99	35	validation	validation	NOUN
cana-1628	99	36	metric	metric	NOUN
cana-1628	99	37	is	be	AUX
cana-1628	99	38	the	the	DET
cana-1628	99	39	dbi	dbi	NOUN
cana-1628	99	40	which	which	PRON
cana-1628	99	41	is	be	AUX
cana-1628	99	42	used	use	VERB
cana-1628	99	43	to	to	PART
cana-1628	99	44	determine	determine	VERB
cana-1628	99	45	the	the	DET
cana-1628	99	46	goodness	goodness	NOUN
cana-1628	99	47	of	of	ADP
cana-1628	99	48	clusters	cluster	NOUN
cana-1628	99	49	.	.	PUNCT
cana-1628	100	1	the	the	DET
cana-1628	100	2	dbi	dbi	NOUN
cana-1628	100	3	for	for	SCONJ
cana-1628	100	4	𝐾	𝐾	PROPN
cana-1628	100	5	clusters	cluster	NOUN
cana-1628	100	6	𝐶𝑖	𝐶𝑖	VERB
cana-1628	100	7	with	with	ADP
cana-1628	100	8	𝑖	𝑖	NOUN
cana-1628	100	9	=	=	SYM
cana-1628	100	10	1	1	NUM
cana-1628	100	11	,	,	PUNCT
cana-1628	100	12	…	…	PUNCT
cana-1628	100	13	,	,	PUNCT
cana-1628	100	14	𝐾	𝐾	PROPN
cana-1628	100	15	is	be	AUX
cana-1628	100	16	defined	define	VERB
cana-1628	100	17	according	accord	VERB
cana-1628	100	18	to	to	ADP
cana-1628	100	19	equation	equation	NOUN
cana-1628	100	20	(	(	PUNCT
cana-1628	100	21	4	4	NUM
cana-1628	100	22	):	):	PUNCT
cana-1628	101	1	𝐷𝐵𝐾	𝐷𝐵𝐾	NOUN
cana-1628	101	2	=	=	SYM
cana-1628	101	3	1	1	NUM
cana-1628	101	4	𝐾	𝐾	PROPN
cana-1628	101	5	∑	∑	NOUN
cana-1628	101	6	  	  	SPACE
cana-1628	101	7	𝐾	𝐾	PROPN
cana-1628	101	8	𝑖=1	𝑖=1	PROPN
cana-1628	101	9	 	 	SPACE
cana-1628	101	10	max	max	PROPN
cana-1628	101	11	𝑗≠𝑖	𝑗≠𝑖	PROPN
cana-1628	101	12	 	 	SPACE
cana-1628	101	13	𝑓𝑖,𝑗	𝑓𝑖,𝑗	NOUN
cana-1628	101	14	(	(	PUNCT
cana-1628	101	15	4	4	NUM
cana-1628	101	16	)	)	PUNCT
cana-1628	101	17	where	where	SCONJ
cana-1628	101	18	:	:	PUNCT
cana-1628	101	19	𝑓𝑖,𝑗	𝑓𝑖,𝑗	NOUN
cana-1628	101	20	=	=	SYM
cana-1628	101	21	diam⁡(𝐶𝑖	diam⁡(𝐶𝑖	PROPN
cana-1628	101	22	)	)	PUNCT
cana-1628	101	23	+	+	SYM
cana-1628	101	24	diam⁡(𝐶𝑗	diam⁡(𝐶𝑗	PROPN
cana-1628	101	25	)	)	PUNCT
cana-1628	101	26	𝑑(𝐶𝑖	𝑑(𝐶𝑖	NOUN
cana-1628	101	27	,	,	PUNCT
cana-1628	101	28	𝐶𝑗	𝐶𝑗	PROPN
cana-1628	101	29	)	)	PUNCT
cana-1628	101	30	(	(	PUNCT
cana-1628	101	31	5	5	NUM
cana-1628	101	32	)	)	PUNCT
cana-1628	101	33	and	and	CCONJ
cana-1628	101	34	,	,	PUNCT
cana-1628	101	35	in	in	ADP
cana-1628	101	36	this	this	DET
cana-1628	101	37	case	case	NOUN
cana-1628	101	38	,	,	PUNCT
cana-1628	101	39	the	the	DET
cana-1628	101	40	diameter	diameter	NOUN
cana-1628	101	41	of	of	ADP
cana-1628	101	42	a	a	DET
cana-1628	101	43	cluster	cluster	NOUN
cana-1628	101	44	is	be	AUX
cana-1628	101	45	defined	define	VERB
cana-1628	101	46	as	as	ADP
cana-1628	101	47	:	:	PUNCT
cana-1628	101	48	diam⁡(𝐶𝑖	diam⁡(𝐶𝑖	PROPN
cana-1628	101	49	)	)	PUNCT
cana-1628	101	50	=	=	PUNCT
cana-1628	101	51	(	(	PUNCT
cana-1628	101	52	1	1	NUM
cana-1628	101	53	𝑛𝑖	𝑛𝑖	NOUN
cana-1628	101	54	∑	∑	DET
cana-1628	101	55	  	  	SPACE
cana-1628	101	56	𝑥∈𝐶𝑖	𝑥∈𝐶𝑖	PROPN
cana-1628	101	57	  	  	SPACE
cana-1628	101	58	∥∥𝑥	∥∥𝑥	NOUN
cana-1628	101	59	−	−	PUNCT
cana-1628	101	60	𝑧𝑖∥∥	𝑧𝑖∥∥	PROPN
cana-1628	101	61	2	2	NUM
cana-1628	101	62	)	)	PUNCT
cana-1628	101	63	1	1	NUM
cana-1628	101	64	2	2	NUM
cana-1628	101	65	(	(	PUNCT
cana-1628	101	66	6	6	NUM
cana-1628	101	67	)	)	PUNCT
cana-1628	101	68	with	with	ADP
cana-1628	101	69	𝑛𝑖	𝑛𝑖	PRON
cana-1628	101	70	the	the	DET
cana-1628	101	71	number	number	NOUN
cana-1628	101	72	of	of	ADP
cana-1628	101	73	data	datum	NOUN
cana-1628	101	74	points	point	NOUN
cana-1628	101	75	and	and	CCONJ
cana-1628	101	76	𝑧𝑖	𝑧𝑖	INTJ
cana-1628	101	77	the	the	DET
cana-1628	101	78	centroid	centroid	NOUN
cana-1628	101	79	of	of	ADP
cana-1628	101	80	cluster	cluster	NOUN
cana-1628	101	81	𝐶𝑖.	𝐶𝑖.	PROPN
cana-1628	101	82	the	the	DET
cana-1628	101	83	dbi	dbi	NOUN
cana-1628	101	84	will	will	AUX
cana-1628	101	85	achieve	achieve	VERB
cana-1628	101	86	very	very	ADV
cana-1628	101	87	small	small	ADJ
cana-1628	101	88	values	value	NOUN
cana-1628	101	89	,	,	PUNCT
cana-1628	101	90	which	which	PRON
cana-1628	101	91	guarantees	guarantee	VERB
cana-1628	101	92	the	the	DET
cana-1628	101	93	presence	presence	NOUN
cana-1628	101	94	of	of	ADP
cana-1628	101	95	high	high	ADJ
cana-1628	101	96	-	-	PUNCT
cana-1628	101	97	quality	quality	NOUN
cana-1628	101	98	clusters	cluster	NOUN
cana-1628	101	99	.	.	PUNCT
cana-1628	102	1	therefore	therefore	ADV
cana-1628	102	2	,	,	PUNCT
cana-1628	102	3	the	the	DET
cana-1628	102	4	ideal	ideal	ADJ
cana-1628	102	5	number	number	NOUN
cana-1628	102	6	of	of	ADP
cana-1628	102	7	clusters	cluster	NOUN
cana-1628	102	8	is	be	AUX
cana-1628	102	9	discovered	discover	VERB
cana-1628	102	10	when	when	SCONJ
cana-1628	102	11	this	this	DET
cana-1628	102	12	index	index	NOUN
cana-1628	102	13	is	be	AUX
cana-1628	102	14	minimized	minimize	VERB
cana-1628	102	15	depending	depend	VERB
cana-1628	102	16	on	on	ADP
cana-1628	102	17	the	the	DET
cana-1628	102	18	input	input	NOUN
cana-1628	102	19	dataset	dataset	NOUN
cana-1628	102	20	.	.	PUNCT
cana-1628	103	1	communications	communication	NOUN
cana-1628	103	2	on	on	ADP
cana-1628	103	3	applied	apply	VERB
cana-1628	103	4	nonlinear	nonlinear	ADJ
cana-1628	103	5	analysis	analysis	NOUN
cana-1628	103	6	issn	issn	NOUN
cana-1628	103	7	:	:	PUNCT
cana-1628	103	8	1074	1074	NUM
cana-1628	103	9	-	-	PUNCT
cana-1628	103	10	133x	133x	NUM
cana-1628	103	11	vol	vol	NOUN
cana-1628	103	12	32	32	NUM
cana-1628	103	13	no	no	NOUN
cana-1628	103	14	.	.	NOUN
cana-1628	103	15	1	1	NUM
cana-1628	103	16	(	(	PUNCT
cana-1628	103	17	2025	2025	NUM
cana-1628	103	18	)	)	PUNCT
cana-1628	103	19	161	161	NUM
cana-1628	103	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	103	21	as	as	SCONJ
cana-1628	103	22	indicated	indicate	VERB
cana-1628	103	23	in	in	ADP
cana-1628	103	24	table	table	NOUN
cana-1628	103	25	2	2	NUM
cana-1628	103	26	,	,	PUNCT
cana-1628	103	27	each	each	DET
cana-1628	103	28	district	district	NOUN
cana-1628	103	29	data	datum	NOUN
cana-1628	103	30	is	be	AUX
cana-1628	103	31	grouped	group	VERB
cana-1628	103	32	into	into	ADP
cana-1628	103	33	the	the	DET
cana-1628	103	34	best	good	ADJ
cana-1628	103	35	similar	similar	ADJ
cana-1628	103	36	cluster	cluster	NOUN
cana-1628	103	37	,	,	PUNCT
cana-1628	103	38	and	and	CCONJ
cana-1628	103	39	each	each	DET
cana-1628	103	40	cluster	cluster	NOUN
cana-1628	103	41	has	have	VERB
cana-1628	103	42	a	a	DET
cana-1628	103	43	different	different	ADJ
cana-1628	103	44	observation	observation	NOUN
cana-1628	103	45	size	size	NOUN
cana-1628	103	46	.	.	PUNCT
cana-1628	104	1	for	for	ADP
cana-1628	104	2	example	example	NOUN
cana-1628	104	3	,	,	PUNCT
cana-1628	104	4	in	in	ADP
cana-1628	104	5	the	the	DET
cana-1628	104	6	south	south	ADJ
cana-1628	104	7	aa	aa	PROPN
cana-1628	104	8	district	district	PROPN
cana-1628	104	9	(	(	PUNCT
cana-1628	104	10	cluter	cluter	NOUN
cana-1628	104	11	1	1	NUM
cana-1628	104	12	=	=	SYM
cana-1628	104	13	111503	111503	NUM
cana-1628	104	14	,	,	PUNCT
cana-1628	104	15	cluster	cluster	NOUN
cana-1628	104	16	2	2	NUM
cana-1628	104	17	=	=	SYM
cana-1628	104	18	152620	152620	NUM
cana-1628	104	19	,	,	PUNCT
cana-1628	104	20	and	and	CCONJ
cana-1628	104	21	cluster	cluster	NOUN
cana-1628	104	22	3	3	NUM
cana-1628	104	23	=	=	SYM
cana-1628	104	24	45183	45183	NUM
cana-1628	104	25	observations	observation	NOUN
cana-1628	104	26	)	)	PUNCT
cana-1628	104	27	.	.	PUNCT
cana-1628	105	1	in	in	ADP
cana-1628	105	2	the	the	DET
cana-1628	105	3	case	case	NOUN
cana-1628	105	4	of	of	ADP
cana-1628	105	5	west	west	PROPN
cana-1628	105	6	aa	aa	PROPN
cana-1628	105	7	data	data	PROPN
cana-1628	105	8	(	(	PUNCT
cana-1628	105	9	cluster	cluster	NOUN
cana-1628	105	10	1	1	NUM
cana-1628	105	11	=	=	SYM
cana-1628	105	12	169800	169800	NUM
cana-1628	105	13	,	,	PUNCT
cana-1628	105	14	cluster	cluster	NOUN
cana-1628	105	15	2	2	NUM
cana-1628	105	16	=	=	SYM
cana-1628	105	17	109227	109227	NUM
cana-1628	105	18	,	,	PUNCT
cana-1628	105	19	and	and	CCONJ
cana-1628	105	20	cluster	cluster	NOUN
cana-1628	105	21	3	3	NUM
cana-1628	105	22	=	=	SYM
cana-1628	105	23	34464	34464	NUM
cana-1628	105	24	observations	observation	NOUN
cana-1628	105	25	)	)	PUNCT
cana-1628	105	26	.	.	PUNCT
cana-1628	106	1	moreover	moreover	ADV
cana-1628	106	2	,	,	PUNCT
cana-1628	106	3	from	from	ADP
cana-1628	106	4	table	table	NOUN
cana-1628	106	5	2	2	NUM
cana-1628	106	6	,	,	PUNCT
cana-1628	106	7	it	it	PRON
cana-1628	106	8	has	have	AUX
cana-1628	106	9	been	be	AUX
cana-1628	106	10	indicated	indicate	VERB
cana-1628	106	11	that	that	SCONJ
cana-1628	106	12	the	the	DET
cana-1628	106	13	silhouette	silhouette	NOUN
cana-1628	106	14	score	score	NOUN
cana-1628	106	15	for	for	ADP
cana-1628	106	16	each	each	DET
cana-1628	106	17	cluster	cluster	NOUN
cana-1628	106	18	is	be	AUX
cana-1628	106	19	>	>	X
cana-1628	106	20	0.5	0.5	NUM
cana-1628	106	21	,	,	PUNCT
cana-1628	106	22	which	which	PRON
cana-1628	106	23	is	be	AUX
cana-1628	106	24	higher	high	ADJ
cana-1628	106	25	,	,	PUNCT
cana-1628	106	26	and	and	CCONJ
cana-1628	106	27	the	the	DET
cana-1628	106	28	data	data	NOUN
cana-1628	106	29	points	point	NOUN
cana-1628	106	30	are	be	AUX
cana-1628	106	31	correctly	correctly	ADV
cana-1628	106	32	grouped	group	VERB
cana-1628	106	33	in	in	ADP
cana-1628	106	34	their	their	PRON
cana-1628	106	35	proper	proper	ADJ
cana-1628	106	36	cluster	cluster	NOUN
cana-1628	106	37	.	.	PUNCT
cana-1628	107	1	furthermore	furthermore	ADV
cana-1628	107	2	,	,	PUNCT
cana-1628	107	3	the	the	DET
cana-1628	107	4	similarity	similarity	NOUN
cana-1628	107	5	(	(	PUNCT
cana-1628	107	6	cohesion	cohesion	NOUN
cana-1628	107	7	)	)	PUNCT
cana-1628	107	8	of	of	ADP
cana-1628	107	9	data	datum	NOUN
cana-1628	107	10	points	point	NOUN
cana-1628	107	11	in	in	ADP
cana-1628	107	12	a	a	DET
cana-1628	107	13	cluster	cluster	NOUN
cana-1628	107	14	is	be	AUX
cana-1628	107	15	also	also	ADV
cana-1628	107	16	very	very	ADV
cana-1628	107	17	high	high	ADJ
cana-1628	107	18	as	as	SCONJ
cana-1628	107	19	the	the	DET
cana-1628	107	20	larger	large	ADJ
cana-1628	107	21	silhouette	silhouette	NOUN
cana-1628	107	22	score	score	NOUN
cana-1628	107	23	reveals	reveal	VERB
cana-1628	107	24	the	the	DET
cana-1628	107	25	closeness	closeness	NOUN
cana-1628	107	26	of	of	ADP
cana-1628	107	27	data	datum	NOUN
cana-1628	107	28	points	point	NOUN
cana-1628	107	29	in	in	ADP
cana-1628	107	30	a	a	DET
cana-1628	107	31	cluster	cluster	NOUN
cana-1628	107	32	.	.	PUNCT
cana-1628	108	1	in	in	ADP
cana-1628	108	2	general	general	ADJ
cana-1628	108	3	,	,	PUNCT
cana-1628	108	4	the	the	DET
cana-1628	108	5	cluster	cluster	NOUN
cana-1628	108	6	validation	validation	NOUN
cana-1628	108	7	results	result	VERB
cana-1628	108	8	in	in	ADP
cana-1628	108	9	table	table	NOUN
cana-1628	108	10	2	2	NUM
cana-1628	108	11	show	show	VERB
cana-1628	108	12	that	that	SCONJ
cana-1628	108	13	k	k	NOUN
cana-1628	108	14	-	-	PUNCT
cana-1628	108	15	means	mean	VERB
cana-1628	108	16	clustering	clustering	NOUN
cana-1628	108	17	is	be	AUX
cana-1628	108	18	a	a	DET
cana-1628	108	19	viable	viable	ADJ
cana-1628	108	20	solution	solution	NOUN
cana-1628	108	21	to	to	PART
cana-1628	108	22	characterize	characterize	VERB
cana-1628	108	23	the	the	DET
cana-1628	108	24	energy	energy	NOUN
cana-1628	108	25	consumption	consumption	NOUN
cana-1628	108	26	profiles	profile	NOUN
cana-1628	108	27	and	and	CCONJ
cana-1628	108	28	generate	generate	VERB
cana-1628	108	29	optimal	optimal	ADJ
cana-1628	108	30	clusters	cluster	NOUN
cana-1628	108	31	that	that	PRON
cana-1628	108	32	will	will	AUX
cana-1628	108	33	improve	improve	VERB
cana-1628	108	34	the	the	DET
cana-1628	108	35	prediction	prediction	NOUN
cana-1628	108	36	accuracy	accuracy	NOUN
cana-1628	108	37	of	of	ADP
cana-1628	108	38	the	the	DET
cana-1628	108	39	subsequent	subsequent	ADJ
cana-1628	108	40	ensemble	ensemble	ADJ
cana-1628	108	41	models	model	NOUN
cana-1628	108	42	.	.	PUNCT
cana-1628	109	1	table	table	NOUN
cana-1628	109	2	2	2	NUM
cana-1628	109	3	:	:	PUNCT
cana-1628	109	4	k	k	X
cana-1628	109	5	-	-	PUNCT
cana-1628	109	6	means	mean	VERB
cana-1628	109	7	clustering	cluster	VERB
cana-1628	109	8	validation	validation	NOUN
cana-1628	109	9	results	result	NOUN
cana-1628	109	10	district	district	NOUN
cana-1628	109	11	dataset	dataset	VERB
cana-1628	109	12	#	#	SYM
cana-1628	109	13	cluster	cluster	NOUN
cana-1628	109	14	silhouette_score	silhouette_score	NOUN
cana-1628	109	15	dbi_score	dbi_score	NOUN
cana-1628	109	16	south	south	ADJ
cana-1628	109	17	aa	aa	NOUN
cana-1628	109	18	309303	309303	NUM
cana-1628	109	19	3	3	NUM
cana-1628	109	20	0.5478	0.5478	NUM
cana-1628	109	21	0.5785	0.5785	NUM
cana-1628	109	22	west	west	ADJ
cana-1628	109	23	aa	aa	NOUN
cana-1628	109	24	313492	313492	NUM
cana-1628	109	25	3	3	NUM
cana-1628	109	26	0.550	0.550	NUM
cana-1628	109	27	0.593	0.593	NUM
cana-1628	109	28	a	a	DET
cana-1628	109	29	dataset	dataset	NOUN
cana-1628	109	30	with	with	ADP
cana-1628	109	31	normal	normal	ADJ
cana-1628	109	32	distribution	distribution	NOUN
cana-1628	109	33	has	have	VERB
cana-1628	109	34	skewness	skewness	NOUN
cana-1628	109	35	and	and	CCONJ
cana-1628	109	36	kurtosis	kurtosis	VERB
cana-1628	109	37	values	value	NOUN
cana-1628	109	38	of	of	ADP
cana-1628	109	39	0	0	NUM
cana-1628	109	40	and	and	CCONJ
cana-1628	109	41	3	3	NUM
cana-1628	109	42	,	,	PUNCT
cana-1628	109	43	respectively	respectively	ADV
cana-1628	109	44	.	.	PUNCT
cana-1628	110	1	however	however	ADV
cana-1628	110	2	,	,	PUNCT
cana-1628	110	3	as	as	ADP
cana-1628	110	4	table	table	NOUN
cana-1628	110	5	1	1	NUM
cana-1628	110	6	and	and	CCONJ
cana-1628	110	7	figure	figure	VERB
cana-1628	110	8	3	3	NUM
cana-1628	110	9	,	,	PUNCT
cana-1628	110	10	figure	figure	NOUN
cana-1628	110	11	4	4	NUM
cana-1628	110	12	,	,	PUNCT
cana-1628	110	13	and	and	CCONJ
cana-1628	110	14	figure	figure	VERB
cana-1628	110	15	5	5	NUM
cana-1628	110	16	show	show	NOUN
cana-1628	110	17	,	,	PUNCT
cana-1628	110	18	our	our	PRON
cana-1628	110	19	dataset	dataset	NOUN
cana-1628	110	20	is	be	AUX
cana-1628	110	21	positively	positively	ADV
cana-1628	110	22	skewed	skewed	ADJ
cana-1628	110	23	.	.	PUNCT
cana-1628	111	1	this	this	DET
cana-1628	111	2	kind	kind	NOUN
cana-1628	111	3	of	of	ADP
cana-1628	111	4	asymmetrical	asymmetrical	ADJ
cana-1628	111	5	data	datum	NOUN
cana-1628	111	6	distribution	distribution	NOUN
cana-1628	111	7	and	and	CCONJ
cana-1628	111	8	complicated	complicated	ADJ
cana-1628	111	9	energy	energy	NOUN
cana-1628	111	10	consumption	consumption	NOUN
cana-1628	111	11	patterns	pattern	NOUN
cana-1628	111	12	[	[	X
cana-1628	111	13	7	7	X
cana-1628	111	14	]	]	PUNCT
cana-1628	111	15	requires	require	VERB
cana-1628	111	16	efficient	efficient	ADJ
cana-1628	111	17	data	datum	NOUN
cana-1628	111	18	clustering	cluster	VERB
cana-1628	111	19	and	and	CCONJ
cana-1628	111	20	an	an	DET
cana-1628	111	21	ensemble	ensemble	ADJ
cana-1628	111	22	deep	deep	ADJ
cana-1628	111	23	learning	learning	NOUN
cana-1628	111	24	model	model	NOUN
cana-1628	111	25	that	that	PRON
cana-1628	111	26	can	can	AUX
cana-1628	111	27	handle	handle	VERB
cana-1628	111	28	much	much	ADV
cana-1628	111	29	better	well	ADJ
cana-1628	111	30	than	than	ADP
cana-1628	111	31	the	the	DET
cana-1628	111	32	classical	classical	ADJ
cana-1628	111	33	machine	machine	NOUN
cana-1628	111	34	learning	learning	NOUN
cana-1628	111	35	models	model	NOUN
cana-1628	111	36	and	and	CCONJ
cana-1628	111	37	statistical	statistical	ADJ
cana-1628	111	38	techniques	technique	NOUN
cana-1628	111	39	.	.	PUNCT
cana-1628	112	1	moreover	moreover	ADV
cana-1628	112	2	,	,	PUNCT
cana-1628	112	3	k	k	X
cana-1628	112	4	-	-	PUNCT
cana-1628	112	5	means	mean	VERB
cana-1628	112	6	clustering	cluster	VERB
cana-1628	112	7	results	result	NOUN
cana-1628	112	8	in	in	ADP
cana-1628	112	9	figure	figure	NOUN
cana-1628	112	10	7	7	NUM
cana-1628	112	11	illustrate	illustrate	VERB
cana-1628	112	12	that	that	SCONJ
cana-1628	112	13	south	south	PROPN
cana-1628	112	14	aa	aa	PROPN
cana-1628	112	15	data	datum	NOUN
cana-1628	112	16	is	be	AUX
cana-1628	112	17	grouped	group	VERB
cana-1628	112	18	into	into	ADP
cana-1628	112	19	3	3	NUM
cana-1628	112	20	clusters	cluster	NOUN
cana-1628	112	21	of	of	ADP
cana-1628	112	22	energy	energy	NOUN
cana-1628	112	23	consumption	consumption	NOUN
cana-1628	112	24	profiles	profile	NOUN
cana-1628	112	25	.	.	PUNCT
cana-1628	113	1	accordingly	accordingly	ADV
cana-1628	113	2	,	,	PUNCT
cana-1628	113	3	cluster	cluster	NOUN
cana-1628	113	4	1	1	NUM
cana-1628	113	5	contains	contain	VERB
cana-1628	113	6	the	the	DET
cana-1628	113	7	medium	medium	ADJ
cana-1628	113	8	size	size	NOUN
cana-1628	113	9	energy	energy	NOUN
cana-1628	113	10	consumption	consumption	NOUN
cana-1628	113	11	profiles	profile	NOUN
cana-1628	113	12	and	and	CCONJ
cana-1628	113	13	the	the	DET
cana-1628	113	14	user	user	NOUN
cana-1628	113	15	’s	’s	PART
cana-1628	113	16	monthly	monthly	ADJ
cana-1628	113	17	energy	energy	NOUN
cana-1628	113	18	usage	usage	NOUN
cana-1628	113	19	is	be	AUX
cana-1628	113	20	between	between	ADP
cana-1628	113	21	275kw	275kw	NOUN
cana-1628	113	22	and	and	CCONJ
cana-1628	113	23	575kw	575kw	NOUN
cana-1628	113	24	.	.	PUNCT
cana-1628	114	1	next	next	ADJ
cana-1628	114	2	to	to	ADP
cana-1628	114	3	cluster	cluster	VERB
cana-1628	114	4	1	1	NUM
cana-1628	114	5	,	,	PUNCT
cana-1628	114	6	cluster	cluster	NOUN
cana-1628	114	7	2	2	NUM
cana-1628	114	8	is	be	AUX
cana-1628	114	9	indicated	indicate	VERB
cana-1628	114	10	in	in	ADP
cana-1628	114	11	the	the	DET
cana-1628	114	12	brown	brown	PROPN
cana-1628	114	13	box	box	PROPN
cana-1628	114	14	and	and	CCONJ
cana-1628	114	15	contains	contain	VERB
cana-1628	114	16	lower	low	ADJ
cana-1628	114	17	energy	energy	NOUN
cana-1628	114	18	users	user	NOUN
cana-1628	114	19	;	;	PUNCT
cana-1628	114	20	their	their	PRON
cana-1628	114	21	monthly	monthly	ADJ
cana-1628	114	22	energy	energy	NOUN
cana-1628	114	23	consumption	consumption	NOUN
cana-1628	114	24	is	be	AUX
cana-1628	114	25	between	between	ADP
cana-1628	114	26	0.1kw	0.1kw	PROPN
cana-1628	114	27	and	and	CCONJ
cana-1628	114	28	275kw	275kw	NOUN
cana-1628	114	29	,	,	PUNCT
cana-1628	114	30	but	but	CCONJ
cana-1628	114	31	the	the	DET
cana-1628	114	32	largest	large	ADJ
cana-1628	114	33	observation	observation	NOUN
cana-1628	114	34	or	or	CCONJ
cana-1628	114	35	energy	energy	NOUN
cana-1628	114	36	consumption	consumption	NOUN
cana-1628	114	37	profiles	profile	NOUN
cana-1628	114	38	are	be	AUX
cana-1628	114	39	grouped	group	VERB
cana-1628	114	40	in	in	ADP
cana-1628	114	41	this	this	DET
cana-1628	114	42	category	category	NOUN
cana-1628	114	43	.	.	PUNCT
cana-1628	115	1	the	the	DET
cana-1628	115	2	last	last	ADJ
cana-1628	115	3	cluster	cluster	NOUN
cana-1628	115	4	consists	consist	VERB
cana-1628	115	5	of	of	ADP
cana-1628	115	6	the	the	DET
cana-1628	115	7	group	group	NOUN
cana-1628	115	8	of	of	ADP
cana-1628	115	9	consumption	consumption	NOUN
cana-1628	115	10	profiles	profile	NOUN
cana-1628	115	11	that	that	PRON
cana-1628	115	12	comprises	comprise	VERB
cana-1628	115	13	the	the	DET
cana-1628	115	14	highest	high	ADJ
cana-1628	115	15	monthly	monthly	ADJ
cana-1628	115	16	energy	energy	NOUN
cana-1628	115	17	consumption	consumption	NOUN
cana-1628	115	18	profiles	profile	NOUN
cana-1628	115	19	whose	whose	DET
cana-1628	115	20	monthly	monthly	ADJ
cana-1628	115	21	consumption	consumption	NOUN
cana-1628	115	22	revolves	revolve	VERB
cana-1628	115	23	between	between	ADP
cana-1628	115	24	575	575	NUM
cana-1628	115	25	kw	kw	NOUN
cana-1628	115	26	and	and	CCONJ
cana-1628	115	27	925	925	NUM
cana-1628	115	28	kw	kw	NOUN
cana-1628	115	29	,	,	PUNCT
cana-1628	115	30	but	but	CCONJ
cana-1628	115	31	this	this	DET
cana-1628	115	32	group	group	NOUN
cana-1628	115	33	accommodates	accommodate	VERB
cana-1628	115	34	the	the	DET
cana-1628	115	35	smallest	small	ADJ
cana-1628	115	36	number	number	NOUN
cana-1628	115	37	of	of	ADP
cana-1628	115	38	observations	observation	NOUN
cana-1628	115	39	.	.	PUNCT
cana-1628	116	1	figure	figure	VERB
cana-1628	116	2	3	3	NUM
cana-1628	116	3	:	:	PUNCT
cana-1628	116	4	skewness	skewness	NOUN
cana-1628	116	5	test	test	NOUN
cana-1628	116	6	for	for	ADP
cana-1628	116	7	south	south	ADJ
cana-1628	116	8	aa	aa	PROPN
cana-1628	116	9	after	after	ADP
cana-1628	116	10	clustering	cluster	VERB
cana-1628	116	11	.	.	PUNCT
cana-1628	117	1	communications	communication	NOUN
cana-1628	117	2	on	on	ADP
cana-1628	117	3	applied	apply	VERB
cana-1628	117	4	nonlinear	nonlinear	ADJ
cana-1628	117	5	analysis	analysis	NOUN
cana-1628	117	6	issn	issn	NOUN
cana-1628	117	7	:	:	PUNCT
cana-1628	117	8	1074	1074	NUM
cana-1628	117	9	-	-	PUNCT
cana-1628	117	10	133x	133x	NUM
cana-1628	117	11	vol	vol	NOUN
cana-1628	117	12	32	32	NUM
cana-1628	117	13	no	no	NOUN
cana-1628	117	14	.	.	NOUN
cana-1628	117	15	1	1	NUM
cana-1628	117	16	(	(	PUNCT
cana-1628	117	17	2025	2025	NUM
cana-1628	117	18	)	)	PUNCT
cana-1628	117	19	162	162	NUM
cana-1628	117	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	117	21	figure	figure	NOUN
cana-1628	117	22	4	4	NUM
cana-1628	117	23	:	:	PUNCT
cana-1628	117	24	skewness	skewness	NOUN
cana-1628	117	25	of	of	ADP
cana-1628	117	26	west	west	PROPN
cana-1628	117	27	aa	aa	PROPN
cana-1628	117	28	data	datum	NOUN
cana-1628	117	29	before	before	ADP
cana-1628	117	30	clustering	clustering	NOUN
cana-1628	117	31	and	and	CCONJ
cana-1628	117	32	after	after	ADP
cana-1628	117	33	clustering	cluster	VERB
cana-1628	117	34	figure	figure	NOUN
cana-1628	117	35	5	5	NUM
cana-1628	117	36	:	:	PUNCT
cana-1628	117	37	skewness	skewness	NOUN
cana-1628	117	38	test	test	NOUN
cana-1628	117	39	for	for	ADP
cana-1628	117	40	west	west	PROPN
cana-1628	117	41	aa	aa	PROPN
cana-1628	117	42	data	datum	NOUN
cana-1628	117	43	after	after	ADP
cana-1628	117	44	clustering	cluster	VERB
cana-1628	117	45	.	.	PUNCT
cana-1628	118	1	figure	figure	VERB
cana-1628	118	2	6	6	NUM
cana-1628	118	3	:	:	PUNCT
cana-1628	118	4	k	k	ADJ
cana-1628	118	5	-	-	PUNCT
cana-1628	118	6	means	mean	VERB
cana-1628	118	7	clustering	clustering	NOUN
cana-1628	118	8	results	result	NOUN
cana-1628	118	9	.	.	PUNCT
cana-1628	119	1	3.3	3.3	NUM
cana-1628	119	2	deep	deep	ADJ
cana-1628	119	3	learning	learning	NOUN
cana-1628	119	4	model	model	NOUN
cana-1628	119	5	deep	deep	ADJ
cana-1628	119	6	learning	learning	NOUN
cana-1628	119	7	methods	method	NOUN
cana-1628	119	8	have	have	AUX
cana-1628	119	9	gained	gain	VERB
cana-1628	119	10	greater	great	ADJ
cana-1628	119	11	attention	attention	NOUN
cana-1628	119	12	because	because	SCONJ
cana-1628	119	13	of	of	ADP
cana-1628	119	14	their	their	PRON
cana-1628	119	15	remarkable	remarkable	ADJ
cana-1628	119	16	performance	performance	NOUN
cana-1628	119	17	in	in	ADP
cana-1628	119	18	image	image	NOUN
cana-1628	119	19	classification	classification	NOUN
cana-1628	119	20	,	,	PUNCT
cana-1628	119	21	natural	natural	ADJ
cana-1628	119	22	language	language	NOUN
cana-1628	119	23	processing	processing	NOUN
cana-1628	119	24	,	,	PUNCT
cana-1628	119	25	and	and	CCONJ
cana-1628	119	26	nonlinear	nonlinear	ADJ
cana-1628	119	27	electric	electric	ADJ
cana-1628	119	28	load	load	NOUN
cana-1628	119	29	consumption	consumption	NOUN
cana-1628	119	30	prediction	prediction	NOUN
cana-1628	119	31	[	[	X
cana-1628	119	32	9	9	NUM
cana-1628	119	33	,	,	PUNCT
cana-1628	119	34	36	36	NUM
cana-1628	119	35	]	]	PUNCT
cana-1628	119	36	.	.	PUNCT
cana-1628	120	1	convolutional	convolutional	ADJ
cana-1628	120	2	neural	neural	ADJ
cana-1628	120	3	networks	network	NOUN
cana-1628	120	4	(	(	PUNCT
cana-1628	120	5	cnn	cnn	PROPN
cana-1628	120	6	)	)	PUNCT
cana-1628	121	1	[	[	X
cana-1628	121	2	18	18	NUM
cana-1628	121	3	]	]	PUNCT
cana-1628	121	4	,	,	PUNCT
cana-1628	121	5	long	long	ADJ
cana-1628	121	6	short	short	ADJ
cana-1628	121	7	-	-	PUNCT
cana-1628	121	8	term	term	NOUN
cana-1628	121	9	memory	memory	NOUN
cana-1628	121	10	networks	network	NOUN
cana-1628	121	11	(	(	PUNCT
cana-1628	121	12	lstm	lstm	NOUN
cana-1628	121	13	)	)	PUNCT
cana-1628	122	1	[	[	X
cana-1628	122	2	8	8	NUM
cana-1628	122	3	]	]	PUNCT
cana-1628	122	4	,	,	PUNCT
cana-1628	122	5	and	and	CCONJ
cana-1628	122	6	gated	gate	VERB
cana-1628	122	7	recurrent	recurrent	ADJ
cana-1628	122	8	networks	network	NOUN
cana-1628	122	9	(	(	PUNCT
cana-1628	122	10	gru	gru	NOUN
cana-1628	122	11	)	)	PUNCT
cana-1628	123	1	[	[	X
cana-1628	123	2	17	17	NUM
cana-1628	123	3	]	]	PUNCT
cana-1628	123	4	are	be	AUX
cana-1628	123	5	the	the	DET
cana-1628	123	6	most	most	ADV
cana-1628	123	7	widely	widely	ADV
cana-1628	123	8	used	use	VERB
cana-1628	123	9	deep	deep	ADJ
cana-1628	123	10	learning	learning	NOUN
cana-1628	123	11	algorithms	algorithm	NOUN
cana-1628	123	12	.	.	PUNCT
cana-1628	124	1	communications	communication	NOUN
cana-1628	124	2	on	on	ADP
cana-1628	124	3	applied	apply	VERB
cana-1628	124	4	nonlinear	nonlinear	ADJ
cana-1628	124	5	analysis	analysis	NOUN
cana-1628	124	6	issn	issn	NOUN
cana-1628	124	7	:	:	PUNCT
cana-1628	124	8	1074	1074	NUM
cana-1628	124	9	-	-	PUNCT
cana-1628	124	10	133x	133x	NUM
cana-1628	124	11	vol	vol	NOUN
cana-1628	124	12	32	32	NUM
cana-1628	124	13	no	no	NOUN
cana-1628	124	14	.	.	NOUN
cana-1628	124	15	1	1	NUM
cana-1628	124	16	(	(	PUNCT
cana-1628	124	17	2025	2025	NUM
cana-1628	124	18	)	)	PUNCT
cana-1628	124	19	163	163	NUM
cana-1628	124	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	124	21	3.3.1	3.3.1	NUM
cana-1628	124	22	long	long	ADJ
cana-1628	124	23	-	-	PUNCT
cana-1628	124	24	short	short	ADJ
cana-1628	124	25	term	term	NOUN
cana-1628	124	26	memory	memory	NOUN
cana-1628	124	27	neural	neural	ADJ
cana-1628	124	28	networks	network	NOUN
cana-1628	124	29	(	(	PUNCT
cana-1628	124	30	lstm	lstm	NOUN
cana-1628	124	31	)	)	PUNCT
cana-1628	124	32	long	long	ADJ
cana-1628	124	33	short	short	ADJ
cana-1628	124	34	-	-	PUNCT
cana-1628	124	35	term	term	NOUN
cana-1628	124	36	memory	memory	NOUN
cana-1628	124	37	network	network	NOUN
cana-1628	124	38	(	(	PUNCT
cana-1628	124	39	lstm	lstm	PROPN
cana-1628	124	40	)	)	PUNCT
cana-1628	124	41	is	be	AUX
cana-1628	124	42	an	an	DET
cana-1628	124	43	improved	improved	ADJ
cana-1628	124	44	version	version	NOUN
cana-1628	124	45	of	of	ADP
cana-1628	124	46	a	a	DET
cana-1628	124	47	recurrent	recurrent	ADJ
cana-1628	124	48	neural	neural	ADJ
cana-1628	124	49	network	network	NOUN
cana-1628	124	50	frequently	frequently	ADV
cana-1628	124	51	used	use	VERB
cana-1628	124	52	in	in	ADP
cana-1628	124	53	time	time	NOUN
cana-1628	124	54	forecasting	forecasting	NOUN
cana-1628	124	55	and	and	CCONJ
cana-1628	124	56	natural	natural	ADJ
cana-1628	124	57	language	language	NOUN
cana-1628	124	58	processing	processing	NOUN
cana-1628	124	59	.	.	PUNCT
cana-1628	125	1	with	with	ADP
cana-1628	125	2	its	its	PRON
cana-1628	125	3	special	special	ADJ
cana-1628	125	4	memory	memory	NOUN
cana-1628	125	5	cell	cell	NOUN
cana-1628	125	6	,	,	PUNCT
cana-1628	125	7	lstm	lstm	NOUN
cana-1628	125	8	is	be	AUX
cana-1628	125	9	capable	capable	ADJ
cana-1628	125	10	of	of	ADP
cana-1628	125	11	storing	store	VERB
cana-1628	125	12	information	information	NOUN
cana-1628	125	13	involving	involve	VERB
cana-1628	125	14	long	long	ADJ
cana-1628	125	15	-	-	PUNCT
cana-1628	125	16	range	range	NOUN
cana-1628	125	17	temporal	temporal	ADJ
cana-1628	125	18	dependencies	dependency	NOUN
cana-1628	125	19	[	[	X
cana-1628	125	20	31	31	NUM
cana-1628	125	21	]	]	PUNCT
cana-1628	125	22	.	.	PUNCT
cana-1628	126	1	lstm	lstm	PROPN
cana-1628	126	2	network	network	NOUN
cana-1628	126	3	can	can	AUX
cana-1628	126	4	establish	establish	VERB
cana-1628	126	5	long	long	ADJ
cana-1628	126	6	-	-	PUNCT
cana-1628	126	7	term	term	NOUN
cana-1628	126	8	temporal	temporal	ADJ
cana-1628	126	9	correlation	correlation	NOUN
cana-1628	126	10	information	information	NOUN
cana-1628	126	11	and	and	CCONJ
cana-1628	126	12	overcome	overcome	VERB
cana-1628	126	13	vanishing	vanish	VERB
cana-1628	126	14	gradient	gradient	NOUN
cana-1628	126	15	problems	problem	NOUN
cana-1628	126	16	of	of	ADP
cana-1628	126	17	rnns	rnns	NOUN
cana-1628	126	18	networks	network	NOUN
cana-1628	126	19	as	as	ADP
cana-1628	126	20	figure	figure	NOUN
cana-1628	126	21	8	8	NUM
cana-1628	126	22	shows	show	VERB
cana-1628	126	23	the	the	DET
cana-1628	126	24	sequential	sequential	ADJ
cana-1628	126	25	learning	learning	NOUN
cana-1628	126	26	capabilities	capability	NOUN
cana-1628	126	27	of	of	ADP
cana-1628	126	28	lstm	lstm	NOUN
cana-1628	126	29	.	.	PUNCT
cana-1628	127	1	the	the	DET
cana-1628	127	2	selfconnection	selfconnection	NOUN
cana-1628	127	3	of	of	ADP
cana-1628	127	4	the	the	DET
cana-1628	127	5	lstm	lstm	PROPN
cana-1628	127	6	memory	memory	NOUN
cana-1628	127	7	block	block	NOUN
cana-1628	127	8	,	,	PUNCT
cana-1628	127	9	referred	refer	VERB
cana-1628	127	10	to	to	ADP
cana-1628	127	11	as	as	ADP
cana-1628	127	12	the	the	DET
cana-1628	127	13	cell	cell	NOUN
cana-1628	127	14	state	state	NOUN
cana-1628	127	15	,	,	PUNCT
cana-1628	127	16	preserves	preserve	NOUN
cana-1628	127	17	(	(	PUNCT
cana-1628	127	18	remembers	remember	NOUN
cana-1628	127	19	)	)	PUNCT
cana-1628	127	20	longerrange	longerrange	VERB
cana-1628	127	21	temporal	temporal	ADJ
cana-1628	127	22	dependencies	dependency	NOUN
cana-1628	127	23	of	of	ADP
cana-1628	127	24	the	the	DET
cana-1628	127	25	data	datum	NOUN
cana-1628	127	26	.	.	PUNCT
cana-1628	128	1	moreover	moreover	ADV
cana-1628	128	2	,	,	PUNCT
cana-1628	128	3	lstm	lstm	ADJ
cana-1628	128	4	architecture	architecture	NOUN
cana-1628	128	5	is	be	AUX
cana-1628	128	6	equipped	equip	VERB
cana-1628	128	7	with	with	ADP
cana-1628	128	8	multiplicative	multiplicative	ADJ
cana-1628	128	9	gate	gate	NOUN
cana-1628	128	10	modules	module	NOUN
cana-1628	128	11	which	which	PRON
cana-1628	128	12	include	include	VERB
cana-1628	128	13	an	an	DET
cana-1628	128	14	input	input	NOUN
cana-1628	128	15	gate	gate	NOUN
cana-1628	128	16	,	,	PUNCT
cana-1628	128	17	forget	forget	VERB
cana-1628	128	18	gate	gate	NOUN
cana-1628	128	19	,	,	PUNCT
cana-1628	128	20	and	and	CCONJ
cana-1628	128	21	output	output	NOUN
cana-1628	128	22	gate	gate	NOUN
cana-1628	129	1	[	[	X
cana-1628	129	2	25	25	NUM
cana-1628	129	3	]	]	PUNCT
cana-1628	129	4	.	.	PUNCT
cana-1628	130	1	the	the	DET
cana-1628	130	2	gate	gate	PROPN
cana-1628	130	3	units	unit	NOUN
cana-1628	130	4	are	be	AUX
cana-1628	130	5	responsible	responsible	ADJ
cana-1628	130	6	for	for	ADP
cana-1628	130	7	regulating	regulate	VERB
cana-1628	130	8	the	the	DET
cana-1628	130	9	flow	flow	NOUN
cana-1628	130	10	of	of	ADP
cana-1628	130	11	information	information	NOUN
cana-1628	130	12	while	while	SCONJ
cana-1628	130	13	sequential	sequential	ADJ
cana-1628	130	14	data	datum	NOUN
cana-1628	130	15	processing	processing	NOUN
cana-1628	130	16	is	be	AUX
cana-1628	130	17	dealt	deal	VERB
cana-1628	130	18	with	with	ADP
cana-1628	130	19	lstm	lstm	PROPN
cana-1628	130	20	model	model	NOUN
cana-1628	130	21	.	.	PUNCT
cana-1628	131	1	figure	figure	VERB
cana-1628	131	2	7	7	NUM
cana-1628	131	3	:	:	PUNCT
cana-1628	131	4	lstm	lstm	ADJ
cana-1628	131	5	sequential	sequential	ADJ
cana-1628	131	6	learning	learning	NOUN
cana-1628	131	7	process	process	NOUN
cana-1628	131	8	�	�	NOUN
cana-1628	131	9	̂	̂	NOUN
cana-1628	131	10	�	�	NOUN
cana-1628	131	11	𝑡+1	𝑡+1	PROPN
cana-1628	131	12	=	=	SYM
cana-1628	131	13	𝑓∑⁡	𝑓∑⁡	PROPN
cana-1628	131	14	(	(	PUNCT
cana-1628	131	15	𝑊𝑖𝑥𝑖	𝑊𝑖𝑥𝑖	PROPN
cana-1628	131	16	∗	∗	VERB
cana-1628	131	17	𝑏𝑖	𝑏𝑖	NOUN
cana-1628	131	18	)	)	PUNCT
cana-1628	131	19	(	(	PUNCT
cana-1628	131	20	7	7	X
cana-1628	131	21	)	)	PUNCT
cana-1628	131	22	where	where	SCONJ
cana-1628	131	23	the	the	DET
cana-1628	131	24	𝑊𝑖	𝑊𝑖	PROPN
cana-1628	131	25	and	and	CCONJ
cana-1628	131	26	𝑥𝑖	𝑥𝑖	PROPN
cana-1628	131	27	are	be	AUX
cana-1628	131	28	the	the	DET
cana-1628	131	29	updated	update	VERB
cana-1628	131	30	weight	weight	NOUN
cana-1628	131	31	vector	vector	NOUN
cana-1628	131	32	and	and	CCONJ
cana-1628	131	33	the	the	DET
cana-1628	131	34	input	input	NOUN
cana-1628	131	35	data	datum	NOUN
cana-1628	131	36	respectively	respectively	ADV
cana-1628	131	37	.	.	PUNCT
cana-1628	132	1	furthermore	furthermore	ADV
cana-1628	132	2	,	,	PUNCT
cana-1628	132	3	f	f	PROPN
cana-1628	132	4	is	be	AUX
cana-1628	132	5	the	the	DET
cana-1628	132	6	activation	activation	NOUN
cana-1628	132	7	function	function	NOUN
cana-1628	132	8	.	.	PUNCT
cana-1628	133	1	3.3.2	3.3.2	NUM
cana-1628	133	2	bidirectional	bidirectional	ADJ
cana-1628	133	3	lstm	lstm	NOUN
cana-1628	133	4	(	(	PUNCT
cana-1628	133	5	bilstm	bilstm	NOUN
cana-1628	133	6	)	)	PUNCT
cana-1628	133	7	bidirectional	bidirectional	ADJ
cana-1628	133	8	long	long	ADJ
cana-1628	133	9	short	short	ADJ
cana-1628	133	10	-	-	PUNCT
cana-1628	133	11	term	term	NOUN
cana-1628	133	12	memory	memory	NOUN
cana-1628	133	13	(	(	PUNCT
cana-1628	133	14	bilstm	bilstm	NOUN
cana-1628	133	15	)	)	PUNCT
cana-1628	133	16	is	be	AUX
cana-1628	133	17	the	the	DET
cana-1628	133	18	defamation	defamation	NOUN
cana-1628	133	19	of	of	ADP
cana-1628	133	20	the	the	DET
cana-1628	133	21	lstm	lstm	ADJ
cana-1628	133	22	algorithm	algorithm	NOUN
cana-1628	133	23	which	which	PRON
cana-1628	133	24	is	be	AUX
cana-1628	133	25	capable	capable	ADJ
cana-1628	133	26	of	of	ADP
cana-1628	133	27	learning	learn	VERB
cana-1628	133	28	sequential	sequential	ADJ
cana-1628	133	29	data	datum	NOUN
cana-1628	133	30	in	in	ADP
cana-1628	133	31	both	both	CCONJ
cana-1628	133	32	forward	forward	ADJ
cana-1628	133	33	and	and	CCONJ
cana-1628	133	34	backward	backward	ADJ
cana-1628	133	35	directions	direction	NOUN
cana-1628	133	36	as	as	ADP
cana-1628	133	37	figure	figure	NOUN
cana-1628	133	38	9	9	NUM
cana-1628	133	39	shows	show	NOUN
cana-1628	133	40	.	.	PUNCT
cana-1628	134	1	it	it	PRON
cana-1628	134	2	is	be	AUX
cana-1628	134	3	more	more	ADV
cana-1628	134	4	effective	effective	ADJ
cana-1628	134	5	in	in	ADP
cana-1628	134	6	learning	learn	VERB
cana-1628	134	7	the	the	DET
cana-1628	134	8	past	past	NOUN
cana-1628	134	9	and	and	CCONJ
cana-1628	134	10	the	the	DET
cana-1628	134	11	future	future	ADJ
cana-1628	134	12	context	context	NOUN
cana-1628	134	13	information	information	NOUN
cana-1628	134	14	in	in	ADP
cana-1628	134	15	two	two	NUM
cana-1628	134	16	ways	way	NOUN
cana-1628	134	17	forward	forward	ADV
cana-1628	134	18	and	and	CCONJ
cana-1628	134	19	backward	backward	ADJ
cana-1628	134	20	directions	direction	NOUN
cana-1628	134	21	allowing	allow	VERB
cana-1628	134	22	it	it	PRON
cana-1628	134	23	to	to	PART
cana-1628	134	24	capture	capture	VERB
cana-1628	134	25	the	the	DET
cana-1628	134	26	context	context	NOUN
cana-1628	134	27	from	from	ADP
cana-1628	134	28	both	both	PRON
cana-1628	134	29	past	past	NOUN
cana-1628	134	30	and	and	CCONJ
cana-1628	134	31	future	future	ADJ
cana-1628	134	32	information	information	NOUN
cana-1628	134	33	.	.	PUNCT
cana-1628	135	1	this	this	PRON
cana-1628	135	2	makes	make	VERB
cana-1628	135	3	bidirectional	bidirectional	ADJ
cana-1628	135	4	lstm	lstm	NOUN
cana-1628	135	5	well	well	ADV
cana-1628	135	6	-	-	PUNCT
cana-1628	135	7	suited	suit	VERB
cana-1628	135	8	for	for	ADP
cana-1628	135	9	tasks	task	NOUN
cana-1628	135	10	involving	involve	VERB
cana-1628	135	11	sequential	sequential	ADJ
cana-1628	135	12	data	datum	NOUN
cana-1628	135	13	such	such	ADJ
cana-1628	135	14	as	as	ADP
cana-1628	135	15	natural	natural	ADJ
cana-1628	135	16	language	language	NOUN
cana-1628	135	17	processing	processing	NOUN
cana-1628	135	18	and	and	CCONJ
cana-1628	135	19	time	time	NOUN
cana-1628	135	20	series	series	PROPN
cana-1628	135	21	forecasting	forecasting	PROPN
cana-1628	135	22	.	.	PUNCT
cana-1628	136	1	the	the	DET
cana-1628	136	2	forward	forward	ADJ
cana-1628	136	3	and	and	CCONJ
cana-1628	136	4	backward	backward	ADJ
cana-1628	136	5	operation	operation	NOUN
cana-1628	136	6	of	of	ADP
cana-1628	136	7	the	the	DET
cana-1628	136	8	bilstm	bilstm	NOUN
cana-1628	136	9	can	can	AUX
cana-1628	136	10	be	be	AUX
cana-1628	136	11	expressed	express	VERB
cana-1628	136	12	using	use	VERB
cana-1628	136	13	equations	equation	NOUN
cana-1628	136	14	8	8	NUM
cana-1628	136	15	-	-	SYM
cana-1628	136	16	10	10	NUM
cana-1628	136	17	.	.	PUNCT
cana-1628	137	1	ℎ⃗	ℎ⃗	NOUN
cana-1628	137	2	𝑡	𝑡	NOUN
cana-1628	137	3	=	=	SYM
cana-1628	137	4	lstm(𝑥𝑡	lstm(𝑥𝑡	NOUN
cana-1628	137	5	,	,	PUNCT
cana-1628	137	6	ℎ⃗	ℎ⃗	NOUN
cana-1628	137	7	𝑡)⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(8	𝑡)⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(8	NOUN
cana-1628	137	8	)	)	PUNCT
cana-1628	137	9	ℎ←𝑡	ℎ←𝑡	NOUN
cana-1628	137	10	=	=	SYM
cana-1628	137	11	lstm(𝑥𝑡	lstm(𝑥𝑡	NOUN
cana-1628	137	12	,	,	PUNCT
cana-1628	137	13	ℎ←𝑡)⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(9	ℎ←𝑡)⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(9	NOUN
cana-1628	137	14	)	)	PUNCT
cana-1628	137	15	�	�	PROPN
cana-1628	137	16	̂	̂	SYM
cana-1628	137	17	�	�	NOUN
cana-1628	137	18	𝑡+1	𝑡+1	NOUN
cana-1628	137	19	=	=	SYM
cana-1628	137	20	𝑤	𝑤	ADP
cana-1628	137	21	→𝑦	→𝑦	NUM
cana-1628	137	22	ℎ	ℎ	PROPN
cana-1628	137	23	ℎ⃗	ℎ⃗	NOUN
cana-1628	137	24	𝑡	𝑡	VERB
cana-1628	137	25	+	+	VERB
cana-1628	137	26	𝑊	𝑊	PROPN
cana-1628	137	27	←ℎ𝑦	←ℎ𝑦	NOUN
cana-1628	137	28	,	,	PUNCT
cana-1628	137	29	ℎ𝑡←	ℎ𝑡←	ADJ
cana-1628	137	30	+	+	CCONJ
cana-1628	137	31	𝑏𝑦⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(10	𝑏𝑦⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(10	ADJ
cana-1628	137	32	)	)	PUNCT
cana-1628	137	33	communications	communication	NOUN
cana-1628	137	34	on	on	ADP
cana-1628	137	35	applied	apply	VERB
cana-1628	137	36	nonlinear	nonlinear	ADJ
cana-1628	137	37	analysis	analysis	NOUN
cana-1628	137	38	issn	issn	NOUN
cana-1628	137	39	:	:	PUNCT
cana-1628	137	40	1074	1074	NUM
cana-1628	137	41	-	-	PUNCT
cana-1628	137	42	133x	133x	NUM
cana-1628	137	43	vol	vol	NOUN
cana-1628	137	44	32	32	NUM
cana-1628	137	45	no	no	NOUN
cana-1628	137	46	.	.	NOUN
cana-1628	137	47	1	1	NUM
cana-1628	137	48	(	(	PUNCT
cana-1628	137	49	2025	2025	NUM
cana-1628	137	50	)	)	PUNCT
cana-1628	137	51	164	164	NUM
cana-1628	137	52	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	137	53	bilstm	bilstm	NOUN
cana-1628	137	54	trains	train	VERB
cana-1628	137	55	two	two	NUM
cana-1628	137	56	lstm	lstm	PROPN
cana-1628	137	57	networks	network	NOUN
cana-1628	137	58	,	,	PUNCT
cana-1628	137	59	where	where	SCONJ
cana-1628	137	60	the	the	DET
cana-1628	137	61	first	first	ADJ
cana-1628	137	62	lstm	lstm	ADJ
cana-1628	137	63	network	network	NOUN
cana-1628	137	64	processes	process	VERB
cana-1628	137	65	the	the	DET
cana-1628	137	66	input	input	NOUN
cana-1628	137	67	sequence	sequence	NOUN
cana-1628	137	68	in	in	ADP
cana-1628	137	69	the	the	DET
cana-1628	137	70	forward	forward	ADJ
cana-1628	137	71	direction	direction	NOUN
cana-1628	137	72	,	,	PUNCT
cana-1628	137	73	and	and	CCONJ
cana-1628	137	74	the	the	DET
cana-1628	137	75	second	second	ADJ
cana-1628	137	76	lstm	lstm	NOUN
cana-1628	137	77	network	network	NOUN
cana-1628	137	78	processes	process	NOUN
cana-1628	137	79	in	in	ADP
cana-1628	137	80	the	the	DET
cana-1628	137	81	reverse	reverse	ADJ
cana-1628	137	82	direction	direction	NOUN
cana-1628	137	83	with	with	ADP
cana-1628	137	84	a	a	DET
cana-1628	137	85	reversed	reverse	VERB
cana-1628	137	86	copy	copy	NOUN
cana-1628	137	87	of	of	ADP
cana-1628	137	88	the	the	DET
cana-1628	137	89	input	input	NOUN
cana-1628	138	1	[	[	X
cana-1628	138	2	15	15	NUM
cana-1628	138	3	]	]	PUNCT
cana-1628	138	4	.	.	PUNCT
cana-1628	139	1	figure	figure	NOUN
cana-1628	139	2	8	8	NUM
cana-1628	139	3	:	:	PUNCT
cana-1628	139	4	bilstm	bilstm	NOUN
cana-1628	139	5	structure	structure	NOUN
cana-1628	139	6	3.3.3	3.3.3	NUM
cana-1628	139	7	1d	1d	NUM
cana-1628	139	8	-	-	PUNCT
cana-1628	139	9	dimensional	dimensional	ADJ
cana-1628	139	10	convolution	convolution	NOUN
cana-1628	139	11	neural	neural	ADJ
cana-1628	139	12	network	network	NOUN
cana-1628	139	13	(	(	PUNCT
cana-1628	139	14	1d	1d	PROPN
cana-1628	139	15	-	-	PUNCT
cana-1628	139	16	cnn	cnn	NOUN
cana-1628	139	17	)	)	PUNCT
cana-1628	139	18	model	model	NOUN
cana-1628	139	19	parameter	parameter	NOUN
cana-1628	139	20	name	name	NOUN
cana-1628	139	21	types	type	NOUN
cana-1628	139	22	/	/	SYM
cana-1628	139	23	range	range	NOUN
cana-1628	139	24	values	value	NOUN
cana-1628	139	25	optimal	optimal	ADJ
cana-1628	139	26	value	value	NOUN
cana-1628	139	27	selected	select	VERB
cana-1628	139	28	1d	1d	NUM
cana-1628	139	29	-	-	PUNCT
cana-1628	139	30	cnn	cnn	NOUN
cana-1628	139	31	number	number	NOUN
cana-1628	139	32	filters	filter	NOUN
cana-1628	139	33	[	[	X
cana-1628	139	34	32,64,128,256	32,64,128,256	X
cana-1628	139	35	]	]	X
cana-1628	139	36	64	64	NUM
cana-1628	139	37	kernel	kernel	NOUN
cana-1628	139	38	size	size	NOUN
cana-1628	139	39	[	[	X
cana-1628	139	40	2,3,4,5	2,3,4,5	NUM
cana-1628	139	41	]	]	SYM
cana-1628	139	42	3	3	NUM
cana-1628	139	43	pool	pool	NOUN
cana-1628	139	44	type	type	NOUN
cana-1628	140	1	[	[	X
cana-1628	140	2	maxpooling1d	maxpooling1d	NOUN
cana-1628	140	3	,	,	PUNCT
cana-1628	140	4	averagepooling1d	averagepooling1d	PROPN
cana-1628	140	5	]	]	PUNCT
cana-1628	140	6	maxpooling1d	maxpooling1d	NOUN
cana-1628	140	7	activation	activation	NOUN
cana-1628	140	8	function	function	NOUN
cana-1628	140	9	[	[	X
cana-1628	140	10	relu	relu	NOUN
cana-1628	140	11	,	,	PUNCT
cana-1628	140	12	tanh	tanh	NOUN
cana-1628	140	13	,	,	PUNCT
cana-1628	140	14	linear	linear	ADJ
cana-1628	140	15	]	]	PUNCT
cana-1628	140	16	relu	relu	NOUN
cana-1628	140	17	epoch	epoch	NOUN
cana-1628	141	1	[	[	X
cana-1628	141	2	40,80,120,160	40,80,120,160	NUM
cana-1628	141	3	]	]	SYM
cana-1628	141	4	80	80	NUM
cana-1628	141	5	optimizer	optimizer	NOUN
cana-1628	141	6	[	[	X
cana-1628	141	7	rmsprop	rmsprop	NOUN
cana-1628	141	8	,	,	PUNCT
cana-1628	141	9	adam	adam	NOUN
cana-1628	141	10	,	,	PUNCT
cana-1628	141	11	adadalta	adadalta	PROPN
cana-1628	141	12	]	]	PUNCT
cana-1628	141	13	adam	adam	PROPN
cana-1628	141	14	batch	batch	NOUN
cana-1628	141	15	size	size	NOUN
cana-1628	141	16	[	[	X
cana-1628	141	17	32,64,120,180	32,64,120,180	X
cana-1628	141	18	]	]	PUNCT
cana-1628	141	19	32	32	NUM
cana-1628	141	20	learning	learning	NOUN
cana-1628	141	21	rate	rate	NOUN
cana-1628	141	22	[	[	X
cana-1628	141	23	0.0001,0.001,0.01,0.1	0.0001,0.001,0.01,0.1	X
cana-1628	141	24	]	]	X
cana-1628	141	25	0.001	0.001	NUM
cana-1628	141	26	bilstm	bilstm	NOUN
cana-1628	141	27	activation	activation	NOUN
cana-1628	141	28	function	function	NOUN
cana-1628	141	29	[	[	X
cana-1628	141	30	relu	relu	NOUN
cana-1628	141	31	,	,	PUNCT
cana-1628	141	32	tanh	tanh	NOUN
cana-1628	141	33	,	,	PUNCT
cana-1628	141	34	linear	linear	ADJ
cana-1628	141	35	]	]	X
cana-1628	141	36	relu	relu	NOUN
cana-1628	141	37	dropout	dropout	NOUN
cana-1628	141	38	rate	rate	NOUN
cana-1628	141	39	[	[	X
cana-1628	141	40	0.1,0.2,0.4,0.5	0.1,0.2,0.4,0.5	NOUN
cana-1628	141	41	]	]	X
cana-1628	141	42	0.2	0.2	NUM
cana-1628	141	43	optimizer	optimizer	NOUN
cana-1628	141	44	[	[	X
cana-1628	141	45	rmsprop	rmsprop	NOUN
cana-1628	141	46	,	,	PUNCT
cana-1628	141	47	adam	adam	NOUN
cana-1628	141	48	,	,	PUNCT
cana-1628	141	49	adadalta	adadalta	PROPN
cana-1628	141	50	]	]	PUNCT
cana-1628	141	51	adam	adam	PROPN
cana-1628	141	52	epoch	epoch	PROPN
cana-1628	142	1	[	[	X
cana-1628	142	2	40,80,120,200	40,80,120,200	X
cana-1628	142	3	]	]	SYM
cana-1628	142	4	120	120	NUM
cana-1628	142	5	batch	batch	NOUN
cana-1628	142	6	size	size	NOUN
cana-1628	142	7	[	[	X
cana-1628	142	8	32,64,128,256	32,64,128,256	X
cana-1628	142	9	]	]	PUNCT
cana-1628	142	10	64	64	NUM
cana-1628	142	11	learning	learning	NOUN
cana-1628	142	12	rate	rate	NOUN
cana-1628	142	13	[	[	X
cana-1628	142	14	0.0001,0.001,0.01,0.1	0.0001,0.001,0.01,0.1	X
cana-1628	142	15	]	]	X
cana-1628	142	16	0.01	0.01	NUM
cana-1628	142	17	lstm	lstm	ADJ
cana-1628	142	18	activation	activation	NOUN
cana-1628	142	19	function	function	NOUN
cana-1628	142	20	[	[	X
cana-1628	142	21	relu	relu	NOUN
cana-1628	142	22	,	,	PUNCT
cana-1628	142	23	tanh	tanh	NOUN
cana-1628	142	24	,	,	PUNCT
cana-1628	142	25	linear	linear	ADJ
cana-1628	142	26	]	]	X
cana-1628	142	27	tanh	tanh	PROPN
cana-1628	142	28	dropout	dropout	NOUN
cana-1628	142	29	rate	rate	NOUN
cana-1628	142	30	[	[	X
cana-1628	142	31	0.1,0.2,0.4,0.5	0.1,0.2,0.4,0.5	NOUN
cana-1628	142	32	]	]	X
cana-1628	142	33	0.2	0.2	NUM
cana-1628	142	34	epoch	epoch	NOUN
cana-1628	143	1	[	[	X
cana-1628	143	2	40,80,120,160	40,80,120,160	X
cana-1628	143	3	]	]	SYM
cana-1628	143	4	160	160	NUM
cana-1628	143	5	batch	batch	NOUN
cana-1628	143	6	size	size	NOUN
cana-1628	143	7	[	[	X
cana-1628	143	8	32,64,120,180	32,64,120,180	X
cana-1628	143	9	]	]	PUNCT
cana-1628	143	10	64	64	NUM
cana-1628	143	11	optimizer	optimizer	NOUN
cana-1628	143	12	[	[	X
cana-1628	143	13	rmsprop	rmsprop	NOUN
cana-1628	143	14	,	,	PUNCT
cana-1628	143	15	adam	adam	NOUN
cana-1628	143	16	,	,	PUNCT
cana-1628	143	17	adadalta	adadalta	PROPN
cana-1628	143	18	]	]	PUNCT
cana-1628	143	19	rmsprop	rmsprop	NOUN
cana-1628	143	20	learning	learning	NOUN
cana-1628	143	21	rate	rate	NOUN
cana-1628	143	22	[	[	X
cana-1628	143	23	0.0001,0.001,0.01,0.1	0.0001,0.001,0.01,0.1	X
cana-1628	143	24	]	]	X
cana-1628	143	25	0.01	0.01	NUM
cana-1628	143	26	convolutional	convolutional	ADJ
cana-1628	143	27	neural	neural	ADJ
cana-1628	143	28	networks	network	NOUN
cana-1628	143	29	(	(	PUNCT
cana-1628	143	30	cnns	cnns	PROPN
cana-1628	143	31	)	)	PUNCT
cana-1628	143	32	are	be	AUX
cana-1628	143	33	the	the	DET
cana-1628	143	34	most	most	ADV
cana-1628	143	35	popular	popular	ADJ
cana-1628	143	36	deep	deep	ADJ
cana-1628	143	37	learning	learning	NOUN
cana-1628	143	38	algorithms	algorithm	NOUN
cana-1628	143	39	with	with	ADP
cana-1628	143	40	similar	similar	ADJ
cana-1628	143	41	human	human	ADJ
cana-1628	143	42	biological	biological	ADJ
cana-1628	143	43	perception	perception	NOUN
cana-1628	143	44	processing	processing	NOUN
cana-1628	143	45	systems	system	NOUN
cana-1628	143	46	[	[	X
cana-1628	143	47	18	18	NUM
cana-1628	143	48	]	]	PUNCT
cana-1628	143	49	.	.	PUNCT
cana-1628	144	1	1d	1d	NUM
cana-1628	144	2	-	-	PUNCT
cana-1628	144	3	cnn	cnn	PROPN
cana-1628	144	4	is	be	AUX
cana-1628	144	5	a	a	DET
cana-1628	144	6	special	special	ADJ
cana-1628	144	7	type	type	NOUN
cana-1628	144	8	of	of	ADP
cana-1628	144	9	cnn	cnn	PROPN
cana-1628	144	10	network	network	NOUN
cana-1628	144	11	with	with	ADP
cana-1628	144	12	powerful	powerful	ADJ
cana-1628	144	13	feature	feature	NOUN
cana-1628	144	14	extraction	extraction	NOUN
cana-1628	144	15	and	and	CCONJ
cana-1628	144	16	time	time	NOUN
cana-1628	144	17	series	series	PROPN
cana-1628	144	18	forecasting	forecasting	NOUN
cana-1628	144	19	capabilities	capability	NOUN
cana-1628	144	20	.	.	PUNCT
cana-1628	145	1	1dcnn	1dcnn	NUM
cana-1628	145	2	is	be	AUX
cana-1628	145	3	composed	compose	VERB
cana-1628	145	4	of	of	ADP
cana-1628	145	5	three	three	NUM
cana-1628	145	6	basic	basic	ADJ
cana-1628	145	7	components	component	NOUN
cana-1628	145	8	which	which	PRON
cana-1628	145	9	include	include	VERB
cana-1628	145	10	the	the	DET
cana-1628	145	11	convolution	convolution	NOUN
cana-1628	145	12	layer	layer	NOUN
cana-1628	145	13	for	for	ADP
cana-1628	145	14	feature	feature	NOUN
cana-1628	145	15	extraction	extraction	NOUN
cana-1628	145	16	,	,	PUNCT
cana-1628	145	17	the	the	DET
cana-1628	145	18	pooling	pooling	NOUN
cana-1628	145	19	layer	layer	NOUN
cana-1628	145	20	for	for	ADP
cana-1628	145	21	dimension	dimension	NOUN
cana-1628	145	22	reduction	reduction	NOUN
cana-1628	145	23	,	,	PUNCT
cana-1628	145	24	and	and	CCONJ
cana-1628	145	25	the	the	DET
cana-1628	145	26	neuron	neuron	NOUN
cana-1628	145	27	in	in	ADP
cana-1628	145	28	fully	fully	ADV
cana-1628	145	29	connected	connected	ADJ
cana-1628	145	30	layers	layer	NOUN
cana-1628	145	31	use	use	VERB
cana-1628	145	32	a	a	DET
cana-1628	145	33	weights	weight	NOUN
cana-1628	145	34	matrix	matrix	NOUN
cana-1628	145	35	to	to	PART
cana-1628	145	36	apply	apply	VERB
cana-1628	145	37	a	a	DET
cana-1628	145	38	linear	linear	ADJ
cana-1628	145	39	transformation	transformation	NOUN
cana-1628	145	40	to	to	ADP
cana-1628	145	41	the	the	DET
cana-1628	145	42	input	input	NOUN
cana-1628	145	43	vector	vector	NOUN
cana-1628	146	1	[	[	X
cana-1628	146	2	12	12	NUM
cana-1628	146	3	]	]	PUNCT
cana-1628	146	4	as	as	SCONJ
cana-1628	146	5	shown	show	VERB
cana-1628	146	6	in	in	ADP
cana-1628	146	7	figure	figure	NOUN
cana-1628	146	8	10	10	NUM
cana-1628	146	9	.	.	PUNCT
cana-1628	147	1	communications	communication	NOUN
cana-1628	147	2	on	on	ADP
cana-1628	147	3	applied	apply	VERB
cana-1628	147	4	nonlinear	nonlinear	ADJ
cana-1628	147	5	analysis	analysis	NOUN
cana-1628	147	6	issn	issn	NOUN
cana-1628	147	7	:	:	PUNCT
cana-1628	147	8	1074	1074	NUM
cana-1628	147	9	-	-	PUNCT
cana-1628	147	10	133x	133x	NUM
cana-1628	147	11	vol	vol	NOUN
cana-1628	147	12	32	32	NUM
cana-1628	147	13	no	no	NOUN
cana-1628	147	14	.	.	NOUN
cana-1628	147	15	1	1	NUM
cana-1628	147	16	(	(	PUNCT
cana-1628	147	17	2025	2025	NUM
cana-1628	147	18	)	)	PUNCT
cana-1628	147	19	165	165	NUM
cana-1628	147	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	147	21	3.3.4	3.3.4	NUM
cana-1628	147	22	ensemble	ensemble	ADJ
cana-1628	147	23	deep	deep	ADJ
cana-1628	147	24	learning	learning	NOUN
cana-1628	147	25	model	model	NOUN
cana-1628	147	26	the	the	DET
cana-1628	147	27	ensemble	ensemble	ADJ
cana-1628	147	28	model	model	NOUN
cana-1628	147	29	integrates	integrate	VERB
cana-1628	147	30	multiple	multiple	ADJ
cana-1628	147	31	learning	learn	VERB
cana-1628	147	32	algorithms	algorithm	NOUN
cana-1628	147	33	to	to	PART
cana-1628	147	34	obtain	obtain	VERB
cana-1628	147	35	models	model	NOUN
cana-1628	147	36	that	that	PRON
cana-1628	147	37	perform	perform	VERB
cana-1628	147	38	better	well	ADV
cana-1628	147	39	than	than	ADP
cana-1628	147	40	the	the	DET
cana-1628	147	41	single	single	ADJ
cana-1628	147	42	constituent	constituent	NOUN
cana-1628	147	43	base	base	NOUN
cana-1628	147	44	models	model	NOUN
cana-1628	147	45	.	.	PUNCT
cana-1628	148	1	this	this	PRON
cana-1628	148	2	means	mean	VERB
cana-1628	148	3	that	that	SCONJ
cana-1628	148	4	the	the	DET
cana-1628	148	5	optimal	optimal	ADJ
cana-1628	148	6	and	and	CCONJ
cana-1628	148	7	strong	strong	ADJ
cana-1628	148	8	generalizable	generalizable	ADJ
cana-1628	148	9	figure	figure	NOUN
cana-1628	148	10	9	9	NUM
cana-1628	148	11	:	:	PUNCT
cana-1628	148	12	1d	1d	NUM
cana-1628	148	13	-	-	PUNCT
cana-1628	148	14	cnn	cnn	NOUN
cana-1628	148	15	architecture	architecture	NOUN
cana-1628	148	16	ensemble	ensemble	ADJ
cana-1628	148	17	model	model	NOUN
cana-1628	148	18	can	can	AUX
cana-1628	148	19	be	be	AUX
cana-1628	148	20	obtained	obtain	VERB
cana-1628	148	21	by	by	ADP
cana-1628	148	22	combining	combine	VERB
cana-1628	148	23	multiple	multiple	ADJ
cana-1628	148	24	base	base	NOUN
cana-1628	148	25	models	model	NOUN
cana-1628	148	26	or	or	CCONJ
cana-1628	148	27	algorithms	algorithm	NOUN
cana-1628	148	28	.	.	PUNCT
cana-1628	149	1	in	in	ADP
cana-1628	149	2	this	this	DET
cana-1628	149	3	paper	paper	NOUN
cana-1628	149	4	to	to	PART
cana-1628	149	5	build	build	VERB
cana-1628	149	6	an	an	DET
cana-1628	149	7	ensemble	ensemble	ADJ
cana-1628	149	8	deep	deep	ADJ
cana-1628	149	9	learning	learning	NOUN
cana-1628	149	10	model	model	NOUN
cana-1628	149	11	,	,	PUNCT
cana-1628	149	12	first	first	ADV
cana-1628	149	13	of	of	ADP
cana-1628	149	14	all	all	PRON
cana-1628	149	15	,	,	PUNCT
cana-1628	149	16	each	each	DET
cana-1628	149	17	base	base	NOUN
cana-1628	149	18	models	model	NOUN
cana-1628	149	19	are	be	AUX
cana-1628	149	20	trained	train	VERB
cana-1628	149	21	with	with	ADP
cana-1628	149	22	their	their	PRON
cana-1628	149	23	optimal	optimal	ADJ
cana-1628	149	24	hyperparameter	hyperparameter	NOUN
cana-1628	149	25	configuration	configuration	NOUN
cana-1628	149	26	as	as	ADP
cana-1628	149	27	table	table	NOUN
cana-1628	149	28	3	3	NUM
cana-1628	149	29	shows	show	VERB
cana-1628	149	30	the	the	DET
cana-1628	149	31	set	set	NOUN
cana-1628	149	32	of	of	ADP
cana-1628	149	33	optimal	optimal	ADJ
cana-1628	149	34	hyperparameters	hyperparameter	NOUN
cana-1628	149	35	of	of	ADP
cana-1628	149	36	each	each	DET
cana-1628	149	37	base	base	NOUN
cana-1628	149	38	model	model	NOUN
cana-1628	149	39	.	.	PUNCT
cana-1628	150	1	the	the	DET
cana-1628	150	2	optimal	optimal	ADJ
cana-1628	150	3	hyperparameter	hyperparameter	NOUN
cana-1628	150	4	combination	combination	NOUN
cana-1628	150	5	of	of	ADP
cana-1628	150	6	each	each	DET
cana-1628	150	7	base	base	NOUN
cana-1628	150	8	model	model	NOUN
cana-1628	150	9	was	be	AUX
cana-1628	150	10	determined	determine	VERB
cana-1628	150	11	using	use	VERB
cana-1628	150	12	the	the	DET
cana-1628	150	13	bayesian	bayesian	NOUN
cana-1628	150	14	optimization	optimization	NOUN
cana-1628	150	15	(	(	PUNCT
cana-1628	150	16	bo	bo	NOUN
cana-1628	150	17	)	)	PUNCT
cana-1628	150	18	algorithm	algorithm	NOUN
cana-1628	150	19	.	.	PUNCT
cana-1628	151	1	bayesian	bayesian	NOUN
cana-1628	151	2	optimization	optimization	NOUN
cana-1628	151	3	algorithm	algorithm	NOUN
cana-1628	151	4	is	be	AUX
cana-1628	151	5	a	a	DET
cana-1628	151	6	metaheuristic	metaheuristic	ADJ
cana-1628	151	7	hyperparameter	hyperparameter	NOUN
cana-1628	151	8	tuning	tune	VERB
cana-1628	151	9	approach	approach	NOUN
cana-1628	151	10	based	base	VERB
cana-1628	151	11	on	on	ADP
cana-1628	151	12	the	the	DET
cana-1628	151	13	probability	probability	NOUN
cana-1628	151	14	model	model	NOUN
cana-1628	151	15	of	of	ADP
cana-1628	151	16	the	the	DET
cana-1628	151	17	global	global	ADJ
cana-1628	151	18	function	function	NOUN
cana-1628	151	19	which	which	PRON
cana-1628	151	20	aims	aim	VERB
cana-1628	151	21	to	to	PART
cana-1628	151	22	intelligently	intelligently	ADV
cana-1628	151	23	identify	identify	VERB
cana-1628	151	24	the	the	DET
cana-1628	151	25	optimal	optimal	ADJ
cana-1628	151	26	combination	combination	NOUN
cana-1628	151	27	of	of	ADP
cana-1628	151	28	hyperparameters	hyperparameter	NOUN
cana-1628	151	29	with	with	ADP
cana-1628	151	30	reasonable	reasonable	ADJ
cana-1628	151	31	computation	computation	NOUN
cana-1628	151	32	time	time	NOUN
cana-1628	151	33	.	.	PUNCT
cana-1628	152	1	table	table	NOUN
cana-1628	152	2	3	3	NUM
cana-1628	152	3	:	:	PUNCT
cana-1628	152	4	hyperparameters	hyperparameter	NOUN
cana-1628	152	5	tuning	tune	VERB
cana-1628	152	6	for	for	ADP
cana-1628	152	7	selected	select	VERB
cana-1628	152	8	deep	deep	ADJ
cana-1628	152	9	learning	learning	NOUN
cana-1628	152	10	models	model	NOUN
cana-1628	152	11	.	.	PUNCT
cana-1628	153	1	after	after	SCONJ
cana-1628	153	2	the	the	DET
cana-1628	153	3	optimal	optimal	ADJ
cana-1628	153	4	clusters	cluster	NOUN
cana-1628	153	5	have	have	AUX
cana-1628	153	6	been	be	AUX
cana-1628	153	7	generated	generate	VERB
cana-1628	153	8	,	,	PUNCT
cana-1628	153	9	the	the	DET
cana-1628	153	10	observations	observation	NOUN
cana-1628	153	11	in	in	ADP
cana-1628	153	12	each	each	DET
cana-1628	153	13	cluster	cluster	NOUN
cana-1628	153	14	are	be	AUX
cana-1628	153	15	divided	divide	VERB
cana-1628	153	16	into	into	ADP
cana-1628	153	17	training	training	NOUN
cana-1628	153	18	and	and	CCONJ
cana-1628	153	19	testing	testing	NOUN
cana-1628	153	20	sets	set	NOUN
cana-1628	153	21	to	to	PART
cana-1628	153	22	train	train	VERB
cana-1628	153	23	the	the	DET
cana-1628	153	24	proposed	propose	VERB
cana-1628	153	25	model	model	NOUN
cana-1628	153	26	with	with	ADP
cana-1628	153	27	each	each	DET
cana-1628	153	28	cluster	cluster	NOUN
cana-1628	153	29	’s	’s	PART
cana-1628	153	30	data	datum	NOUN
cana-1628	153	31	as	as	ADP
cana-1628	153	32	figure	figure	NOUN
cana-1628	153	33	10	10	NUM
cana-1628	153	34	illustrates	illustrate	VERB
cana-1628	153	35	.	.	PUNCT
cana-1628	154	1	despite	despite	SCONJ
cana-1628	154	2	the	the	DET
cana-1628	154	3	varying	vary	VERB
cana-1628	154	4	dataset	dataset	NOUN
cana-1628	154	5	size	size	NOUN
cana-1628	154	6	,	,	PUNCT
cana-1628	154	7	70	70	NUM
cana-1628	154	8	%	%	NOUN
cana-1628	154	9	of	of	ADP
cana-1628	154	10	the	the	DET
cana-1628	154	11	instances	instance	NOUN
cana-1628	154	12	comprising	comprise	VERB
cana-1628	154	13	the	the	DET
cana-1628	154	14	first	first	ADJ
cana-1628	154	15	16	16	NUM
cana-1628	154	16	months	month	NOUN
cana-1628	154	17	of	of	ADP
cana-1628	154	18	load	load	NOUN
cana-1628	154	19	consumption	consumption	NOUN
cana-1628	154	20	data	datum	NOUN
cana-1628	154	21	of	of	ADP
cana-1628	154	22	each	each	DET
cana-1628	154	23	cluster	cluster	NOUN
cana-1628	154	24	was	be	AUX
cana-1628	154	25	used	use	VERB
cana-1628	154	26	for	for	ADP
cana-1628	154	27	model	model	NOUN
cana-1628	154	28	training	training	NOUN
cana-1628	154	29	by	by	ADP
cana-1628	154	30	keeping	keep	VERB
cana-1628	154	31	the	the	DET
cana-1628	154	32	sequential	sequential	ADJ
cana-1628	154	33	order	order	NOUN
cana-1628	154	34	of	of	ADP
cana-1628	154	35	the	the	DET
cana-1628	154	36	monthly	monthly	ADJ
cana-1628	154	37	energy	energy	NOUN
cana-1628	154	38	consumption	consumption	NOUN
cana-1628	154	39	is	be	AUX
cana-1628	154	40	relevant	relevant	ADJ
cana-1628	154	41	.	.	PUNCT
cana-1628	155	1	then	then	ADV
cana-1628	155	2	,	,	PUNCT
cana-1628	155	3	the	the	DET
cana-1628	155	4	remaining	remain	VERB
cana-1628	155	5	30	30	NUM
cana-1628	155	6	%	%	NOUN
cana-1628	155	7	approximately	approximately	ADV
cana-1628	155	8	the	the	DET
cana-1628	155	9	final	final	ADJ
cana-1628	155	10	four	four	NUM
cana-1628	155	11	months	month	NOUN
cana-1628	155	12	of	of	ADP
cana-1628	155	13	the	the	DET
cana-1628	155	14	dataset	dataset	NOUN
cana-1628	155	15	was	be	AUX
cana-1628	155	16	used	use	VERB
cana-1628	155	17	to	to	PART
cana-1628	155	18	test	test	VERB
cana-1628	155	19	the	the	DET
cana-1628	155	20	model	model	NOUN
cana-1628	155	21	’s	’s	PART
cana-1628	155	22	performance	performance	NOUN
cana-1628	155	23	.	.	PUNCT
cana-1628	156	1	this	this	PRON
cana-1628	156	2	is	be	AUX
cana-1628	156	3	because	because	SCONJ
cana-1628	156	4	we	we	PRON
cana-1628	156	5	aimed	aim	VERB
cana-1628	156	6	to	to	PART
cana-1628	156	7	predict	predict	VERB
cana-1628	156	8	the	the	DET
cana-1628	156	9	next	next	ADJ
cana-1628	156	10	four	four	NUM
cana-1628	156	11	months	month	NOUN
cana-1628	156	12	’	'	PUNCT
cana-1628	156	13	aggregate	aggregate	ADJ
cana-1628	156	14	monthly	monthly	ADJ
cana-1628	156	15	load	load	NOUN
cana-1628	156	16	consumption	consumption	NOUN
cana-1628	156	17	using	use	VERB
cana-1628	156	18	the	the	DET
cana-1628	156	19	previous	previous	ADJ
cana-1628	156	20	16	16	NUM
cana-1628	156	21	months	month	NOUN
cana-1628	156	22	’	'	PUNCT
cana-1628	156	23	load	load	NOUN
cana-1628	156	24	consumption	consumption	NOUN
cana-1628	156	25	data	datum	NOUN
cana-1628	156	26	.	.	PUNCT
cana-1628	157	1	the	the	DET
cana-1628	157	2	pseudocode	pseudocode	PROPN
cana-1628	157	3	detailed	detail	VERB
cana-1628	157	4	in	in	ADP
cana-1628	157	5	algorithm	algorithm	NOUN
cana-1628	157	6	2	2	NUM
cana-1628	157	7	shows	show	VERB
cana-1628	157	8	the	the	DET
cana-1628	157	9	proposed	propose	VERB
cana-1628	157	10	model	model	NOUN
cana-1628	157	11	execution	execution	NOUN
cana-1628	157	12	process	process	NOUN
cana-1628	157	13	,	,	PUNCT
cana-1628	157	14	including	include	VERB
cana-1628	157	15	data	datum	NOUN
cana-1628	157	16	partitioning	partition	VERB
cana-1628	157	17	into	into	ADP
cana-1628	157	18	training	training	NOUN
cana-1628	157	19	and	and	CCONJ
cana-1628	157	20	test	test	NOUN
cana-1628	157	21	datasets	dataset	NOUN
cana-1628	157	22	.	.	PUNCT
cana-1628	158	1	thus	thus	ADV
cana-1628	158	2	,	,	PUNCT
cana-1628	158	3	given	give	VERB
cana-1628	158	4	lstm	lstm	NOUN
cana-1628	158	5	,	,	PUNCT
cana-1628	158	6	bilstm	bilstm	NOUN
cana-1628	158	7	,	,	PUNCT
cana-1628	158	8	and	and	CCONJ
cana-1628	158	9	cnn	cnn	PROPN
cana-1628	158	10	-	-	PUNCT
cana-1628	158	11	gru	gru	PROPN
cana-1628	158	12	are	be	AUX
cana-1628	158	13	chosen	choose	VERB
cana-1628	158	14	algorithms	algorithm	NOUN
cana-1628	158	15	to	to	PART
cana-1628	158	16	build	build	VERB
cana-1628	158	17	the	the	DET
cana-1628	158	18	base	base	NOUN
cana-1628	158	19	models	model	NOUN
cana-1628	158	20	,	,	PUNCT
cana-1628	158	21	then	then	ADV
cana-1628	158	22	each	each	DET
cana-1628	158	23	model	model	NOUN
cana-1628	158	24	prediction	prediction	NOUN
cana-1628	158	25	output	output	NOUN
cana-1628	158	26	can	can	AUX
cana-1628	158	27	be	be	AUX
cana-1628	158	28	represented	represent	VERB
cana-1628	158	29	by	by	ADP
cana-1628	158	30	ˆ	ˆ	NOUN
cana-1628	158	31	m1	m1	NOUN
cana-1628	158	32	,	,	PUNCT
cana-1628	158	33	ˆ	ˆ	PROPN
cana-1628	158	34	m2	m2	PROPN
cana-1628	158	35	and	and	CCONJ
cana-1628	158	36	ˆ	ˆ	PROPN
cana-1628	158	37	m3	m3	PROPN
cana-1628	158	38	,	,	PUNCT
cana-1628	158	39	respectively	respectively	ADV
cana-1628	158	40	.	.	PUNCT
cana-1628	159	1	similarly	similarly	ADV
cana-1628	159	2	,	,	PUNCT
cana-1628	159	3	their	their	PRON
cana-1628	159	4	respective	respective	ADJ
cana-1628	159	5	weights	weight	NOUN
cana-1628	159	6	can	can	AUX
cana-1628	159	7	be	be	AUX
cana-1628	159	8	generated	generate	VERB
cana-1628	159	9	as	as	ADP
cana-1628	159	10	w1	w1	NOUN
cana-1628	159	11	,	,	PUNCT
cana-1628	159	12	w2	w2	NOUN
cana-1628	159	13	and	and	CCONJ
cana-1628	159	14	w3	w3	PROPN
cana-1628	159	15	,	,	PUNCT
cana-1628	159	16	for	for	ADP
cana-1628	159	17	ˆ	ˆ	PROPN
cana-1628	159	18	m1	m1	NOUN
cana-1628	159	19	,	,	PUNCT
cana-1628	159	20	ˆ	ˆ	PROPN
cana-1628	159	21	m2	m2	PROPN
cana-1628	159	22	and	and	CCONJ
cana-1628	159	23	ˆ	ˆ	PROPN
cana-1628	159	24	m3	m3	PROPN
cana-1628	159	25	models	model	NOUN
cana-1628	159	26	,	,	PUNCT
cana-1628	159	27	respectively	respectively	ADV
cana-1628	159	28	,	,	PUNCT
cana-1628	159	29	with	with	ADP
cana-1628	159	30	different	different	ADJ
cana-1628	159	31	weight	weight	NOUN
cana-1628	159	32	values	value	NOUN
cana-1628	159	33	based	base	VERB
cana-1628	159	34	on	on	ADP
cana-1628	159	35	the	the	DET
cana-1628	159	36	proportion	proportion	NOUN
cana-1628	159	37	of	of	ADP
cana-1628	159	38	the	the	DET
cana-1628	159	39	performance	performance	NOUN
cana-1628	159	40	of	of	ADP
cana-1628	159	41	each	each	DET
cana-1628	159	42	base	base	NOUN
cana-1628	159	43	model	model	NOUN
cana-1628	159	44	that	that	PRON
cana-1628	159	45	will	will	AUX
cana-1628	159	46	yield	yield	VERB
cana-1628	159	47	an	an	DET
cana-1628	159	48	optimal	optimal	ADJ
cana-1628	159	49	ensemble	ensemble	ADJ
cana-1628	159	50	model	model	NOUN
cana-1628	159	51	.	.	PUNCT
cana-1628	160	1	the	the	DET
cana-1628	160	2	weight	weight	NOUN
cana-1628	160	3	value	value	NOUN
cana-1628	160	4	for	for	ADP
cana-1628	160	5	each	each	DET
cana-1628	160	6	base	base	NOUN
cana-1628	160	7	model	model	NOUN
cana-1628	160	8	should	should	AUX
cana-1628	160	9	be	be	AUX
cana-1628	160	10	a	a	DET
cana-1628	160	11	small	small	ADJ
cana-1628	160	12	fraction	fraction	NOUN
cana-1628	160	13	number	number	NOUN
cana-1628	160	14	ranging	range	VERB
cana-1628	160	15	from	from	ADP
cana-1628	160	16	0	0	NUM
cana-1628	160	17	to	to	ADP
cana-1628	160	18	1	1	NUM
cana-1628	160	19	.	.	PUNCT
cana-1628	161	1	this	this	PRON
cana-1628	161	2	means	mean	VERB
cana-1628	161	3	that	that	SCONJ
cana-1628	161	4	the	the	DET
cana-1628	161	5	sum	sum	NOUN
cana-1628	161	6	of	of	ADP
cana-1628	161	7	the	the	DET
cana-1628	161	8	weight	weight	NOUN
cana-1628	161	9	values	value	NOUN
cana-1628	161	10	of	of	ADP
cana-1628	161	11	all	all	DET
cana-1628	161	12	base	base	NOUN
cana-1628	161	13	models	model	NOUN
cana-1628	161	14	should	should	AUX
cana-1628	161	15	be	be	AUX
cana-1628	161	16	≤	≤	NUM
cana-1628	161	17	1	1	NUM
cana-1628	161	18	.	.	PUNCT
cana-1628	162	1	then	then	ADV
cana-1628	162	2	,	,	PUNCT
cana-1628	162	3	the	the	DET
cana-1628	162	4	final	final	ADJ
cana-1628	162	5	weighted	weight	VERB
cana-1628	162	6	average	average	ADJ
cana-1628	162	7	ensemble	ensemble	ADJ
cana-1628	162	8	model	model	NOUN
cana-1628	162	9	can	can	AUX
cana-1628	162	10	be	be	AUX
cana-1628	162	11	,	,	PUNCT
cana-1628	162	12	represented	represent	VERB
cana-1628	162	13	as	as	ADP
cana-1628	162	14	wae	wae	NOUN
cana-1628	162	15	,	,	PUNCT
cana-1628	162	16	and	and	CCONJ
cana-1628	162	17	be	be	AUX
cana-1628	162	18	found	find	VERB
cana-1628	162	19	by	by	ADP
cana-1628	162	20	merging	merge	VERB
cana-1628	162	21	base	base	NOUN
cana-1628	162	22	models	model	NOUN
cana-1628	162	23	as	as	SCONJ
cana-1628	162	24	illustrated	illustrate	VERB
cana-1628	162	25	in	in	ADP
cana-1628	162	26	equation	equation	NOUN
cana-1628	162	27	12	12	NUM
cana-1628	162	28	.	.	PUNCT
cana-1628	163	1	𝑊𝐴𝐸	𝑊𝐴𝐸	PROPN
cana-1628	163	2	=	=	SYM
cana-1628	163	3	(	(	PUNCT
cana-1628	163	4	(	(	PUNCT
cana-1628	163	5	𝑤1	𝑤1	VERB
cana-1628	163	6	∗	∗	X
cana-1628	163	7	�	�	PROPN
cana-1628	163	8	̂	̂	VERB
cana-1628	163	9	�	�	NOUN
cana-1628	163	10	1	1	NUM
cana-1628	163	11	)	)	PUNCT
cana-1628	163	12	+	+	CCONJ
cana-1628	163	13	(	(	PUNCT
cana-1628	163	14	𝑤2	𝑤2	NOUN
cana-1628	163	15	∗	∗	NOUN
cana-1628	163	16	�	�	PROPN
cana-1628	163	17	̂	̂	VERB
cana-1628	163	18	�	�	NOUN
cana-1628	163	19	2	2	NUM
cana-1628	163	20	)	)	PUNCT
cana-1628	163	21	+	+	CCONJ
cana-1628	163	22	(	(	PUNCT
cana-1628	163	23	𝑤3	𝑤3	PROPN
cana-1628	163	24	∗	∗	PRON
cana-1628	163	25	�	�	PROPN
cana-1628	163	26	̂	̂	VERB
cana-1628	163	27	�	�	NOUN
cana-1628	163	28	3	3	NUM
cana-1628	163	29	)	)	PUNCT
cana-1628	163	30	)	)	PUNCT
cana-1628	163	31	(	(	PUNCT
cana-1628	163	32	11	11	X
cana-1628	163	33	)	)	PUNCT
cana-1628	163	34	communications	communication	NOUN
cana-1628	163	35	on	on	ADP
cana-1628	163	36	applied	apply	VERB
cana-1628	163	37	nonlinear	nonlinear	ADJ
cana-1628	163	38	analysis	analysis	NOUN
cana-1628	163	39	issn	issn	NOUN
cana-1628	163	40	:	:	PUNCT
cana-1628	163	41	1074	1074	NUM
cana-1628	163	42	-	-	PUNCT
cana-1628	163	43	133x	133x	NUM
cana-1628	163	44	vol	vol	NOUN
cana-1628	163	45	32	32	NUM
cana-1628	163	46	no	no	NOUN
cana-1628	163	47	.	.	NOUN
cana-1628	163	48	1	1	NUM
cana-1628	163	49	(	(	PUNCT
cana-1628	163	50	2025	2025	NUM
cana-1628	163	51	)	)	PUNCT
cana-1628	163	52	166	166	NUM
cana-1628	163	53	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	163	54	where	where	SCONJ
cana-1628	163	55	𝑤1	𝑤1	VERB
cana-1628	163	56	,	,	PUNCT
cana-1628	163	57	𝑤2	𝑤2	NOUN
cana-1628	163	58	and	and	CCONJ
cana-1628	163	59	𝑤3	𝑤3	PROPN
cana-1628	163	60	are	be	AUX
cana-1628	163	61	the	the	DET
cana-1628	163	62	weights	weight	NOUN
cana-1628	163	63	for	for	ADP
cana-1628	163	64	�	�	NOUN
cana-1628	163	65	̂	̂	VERB
cana-1628	163	66	�	�	NOUN
cana-1628	163	67	1	1	NUM
cana-1628	163	68	,	,	PUNCT
cana-1628	163	69	�	�	NOUN
cana-1628	163	70	̂	̂	NOUN
cana-1628	163	71	�	�	NOUN
cana-1628	163	72	2	2	NUM
cana-1628	163	73	and	and	CCONJ
cana-1628	163	74	�	�	PROPN
cana-1628	163	75	̂	̂	VERB
cana-1628	163	76	�	�	NOUN
cana-1628	163	77	3	3	NUM
cana-1628	163	78	models	model	NOUN
cana-1628	163	79	respectively	respectively	ADV
cana-1628	163	80	.	.	PUNCT
cana-1628	164	1	communications	communication	NOUN
cana-1628	164	2	on	on	ADP
cana-1628	164	3	applied	apply	VERB
cana-1628	164	4	nonlinear	nonlinear	ADJ
cana-1628	164	5	analysis	analysis	NOUN
cana-1628	164	6	issn	issn	NOUN
cana-1628	164	7	:	:	PUNCT
cana-1628	164	8	1074	1074	NUM
cana-1628	164	9	-	-	PUNCT
cana-1628	164	10	133x	133x	NUM
cana-1628	164	11	vol	vol	NOUN
cana-1628	164	12	32	32	NUM
cana-1628	164	13	no	no	NOUN
cana-1628	164	14	.	.	NOUN
cana-1628	164	15	1	1	NUM
cana-1628	164	16	(	(	PUNCT
cana-1628	164	17	2025	2025	NUM
cana-1628	164	18	)	)	PUNCT
cana-1628	164	19	167	167	NUM
cana-1628	164	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	164	21	figure	figure	NOUN
cana-1628	164	22	10	10	NUM
cana-1628	164	23	:	:	PUNCT
cana-1628	164	24	the	the	DET
cana-1628	164	25	proposed	propose	VERB
cana-1628	164	26	ensemble	ensemble	ADJ
cana-1628	164	27	model	model	NOUN
cana-1628	164	28	3.3.5	3.3.5	NUM
cana-1628	164	29	performance	performance	NOUN
cana-1628	164	30	evaluation	evaluation	NOUN
cana-1628	164	31	whether	whether	SCONJ
cana-1628	164	32	machine	machine	NOUN
cana-1628	164	33	learning	learning	NOUN
cana-1628	164	34	models	model	NOUN
cana-1628	164	35	are	be	AUX
cana-1628	164	36	developed	develop	VERB
cana-1628	164	37	for	for	ADP
cana-1628	164	38	classification	classification	NOUN
cana-1628	164	39	or	or	CCONJ
cana-1628	164	40	regression	regression	NOUN
cana-1628	164	41	tasks	task	NOUN
cana-1628	164	42	,	,	PUNCT
cana-1628	164	43	several	several	ADJ
cana-1628	164	44	metrics	metric	NOUN
cana-1628	164	45	can	can	AUX
cana-1628	164	46	be	be	AUX
cana-1628	164	47	used	use	VERB
cana-1628	164	48	to	to	PART
cana-1628	164	49	quantitatively	quantitatively	ADV
cana-1628	164	50	evaluate	evaluate	VERB
cana-1628	164	51	their	their	PRON
cana-1628	164	52	efficacy	efficacy	NOUN
cana-1628	164	53	and	and	CCONJ
cana-1628	164	54	performance	performance	NOUN
cana-1628	164	55	.	.	PUNCT
cana-1628	165	1	1	1	X
cana-1628	165	2	.	.	X
cana-1628	165	3	mean	mean	VERB
cana-1628	165	4	absolute	absolute	ADJ
cana-1628	165	5	error	error	NOUN
cana-1628	165	6	(	(	PUNCT
cana-1628	165	7	mae	mae	PROPN
cana-1628	165	8	):	):	PUNCT
cana-1628	165	9	mae	mae	PROPN
cana-1628	165	10	is	be	AUX
cana-1628	165	11	a	a	DET
cana-1628	165	12	statistical	statistical	ADJ
cana-1628	165	13	metric	metric	NOUN
cana-1628	165	14	that	that	PRON
cana-1628	165	15	can	can	AUX
cana-1628	165	16	be	be	AUX
cana-1628	165	17	used	use	VERB
cana-1628	165	18	to	to	PART
cana-1628	165	19	quantify	quantify	VERB
cana-1628	165	20	the	the	DET
cana-1628	165	21	discrepancies	discrepancy	NOUN
cana-1628	165	22	between	between	ADP
cana-1628	165	23	the	the	DET
cana-1628	165	24	expected	expect	VERB
cana-1628	165	25	and	and	CCONJ
cana-1628	165	26	target	target	VERB
cana-1628	165	27	values	value	NOUN
cana-1628	165	28	[	[	X
cana-1628	165	29	4	4	NUM
cana-1628	165	30	]	]	PUNCT
cana-1628	165	31	.	.	PUNCT
cana-1628	166	1	2	2	X
cana-1628	166	2	.	.	X
cana-1628	166	3	root	root	NOUN
cana-1628	166	4	mean	mean	VERB
cana-1628	166	5	square	square	ADJ
cana-1628	166	6	error	error	NOUN
cana-1628	166	7	(	(	PUNCT
cana-1628	166	8	rmse	rmse	NOUN
cana-1628	166	9	):	):	PUNCT
cana-1628	166	10	rmse	rmse	PROPN
cana-1628	166	11	is	be	AUX
cana-1628	166	12	one	one	NUM
cana-1628	166	13	of	of	ADP
cana-1628	166	14	the	the	DET
cana-1628	166	15	popular	popular	ADJ
cana-1628	166	16	statistical	statistical	ADJ
cana-1628	166	17	metrics	metric	NOUN
cana-1628	166	18	employed	employ	VERB
cana-1628	166	19	to	to	PART
cana-1628	166	20	calculate	calculate	VERB
cana-1628	166	21	the	the	DET
cana-1628	166	22	difference	difference	NOUN
cana-1628	166	23	between	between	ADP
cana-1628	166	24	the	the	DET
cana-1628	166	25	actual	actual	ADJ
cana-1628	166	26	and	and	CCONJ
cana-1628	166	27	expected	expect	VERB
cana-1628	166	28	predicted	predict	VERB
cana-1628	166	29	values	value	NOUN
cana-1628	166	30	in	in	ADP
cana-1628	166	31	regression	regression	NOUN
cana-1628	166	32	model	model	NOUN
cana-1628	166	33	evaluation	evaluation	NOUN
cana-1628	166	34	as	as	SCONJ
cana-1628	166	35	expressed	express	VERB
cana-1628	166	36	in	in	ADP
cana-1628	166	37	equation	equation	NOUN
cana-1628	166	38	13	13	NUM
cana-1628	166	39	.	.	PUNCT
cana-1628	167	1	3	3	X
cana-1628	167	2	.	.	X
cana-1628	167	3	mean	mean	VERB
cana-1628	167	4	absolute	absolute	ADJ
cana-1628	167	5	percentage	percentage	NOUN
cana-1628	167	6	error	error	NOUN
cana-1628	167	7	(	(	PUNCT
cana-1628	167	8	mape	mape	NOUN
cana-1628	167	9	):	):	PUNCT
cana-1628	167	10	the	the	DET
cana-1628	167	11	mape	mape	NOUN
cana-1628	167	12	is	be	AUX
cana-1628	167	13	used	use	VERB
cana-1628	167	14	to	to	PART
cana-1628	167	15	calculate	calculate	VERB
cana-1628	167	16	the	the	DET
cana-1628	167	17	percentage	percentage	NOUN
cana-1628	167	18	difference	difference	NOUN
cana-1628	167	19	between	between	ADP
cana-1628	167	20	the	the	DET
cana-1628	167	21	actual	actual	ADJ
cana-1628	167	22	and	and	CCONJ
cana-1628	167	23	expected	expect	VERB
cana-1628	167	24	values	value	NOUN
cana-1628	167	25	of	of	ADP
cana-1628	167	26	load	load	NOUN
cana-1628	167	27	consumption	consumption	NOUN
cana-1628	167	28	data	datum	NOUN
cana-1628	167	29	.	.	PUNCT
cana-1628	168	1	[	[	X
cana-1628	168	2	3	3	NUM
cana-1628	168	3	]	]	PUNCT
cana-1628	168	4	.	.	PUNCT
cana-1628	169	1	the	the	DET
cana-1628	169	2	mathematical	mathematical	ADJ
cana-1628	169	3	formula	formula	NOUN
cana-1628	169	4	for	for	ADP
cana-1628	169	5	mape	mape	NOUN
cana-1628	169	6	is	be	AUX
cana-1628	169	7	given	give	VERB
cana-1628	169	8	in	in	ADP
cana-1628	169	9	equation	equation	NOUN
cana-1628	169	10	14	14	NUM
cana-1628	169	11	.	.	PUNCT
cana-1628	170	1	mae	mae	PROPN
cana-1628	170	2	⁡	⁡	PROPN
cana-1628	170	3	=	=	SYM
cana-1628	171	1	1	1	NUM
cana-1628	171	2	𝑛	𝑛	PRON
cana-1628	171	3	∑	∑	ADP
cana-1628	171	4	  	  	SPACE
cana-1628	171	5	𝑛	𝑛	PRON
cana-1628	171	6	𝑖=1	𝑖=1	PROPN
cana-1628	171	7	  	  	SPACE
cana-1628	171	8	|𝑦	|𝑦	PROPN
cana-1628	171	9	−	−	PROPN
cana-1628	171	10	�	�	PROPN
cana-1628	171	11	̂	̂	PROPN
cana-1628	171	12	�	�	NOUN
cana-1628	171	13	|	|	CCONJ
cana-1628	171	14	(	(	PUNCT
cana-1628	171	15	12	12	NUM
cana-1628	171	16	)	)	PUNCT
cana-1628	171	17	𝑅𝑀𝑆𝐸⁡	𝑅𝑀𝑆𝐸⁡	NOUN
cana-1628	171	18	=	=	SYM
cana-1628	172	1	√	√	PROPN
cana-1628	172	2	1	1	NUM
cana-1628	172	3	𝑛	𝑛	PRON
cana-1628	172	4	∑	∑	ADP
cana-1628	172	5	  	  	SPACE
cana-1628	172	6	𝑛	𝑛	PRON
cana-1628	172	7	𝑖=1	𝑖=1	PROPN
cana-1628	172	8	  	  	SPACE
cana-1628	172	9	(	(	PUNCT
cana-1628	172	10	𝑦	𝑦	NOUN
cana-1628	172	11	−	−	PROPN
cana-1628	172	12	�	�	PROPN
cana-1628	172	13	̂	̂	NOUN
cana-1628	172	14	�	�	NOUN
cana-1628	172	15	)2	)2	PUNCT
cana-1628	172	16	(	(	PUNCT
cana-1628	172	17	13	13	NUM
cana-1628	172	18	)	)	PUNCT
cana-1628	172	19	mape	mape	NOUN
cana-1628	172	20	⁡	⁡	PUNCT
cana-1628	172	21	=	=	X
cana-1628	172	22	1	1	NUM
cana-1628	172	23	𝑛	𝑛	PRON
cana-1628	172	24	∑	∑	ADP
cana-1628	172	25	  	  	SPACE
cana-1628	172	26	𝑛	𝑛	PRON
cana-1628	172	27	𝑖=1	𝑖=1	PROPN
cana-1628	172	28	  	  	SPACE
cana-1628	172	29	|𝑦	|𝑦	PROPN
cana-1628	172	30	−	−	PROPN
cana-1628	172	31	�	�	PROPN
cana-1628	172	32	̂	̂	NOUN
cana-1628	172	33	�	�	NOUN
cana-1628	172	34	|	|	ADV
cana-1628	172	35	𝑦	𝑦	NOUN
cana-1628	172	36	∗	∗	NOUN
cana-1628	172	37	100	100	NUM
cana-1628	172	38	(	(	PUNCT
cana-1628	172	39	14	14	NUM
cana-1628	172	40	)	)	PUNCT
cana-1628	172	41	communications	communication	NOUN
cana-1628	172	42	on	on	ADP
cana-1628	172	43	applied	apply	VERB
cana-1628	172	44	nonlinear	nonlinear	ADJ
cana-1628	172	45	analysis	analysis	NOUN
cana-1628	172	46	issn	issn	NOUN
cana-1628	172	47	:	:	PUNCT
cana-1628	172	48	1074	1074	NUM
cana-1628	172	49	-	-	PUNCT
cana-1628	172	50	133x	133x	NUM
cana-1628	172	51	vol	vol	NOUN
cana-1628	172	52	32	32	NUM
cana-1628	172	53	no	no	NOUN
cana-1628	172	54	.	.	NOUN
cana-1628	172	55	1	1	NUM
cana-1628	172	56	(	(	PUNCT
cana-1628	172	57	2025	2025	NUM
cana-1628	172	58	)	)	PUNCT
cana-1628	172	59	168	168	NUM
cana-1628	172	60	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	172	61	where	where	SCONJ
cana-1628	172	62	y	y	PROPN
cana-1628	172	63	and	and	CCONJ
cana-1628	172	64	ˆ	ˆ	PROPN
cana-1628	172	65	y	y	PROPN
cana-1628	172	66	represent	represent	VERB
cana-1628	172	67	the	the	DET
cana-1628	172	68	actual	actual	ADJ
cana-1628	172	69	and	and	CCONJ
cana-1628	172	70	predicted	predict	VERB
cana-1628	172	71	values	value	NOUN
cana-1628	172	72	respectively	respectively	ADV
cana-1628	172	73	.	.	PUNCT
cana-1628	173	1	furthermore	furthermore	ADV
cana-1628	173	2	,	,	PUNCT
cana-1628	173	3	n	n	PRON
cana-1628	173	4	denotes	denote	VERB
cana-1628	173	5	the	the	DET
cana-1628	173	6	total	total	ADJ
cana-1628	173	7	number	number	NOUN
cana-1628	173	8	of	of	ADP
cana-1628	173	9	data	datum	NOUN
cana-1628	173	10	that	that	PRON
cana-1628	173	11	were	be	AUX
cana-1628	173	12	utilized	utilize	VERB
cana-1628	173	13	in	in	ADP
cana-1628	173	14	the	the	DET
cana-1628	173	15	model	model	NOUN
cana-1628	173	16	evaluation	evaluation	NOUN
cana-1628	173	17	process	process	NOUN
cana-1628	173	18	.	.	PUNCT
cana-1628	174	1	3.4	3.4	NUM
cana-1628	174	2	results	result	VERB
cana-1628	174	3	analysis	analysis	NOUN
cana-1628	174	4	this	this	DET
cana-1628	174	5	section	section	NOUN
cana-1628	174	6	provides	provide	VERB
cana-1628	174	7	the	the	DET
cana-1628	174	8	experimental	experimental	ADJ
cana-1628	174	9	results	result	NOUN
cana-1628	174	10	discussion	discussion	NOUN
cana-1628	174	11	and	and	CCONJ
cana-1628	174	12	comparative	comparative	ADJ
cana-1628	174	13	analysis	analysis	NOUN
cana-1628	174	14	of	of	ADP
cana-1628	174	15	the	the	DET
cana-1628	174	16	proposed	propose	VERB
cana-1628	174	17	ensemble	ensemble	ADJ
cana-1628	174	18	deep	deep	ADJ
cana-1628	174	19	learning	learning	NOUN
cana-1628	174	20	model	model	NOUN
cana-1628	174	21	and	and	CCONJ
cana-1628	174	22	the	the	DET
cana-1628	174	23	base	base	NOUN
cana-1628	174	24	models	model	NOUN
cana-1628	174	25	using	use	VERB
cana-1628	174	26	different	different	ADJ
cana-1628	174	27	case	case	NOUN
cana-1628	174	28	study	study	NOUN
cana-1628	174	29	data	datum	NOUN
cana-1628	174	30	,	,	PUNCT
cana-1628	174	31	which	which	PRON
cana-1628	174	32	includes	include	VERB
cana-1628	174	33	south	south	ADJ
cana-1628	174	34	aa	aa	NOUN
cana-1628	174	35	and	and	CCONJ
cana-1628	174	36	west	west	PROPN
cana-1628	174	37	aa	aa	PROPN
cana-1628	174	38	.	.	PUNCT
cana-1628	174	39	table	table	NOUN
cana-1628	174	40	4	4	NUM
cana-1628	174	41	:	:	PUNCT
cana-1628	174	42	comparison	comparison	NOUN
cana-1628	174	43	of	of	ADP
cana-1628	174	44	the	the	DET
cana-1628	174	45	proposed	propose	VERB
cana-1628	174	46	and	and	CCONJ
cana-1628	174	47	baseline	baseline	VERB
cana-1628	174	48	deep	deep	ADJ
cana-1628	174	49	learning	learning	NOUN
cana-1628	174	50	models	model	NOUN
cana-1628	174	51	on	on	ADP
cana-1628	174	52	south	south	PROPN
cana-1628	174	53	aa	aa	PROPN
cana-1628	174	54	data	data	PROPN
cana-1628	174	55	.	.	PUNCT
cana-1628	175	1	data	datum	NOUN
cana-1628	175	2	algorithm	algorithm	PROPN
cana-1628	175	3	mae	mae	PROPN
cana-1628	175	4	rmse	rmse	PROPN
cana-1628	175	5	mape(%	mape(%	PROPN
cana-1628	175	6	)	)	PUNCT
cana-1628	175	7	cluster	cluster	NOUN
cana-1628	175	8	1	1	NUM
cana-1628	175	9	1d	1d	NUM
cana-1628	175	10	-	-	PUNCT
cana-1628	175	11	cnn	cnn	PROPN
cana-1628	175	12	56.097	56.097	NUM
cana-1628	175	13	65.869	65.869	NUM
cana-1628	175	14	0.195	0.195	NUM
cana-1628	175	15	gru	gru	NOUN
cana-1628	175	16	63.499	63.499	NUM
cana-1628	175	17	74.679	74.679	NUM
cana-1628	175	18	0.210	0.210	NUM
cana-1628	175	19	lstm	lstm	VERB
cana-1628	175	20	56.658	56.658	NUM
cana-1628	175	21	66.646	66.646	NUM
cana-1628	175	22	0.179	0.179	NUM
cana-1628	175	23	bilstm	bilstm	NOUN
cana-1628	175	24	57.904	57.904	NUM
cana-1628	175	25	67.777	67.777	NUM
cana-1628	175	26	0.186	0.186	NUM
cana-1628	175	27	ensemble	ensemble	ADJ
cana-1628	175	28	54.472	54.472	NUM
cana-1628	175	29	65.509	65.509	NUM
cana-1628	175	30	0.160	0.160	NUM
cana-1628	175	31	cluster	cluster	NOUN
cana-1628	175	32	2	2	NUM
cana-1628	175	33	1d	1d	NUM
cana-1628	175	34	-	-	PUNCT
cana-1628	175	35	cnn	cnn	NOUN
cana-1628	175	36	60.916	60.916	NUM
cana-1628	175	37	73.170	73.170	NUM
cana-1628	175	38	3.154	3.154	NUM
cana-1628	175	39	gru	gru	NOUN
cana-1628	176	1	51.953	51.953	NUM
cana-1628	176	2	63.904	63.904	NUM
cana-1628	176	3	3.180	3.180	NUM
cana-1628	176	4	lstm	lstm	NOUN
cana-1628	176	5	51.965	51.965	NUM
cana-1628	176	6	64.054	64.054	NUM
cana-1628	176	7	3.195	3.195	NUM
cana-1628	176	8	bilstm	bilstm	NOUN
cana-1628	176	9	59.711	59.711	NUM
cana-1628	176	10	69.964	69.964	NUM
cana-1628	176	11	0.195	0.195	NUM
cana-1628	176	12	ensemble	ensemble	ADJ
cana-1628	176	13	51.389	51.389	NUM
cana-1628	176	14	62.385	62.385	NUM
cana-1628	176	15	3.033	3.033	NUM
cana-1628	176	16	cluster	cluster	NOUN
cana-1628	176	17	3	3	NUM
cana-1628	176	18	1d	1d	NUM
cana-1628	176	19	-	-	PUNCT
cana-1628	176	20	cnn	cnn	NOUN
cana-1628	176	21	99.237	99.237	NUM
cana-1628	176	22	116.411	116.411	NUM
cana-1628	176	23	0.164	0.164	NUM
cana-1628	176	24	gru	gru	NOUN
cana-1628	176	25	103.903	103.903	NUM
cana-1628	176	26	120.980	120.980	NUM
cana-1628	176	27	0.175	0.175	NUM
cana-1628	176	28	lstm	lstm	PROPN
cana-1628	176	29	99.014	99.014	NUM
cana-1628	176	30	116.439	116.439	NUM
cana-1628	176	31	0.163	0.163	NUM
cana-1628	176	32	bilstm	bilstm	NOUN
cana-1628	176	33	97.567	97.567	NUM
cana-1628	176	34	117.137	117.137	NUM
cana-1628	176	35	0.146	0.146	NUM
cana-1628	176	36	ensemble	ensemble	ADJ
cana-1628	176	37	98.082	98.082	NUM
cana-1628	176	38	121.360	121.360	NUM
cana-1628	176	39	0.151	0.151	NUM
cana-1628	176	40	table	table	NOUN
cana-1628	176	41	4	4	NUM
cana-1628	176	42	shows	show	VERB
cana-1628	176	43	the	the	DET
cana-1628	176	44	proposed	propose	VERB
cana-1628	176	45	model	model	NOUN
cana-1628	176	46	performance	performance	NOUN
cana-1628	176	47	in	in	ADP
cana-1628	176	48	comparison	comparison	NOUN
cana-1628	176	49	with	with	ADP
cana-1628	176	50	base	base	NOUN
cana-1628	176	51	models	model	NOUN
cana-1628	176	52	(	(	PUNCT
cana-1628	176	53	1dcnn	1dcnn	NUM
cana-1628	176	54	,	,	PUNCT
cana-1628	176	55	lstm	lstm	ADJ
cana-1628	176	56	,	,	PUNCT
cana-1628	176	57	bilstm	bilstm	NOUN
cana-1628	176	58	)	)	PUNCT
cana-1628	176	59	models	model	NOUN
cana-1628	176	60	using	use	VERB
cana-1628	176	61	mae	mae	PROPN
cana-1628	176	62	,	,	PUNCT
cana-1628	176	63	rmse	rmse	NOUN
cana-1628	176	64	,	,	PUNCT
cana-1628	176	65	and	and	CCONJ
cana-1628	176	66	mape	mape	NOUN
cana-1628	176	67	metrics	metric	NOUN
cana-1628	176	68	.	.	PUNCT
cana-1628	177	1	accordingly	accordingly	ADV
cana-1628	177	2	,	,	PUNCT
cana-1628	177	3	the	the	DET
cana-1628	177	4	proposed	propose	VERB
cana-1628	177	5	ensemble	ensemble	ADJ
cana-1628	177	6	model	model	NOUN
cana-1628	177	7	shows	show	VERB
cana-1628	177	8	superior	superior	ADJ
cana-1628	177	9	performance	performance	NOUN
cana-1628	177	10	than	than	ADP
cana-1628	177	11	the	the	DET
cana-1628	177	12	base	base	NOUN
cana-1628	177	13	models	model	NOUN
cana-1628	177	14	on	on	ADP
cana-1628	177	15	cluster	cluster	NOUN
cana-1628	177	16	1	1	NUM
cana-1628	177	17	data	datum	NOUN
cana-1628	177	18	with	with	ADP
cana-1628	177	19	lower	low	ADJ
cana-1628	177	20	prediction	prediction	NOUN
cana-1628	177	21	error	error	NOUN
cana-1628	177	22	values	value	NOUN
cana-1628	177	23	54.472	54.472	NUM
cana-1628	177	24	,	,	PUNCT
cana-1628	177	25	65.509	65.509	NUM
cana-1628	177	26	,	,	PUNCT
cana-1628	177	27	and	and	CCONJ
cana-1628	177	28	0.16o	0.16o	PROPN
cana-1628	177	29	for	for	ADP
cana-1628	177	30	mae	mae	PROPN
cana-1628	177	31	,	,	PUNCT
cana-1628	177	32	rmse	rmse	NOUN
cana-1628	177	33	,	,	PUNCT
cana-1628	177	34	and	and	CCONJ
cana-1628	177	35	mape	mape	NOUN
cana-1628	177	36	respectively	respectively	ADV
cana-1628	177	37	.	.	PUNCT
cana-1628	178	1	similarly	similarly	ADV
cana-1628	178	2	,	,	PUNCT
cana-1628	178	3	the	the	DET
cana-1628	178	4	ensemble	ensemble	ADJ
cana-1628	178	5	model	model	NOUN
cana-1628	178	6	outperforms	outperform	VERB
cana-1628	178	7	the	the	DET
cana-1628	178	8	base	base	NOUN
cana-1628	178	9	models	model	NOUN
cana-1628	178	10	on	on	ADP
cana-1628	178	11	cluster	cluster	NOUN
cana-1628	178	12	2	2	NUM
cana-1628	178	13	data	datum	NOUN
cana-1628	178	14	based	base	VERB
cana-1628	178	15	on	on	ADP
cana-1628	178	16	mae(51.389	mae(51.389	NUM
cana-1628	178	17	)	)	PUNCT
cana-1628	178	18	and	and	CCONJ
cana-1628	178	19	rmse(62.385	rmse(62.385	VERB
cana-1628	178	20	)	)	PUNCT
cana-1628	178	21	regardless	regardless	ADV
cana-1628	178	22	of	of	ADP
cana-1628	178	23	the	the	DET
cana-1628	178	24	larger	large	ADJ
cana-1628	178	25	mape	mape	NOUN
cana-1628	178	26	values	value	NOUN
cana-1628	178	27	.	.	PUNCT
cana-1628	179	1	but	but	CCONJ
cana-1628	179	2	,	,	PUNCT
cana-1628	179	3	in	in	ADP
cana-1628	179	4	the	the	DET
cana-1628	179	5	case	case	NOUN
cana-1628	179	6	of	of	ADP
cana-1628	179	7	cluster	cluster	NOUN
cana-1628	179	8	3	3	NUM
cana-1628	179	9	data	datum	NOUN
cana-1628	179	10	,	,	PUNCT
cana-1628	179	11	bilstm	bilstm	NOUN
cana-1628	179	12	has	have	AUX
cana-1628	179	13	achieved	achieve	VERB
cana-1628	179	14	the	the	DET
cana-1628	179	15	best	good	ADJ
cana-1628	179	16	performance	performance	NOUN
cana-1628	179	17	with	with	ADP
cana-1628	179	18	lower	low	ADJ
cana-1628	179	19	97.567	97.567	NUM
cana-1628	179	20	and	and	CCONJ
cana-1628	179	21	0.146	0.146	NUM
cana-1628	179	22	error	error	NOUN
cana-1628	179	23	values	value	NOUN
cana-1628	179	24	for	for	ADP
cana-1628	179	25	mae	mae	PROPN
cana-1628	179	26	and	and	CCONJ
cana-1628	179	27	mape	mape	NOUN
cana-1628	179	28	respectively	respectively	ADV
cana-1628	179	29	.	.	PUNCT
cana-1628	180	1	table	table	NOUN
cana-1628	180	2	5	5	NUM
cana-1628	180	3	:	:	PUNCT
cana-1628	180	4	model	model	NOUN
cana-1628	180	5	performance	performance	NOUN
cana-1628	180	6	comparison	comparison	NOUN
cana-1628	180	7	on	on	ADP
cana-1628	180	8	training	training	NOUN
cana-1628	180	9	and	and	CCONJ
cana-1628	180	10	test	test	NOUN
cana-1628	180	11	set	set	VERB
cana-1628	180	12	with	with	ADP
cana-1628	180	13	clustering	clustering	NOUN
cana-1628	180	14	and	and	CCONJ
cana-1628	180	15	without	without	ADP
cana-1628	180	16	clustering	cluster	VERB
cana-1628	180	17	,	,	PUNCT
cana-1628	180	18	south	south	ADJ
cana-1628	180	19	aa	aa	NOUN
cana-1628	180	20	case	case	NOUN
cana-1628	180	21	study	study	NOUN
cana-1628	180	22	data	datum	NOUN
cana-1628	180	23	data	datum	NOUN
cana-1628	180	24	algorithm	algorithm	PROPN
cana-1628	180	25	training	training	NOUN
cana-1628	180	26	test	test	NOUN
cana-1628	180	27	mae	mae	PROPN
cana-1628	180	28	rmse	rmse	PROPN
cana-1628	180	29	mae	mae	PROPN
cana-1628	180	30	rmse	rmse	PROPN
cana-1628	180	31	clustered	clustered	ADJ
cana-1628	180	32	1d	1d	NUM
cana-1628	180	33	-	-	PUNCT
cana-1628	180	34	cnn	cnn	PROPN
cana-1628	180	35	58.358	58.358	NUM
cana-1628	180	36	70.057	70.057	NUM
cana-1628	180	37	60.916	60.916	NUM
cana-1628	180	38	73.170	73.170	NUM
cana-1628	180	39	lstm	lstm	NOUN
cana-1628	180	40	58.579	58.579	NUM
cana-1628	180	41	70.123	70.123	NUM
cana-1628	180	42	51.965	51.965	NUM
cana-1628	180	43	64.054	64.054	NUM
cana-1628	180	44	bilstm	bilstm	NOUN
cana-1628	181	1	61.927	61.927	NUM
cana-1628	182	1	74.520	74.520	NUM
cana-1628	182	2	59.711	59.711	NUM
cana-1628	182	3	69.964	69.964	NUM
cana-1628	182	4	ensemble	ensemble	ADJ
cana-1628	182	5	58.287	58.287	NUM
cana-1628	182	6	69.975	69.975	NUM
cana-1628	182	7	51.389	51.389	NUM
cana-1628	182	8	62.385	62.385	NUM
cana-1628	182	9	un	un	ADJ
cana-1628	182	10	-	-	ADJ
cana-1628	182	11	clustered	clustered	ADJ
cana-1628	182	12	1d	1d	NOUN
cana-1628	182	13	-	-	PUNCT
cana-1628	182	14	cnn	cnn	NOUN
cana-1628	182	15	178.995	178.995	NUM
cana-1628	182	16	256.540	256.540	NUM
cana-1628	182	17	182.874	182.874	NUM
cana-1628	182	18	266.111	266.111	NUM
cana-1628	182	19	lstm	lstm	VERB
cana-1628	182	20	162.288	162.288	NUM
cana-1628	182	21	250.252	250.252	NUM
cana-1628	182	22	167.094	167.094	NUM
cana-1628	182	23	261.151	261.151	NUM
cana-1628	182	24	bilstm	bilstm	NOUN
cana-1628	182	25	174.277	174.277	NUM
cana-1628	182	26	255.662	255.662	NUM
cana-1628	182	27	179.108	179.108	NUM
cana-1628	182	28	261.151	261.151	NUM
cana-1628	182	29	communications	communication	NOUN
cana-1628	182	30	on	on	ADP
cana-1628	182	31	applied	apply	VERB
cana-1628	182	32	nonlinear	nonlinear	ADJ
cana-1628	182	33	analysis	analysis	NOUN
cana-1628	182	34	issn	issn	NOUN
cana-1628	182	35	:	:	PUNCT
cana-1628	182	36	1074	1074	NUM
cana-1628	182	37	-	-	PUNCT
cana-1628	182	38	133x	133x	NUM
cana-1628	182	39	vol	vol	NOUN
cana-1628	182	40	32	32	NUM
cana-1628	182	41	no	no	NOUN
cana-1628	182	42	.	.	NOUN
cana-1628	182	43	1	1	NUM
cana-1628	182	44	(	(	PUNCT
cana-1628	182	45	2025	2025	NUM
cana-1628	182	46	)	)	PUNCT
cana-1628	182	47	169	169	NUM
cana-1628	182	48	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	182	49	ensemble	ensemble	VERB
cana-1628	182	50	163.369	163.369	NUM
cana-1628	182	51	250.762	250.762	NUM
cana-1628	182	52	168.630	168.630	NUM
cana-1628	182	53	260.684	260.684	NUM
cana-1628	182	54	table	table	NOUN
cana-1628	182	55	5	5	NUM
cana-1628	182	56	summarizes	summarize	NOUN
cana-1628	182	57	the	the	DET
cana-1628	182	58	performance	performance	NOUN
cana-1628	182	59	of	of	ADP
cana-1628	182	60	training	training	NOUN
cana-1628	182	61	and	and	CCONJ
cana-1628	182	62	test	test	NOUN
cana-1628	182	63	data	datum	NOUN
cana-1628	182	64	accuracy	accuracy	NOUN
cana-1628	182	65	on	on	ADP
cana-1628	182	66	post	post	ADJ
cana-1628	182	67	-	-	ADJ
cana-1628	182	68	clustering	clustering	ADJ
cana-1628	182	69	and	and	CCONJ
cana-1628	182	70	withoutclustering	withoutclustere	VERB
cana-1628	182	71	energy	energy	NOUN
cana-1628	182	72	consumption	consumption	NOUN
cana-1628	182	73	data	datum	NOUN
cana-1628	182	74	.	.	PUNCT
cana-1628	183	1	the	the	DET
cana-1628	183	2	results	result	NOUN
cana-1628	183	3	have	have	AUX
cana-1628	183	4	demonstrated	demonstrate	VERB
cana-1628	183	5	that	that	SCONJ
cana-1628	183	6	the	the	DET
cana-1628	183	7	proposed	propose	VERB
cana-1628	183	8	ensemble	ensemble	ADJ
cana-1628	183	9	model	model	NOUN
cana-1628	183	10	and	and	CCONJ
cana-1628	183	11	the	the	DET
cana-1628	183	12	base	base	NOUN
cana-1628	183	13	models	model	NOUN
cana-1628	183	14	have	have	AUX
cana-1628	183	15	achieved	achieve	VERB
cana-1628	183	16	lower	low	ADJ
cana-1628	183	17	mae	mae	PROPN
cana-1628	183	18	and	and	CCONJ
cana-1628	183	19	rmse	rmse	ADJ
cana-1628	183	20	prediction	prediction	NOUN
cana-1628	183	21	errors	error	NOUN
cana-1628	183	22	on	on	ADP
cana-1628	183	23	post	post	ADJ
cana-1628	183	24	-	-	ADJ
cana-1628	183	25	cluster	cluster	ADJ
cana-1628	183	26	data	datum	NOUN
cana-1628	183	27	about	about	ADP
cana-1628	183	28	the	the	DET
cana-1628	183	29	training	training	NOUN
cana-1628	183	30	and	and	CCONJ
cana-1628	183	31	test	test	NOUN
cana-1628	183	32	accuracy	accuracy	NOUN
cana-1628	183	33	.	.	PUNCT
cana-1628	184	1	moreover	moreover	ADV
cana-1628	184	2	,	,	PUNCT
cana-1628	184	3	the	the	DET
cana-1628	184	4	mae	mae	PROPN
cana-1628	184	5	and	and	CCONJ
cana-1628	184	6	rmse	rmse	ADJ
cana-1628	184	7	errors	error	NOUN
cana-1628	184	8	on	on	ADP
cana-1628	184	9	test	test	NOUN
cana-1628	184	10	data	datum	NOUN
cana-1628	184	11	are	be	AUX
cana-1628	184	12	much	much	ADV
cana-1628	184	13	lower	low	ADJ
cana-1628	184	14	compared	compare	VERB
cana-1628	184	15	with	with	ADP
cana-1628	184	16	the	the	DET
cana-1628	184	17	training	training	NOUN
cana-1628	184	18	errors	error	NOUN
cana-1628	184	19	.	.	PUNCT
cana-1628	185	1	from	from	ADP
cana-1628	185	2	these	these	DET
cana-1628	185	3	results	result	NOUN
cana-1628	185	4	,	,	PUNCT
cana-1628	185	5	we	we	PRON
cana-1628	185	6	can	can	AUX
cana-1628	185	7	conclude	conclude	VERB
cana-1628	185	8	that	that	SCONJ
cana-1628	185	9	integrating	integrate	VERB
cana-1628	185	10	k	k	ADJ
cana-1628	185	11	-	-	ADJ
cana-1628	185	12	means++	means++	ADV
cana-1628	185	13	clustering	clustering	NOUN
cana-1628	185	14	with	with	ADP
cana-1628	185	15	deep	deep	ADJ
cana-1628	185	16	learning	learning	NOUN
cana-1628	185	17	methods	method	NOUN
cana-1628	185	18	enables	enable	VERB
cana-1628	185	19	the	the	DET
cana-1628	185	20	model	model	NOUN
cana-1628	185	21	to	to	PART
cana-1628	185	22	learn	learn	VERB
cana-1628	185	23	the	the	DET
cana-1628	185	24	new	new	ADJ
cana-1628	185	25	data	datum	NOUN
cana-1628	185	26	very	very	ADV
cana-1628	185	27	well	well	ADV
cana-1628	185	28	and	and	CCONJ
cana-1628	185	29	make	make	VERB
cana-1628	185	30	better	well	ADJ
cana-1628	185	31	generalizations	generalization	NOUN
cana-1628	185	32	[	[	X
cana-1628	185	33	20	20	NUM
cana-1628	185	34	,	,	PUNCT
cana-1628	185	35	33	33	NUM
cana-1628	185	36	]	]	PUNCT
cana-1628	185	37	.	.	PUNCT
cana-1628	186	1	moreover	moreover	ADV
cana-1628	186	2	,	,	PUNCT
cana-1628	186	3	the	the	DET
cana-1628	186	4	post	post	ADJ
cana-1628	186	5	-	-	ADJ
cana-1628	186	6	clustering	clustering	ADJ
cana-1628	186	7	based	base	VERB
cana-1628	186	8	ensemble	ensemble	ADJ
cana-1628	186	9	model	model	NOUN
cana-1628	186	10	demonstrates	demonstrate	VERB
cana-1628	186	11	superior	superior	ADJ
cana-1628	186	12	performance	performance	NOUN
cana-1628	186	13	with	with	ADP
cana-1628	186	14	a	a	DET
cana-1628	186	15	significant	significant	ADJ
cana-1628	186	16	prediction	prediction	NOUN
cana-1628	186	17	error	error	NOUN
cana-1628	186	18	decrease	decrease	NOUN
cana-1628	186	19	of	of	ADP
cana-1628	186	20	mae	mae	PROPN
cana-1628	186	21	(	(	PUNCT
cana-1628	186	22	64.321	64.321	NUM
cana-1628	186	23	%	%	NOUN
cana-1628	186	24	)	)	PUNCT
cana-1628	186	25	and	and	CCONJ
cana-1628	186	26	rmse	rmse	ADJ
cana-1628	186	27	(	(	PUNCT
cana-1628	186	28	72.095	72.095	NUM
cana-1628	186	29	%	%	NOUN
cana-1628	186	30	)	)	PUNCT
cana-1628	186	31	on	on	ADP
cana-1628	186	32	training	training	NOUN
cana-1628	186	33	data	datum	NOUN
cana-1628	186	34	,	,	PUNCT
cana-1628	186	35	and	and	CCONJ
cana-1628	186	36	mae(69.525	mae(69.525	NUM
cana-1628	186	37	%	%	NOUN
cana-1628	186	38	)	)	PUNCT
cana-1628	186	39	and	and	CCONJ
cana-1628	186	40	rmse	rmse	ADJ
cana-1628	186	41	(	(	PUNCT
cana-1628	186	42	76.068	76.068	NUM
cana-1628	186	43	%	%	NOUN
cana-1628	186	44	)	)	PUNCT
cana-1628	186	45	on	on	ADP
cana-1628	186	46	test	test	NOUN
cana-1628	186	47	data	datum	NOUN
cana-1628	186	48	,	,	PUNCT
cana-1628	186	49	as	as	SCONJ
cana-1628	186	50	compared	compare	VERB
cana-1628	186	51	to	to	ADP
cana-1628	186	52	the	the	DET
cana-1628	186	53	performance	performance	NOUN
cana-1628	186	54	obtained	obtain	VERB
cana-1628	186	55	without	without	ADP
cana-1628	186	56	clustering	cluster	VERB
cana-1628	186	57	.	.	PUNCT
cana-1628	187	1	overall	overall	ADV
cana-1628	187	2	,	,	PUNCT
cana-1628	187	3	the	the	DET
cana-1628	187	4	results	result	NOUN
cana-1628	187	5	show	show	VERB
cana-1628	187	6	that	that	SCONJ
cana-1628	187	7	the	the	DET
cana-1628	187	8	best	good	ADJ
cana-1628	187	9	prediction	prediction	NOUN
cana-1628	187	10	accuracy	accuracy	NOUN
cana-1628	187	11	and	and	CCONJ
cana-1628	187	12	the	the	DET
cana-1628	187	13	lowest	low	ADJ
cana-1628	187	14	mae	mae	PROPN
cana-1628	187	15	,	,	PUNCT
cana-1628	187	16	rmse	rmse	NOUN
cana-1628	187	17	,	,	PUNCT
cana-1628	187	18	and	and	CCONJ
cana-1628	187	19	mape	mape	NOUN
cana-1628	187	20	errors	error	NOUN
cana-1628	187	21	are	be	AUX
cana-1628	187	22	obtained	obtain	VERB
cana-1628	187	23	when	when	SCONJ
cana-1628	187	24	multiple	multiple	ADJ
cana-1628	187	25	deep	deep	ADJ
cana-1628	187	26	learning	learning	NOUN
cana-1628	187	27	techniques	technique	NOUN
cana-1628	187	28	are	be	AUX
cana-1628	187	29	combined	combine	VERB
cana-1628	187	30	and	and	CCONJ
cana-1628	187	31	utilized	utilize	VERB
cana-1628	187	32	in	in	ADP
cana-1628	187	33	the	the	DET
cana-1628	187	34	form	form	NOUN
cana-1628	187	35	of	of	ADP
cana-1628	187	36	an	an	DET
cana-1628	187	37	ensemble	ensemble	ADJ
cana-1628	187	38	model	model	NOUN
cana-1628	187	39	[	[	X
cana-1628	187	40	28	28	NUM
cana-1628	187	41	,	,	PUNCT
cana-1628	187	42	37	37	NUM
cana-1628	187	43	]	]	PUNCT
cana-1628	187	44	,	,	PUNCT
cana-1628	187	45	coupled	couple	VERB
cana-1628	187	46	with	with	ADP
cana-1628	187	47	hyperparameter	hyperparameter	NOUN
cana-1628	187	48	optimization	optimization	NOUN
cana-1628	187	49	on	on	ADP
cana-1628	187	50	post	post	ADJ
cana-1628	187	51	-	-	ADJ
cana-1628	187	52	clustering	clustering	ADJ
cana-1628	187	53	data	datum	NOUN
cana-1628	187	54	.	.	PUNCT
cana-1628	188	1	table	table	NOUN
cana-1628	188	2	6	6	NUM
cana-1628	188	3	:	:	PUNCT
cana-1628	188	4	comparison	comparison	NOUN
cana-1628	188	5	of	of	ADP
cana-1628	188	6	the	the	DET
cana-1628	188	7	proposed	propose	VERB
cana-1628	188	8	model	model	NOUN
cana-1628	188	9	and	and	CCONJ
cana-1628	188	10	baseline	baseline	VERB
cana-1628	188	11	deep	deep	ADJ
cana-1628	188	12	learning	learning	NOUN
cana-1628	188	13	models	model	NOUN
cana-1628	188	14	on	on	ADP
cana-1628	188	15	west	west	PROPN
cana-1628	188	16	aa	aa	PROPN
cana-1628	188	17	data	data	PROPN
cana-1628	188	18	.	.	PUNCT
cana-1628	189	1	data	datum	NOUN
cana-1628	189	2	algorithm	algorithm	PROPN
cana-1628	189	3	mae	mae	PROPN
cana-1628	189	4	rmse	rmse	PROPN
cana-1628	189	5	mape(%	mape(%	PROPN
cana-1628	189	6	)	)	PUNCT
cana-1628	189	7	cluster	cluster	NOUN
cana-1628	189	8	1	1	NUM
cana-1628	189	9	1d	1d	NUM
cana-1628	189	10	-	-	PUNCT
cana-1628	189	11	cnn	cnn	NOUN
cana-1628	189	12	58.071	58.071	NUM
cana-1628	189	13	69.092	69.092	NUM
cana-1628	189	14	12.097	12.097	NUM
cana-1628	189	15	gru	gru	NOUN
cana-1628	189	16	58.538	58.538	NUM
cana-1628	189	17	69.884	69.884	NUM
cana-1628	189	18	12.418	12.418	NUM
cana-1628	189	19	lstm	lstm	NOUN
cana-1628	189	20	58.039	58.039	NUM
cana-1628	189	21	69.079	69.079	NUM
cana-1628	189	22	12.086	12.086	NUM
cana-1628	189	23	bilstm	bilstm	NOUN
cana-1628	189	24	58.487	58.487	NUM
cana-1628	189	25	69.758	69.758	NUM
cana-1628	189	26	12.312	12.312	NUM
cana-1628	189	27	ensemble	ensemble	ADJ
cana-1628	189	28	57.584	57.584	NUM
cana-1628	189	29	68.136	68.136	NUM
cana-1628	189	30	11.601	11.601	NUM
cana-1628	189	31	cluster	cluster	NOUN
cana-1628	189	32	2	2	NUM
cana-1628	189	33	1d	1d	NUM
cana-1628	189	34	-	-	PUNCT
cana-1628	189	35	cnn	cnn	NOUN
cana-1628	189	36	63.511	63.511	NUM
cana-1628	189	37	74.157	74.157	NUM
cana-1628	189	38	0.197	0.197	NUM
cana-1628	189	39	gru	gru	NOUN
cana-1628	190	1	67.750	67.750	NUM
cana-1628	190	2	78.317	78.317	NUM
cana-1628	190	3	0.218	0.218	NUM
cana-1628	190	4	lstm	lstm	ADJ
cana-1628	190	5	62.458	62.458	NUM
cana-1628	190	6	76.379	76.379	NUM
cana-1628	190	7	0.180	0.180	NUM
cana-1628	190	8	bilstm	bilstm	NOUN
cana-1628	190	9	65.867	65.867	NUM
cana-1628	190	10	76.337	76.337	NUM
cana-1628	190	11	0.210	0.210	NUM
cana-1628	190	12	ensemble	ensemble	ADJ
cana-1628	190	13	62.335	62.335	NUM
cana-1628	190	14	73.765	73.765	NUM
cana-1628	190	15	0.181	0.181	NUM
cana-1628	190	16	cluster	cluster	NOUN
cana-1628	190	17	3	3	NUM
cana-1628	190	18	1d	1d	NUM
cana-1628	190	19	-	-	PUNCT
cana-1628	190	20	cnn	cnn	PROPN
cana-1628	190	21	69.394	69.394	NUM
cana-1628	190	22	87.537	87.537	NUM
cana-1628	190	23	0.113	0.113	NUM
cana-1628	190	24	gru	gru	NOUN
cana-1628	190	25	69.007	69.007	NUM
cana-1628	190	26	87.457	87.457	NUM
cana-1628	190	27	0.115	0.115	NUM
cana-1628	190	28	lstm	lstm	VERB
cana-1628	190	29	69.138	69.138	NUM
cana-1628	190	30	88.323	88.323	NUM
cana-1628	190	31	0.110	0.110	NUM
cana-1628	190	32	bilstm	bilstm	NOUN
cana-1628	190	33	70.374	70.374	NUM
cana-1628	190	34	90.007	90.007	NUM
cana-1628	190	35	0.171	0.171	NUM
cana-1628	190	36	ensemble	ensemble	ADJ
cana-1628	190	37	68.581	68.581	NUM
cana-1628	190	38	85.730	85.730	NUM
cana-1628	190	39	0.112	0.112	NUM
cana-1628	190	40	moreover	moreover	ADV
cana-1628	190	41	,	,	PUNCT
cana-1628	190	42	table	table	NOUN
cana-1628	190	43	6	6	NUM
cana-1628	190	44	summarizes	summarize	NOUN
cana-1628	190	45	the	the	DET
cana-1628	190	46	performance	performance	NOUN
cana-1628	190	47	of	of	ADP
cana-1628	190	48	the	the	DET
cana-1628	190	49	proposed	propose	VERB
cana-1628	190	50	model	model	NOUN
cana-1628	190	51	and	and	CCONJ
cana-1628	190	52	the	the	DET
cana-1628	190	53	base	base	NOUN
cana-1628	190	54	models	model	NOUN
cana-1628	190	55	on	on	ADP
cana-1628	190	56	the	the	DET
cana-1628	190	57	west	west	PROPN
cana-1628	190	58	aa	aa	PROPN
cana-1628	190	59	case	case	NOUN
cana-1628	190	60	study	study	NOUN
cana-1628	190	61	data	datum	NOUN
cana-1628	190	62	.	.	PUNCT
cana-1628	191	1	from	from	ADP
cana-1628	191	2	this	this	DET
cana-1628	191	3	result	result	NOUN
cana-1628	191	4	,	,	PUNCT
cana-1628	191	5	the	the	DET
cana-1628	191	6	ensemble	ensemble	ADJ
cana-1628	191	7	model	model	NOUN
cana-1628	191	8	outperforms	outperform	VERB
cana-1628	191	9	the	the	DET
cana-1628	191	10	base	base	NOUN
cana-1628	191	11	models	model	NOUN
cana-1628	191	12	in	in	ADP
cana-1628	191	13	the	the	DET
cana-1628	191	14	case	case	NOUN
cana-1628	191	15	of	of	ADP
cana-1628	191	16	cluster	cluster	NOUN
cana-1628	191	17	1	1	NUM
cana-1628	191	18	data	datum	NOUN
cana-1628	191	19	showing	show	VERB
cana-1628	191	20	a	a	DET
cana-1628	191	21	significant	significant	ADJ
cana-1628	191	22	error	error	NOUN
cana-1628	191	23	decrease	decrease	NOUN
cana-1628	191	24	of	of	ADP
cana-1628	191	25	mae(1.543	mae(1.543	NOUN
cana-1628	191	26	%	%	NOUN
cana-1628	191	27	)	)	PUNCT
cana-1628	191	28	,	,	PUNCT
cana-1628	191	29	and	and	CCONJ
cana-1628	191	30	rmse(2.325	rmse(2.325	PROPN
cana-1628	191	31	%	%	NOUN
cana-1628	191	32	)	)	PUNCT
cana-1628	191	33	compared	compare	VERB
cana-1628	191	34	to	to	ADP
cana-1628	191	35	the	the	DET
cana-1628	191	36	bilstm	bilstm	NOUN
cana-1628	191	37	which	which	PRON
cana-1628	191	38	is	be	AUX
cana-1628	191	39	the	the	DET
cana-1628	191	40	worst	worst	ADV
cana-1628	191	41	-	-	PUNCT
cana-1628	191	42	performing	perform	VERB
cana-1628	191	43	base	base	NOUN
cana-1628	191	44	model	model	NOUN
cana-1628	191	45	.	.	PUNCT
cana-1628	192	1	communications	communication	NOUN
cana-1628	192	2	on	on	ADP
cana-1628	192	3	applied	apply	VERB
cana-1628	192	4	nonlinear	nonlinear	ADJ
cana-1628	192	5	analysis	analysis	NOUN
cana-1628	192	6	issn	issn	NOUN
cana-1628	192	7	:	:	PUNCT
cana-1628	192	8	1074	1074	NUM
cana-1628	192	9	-	-	PUNCT
cana-1628	192	10	133x	133x	NUM
cana-1628	192	11	vol	vol	NOUN
cana-1628	192	12	32	32	NUM
cana-1628	192	13	no	no	NOUN
cana-1628	192	14	.	.	NOUN
cana-1628	192	15	1	1	NUM
cana-1628	192	16	(	(	PUNCT
cana-1628	192	17	2025	2025	NUM
cana-1628	192	18	)	)	PUNCT
cana-1628	192	19	170	170	NUM
cana-1628	192	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	192	21	figure	figure	NOUN
cana-1628	192	22	11	11	NUM
cana-1628	192	23	:	:	PUNCT
cana-1628	192	24	mae	mae	PROPN
cana-1628	192	25	values	value	NOUN
cana-1628	192	26	for	for	ADP
cana-1628	192	27	clustered	clustered	ADJ
cana-1628	192	28	and	and	CCONJ
cana-1628	192	29	un	un	ADJ
cana-1628	192	30	-	-	ADJ
cana-1628	192	31	clustered	clustered	ADJ
cana-1628	192	32	,	,	PUNCT
cana-1628	192	33	south	south	ADJ
cana-1628	192	34	aa	aa	PROPN
cana-1628	192	35	data	datum	NOUN
cana-1628	192	36	figure	figure	NOUN
cana-1628	192	37	12	12	NUM
cana-1628	192	38	:	:	PUNCT
cana-1628	192	39	mae	mae	PROPN
cana-1628	192	40	values	value	NOUN
cana-1628	192	41	for	for	ADP
cana-1628	192	42	clustered	clustered	ADJ
cana-1628	192	43	vs	vs	ADP
cana-1628	192	44	un	un	ADJ
cana-1628	192	45	-	-	ADJ
cana-1628	192	46	clustered	clustered	ADJ
cana-1628	192	47	,	,	PUNCT
cana-1628	192	48	west	west	NOUN
cana-1628	192	49	aa	aa	PROPN
cana-1628	192	50	data	data	PROPN
cana-1628	192	51	figure	figure	NOUN
cana-1628	192	52	11	11	NUM
cana-1628	192	53	and	and	CCONJ
cana-1628	192	54	figure	figure	NOUN
cana-1628	192	55	12	12	NUM
cana-1628	192	56	,	,	PUNCT
cana-1628	192	57	shows	show	VERB
cana-1628	192	58	that	that	SCONJ
cana-1628	192	59	the	the	DET
cana-1628	192	60	performance	performance	NOUN
cana-1628	192	61	of	of	ADP
cana-1628	192	62	the	the	DET
cana-1628	192	63	ensemble	ensemble	ADJ
cana-1628	192	64	model	model	NOUN
cana-1628	192	65	significantly	significantly	ADV
cana-1628	192	66	improved	improve	VERB
cana-1628	192	67	in	in	ADP
cana-1628	192	68	the	the	DET
cana-1628	192	69	case	case	NOUN
cana-1628	192	70	of	of	ADP
cana-1628	192	71	cluster	cluster	NOUN
cana-1628	192	72	generated	generate	VERB
cana-1628	192	73	data	datum	NOUN
cana-1628	192	74	with	with	ADP
cana-1628	192	75	mae(69.696	mae(69.696	NOUN
cana-1628	192	76	%	%	NOUN
cana-1628	192	77	)	)	PUNCT
cana-1628	192	78	error	error	NOUN
cana-1628	192	79	decrease	decrease	NOUN
cana-1628	192	80	south	south	ADJ
cana-1628	192	81	aa	aa	PROPN
cana-1628	192	82	case	case	NOUN
cana-1628	192	83	study	study	NOUN
cana-1628	192	84	data	datum	NOUN
cana-1628	192	85	and	and	CCONJ
cana-1628	192	86	mae(55.595	mae(55.595	NOUN
cana-1628	192	87	%	%	NOUN
cana-1628	192	88	)	)	PUNCT
cana-1628	192	89	error	error	NOUN
cana-1628	192	90	decrease	decrease	NOUN
cana-1628	192	91	on	on	ADP
cana-1628	192	92	the	the	DET
cana-1628	192	93	west	west	PROPN
cana-1628	192	94	aa	aa	PROPN
cana-1628	192	95	data	data	PROPN
cana-1628	192	96	.	.	PUNCT
cana-1628	193	1	from	from	ADP
cana-1628	193	2	these	these	DET
cana-1628	193	3	results	result	NOUN
cana-1628	193	4	,	,	PUNCT
cana-1628	193	5	we	we	PRON
cana-1628	193	6	can	can	AUX
cana-1628	193	7	conclude	conclude	VERB
cana-1628	193	8	that	that	SCONJ
cana-1628	193	9	the	the	DET
cana-1628	193	10	potential	potential	NOUN
cana-1628	193	11	of	of	ADP
cana-1628	193	12	k	k	NOUN
cana-1628	193	13	-	-	PUNCT
cana-1628	193	14	means	means	NOUN
cana-1628	193	15	clustering	cluster	VERB
cana-1628	193	16	to	to	PART
cana-1628	193	17	find	find	VERB
cana-1628	193	18	the	the	DET
cana-1628	193	19	optimal	optimal	ADJ
cana-1628	193	20	clusters	cluster	NOUN
cana-1628	193	21	of	of	ADP
cana-1628	193	22	the	the	DET
cana-1628	193	23	energy	energy	NOUN
cana-1628	193	24	data	datum	NOUN
cana-1628	193	25	significantly	significantly	ADV
cana-1628	193	26	contributes	contribute	VERB
cana-1628	193	27	to	to	ADP
cana-1628	193	28	the	the	DET
cana-1628	193	29	ensemble	ensemble	ADJ
cana-1628	193	30	deep	deep	ADJ
cana-1628	193	31	model	model	NOUN
cana-1628	193	32	to	to	PART
cana-1628	193	33	learn	learn	VERB
cana-1628	193	34	complex	complex	ADJ
cana-1628	193	35	energy	energy	NOUN
cana-1628	193	36	data	datum	NOUN
cana-1628	193	37	effectively	effectively	ADV
cana-1628	193	38	and	and	CCONJ
cana-1628	193	39	exhibits	exhibit	VERB
cana-1628	193	40	lower	low	ADJ
cana-1628	193	41	prediction	prediction	NOUN
cana-1628	193	42	errors	error	NOUN
cana-1628	193	43	in	in	ADP
cana-1628	193	44	the	the	DET
cana-1628	193	45	new	new	ADJ
cana-1628	193	46	dataset	dataset	NOUN
cana-1628	193	47	compared	compare	VERB
cana-1628	193	48	to	to	ADP
cana-1628	193	49	the	the	DET
cana-1628	193	50	model	model	NOUN
cana-1628	193	51	performance	performance	NOUN
cana-1628	193	52	on	on	ADP
cana-1628	193	53	un	un	ADJ
cana-1628	193	54	-	-	ADJ
cana-1628	193	55	cluster	cluster	NOUN
cana-1628	193	56	data	datum	NOUN
cana-1628	193	57	concerning	concern	VERB
cana-1628	193	58	mae	mae	PROPN
cana-1628	193	59	performance	performance	NOUN
cana-1628	193	60	metric	metric	ADJ
cana-1628	193	61	.	.	PUNCT
cana-1628	194	1	to	to	PART
cana-1628	194	2	generalize	generalize	VERB
cana-1628	194	3	the	the	DET
cana-1628	194	4	training	training	NOUN
cana-1628	194	5	of	of	ADP
cana-1628	194	6	deep	deep	ADJ
cana-1628	194	7	learning	learning	NOUN
cana-1628	194	8	and	and	CCONJ
cana-1628	194	9	their	their	PRON
cana-1628	194	10	ensemble	ensemble	ADJ
cana-1628	194	11	models	model	NOUN
cana-1628	194	12	with	with	ADP
cana-1628	194	13	postclustering	postclustere	VERB
cana-1628	194	14	data	datum	NOUN
cana-1628	194	15	affirms	affirm	VERB
cana-1628	194	16	the	the	DET
cana-1628	194	17	better	well	ADJ
cana-1628	194	18	performance	performance	NOUN
cana-1628	194	19	.	.	PUNCT
cana-1628	195	1	this	this	PRON
cana-1628	195	2	is	be	AUX
cana-1628	195	3	because	because	SCONJ
cana-1628	195	4	in	in	ADP
cana-1628	195	5	addition	addition	NOUN
cana-1628	195	6	to	to	ADP
cana-1628	195	7	outlier	outlier	ADJ
cana-1628	195	8	treatment	treatment	NOUN
cana-1628	195	9	,	,	PUNCT
cana-1628	195	10	clustering	clustering	NOUN
cana-1628	195	11	of	of	ADP
cana-1628	195	12	highly	highly	ADV
cana-1628	195	13	variable	variable	ADJ
cana-1628	195	14	energy	energy	NOUN
cana-1628	195	15	consumption	consumption	NOUN
cana-1628	195	16	data	datum	NOUN
cana-1628	195	17	[	[	X
cana-1628	195	18	17	17	NUM
cana-1628	195	19	,	,	PUNCT
cana-1628	195	20	34	34	NUM
cana-1628	195	21	]	]	PUNCT
cana-1628	195	22	into	into	ADP
cana-1628	195	23	more	more	ADV
cana-1628	195	24	similar	similar	ADJ
cana-1628	195	25	consumption	consumption	NOUN
cana-1628	195	26	patterns	pattern	NOUN
cana-1628	195	27	enables	enable	VERB
cana-1628	195	28	the	the	DET
cana-1628	195	29	proposed	propose	VERB
cana-1628	195	30	model	model	NOUN
cana-1628	195	31	to	to	PART
cana-1628	195	32	learn	learn	VERB
cana-1628	195	33	the	the	DET
cana-1628	195	34	detailed	detailed	ADJ
cana-1628	195	35	features	feature	NOUN
cana-1628	195	36	of	of	ADP
cana-1628	195	37	the	the	DET
cana-1628	195	38	input	input	NOUN
cana-1628	195	39	data	datum	NOUN
cana-1628	195	40	.	.	PUNCT
cana-1628	196	1	4	4	NUM
cana-1628	196	2	conclusion	conclusion	NOUN
cana-1628	196	3	in	in	ADP
cana-1628	196	4	this	this	DET
cana-1628	196	5	study	study	NOUN
cana-1628	196	6	,	,	PUNCT
cana-1628	196	7	the	the	DET
cana-1628	196	8	effectiveness	effectiveness	NOUN
cana-1628	196	9	of	of	ADP
cana-1628	196	10	deep	deep	ADJ
cana-1628	196	11	learning	learning	NOUN
cana-1628	196	12	models	model	NOUN
cana-1628	196	13	(	(	PUNCT
cana-1628	196	14	1d	1d	NUM
cana-1628	196	15	-	-	PUNCT
cana-1628	196	16	cnn	cnn	PROPN
cana-1628	196	17	,	,	PUNCT
cana-1628	196	18	lstm	lstm	PROPN
cana-1628	196	19	,	,	PUNCT
cana-1628	196	20	bilstm	bilstm	NOUN
cana-1628	196	21	and	and	CCONJ
cana-1628	196	22	gru	gru	PROPN
cana-1628	196	23	)	)	PUNCT
cana-1628	196	24	and	and	CCONJ
cana-1628	196	25	ensemble	ensemble	ADJ
cana-1628	196	26	model	model	NOUN
cana-1628	196	27	is	be	AUX
cana-1628	196	28	investigated	investigate	VERB
cana-1628	196	29	for	for	ADP
cana-1628	196	30	aggregate	aggregate	ADJ
cana-1628	196	31	energy	energy	NOUN
cana-1628	196	32	consumption	consumption	NOUN
cana-1628	196	33	prediction	prediction	NOUN
cana-1628	196	34	focusing	focus	VERB
cana-1628	196	35	on	on	ADP
cana-1628	196	36	the	the	DET
cana-1628	196	37	residential	residential	ADJ
cana-1628	196	38	users	user	NOUN
cana-1628	196	39	category	category	NOUN
cana-1628	196	40	.	.	PUNCT
cana-1628	197	1	the	the	DET
cana-1628	197	2	model	model	NOUN
cana-1628	197	3	’s	’s	PART
cana-1628	197	4	performance	performance	NOUN
cana-1628	197	5	was	be	AUX
cana-1628	197	6	assessed	assess	VERB
cana-1628	197	7	on	on	ADP
cana-1628	197	8	both	both	CCONJ
cana-1628	197	9	the	the	DET
cana-1628	197	10	un	un	NOUN
cana-1628	197	11	-	-	NOUN
cana-1628	197	12	cluster	cluster	NOUN
cana-1628	197	13	and	and	CCONJ
cana-1628	197	14	postclustered	postclustere	VERB
cana-1628	197	15	energy	energy	NOUN
cana-1628	197	16	datasets	dataset	NOUN
cana-1628	197	17	.	.	PUNCT
cana-1628	198	1	the	the	DET
cana-1628	198	2	integration	integration	NOUN
cana-1628	198	3	of	of	ADP
cana-1628	198	4	k	k	NOUN
cana-1628	198	5	-	-	PUNCT
cana-1628	198	6	means	means	NOUN
cana-1628	198	7	clustering	cluster	VERB
cana-1628	198	8	with	with	ADP
cana-1628	198	9	an	an	DET
cana-1628	198	10	ensemble	ensemble	ADJ
cana-1628	198	11	model	model	NOUN
cana-1628	198	12	to	to	PART
cana-1628	198	13	find	find	VERB
cana-1628	198	14	the	the	DET
cana-1628	198	15	optimal	optimal	ADJ
cana-1628	198	16	cluster	cluster	NOUN
cana-1628	198	17	that	that	PRON
cana-1628	198	18	minimizes	minimize	VERB
cana-1628	198	19	the	the	DET
cana-1628	198	20	high	high	ADJ
cana-1628	198	21	variability	variability	NOUN
cana-1628	198	22	and	and	CCONJ
cana-1628	198	23	complexity	complexity	NOUN
cana-1628	198	24	of	of	ADP
cana-1628	198	25	energy	energy	NOUN
cana-1628	198	26	consumption	consumption	NOUN
cana-1628	198	27	data	datum	NOUN
cana-1628	198	28	has	have	AUX
cana-1628	198	29	been	be	AUX
cana-1628	198	30	investigated	investigate	VERB
cana-1628	198	31	.	.	PUNCT
cana-1628	199	1	the	the	DET
cana-1628	199	2	viability	viability	NOUN
cana-1628	199	3	of	of	ADP
cana-1628	199	4	the	the	DET
cana-1628	199	5	clustering	clustering	ADJ
cana-1628	199	6	technique	technique	NOUN
cana-1628	199	7	to	to	ADP
cana-1628	199	8	group	group	NOUN
cana-1628	199	9	energy	energy	NOUN
cana-1628	199	10	consumption	consumption	NOUN
cana-1628	199	11	data	datum	NOUN
cana-1628	199	12	into	into	ADP
cana-1628	199	13	a	a	DET
cana-1628	199	14	more	more	ADV
cana-1628	199	15	similar	similar	ADJ
cana-1628	199	16	consumption	consumption	NOUN
cana-1628	199	17	profile	profile	NOUN
cana-1628	199	18	was	be	AUX
cana-1628	199	19	validated	validate	VERB
cana-1628	199	20	and	and	CCONJ
cana-1628	199	21	promising	promising	ADJ
cana-1628	199	22	results	result	NOUN
cana-1628	199	23	were	be	AUX
cana-1628	199	24	found	find	VERB
cana-1628	199	25	which	which	PRON
cana-1628	199	26	has	have	AUX
cana-1628	199	27	enabled	enable	VERB
cana-1628	199	28	the	the	DET
cana-1628	199	29	ensemble	ensemble	ADJ
cana-1628	199	30	deep	deep	ADJ
cana-1628	199	31	model	model	NOUN
cana-1628	199	32	to	to	PART
cana-1628	199	33	learn	learn	VERB
cana-1628	199	34	the	the	DET
cana-1628	199	35	complete	complete	ADJ
cana-1628	199	36	and	and	CCONJ
cana-1628	199	37	intrinsic	intrinsic	ADJ
cana-1628	199	38	nature	nature	NOUN
cana-1628	199	39	of	of	ADP
cana-1628	199	40	the	the	DET
cana-1628	199	41	energy	energy	NOUN
cana-1628	199	42	consumption	consumption	NOUN
cana-1628	199	43	data	datum	NOUN
cana-1628	199	44	.	.	PUNCT
cana-1628	200	1	hence	hence	ADV
cana-1628	200	2	,	,	PUNCT
cana-1628	200	3	the	the	DET
cana-1628	200	4	integration	integration	NOUN
cana-1628	200	5	of	of	ADP
cana-1628	200	6	clustering	clustering	ADJ
cana-1628	200	7	approach	approach	NOUN
cana-1628	200	8	with	with	ADP
cana-1628	200	9	deep	deep	ADJ
cana-1628	200	10	learning	learning	NOUN
cana-1628	200	11	and	and	CCONJ
cana-1628	200	12	ensemble	ensemble	ADJ
cana-1628	200	13	techniques	technique	NOUN
cana-1628	200	14	significantly	significantly	ADV
cana-1628	200	15	improves	improve	VERB
cana-1628	200	16	the	the	DET
cana-1628	200	17	prediction	prediction	NOUN
cana-1628	200	18	performance	performance	NOUN
cana-1628	200	19	of	of	ADP
cana-1628	200	20	the	the	DET
cana-1628	200	21	proposed	propose	VERB
cana-1628	200	22	model	model	NOUN
cana-1628	200	23	with	with	ADP
cana-1628	200	24	very	very	ADV
cana-1628	200	25	low	low	ADJ
cana-1628	200	26	prediction	prediction	NOUN
cana-1628	200	27	errors	error	NOUN
cana-1628	200	28	when	when	SCONJ
cana-1628	200	29	compared	compare	VERB
cana-1628	200	30	to	to	ADP
cana-1628	200	31	the	the	DET
cana-1628	200	32	performance	performance	NOUN
cana-1628	200	33	obtained	obtain	VERB
cana-1628	200	34	without	without	ADP
cana-1628	200	35	clustering	clustering	NOUN
cana-1628	200	36	.	.	PUNCT
cana-1628	201	1	furthermore	furthermore	ADV
cana-1628	201	2	,	,	PUNCT
cana-1628	201	3	while	while	SCONJ
cana-1628	201	4	properly	properly	ADV
cana-1628	201	5	combining	combine	VERB
cana-1628	201	6	the	the	DET
cana-1628	201	7	capabilities	capability	NOUN
cana-1628	201	8	of	of	ADP
cana-1628	201	9	multiple	multiple	ADJ
cana-1628	201	10	deep	deep	ADJ
cana-1628	201	11	learning	learning	NOUN
cana-1628	201	12	algorithms	algorithm	NOUN
cana-1628	201	13	,	,	PUNCT
cana-1628	201	14	results	result	NOUN
cana-1628	201	15	indicate	indicate	VERB
cana-1628	201	16	that	that	SCONJ
cana-1628	201	17	the	the	DET
cana-1628	201	18	proposed	propose	VERB
cana-1628	201	19	ensemble	ensemble	ADJ
cana-1628	201	20	model	model	NOUN
cana-1628	201	21	has	have	AUX
cana-1628	201	22	outperformed	outperform	VERB
cana-1628	201	23	the	the	DET
cana-1628	201	24	optimal	optimal	ADJ
cana-1628	201	25	base	base	NOUN
cana-1628	201	26	model	model	NOUN
cana-1628	201	27	performance	performance	NOUN
cana-1628	201	28	in	in	ADP
cana-1628	201	29	all	all	DET
cana-1628	201	30	case	case	NOUN
cana-1628	201	31	study	study	NOUN
cana-1628	201	32	data	datum	NOUN
cana-1628	201	33	sets	set	NOUN
cana-1628	201	34	used	use	VERB
cana-1628	201	35	in	in	ADP
cana-1628	201	36	this	this	DET
cana-1628	201	37	study	study	NOUN
cana-1628	201	38	.	.	PUNCT
cana-1628	202	1	in	in	ADP
cana-1628	202	2	addition	addition	NOUN
cana-1628	202	3	,	,	PUNCT
cana-1628	202	4	enhanced	enhance	VERB
cana-1628	202	5	by	by	ADP
cana-1628	202	6	the	the	DET
cana-1628	202	7	metaheuristic	metaheuristic	ADJ
cana-1628	202	8	bayesian	bayesian	NOUN
cana-1628	202	9	based	base	VERB
cana-1628	202	10	hyperparameter	hyperparameter	NOUN
cana-1628	202	11	tuning	tuning	NOUN
cana-1628	202	12	method	method	NOUN
cana-1628	202	13	,	,	PUNCT
cana-1628	202	14	the	the	DET
cana-1628	202	15	proposed	propose	VERB
cana-1628	202	16	ensemble	ensemble	ADJ
cana-1628	202	17	deep	deep	ADJ
cana-1628	202	18	learning	learning	NOUN
cana-1628	202	19	model	model	NOUN
cana-1628	202	20	has	have	AUX
cana-1628	202	21	demonstrated	demonstrate	VERB
cana-1628	202	22	the	the	DET
cana-1628	202	23	best	good	ADJ
cana-1628	202	24	performance	performance	NOUN
cana-1628	202	25	and	and	CCONJ
cana-1628	202	26	better	well	ADJ
cana-1628	202	27	generalization	generalization	NOUN
cana-1628	202	28	abilities	ability	NOUN
cana-1628	202	29	without	without	ADP
cana-1628	202	30	facing	face	VERB
cana-1628	202	31	the	the	DET
cana-1628	202	32	problem	problem	NOUN
cana-1628	202	33	of	of	ADP
cana-1628	202	34	model	model	NOUN
cana-1628	202	35	overfitting	overfitting	NOUN
cana-1628	202	36	while	while	SCONJ
cana-1628	202	37	the	the	DET
cana-1628	202	38	trained	train	VERB
cana-1628	202	39	model	model	NOUN
cana-1628	202	40	is	be	AUX
cana-1628	202	41	exposed	expose	VERB
cana-1628	202	42	to	to	ADP
cana-1628	202	43	test	test	NOUN
cana-1628	202	44	data	datum	NOUN
cana-1628	202	45	.	.	PUNCT
cana-1628	203	1	communications	communication	NOUN
cana-1628	203	2	on	on	ADP
cana-1628	203	3	applied	apply	VERB
cana-1628	203	4	nonlinear	nonlinear	ADJ
cana-1628	203	5	analysis	analysis	NOUN
cana-1628	203	6	issn	issn	NOUN
cana-1628	203	7	:	:	PUNCT
cana-1628	203	8	1074	1074	NUM
cana-1628	203	9	-	-	PUNCT
cana-1628	203	10	133x	133x	NUM
cana-1628	203	11	vol	vol	NOUN
cana-1628	203	12	32	32	NUM
cana-1628	203	13	no	no	NOUN
cana-1628	203	14	.	.	NOUN
cana-1628	203	15	1	1	NUM
cana-1628	203	16	(	(	PUNCT
cana-1628	203	17	2025	2025	NUM
cana-1628	203	18	)	)	PUNCT
cana-1628	203	19	171	171	NUM
cana-1628	203	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	203	21	overall	overall	ADJ
cana-1628	203	22	,	,	PUNCT
cana-1628	203	23	the	the	DET
cana-1628	203	24	ensemble	ensemble	ADJ
cana-1628	203	25	model	model	NOUN
cana-1628	203	26	proposed	propose	VERB
cana-1628	203	27	in	in	ADP
cana-1628	203	28	this	this	DET
cana-1628	203	29	study	study	NOUN
cana-1628	203	30	has	have	AUX
cana-1628	203	31	demonstrated	demonstrate	VERB
cana-1628	203	32	better	well	ADJ
cana-1628	203	33	capabilities	capability	NOUN
cana-1628	203	34	for	for	ADP
cana-1628	203	35	learning	learn	VERB
cana-1628	203	36	the	the	DET
cana-1628	203	37	complex	complex	ADJ
cana-1628	203	38	energy	energy	NOUN
cana-1628	203	39	consumption	consumption	NOUN
cana-1628	203	40	data	datum	NOUN
cana-1628	203	41	and	and	CCONJ
cana-1628	203	42	provides	provide	VERB
cana-1628	203	43	a	a	DET
cana-1628	203	44	significant	significant	ADJ
cana-1628	203	45	mae	mae	PROPN
cana-1628	203	46	,	,	PUNCT
cana-1628	203	47	rmse	rmse	NOUN
cana-1628	203	48	and	and	CCONJ
cana-1628	203	49	mape	mape	NOUN
cana-1628	203	50	error	error	NOUN
cana-1628	203	51	decrease	decrease	NOUN
cana-1628	203	52	in	in	ADP
cana-1628	203	53	both	both	DET
cana-1628	203	54	case	case	NOUN
cana-1628	203	55	study	study	NOUN
cana-1628	203	56	data	datum	NOUN
cana-1628	203	57	as	as	ADP
cana-1628	203	58	compared	compare	VERB
cana-1628	203	59	to	to	ADP
cana-1628	203	60	base	base	NOUN
cana-1628	203	61	algorithms	algorithm	NOUN
cana-1628	203	62	,	,	PUNCT
cana-1628	203	63	i.e.	i.e.	X
cana-1628	203	64	,	,	PUNCT
cana-1628	203	65	lstm	lstm	ADJ
cana-1628	203	66	,	,	PUNCT
cana-1628	203	67	bilstm	bilstm	NOUN
cana-1628	203	68	and	and	CCONJ
cana-1628	203	69	1d	1d	NUM
cana-1628	203	70	-	-	PUNCT
cana-1628	203	71	cnn	cnn	NOUN
cana-1628	203	72	performance	performance	NOUN
cana-1628	203	73	.	.	PUNCT
cana-1628	204	1	in	in	ADP
cana-1628	204	2	the	the	DET
cana-1628	204	3	future	future	NOUN
cana-1628	204	4	,	,	PUNCT
cana-1628	204	5	the	the	DET
cana-1628	204	6	income	income	NOUN
cana-1628	204	7	level	level	NOUN
cana-1628	204	8	and	and	CCONJ
cana-1628	204	9	family	family	NOUN
cana-1628	204	10	size	size	NOUN
cana-1628	204	11	information	information	NOUN
cana-1628	204	12	about	about	ADP
cana-1628	204	13	the	the	DET
cana-1628	204	14	customers	customer	NOUN
cana-1628	204	15	should	should	AUX
cana-1628	204	16	be	be	AUX
cana-1628	204	17	incorporated	incorporate	VERB
cana-1628	204	18	as	as	ADP
cana-1628	204	19	exogenous	exogenous	ADJ
cana-1628	204	20	variables	variable	NOUN
cana-1628	204	21	to	to	PART
cana-1628	204	22	enhance	enhance	VERB
cana-1628	204	23	the	the	DET
cana-1628	204	24	prediction	prediction	NOUN
cana-1628	204	25	accuracy	accuracy	NOUN
cana-1628	204	26	of	of	ADP
cana-1628	204	27	the	the	DET
cana-1628	204	28	energy	energy	NOUN
cana-1628	204	29	consumption	consumption	NOUN
cana-1628	204	30	demand	demand	NOUN
cana-1628	204	31	.	.	PUNCT
cana-1628	205	1	additionally	additionally	ADV
cana-1628	205	2	,	,	PUNCT
cana-1628	205	3	optimal	optimal	ADJ
cana-1628	205	4	time	time	NOUN
cana-1628	205	5	steps	step	NOUN
cana-1628	205	6	should	should	AUX
cana-1628	205	7	be	be	AUX
cana-1628	205	8	determined	determine	VERB
cana-1628	205	9	using	use	VERB
cana-1628	205	10	automatic	automatic	ADJ
cana-1628	205	11	optimization	optimization	NOUN
cana-1628	205	12	methods	method	NOUN
cana-1628	205	13	.	.	PUNCT
cana-1628	206	1	references	reference	NOUN
cana-1628	206	2	[	[	X
cana-1628	206	3	1	1	NUM
cana-1628	206	4	]	]	PUNCT
cana-1628	206	5	musaed	musae	VERB
cana-1628	206	6	alhussein	alhussein	NOUN
cana-1628	206	7	,	,	PUNCT
cana-1628	206	8	khursheed	khursheed	NOUN
cana-1628	206	9	aurangzeb	aurangzeb	NOUN
cana-1628	206	10	,	,	PUNCT
cana-1628	206	11	and	and	CCONJ
cana-1628	206	12	syed	syed	PROPN
cana-1628	206	13	irtaza	irtaza	PROPN
cana-1628	206	14	haider	haider	PROPN
cana-1628	206	15	.	.	PUNCT
cana-1628	207	1	hybrid	hybrid	PROPN
cana-1628	207	2	cnn	cnn	PROPN
cana-1628	207	3	-	-	PUNCT
cana-1628	207	4	lstm	lstm	PROPN
cana-1628	207	5	model	model	NOUN
cana-1628	207	6	for	for	ADP
cana-1628	207	7	short	short	ADJ
cana-1628	207	8	term	term	NOUN
cana-1628	207	9	individual	individual	ADJ
cana-1628	207	10	household	household	NOUN
cana-1628	207	11	load	load	NOUN
cana-1628	207	12	forecasting	forecasting	NOUN
cana-1628	207	13	.	.	PUNCT
cana-1628	208	1	ieee	ieee	NOUN
cana-1628	208	2	access	access	NOUN
cana-1628	208	3	,	,	PUNCT
cana-1628	208	4	8:180544–180557	8:180544–180557	NUM
cana-1628	208	5	,	,	PUNCT
cana-1628	208	6	2020	2020	NUM
cana-1628	208	7	.	.	PUNCT
cana-1628	209	1	[	[	X
cana-1628	209	2	2	2	NUM
cana-1628	209	3	]	]	X
cana-1628	209	4	mengmeng	mengmeng	PROPN
cana-1628	209	5	cai	cai	PROPN
cana-1628	209	6	,	,	PUNCT
cana-1628	209	7	manisa	manisa	PROPN
cana-1628	209	8	pipattanasomporn	pipattanasomporn	VERB
cana-1628	209	9	,	,	PUNCT
cana-1628	209	10	and	and	CCONJ
cana-1628	209	11	saifur	saifur	PROPN
cana-1628	209	12	rahman	rahman	PROPN
cana-1628	209	13	.	.	PUNCT
cana-1628	210	1	day	day	NOUN
cana-1628	210	2	-	-	PUNCT
cana-1628	210	3	ahead	ahead	ADV
cana-1628	210	4	building	building	NOUN
cana-1628	210	5	-	-	PUNCT
cana-1628	210	6	level	level	NOUN
cana-1628	210	7	load	load	NOUN
cana-1628	210	8	forecasts	forecast	NOUN
cana-1628	210	9	using	use	VERB
cana-1628	210	10	deep	deep	ADJ
cana-1628	210	11	learning	learning	NOUN
cana-1628	210	12	vs.	vs.	ADP
cana-1628	210	13	traditional	traditional	ADJ
cana-1628	210	14	time	time	NOUN
cana-1628	210	15	-	-	PUNCT
cana-1628	210	16	series	series	NOUN
cana-1628	210	17	techniques	technique	NOUN
cana-1628	210	18	.	.	PUNCT
cana-1628	211	1	applied	apply	VERB
cana-1628	211	2	energy	energy	NOUN
cana-1628	211	3	,	,	PUNCT
cana-1628	211	4	236:1078–1088	236:1078–1088	NUM
cana-1628	211	5	,	,	PUNCT
cana-1628	211	6	2019	2019	NUM
cana-1628	211	7	.	.	PUNCT
cana-1628	212	1	[	[	X
cana-1628	212	2	3	3	X
cana-1628	212	3	]	]	X
cana-1628	212	4	yaogang	yaogang	PROPN
cana-1628	212	5	chen	chen	PROPN
cana-1628	212	6	,	,	PUNCT
cana-1628	212	7	guoyin	guoyin	PROPN
cana-1628	212	8	fu	fu	PROPN
cana-1628	212	9	,	,	PUNCT
cana-1628	212	10	and	and	CCONJ
cana-1628	212	11	xuefeng	xuefeng	PROPN
cana-1628	212	12	liu	liu	PROPN
cana-1628	212	13	.	.	PUNCT
cana-1628	212	14	air	air	NOUN
cana-1628	212	15	-	-	PUNCT
cana-1628	212	16	conditioning	conditioning	NOUN
cana-1628	212	17	load	load	NOUN
cana-1628	212	18	forecasting	forecasting	NOUN
cana-1628	212	19	for	for	ADP
cana-1628	212	20	prosumer	prosumer	NOUN
cana-1628	212	21	based	base	VERB
cana-1628	212	22	on	on	ADP
cana-1628	212	23	meta	meta	PROPN
cana-1628	212	24	ensemble	ensemble	ADJ
cana-1628	212	25	learning	learning	NOUN
cana-1628	212	26	.	.	PUNCT
cana-1628	213	1	ieee	ieee	NOUN
cana-1628	213	2	access	access	NOUN
cana-1628	213	3	,	,	PUNCT
cana-1628	213	4	8:123673–123682	8:123673–123682	NUM
cana-1628	213	5	,	,	PUNCT
cana-1628	213	6	2020	2020	NUM
cana-1628	213	7	.	.	PUNCT
cana-1628	214	1	[	[	X
cana-1628	214	2	4	4	NUM
cana-1628	214	3	]	]	X
cana-1628	214	4	jui	jui	PROPN
cana-1628	214	5	-	-	PUNCT
cana-1628	214	6	sheng	sheng	PROPN
cana-1628	214	7	chou	chou	PROPN
cana-1628	214	8	,	,	PUNCT
cana-1628	214	9	dinh	dinh	NOUN
cana-1628	214	10	-	-	PUNCT
cana-1628	214	11	nhat	nhat	NOUN
cana-1628	214	12	truong	truong	NOUN
cana-1628	214	13	,	,	PUNCT
cana-1628	214	14	and	and	CCONJ
cana-1628	214	15	ching	ching	PROPN
cana-1628	214	16	-	-	PUNCT
cana-1628	214	17	chiun	chiun	PROPN
cana-1628	214	18	kuo	kuo	PROPN
cana-1628	214	19	.	.	PUNCT
cana-1628	215	1	imaging	imaging	PROPN
cana-1628	215	2	time	time	NOUN
cana-1628	215	3	-	-	PUNCT
cana-1628	215	4	series	series	NOUN
cana-1628	215	5	with	with	ADP
cana-1628	215	6	features	feature	NOUN
cana-1628	215	7	to	to	PART
cana-1628	215	8	enable	enable	VERB
cana-1628	215	9	visual	visual	ADJ
cana-1628	215	10	recognition	recognition	NOUN
cana-1628	215	11	of	of	ADP
cana-1628	215	12	regional	regional	ADJ
cana-1628	215	13	energy	energy	NOUN
cana-1628	215	14	consumption	consumption	NOUN
cana-1628	215	15	by	by	ADP
cana-1628	215	16	bio	bio	ADJ
cana-1628	215	17	-	-	PUNCT
cana-1628	215	18	inspired	inspired	ADJ
cana-1628	215	19	optimization	optimization	NOUN
cana-1628	215	20	of	of	ADP
cana-1628	215	21	deep	deep	ADJ
cana-1628	215	22	learning	learning	NOUN
cana-1628	215	23	.	.	PUNCT
cana-1628	216	1	energy	energy	NOUN
cana-1628	216	2	,	,	PUNCT
cana-1628	216	3	224:120100	224:120100	NUM
cana-1628	216	4	,	,	PUNCT
cana-1628	216	5	2021	2021	NUM
cana-1628	216	6	.	.	PUNCT
cana-1628	217	1	[	[	X
cana-1628	217	2	5	5	NUM
cana-1628	217	3	]	]	PUNCT
cana-1628	217	4	behnam	behnam	PROPN
cana-1628	217	5	farsi	farsi	PROPN
cana-1628	217	6	,	,	PUNCT
cana-1628	217	7	manar	manar	PROPN
cana-1628	217	8	amayri	amayri	PROPN
cana-1628	217	9	,	,	PUNCT
cana-1628	217	10	nizar	nizar	PROPN
cana-1628	217	11	bouguila	bouguila	PROPN
cana-1628	217	12	,	,	PUNCT
cana-1628	217	13	and	and	CCONJ
cana-1628	217	14	ursula	ursula	PROPN
cana-1628	217	15	eicker	eicker	PROPN
cana-1628	217	16	.	.	PUNCT
cana-1628	218	1	on	on	ADP
cana-1628	218	2	short	short	ADJ
cana-1628	218	3	-	-	PUNCT
cana-1628	218	4	term	term	NOUN
cana-1628	218	5	load	load	NOUN
cana-1628	218	6	forecasting	forecasting	NOUN
cana-1628	218	7	using	use	VERB
cana-1628	218	8	machine	machine	NOUN
cana-1628	218	9	learning	learn	VERB
cana-1628	218	10	techniques	technique	NOUN
cana-1628	218	11	and	and	CCONJ
cana-1628	218	12	a	a	DET
cana-1628	218	13	novel	novel	NOUN
cana-1628	218	14	parallel	parallel	ADJ
cana-1628	218	15	deep	deep	ADJ
cana-1628	218	16	lstm	lstm	PROPN
cana-1628	218	17	-	-	PUNCT
cana-1628	218	18	cnn	cnn	PROPN
cana-1628	218	19	approach	approach	NOUN
cana-1628	218	20	.	.	PUNCT
cana-1628	219	1	ieee	ieee	NOUN
cana-1628	219	2	access	access	NOUN
cana-1628	219	3	,	,	PUNCT
cana-1628	219	4	9:31191	9:31191	NUM
cana-1628	219	5	–	–	PUNCT
cana-1628	219	6	31212	31212	NUM
cana-1628	219	7	,	,	PUNCT
cana-1628	219	8	2021	2021	NUM
cana-1628	219	9	.	.	PUNCT
cana-1628	220	1	[	[	X
cana-1628	220	2	6	6	NUM
cana-1628	220	3	]	]	SYM
cana-1628	220	4	dawit	dawit	ADJ
cana-1628	220	5	habtu	habtu	NOUN
cana-1628	220	6	gebremeskel	gebremeskel	NOUN
cana-1628	220	7	,	,	PUNCT
cana-1628	220	8	erik	erik	PROPN
cana-1628	220	9	o	o	PROPN
cana-1628	220	10	ahlgren	ahlgren	PROPN
cana-1628	220	11	,	,	PUNCT
cana-1628	220	12	and	and	CCONJ
cana-1628	220	13	getachew	getachew	VERB
cana-1628	220	14	bekele	bekele	PROPN
cana-1628	220	15	beyene	beyene	PROPN
cana-1628	220	16	.	.	PUNCT
cana-1628	221	1	long	long	ADJ
cana-1628	221	2	-	-	PUNCT
cana-1628	221	3	term	term	NOUN
cana-1628	221	4	evolution	evolution	NOUN
cana-1628	221	5	of	of	ADP
cana-1628	221	6	energy	energy	NOUN
cana-1628	221	7	and	and	CCONJ
cana-1628	221	8	electricity	electricity	NOUN
cana-1628	221	9	demand	demand	NOUN
cana-1628	221	10	forecasting	forecasting	NOUN
cana-1628	221	11	:	:	PUNCT
cana-1628	221	12	the	the	DET
cana-1628	221	13	case	case	NOUN
cana-1628	221	14	of	of	ADP
cana-1628	221	15	ethiopia	ethiopia	PROPN
cana-1628	221	16	.	.	PUNCT
cana-1628	222	1	energy	energy	NOUN
cana-1628	222	2	strategy	strategy	NOUN
cana-1628	222	3	reviews	review	NOUN
cana-1628	222	4	,	,	PUNCT
cana-1628	222	5	36:100671	36:100671	NUM
cana-1628	222	6	,	,	PUNCT
cana-1628	222	7	2021	2021	NUM
cana-1628	222	8	.	.	PUNCT
cana-1628	223	1	[	[	X
cana-1628	223	2	7	7	NUM
cana-1628	223	3	]	]	X
cana-1628	223	4	haibo	haibo	ADJ
cana-1628	223	5	guo	guo	PROPN
cana-1628	223	6	,	,	PUNCT
cana-1628	223	7	lingling	lingling	NOUN
cana-1628	223	8	tang	tang	PROPN
cana-1628	223	9	,	,	PUNCT
cana-1628	223	10	and	and	CCONJ
cana-1628	223	11	yuexing	yuexe	VERB
cana-1628	223	12	peng	peng	PROPN
cana-1628	223	13	.	.	PUNCT
cana-1628	224	1	ensemble	ensemble	ADJ
cana-1628	224	2	deep	deep	ADJ
cana-1628	224	3	learning	learning	NOUN
cana-1628	224	4	method	method	NOUN
cana-1628	224	5	for	for	ADP
cana-1628	224	6	shortterm	shortterm	PROPN
cana-1628	224	7	load	load	NOUN
cana-1628	224	8	forecasting	forecasting	NOUN
cana-1628	224	9	.	.	PUNCT
cana-1628	225	1	in	in	ADP
cana-1628	225	2	2018	2018	NUM
cana-1628	225	3	14th	14th	ADJ
cana-1628	225	4	international	international	ADJ
cana-1628	225	5	conference	conference	NOUN
cana-1628	225	6	on	on	ADP
cana-1628	225	7	mobile	mobile	ADJ
cana-1628	225	8	ad	ad	NOUN
cana-1628	225	9	-	-	PUNCT
cana-1628	225	10	hoc	hoc	ADJ
cana-1628	225	11	and	and	CCONJ
cana-1628	225	12	sensor	sensor	NOUN
cana-1628	225	13	networks	network	NOUN
cana-1628	225	14	(	(	PUNCT
cana-1628	225	15	msn	msn	PROPN
cana-1628	225	16	)	)	PUNCT
cana-1628	225	17	,	,	PUNCT
cana-1628	225	18	pages	page	NOUN
cana-1628	225	19	86–90	86–90	NUM
cana-1628	225	20	.	.	PUNCT
cana-1628	226	1	ieee	ieee	PROPN
cana-1628	226	2	,	,	PUNCT
cana-1628	226	3	2018	2018	NUM
cana-1628	226	4	.	.	PUNCT
cana-1628	227	1	[	[	X
cana-1628	227	2	8	8	NUM
cana-1628	227	3	]	]	PUNCT
cana-1628	227	4	ejigu	ejigu	ADJ
cana-1628	227	5	tefera	tefera	PROPN
cana-1628	227	6	habtemariam	habtemariam	PROPN
cana-1628	227	7	,	,	PUNCT
cana-1628	227	8	kula	kula	PROPN
cana-1628	227	9	kekeba	kekeba	PROPN
cana-1628	227	10	,	,	PUNCT
cana-1628	227	11	mar´ıa	mar´ıa	PROPN
cana-1628	227	12	mart´ınez	mart´ınez	PROPN
cana-1628	227	13	-	-	PUNCT
cana-1628	227	14	ballesteros	ballesteros	PROPN
cana-1628	227	15	,	,	PUNCT
cana-1628	227	16	and	and	CCONJ
cana-1628	227	17	francisco	francisco	PROPN
cana-1628	227	18	mart´ınez	mart´ınez	PROPN
cana-1628	227	19	-	-	PROPN
cana-1628	227	20	alvarez	alvarez	PROPN
cana-1628	227	21	.	.	PUNCT
cana-1628	228	1	a	a	DET
cana-1628	228	2	bayesian	bayesian	NOUN
cana-1628	228	3	optimization	optimization	NOUN
cana-1628	228	4	-	-	PUNCT
cana-1628	228	5	based	base	VERB
cana-1628	228	6	lstm	lstm	NOUN
cana-1628	228	7	model	model	NOUN
cana-1628	228	8	for	for	ADP
cana-1628	228	9	wind	wind	NOUN
cana-1628	228	10	power	power	NOUN
cana-1628	228	11	forecast	forecast	NOUN
cana-1628	228	12	´	´	NOUN
cana-1628	228	13	ing	e	VERB
cana-1628	228	14	in	in	ADP
cana-1628	228	15	the	the	DET
cana-1628	228	16	adama	adama	PROPN
cana-1628	228	17	district	district	PROPN
cana-1628	228	18	,	,	PUNCT
cana-1628	228	19	ethiopia	ethiopia	PROPN
cana-1628	228	20	.	.	PUNCT
cana-1628	229	1	energies	energy	NOUN
cana-1628	229	2	,	,	PUNCT
cana-1628	229	3	16(5):2317	16(5):2317	NUM
cana-1628	229	4	,	,	PUNCT
cana-1628	229	5	2023	2023	NUM
cana-1628	229	6	.	.	PUNCT
cana-1628	230	1	[	[	X
cana-1628	230	2	9	9	NUM
cana-1628	230	3	]	]	PUNCT
cana-1628	230	4	ejigu	ejigu	PROPN
cana-1628	230	5	t	t	PROPN
cana-1628	230	6	habtermariam	habtermariam	PROPN
cana-1628	230	7	,	,	PUNCT
cana-1628	230	8	kula	kula	PROPN
cana-1628	230	9	kekeba	kekeba	PROPN
cana-1628	230	10	,	,	PUNCT
cana-1628	230	11	alicia	alicia	PROPN
cana-1628	230	12	troncoso	troncoso	NOUN
cana-1628	230	13	,	,	PUNCT
cana-1628	230	14	and	and	CCONJ
cana-1628	230	15	francisco	francisco	PROPN
cana-1628	230	16	mart´ınez	mart´ınez	PROPN
cana-1628	230	17	-	-	PROPN
cana-1628	230	18	alvarez	alvarez	PROPN
cana-1628	230	19	.	.	PUNCT
cana-1628	230	20	´	´	VERB
cana-1628	230	21	a	a	DET
cana-1628	230	22	cluster	cluster	NOUN
cana-1628	230	23	-	-	PUNCT
cana-1628	230	24	based	base	VERB
cana-1628	230	25	deep	deep	ADJ
cana-1628	230	26	learning	learning	NOUN
cana-1628	230	27	model	model	NOUN
cana-1628	230	28	for	for	ADP
cana-1628	230	29	energy	energy	NOUN
cana-1628	230	30	consumption	consumption	NOUN
cana-1628	230	31	forecasting	forecasting	NOUN
cana-1628	230	32	in	in	ADP
cana-1628	230	33	ethiopia	ethiopia	PROPN
cana-1628	230	34	.	.	PUNCT
cana-1628	231	1	in	in	ADP
cana-1628	231	2	international	international	ADJ
cana-1628	231	3	workshop	workshop	NOUN
cana-1628	231	4	on	on	ADP
cana-1628	231	5	soft	soft	ADJ
cana-1628	231	6	computing	computing	NOUN
cana-1628	231	7	models	model	NOUN
cana-1628	231	8	in	in	ADP
cana-1628	231	9	industrial	industrial	ADJ
cana-1628	231	10	and	and	CCONJ
cana-1628	231	11	environmental	environmental	ADJ
cana-1628	231	12	applications	application	NOUN
cana-1628	231	13	,	,	PUNCT
cana-1628	231	14	pages	page	NOUN
cana-1628	231	15	423–432	423–432	NUM
cana-1628	231	16	.	.	PUNCT
cana-1628	231	17	springer	springer	NOUN
cana-1628	231	18	,	,	PUNCT
cana-1628	231	19	2022	2022	NUM
cana-1628	231	20	.	.	PUNCT
cana-1628	232	1	[	[	X
cana-1628	232	2	10	10	NUM
cana-1628	232	3	]	]	X
cana-1628	232	4	d.	d.	PROPN
cana-1628	232	5	hadjout	hadjout	PROPN
cana-1628	232	6	,	,	PUNCT
cana-1628	232	7	j.	j.	PROPN
cana-1628	232	8	f.	f.	PROPN
cana-1628	232	9	torres	torres	PROPN
cana-1628	232	10	,	,	PUNCT
cana-1628	232	11	a.	a.	NOUN
cana-1628	232	12	troncoso	troncoso	PROPN
cana-1628	232	13	,	,	PUNCT
cana-1628	232	14	a.	a.	PROPN
cana-1628	232	15	sebaa	sebaa	PROPN
cana-1628	232	16	,	,	PUNCT
cana-1628	232	17	and	and	CCONJ
cana-1628	232	18	f.	f.	PROPN
cana-1628	232	19	mart´ınez	mart´ınez	PROPN
cana-1628	232	20	-	-	PROPN
cana-1628	232	21	alvarez	alvarez	PROPN
cana-1628	232	22	.	.	PUNCT
cana-1628	233	1	electricity	electricity	NOUN
cana-1628	233	2	´	´	NOUN
cana-1628	233	3	consumption	consumption	NOUN
cana-1628	233	4	forecasting	forecasting	NOUN
cana-1628	233	5	based	base	VERB
cana-1628	233	6	on	on	ADP
cana-1628	233	7	ensemble	ensemble	ADJ
cana-1628	233	8	deep	deep	ADJ
cana-1628	233	9	learning	learning	NOUN
cana-1628	233	10	with	with	ADP
cana-1628	233	11	application	application	NOUN
cana-1628	233	12	to	to	ADP
cana-1628	233	13	the	the	DET
cana-1628	233	14	algerian	algerian	ADJ
cana-1628	233	15	market	market	NOUN
cana-1628	233	16	.	.	PUNCT
cana-1628	234	1	energy	energy	NOUN
cana-1628	234	2	,	,	PUNCT
cana-1628	234	3	243:123060	243:123060	NOUN
cana-1628	234	4	,	,	PUNCT
cana-1628	234	5	2022	2022	NUM
cana-1628	234	6	.	.	PUNCT
cana-1628	235	1	[	[	X
cana-1628	235	2	11	11	NUM
cana-1628	235	3	]	]	X
cana-1628	235	4	ghulam	ghulam	PROPN
cana-1628	235	5	hafeez	hafeez	PROPN
cana-1628	235	6	,	,	PUNCT
cana-1628	235	7	khurram	khurram	PROPN
cana-1628	235	8	saleem	saleem	PROPN
cana-1628	235	9	alimgeer	alimgeer	PROPN
cana-1628	235	10	,	,	PUNCT
cana-1628	235	11	and	and	CCONJ
cana-1628	235	12	imran	imran	PROPN
cana-1628	235	13	khan	khan	PROPN
cana-1628	235	14	.	.	PUNCT
cana-1628	236	1	electric	electric	PROPN
cana-1628	236	2	load	load	NOUN
cana-1628	236	3	forecasting	forecasting	NOUN
cana-1628	236	4	based	base	VERB
cana-1628	236	5	on	on	ADP
cana-1628	236	6	deep	deep	ADJ
cana-1628	236	7	learning	learning	NOUN
cana-1628	236	8	and	and	CCONJ
cana-1628	236	9	optimized	optimize	VERB
cana-1628	236	10	by	by	ADP
cana-1628	236	11	heuristic	heuristic	ADJ
cana-1628	236	12	algorithm	algorithm	NOUN
cana-1628	236	13	in	in	ADP
cana-1628	236	14	smart	smart	ADJ
cana-1628	236	15	grid	grid	NOUN
cana-1628	236	16	.	.	PUNCT
cana-1628	237	1	applied	apply	VERB
cana-1628	237	2	energy	energy	NOUN
cana-1628	237	3	,	,	PUNCT
cana-1628	237	4	269:114915	269:114915	NUM
cana-1628	237	5	,	,	PUNCT
cana-1628	237	6	2020	2020	NUM
cana-1628	237	7	.	.	PUNCT
cana-1628	238	1	[	[	X
cana-1628	238	2	12	12	NUM
cana-1628	238	3	]	]	X
cana-1628	238	4	ying	ying	PROPN
cana-1628	238	5	-	-	PUNCT
cana-1628	238	6	yi	yi	PROPN
cana-1628	238	7	hong	hong	PROPN
cana-1628	238	8	,	,	PUNCT
cana-1628	238	9	jonathan	jonathan	PROPN
cana-1628	238	10	v.	v.	PROPN
cana-1628	238	11	taylar	taylar	ADJ
cana-1628	238	12	,	,	PUNCT
cana-1628	238	13	and	and	CCONJ
cana-1628	238	14	arnel	arnel	PROPN
cana-1628	238	15	c.	c.	PROPN
cana-1628	238	16	fajardo	fajardo	PROPN
cana-1628	238	17	.	.	PUNCT
cana-1628	239	1	locational	locational	ADJ
cana-1628	239	2	marginal	marginal	ADJ
cana-1628	239	3	price	price	NOUN
cana-1628	239	4	forecasting	forecasting	NOUN
cana-1628	239	5	in	in	ADP
cana-1628	239	6	a	a	DET
cana-1628	239	7	day	day	NOUN
cana-1628	239	8	-	-	PUNCT
cana-1628	239	9	ahead	ahead	NOUN
cana-1628	239	10	power	power	NOUN
cana-1628	239	11	market	market	NOUN
cana-1628	239	12	using	use	VERB
cana-1628	239	13	spatiotemporal	spatiotemporal	ADJ
cana-1628	239	14	deep	deep	ADJ
cana-1628	239	15	learning	learning	NOUN
cana-1628	239	16	network	network	NOUN
cana-1628	239	17	.	.	PUNCT
cana-1628	240	1	sustainable	sustainable	ADJ
cana-1628	240	2	energy	energy	NOUN
cana-1628	240	3	,	,	PUNCT
cana-1628	240	4	grids	grid	NOUN
cana-1628	240	5	and	and	CCONJ
cana-1628	240	6	networks	network	NOUN
cana-1628	240	7	,	,	PUNCT
cana-1628	240	8	24:100406	24:100406	NUM
cana-1628	240	9	,	,	PUNCT
cana-1628	240	10	2020	2020	NUM
cana-1628	240	11	.	.	PUNCT
cana-1628	241	1	[	[	X
cana-1628	241	2	13	13	NUM
cana-1628	241	3	]	]	PUNCT
cana-1628	241	4	idowu	idowu	NOUN
cana-1628	241	5	david	david	PROPN
cana-1628	241	6	ibrahim	ibrahim	PROPN
cana-1628	241	7	,	,	PUNCT
cana-1628	241	8	y	y	PROPN
cana-1628	241	9	hamam	hamam	PROPN
cana-1628	241	10	,	,	PUNCT
cana-1628	241	11	yasser	yasser	PROPN
cana-1628	241	12	alayli	alayli	NOUN
cana-1628	241	13	,	,	PUNCT
cana-1628	241	14	tamba	tamba	NOUN
cana-1628	241	15	jamiru	jamiru	NOUN
cana-1628	241	16	,	,	PUNCT
cana-1628	241	17	emmanuel	emmanuel	PROPN
cana-1628	241	18	rotimi	rotimi	PROPN
cana-1628	241	19	sadiku	sadiku	PROPN
cana-1628	241	20	,	,	PUNCT
cana-1628	241	21	williams	williams	PROPN
cana-1628	241	22	kehinde	kehinde	PROPN
cana-1628	241	23	kupolati	kupolati	PROPN
cana-1628	241	24	,	,	PUNCT
cana-1628	241	25	julius	julius	PROPN
cana-1628	241	26	musyoka	musyoka	PROPN
cana-1628	241	27	ndambuki	ndambuki	VERB
cana-1628	241	28	,	,	PUNCT
cana-1628	241	29	and	and	CCONJ
cana-1628	241	30	azunna	azunna	ADJ
cana-1628	241	31	agwo	agwo	ADJ
cana-1628	241	32	eze	eze	PROPN
cana-1628	241	33	.	.	PUNCT
cana-1628	242	1	a	a	DET
cana-1628	242	2	review	review	NOUN
cana-1628	242	3	on	on	ADP
cana-1628	242	4	africa	africa	PROPN
cana-1628	242	5	energy	energy	NOUN
cana-1628	242	6	supply	supply	NOUN
cana-1628	242	7	through	through	ADP
cana-1628	242	8	renewable	renewable	ADJ
cana-1628	242	9	energy	energy	NOUN
cana-1628	242	10	production	production	NOUN
cana-1628	242	11	:	:	PUNCT
cana-1628	242	12	nigeria	nigeria	PROPN
cana-1628	242	13	,	,	PUNCT
cana-1628	242	14	cameroon	cameroon	PROPN
cana-1628	242	15	,	,	PUNCT
cana-1628	242	16	ghana	ghana	PROPN
cana-1628	242	17	and	and	CCONJ
cana-1628	242	18	south	south	PROPN
cana-1628	242	19	africa	africa	PROPN
cana-1628	242	20	as	as	ADP
cana-1628	242	21	a	a	DET
cana-1628	242	22	case	case	NOUN
cana-1628	242	23	study	study	NOUN
cana-1628	242	24	.	.	PUNCT
cana-1628	243	1	energy	energy	NOUN
cana-1628	243	2	strategy	strategy	NOUN
cana-1628	243	3	reviews	review	NOUN
cana-1628	243	4	,	,	PUNCT
cana-1628	243	5	38:100740	38:100740	NUM
cana-1628	243	6	,	,	PUNCT
cana-1628	243	7	2021	2021	NUM
cana-1628	243	8	.	.	PUNCT
cana-1628	244	1	[	[	X
cana-1628	244	2	14	14	NUM
cana-1628	244	3	]	]	X
cana-1628	244	4	muhammad	muhammad	PROPN
cana-1628	244	5	ishaq	ishaq	PROPN
cana-1628	244	6	,	,	PUNCT
cana-1628	244	7	soonil	soonil	PROPN
cana-1628	244	8	kwon	kwon	PROPN
cana-1628	244	9	,	,	PUNCT
cana-1628	244	10	et	et	PROPN
cana-1628	244	11	al	al	PROPN
cana-1628	244	12	.	.	PUNCT
cana-1628	244	13	short	short	ADJ
cana-1628	244	14	-	-	PUNCT
cana-1628	244	15	term	term	NOUN
cana-1628	244	16	energy	energy	NOUN
cana-1628	244	17	forecasting	forecasting	NOUN
cana-1628	244	18	framework	framework	NOUN
cana-1628	244	19	using	use	VERB
cana-1628	244	20	an	an	DET
cana-1628	244	21	ensemble	ensemble	ADJ
cana-1628	244	22	deep	deep	ADJ
cana-1628	244	23	learning	learning	NOUN
cana-1628	244	24	approach	approach	NOUN
cana-1628	244	25	.	.	PUNCT
cana-1628	245	1	ieee	ieee	NOUN
cana-1628	245	2	access	access	NOUN
cana-1628	245	3	,	,	PUNCT
cana-1628	245	4	9:94262–94271	9:94262–94271	NUM
cana-1628	245	5	,	,	PUNCT
cana-1628	245	6	2021	2021	NUM
cana-1628	245	7	.	.	PUNCT
cana-1628	246	1	[	[	X
cana-1628	246	2	15	15	NUM
cana-1628	246	3	]	]	PUNCT
cana-1628	246	4	k.	k.	PROPN
cana-1628	246	5	u.	u.	PROPN
cana-1628	246	6	jaseena	jaseena	PROPN
cana-1628	246	7	and	and	CCONJ
cana-1628	246	8	b.	b.	PROPN
cana-1628	246	9	c.	c.	PROPN
cana-1628	246	10	kovoor	kovoor	PROPN
cana-1628	246	11	.	.	PUNCT
cana-1628	247	1	decomposition	decomposition	NOUN
cana-1628	247	2	-	-	PUNCT
cana-1628	247	3	based	base	VERB
cana-1628	247	4	hybrid	hybrid	ADJ
cana-1628	247	5	wind	wind	NOUN
cana-1628	247	6	speed	speed	NOUN
cana-1628	247	7	forecasting	forecasting	NOUN
cana-1628	247	8	model	model	NOUN
cana-1628	247	9	using	use	VERB
cana-1628	247	10	deep	deep	ADJ
cana-1628	247	11	bidirectional	bidirectional	ADJ
cana-1628	247	12	lstm	lstm	NOUN
cana-1628	247	13	networks	network	NOUN
cana-1628	247	14	.	.	PUNCT
cana-1628	248	1	energy	energy	NOUN
cana-1628	248	2	conversion	conversion	NOUN
cana-1628	248	3	and	and	CCONJ
cana-1628	248	4	management	management	NOUN
cana-1628	248	5	,	,	PUNCT
cana-1628	248	6	234:113944	234:113944	NUM
cana-1628	248	7	,	,	PUNCT
cana-1628	248	8	2021.16	2021.16	NUM
cana-1628	249	1	[	[	X
cana-1628	249	2	16	16	NUM
cana-1628	249	3	]	]	X
cana-1628	249	4	waqas	waqas	PROPN
cana-1628	249	5	khan	khan	PROPN
cana-1628	249	6	,	,	PUNCT
cana-1628	249	7	shalika	shalika	X
cana-1628	249	8	walker	walker	PROPN
cana-1628	249	9	,	,	PUNCT
cana-1628	249	10	and	and	CCONJ
cana-1628	249	11	wim	wim	PROPN
cana-1628	249	12	zeiler	zeiler	PROPN
cana-1628	249	13	.	.	PUNCT
cana-1628	250	1	improved	improve	VERB
cana-1628	250	2	solar	solar	ADJ
cana-1628	250	3	photovoltaic	photovoltaic	NOUN
cana-1628	250	4	energy	energy	NOUN
cana-1628	250	5	generation	generation	NOUN
cana-1628	250	6	forecast	forecast	NOUN
cana-1628	250	7	using	use	VERB
cana-1628	250	8	deep	deep	ADJ
cana-1628	250	9	learning	learning	NOUN
cana-1628	250	10	-	-	PUNCT
cana-1628	250	11	based	base	VERB
cana-1628	250	12	ensemble	ensemble	ADJ
cana-1628	250	13	stacking	stacking	NOUN
cana-1628	250	14	approach	approach	NOUN
cana-1628	250	15	.	.	PUNCT
cana-1628	251	1	energy	energy	NOUN
cana-1628	251	2	,	,	PUNCT
cana-1628	251	3	240:122812	240:122812	NOUN
cana-1628	251	4	,	,	PUNCT
cana-1628	251	5	2022	2022	NUM
cana-1628	251	6	.	.	PUNCT
cana-1628	252	1	[	[	X
cana-1628	252	2	17	17	NUM
cana-1628	252	3	]	]	X
cana-1628	252	4	pratima	pratima	PROPN
cana-1628	252	5	kumari	kumari	PROPN
cana-1628	252	6	and	and	CCONJ
cana-1628	252	7	durga	durga	PROPN
cana-1628	252	8	toshniwal	toshniwal	PROPN
cana-1628	252	9	.	.	PUNCT
cana-1628	253	1	deep	deep	ADJ
cana-1628	253	2	learning	learning	NOUN
cana-1628	253	3	models	model	NOUN
cana-1628	253	4	for	for	ADP
cana-1628	253	5	solar	solar	ADJ
cana-1628	253	6	irradiance	irradiance	NOUN
cana-1628	253	7	forecasting	forecasting	NOUN
cana-1628	253	8	:	:	PUNCT
cana-1628	253	9	a	a	DET
cana-1628	253	10	comprehensive	comprehensive	ADJ
cana-1628	253	11	review	review	NOUN
cana-1628	253	12	.	.	PUNCT
cana-1628	254	1	journal	journal	PROPN
cana-1628	254	2	of	of	ADP
cana-1628	254	3	cleaner	clean	ADJ
cana-1628	254	4	production	production	NOUN
cana-1628	254	5	,	,	PUNCT
cana-1628	254	6	318:128566	318:128566	NUM
cana-1628	254	7	,	,	PUNCT
cana-1628	254	8	2021	2021	NUM
cana-1628	254	9	.	.	PUNCT
cana-1628	255	1	[	[	X
cana-1628	255	2	18	18	NUM
cana-1628	255	3	]	]	PUNCT
cana-1628	255	4	liying	liye	VERB
cana-1628	255	5	liu	liu	PROPN
cana-1628	255	6	and	and	CCONJ
cana-1628	255	7	yain	yain	NOUN
cana-1628	255	8	-	-	PUNCT
cana-1628	255	9	whar	whar	NOUN
cana-1628	255	10	si	si	NOUN
cana-1628	255	11	.	.	PROPN
cana-1628	255	12	1d	1d	NUM
cana-1628	255	13	convolutional	convolutional	ADJ
cana-1628	255	14	neural	neural	ADJ
cana-1628	255	15	networks	network	NOUN
cana-1628	255	16	for	for	ADP
cana-1628	255	17	chart	chart	NOUN
cana-1628	255	18	pattern	pattern	NOUN
cana-1628	255	19	classification	classification	NOUN
cana-1628	255	20	in	in	ADP
cana-1628	255	21	financial	financial	ADJ
cana-1628	255	22	time	time	NOUN
cana-1628	255	23	series	series	NOUN
cana-1628	255	24	.	.	PUNCT
cana-1628	256	1	the	the	DET
cana-1628	256	2	journal	journal	PROPN
cana-1628	256	3	of	of	ADP
cana-1628	256	4	supercomputing	supercomputing	NOUN
cana-1628	256	5	,	,	PUNCT
cana-1628	256	6	78(12):14191–14214	78(12):14191–14214	NUM
cana-1628	256	7	,	,	PUNCT
cana-1628	256	8	2022	2022	NUM
cana-1628	256	9	.	.	PUNCT
cana-1628	257	1	communications	communication	NOUN
cana-1628	257	2	on	on	ADP
cana-1628	257	3	applied	apply	VERB
cana-1628	257	4	nonlinear	nonlinear	ADJ
cana-1628	257	5	analysis	analysis	NOUN
cana-1628	257	6	issn	issn	NOUN
cana-1628	257	7	:	:	PUNCT
cana-1628	257	8	1074	1074	NUM
cana-1628	257	9	-	-	PUNCT
cana-1628	257	10	133x	133x	NUM
cana-1628	257	11	vol	vol	NOUN
cana-1628	257	12	32	32	NUM
cana-1628	257	13	no	no	NOUN
cana-1628	257	14	.	.	NOUN
cana-1628	257	15	1	1	NUM
cana-1628	257	16	(	(	PUNCT
cana-1628	257	17	2025	2025	NUM
cana-1628	257	18	)	)	PUNCT
cana-1628	257	19	172	172	NUM
cana-1628	257	20	https://internationalpubls.com	https://internationalpubls.com	X
cana-1628	258	1	[	[	X
cana-1628	258	2	19	19	NUM
cana-1628	258	3	]	]	X
cana-1628	258	4	haris	haris	PROPN
cana-1628	258	5	mansoor	mansoor	PROPN
cana-1628	258	6	,	,	PUNCT
cana-1628	258	7	huzaifa	huzaifa	PROPN
cana-1628	258	8	rauf	rauf	PROPN
cana-1628	258	9	,	,	PUNCT
cana-1628	258	10	muhammad	muhammad	PROPN
cana-1628	258	11	mubashar	mubashar	PROPN
cana-1628	258	12	,	,	PUNCT
cana-1628	258	13	muhammad	muhammad	PROPN
cana-1628	258	14	khalid	khalid	PROPN
cana-1628	258	15	,	,	PUNCT
cana-1628	258	16	and	and	CCONJ
cana-1628	258	17	naveed	naveed	PROPN
cana-1628	258	18	arshad	arshad	PROPN
cana-1628	258	19	.	.	PROPN
cana-1628	259	1	past	past	ADP
cana-1628	259	2	vector	vector	NOUN
cana-1628	259	3	similarity	similarity	NOUN
cana-1628	259	4	for	for	ADP
cana-1628	259	5	short	short	ADJ
cana-1628	259	6	term	term	NOUN
cana-1628	259	7	electrical	electrical	ADJ
cana-1628	259	8	load	load	NOUN
cana-1628	259	9	forecasting	forecasting	NOUN
cana-1628	259	10	at	at	ADP
cana-1628	259	11	the	the	DET
cana-1628	259	12	individual	individual	ADJ
cana-1628	259	13	household	household	NOUN
cana-1628	259	14	level	level	NOUN
cana-1628	259	15	.	.	PUNCT
cana-1628	260	1	ieee	ieee	NOUN
cana-1628	260	2	access	access	NOUN
cana-1628	260	3	,	,	PUNCT
cana-1628	260	4	9:42771–42785	9:42771–42785	NUM
cana-1628	260	5	,	,	PUNCT
cana-1628	260	6	2021	2021	NUM
cana-1628	260	7	.	.	PUNCT
cana-1628	261	1	[	[	X
cana-1628	261	2	20	20	NUM
cana-1628	261	3	]	]	X
cana-1628	261	4	f.	f.	PROPN
cana-1628	261	5	mateo	mateo	PROPN
cana-1628	261	6	,	,	PUNCT
cana-1628	261	7	j.	j.	PROPN
cana-1628	261	8	j.	j.	PROPN
cana-1628	261	9	carrasco	carrasco	PROPN
cana-1628	261	10	,	,	PUNCT
cana-1628	261	11	a.	a.	NOUN
cana-1628	261	12	sellami	sellami	PROPN
cana-1628	261	13	,	,	PUNCT
cana-1628	261	14	m.	m.	PROPN
cana-1628	261	15	millan	millan	PROPN
cana-1628	261	16	-	-	PUNCT
cana-1628	261	17	giraldo	giraldo	PROPN
cana-1628	261	18	,	,	PUNCT
cana-1628	261	19	m.	m.	NOUN
cana-1628	261	20	dom	dom	NOUN
cana-1628	261	21	´	´	NOUN
cana-1628	261	22	´	´	PROPN
cana-1628	261	23	ınguez	ınguez	NOUN
cana-1628	261	24	,	,	PUNCT
cana-1628	261	25	and	and	CCONJ
cana-1628	261	26	e.	e.	PROPN
cana-1628	261	27	soriaolivas	soriaolivas	PROPN
cana-1628	261	28	.	.	PUNCT
cana-1628	262	1	machine	machine	NOUN
cana-1628	262	2	learning	learn	VERB
cana-1628	262	3	methods	method	NOUN
cana-1628	262	4	to	to	PART
cana-1628	262	5	forecast	forecast	VERB
cana-1628	262	6	temperature	temperature	NOUN
cana-1628	262	7	in	in	ADP
cana-1628	262	8	buildings	building	NOUN
cana-1628	262	9	.	.	PUNCT
cana-1628	263	1	expert	expert	NOUN
cana-1628	263	2	systems	system	NOUN
cana-1628	263	3	with	with	ADP
cana-1628	263	4	applications	application	NOUN
cana-1628	263	5	,	,	PUNCT
cana-1628	263	6	40(4):1061–1068	40(4):1061–1068	NUM
cana-1628	263	7	,	,	PUNCT
cana-1628	263	8	2013	2013	NUM
cana-1628	263	9	.	.	PUNCT
cana-1628	264	1	[	[	X
cana-1628	264	2	21	21	NUM
cana-1628	264	3	]	]	X
cana-1628	264	4	manohar	manohar	PROPN
cana-1628	264	5	mishra	mishra	PROPN
cana-1628	264	6	,	,	PUNCT
cana-1628	264	7	janmenjoy	janmenjoy	PROPN
cana-1628	264	8	nayak	nayak	PROPN
cana-1628	264	9	,	,	PUNCT
cana-1628	264	10	bighnaraj	bighnaraj	PROPN
cana-1628	264	11	naik	naik	PROPN
cana-1628	264	12	,	,	PUNCT
cana-1628	264	13	and	and	CCONJ
cana-1628	264	14	ajith	ajith	PROPN
cana-1628	264	15	abraham	abraham	PROPN
cana-1628	264	16	.	.	PUNCT
cana-1628	265	1	deep	deep	ADJ
cana-1628	265	2	learning	learning	NOUN
cana-1628	265	3	in	in	ADP
cana-1628	265	4	electrical	electrical	ADJ
cana-1628	265	5	utility	utility	NOUN
cana-1628	265	6	industry	industry	NOUN
cana-1628	265	7	:	:	PUNCT
cana-1628	265	8	a	a	DET
cana-1628	265	9	comprehensive	comprehensive	ADJ
cana-1628	265	10	review	review	NOUN
cana-1628	265	11	of	of	ADP
cana-1628	265	12	a	a	DET
cana-1628	265	13	decade	decade	NOUN
cana-1628	265	14	of	of	ADP
cana-1628	265	15	research	research	NOUN
cana-1628	265	16	.	.	PUNCT
cana-1628	266	1	engineering	engineering	NOUN
cana-1628	266	2	applications	application	NOUN
cana-1628	266	3	of	of	ADP
cana-1628	266	4	artificial	artificial	ADJ
cana-1628	266	5	intelligence	intelligence	NOUN
cana-1628	266	6	,	,	PUNCT
cana-1628	266	7	96:104000	96:104000	NUM
cana-1628	266	8	,	,	PUNCT
cana-1628	266	9	2020	2020	NUM
cana-1628	266	10	.	.	PUNCT
cana-1628	267	1	[	[	X
cana-1628	267	2	22	22	NUM
cana-1628	267	3	]	]	X
cana-1628	267	4	tiago	tiago	PROPN
cana-1628	267	5	pinto	pinto	PROPN
cana-1628	267	6	,	,	PUNCT
cana-1628	267	7	isabel	isabel	PROPN
cana-1628	267	8	prac¸a	prac¸a	PROPN
cana-1628	267	9	,	,	PUNCT
cana-1628	267	10	zita	zita	PROPN
cana-1628	267	11	vale	vale	NOUN
cana-1628	267	12	,	,	PUNCT
cana-1628	267	13	and	and	CCONJ
cana-1628	267	14	jose	jose	PROPN
cana-1628	267	15	silva	silva	PROPN
cana-1628	267	16	.	.	PUNCT
cana-1628	268	1	ensemble	ensemble	ADJ
cana-1628	268	2	learning	learning	NOUN
cana-1628	268	3	for	for	ADP
cana-1628	268	4	electricity	electricity	NOUN
cana-1628	268	5	consumption	consumption	NOUN
cana-1628	268	6	forecasting	forecasting	NOUN
cana-1628	268	7	in	in	ADP
cana-1628	268	8	office	office	NOUN
cana-1628	268	9	buildings	building	NOUN
cana-1628	268	10	.	.	PUNCT
cana-1628	269	1	neurocomputing	neurocompute	VERB
cana-1628	269	2	,	,	PUNCT
cana-1628	269	3	423:747–755	423:747–755	NUM
cana-1628	269	4	,	,	PUNCT
cana-1628	269	5	2021	2021	NUM
cana-1628	269	6	.	.	PUNCT
cana-1628	270	1	[	[	X
cana-1628	270	2	23	23	NUM
cana-1628	270	3	]	]	X
cana-1628	270	4	zahra	zahra	PROPN
cana-1628	270	5	qavidelfardi	qavidelfardi	PROPN
cana-1628	270	6	,	,	PUNCT
cana-1628	270	7	mohammad	mohammad	PROPN
cana-1628	270	8	tahsildoost	tahsildoost	PROPN
cana-1628	270	9	,	,	PUNCT
cana-1628	270	10	and	and	CCONJ
cana-1628	270	11	zahra	zahra	PROPN
cana-1628	270	12	sadat	sadat	PROPN
cana-1628	270	13	zomorodian	zomorodian	PROPN
cana-1628	270	14	.	.	PUNCT
cana-1628	271	1	using	use	VERB
cana-1628	271	2	an	an	DET
cana-1628	271	3	ensemble	ensemble	ADJ
cana-1628	271	4	learning	learning	NOUN
cana-1628	271	5	framework	framework	NOUN
cana-1628	271	6	to	to	PART
cana-1628	271	7	predict	predict	VERB
cana-1628	271	8	residential	residential	ADJ
cana-1628	271	9	energy	energy	NOUN
cana-1628	271	10	consumption	consumption	NOUN
cana-1628	271	11	in	in	ADP
cana-1628	271	12	the	the	DET
cana-1628	271	13	hot	hot	ADJ
cana-1628	271	14	and	and	CCONJ
cana-1628	271	15	humid	humid	ADJ
cana-1628	271	16	climate	climate	NOUN
cana-1628	271	17	of	of	ADP
cana-1628	271	18	iran	iran	PROPN
cana-1628	271	19	.	.	PUNCT
cana-1628	272	1	energy	energy	NOUN
cana-1628	272	2	reports	report	NOUN
cana-1628	272	3	,	,	PUNCT
cana-1628	272	4	8:12327–12347	8:12327–12347	NUM
cana-1628	272	5	,	,	PUNCT
cana-1628	272	6	2022	2022	NUM
cana-1628	272	7	.	.	PUNCT
cana-1628	273	1	[	[	X
cana-1628	273	2	24	24	NUM
cana-1628	273	3	]	]	PUNCT
cana-1628	273	4	abdulwahed	abdulwahe	VERB
cana-1628	273	5	salam	salam	PROPN
cana-1628	273	6	and	and	CCONJ
cana-1628	273	7	abdelaaziz	abdelaaziz	PROPN
cana-1628	273	8	el	el	PROPN
cana-1628	273	9	hibaoui	hibaoui	PROPN
cana-1628	273	10	.	.	PUNCT
cana-1628	274	1	energy	energy	NOUN
cana-1628	274	2	consumption	consumption	NOUN
cana-1628	274	3	prediction	prediction	NOUN
cana-1628	274	4	model	model	NOUN
cana-1628	274	5	with	with	ADP
cana-1628	274	6	deep	deep	ADJ
cana-1628	274	7	inception	inception	ADJ
cana-1628	274	8	residual	residual	ADJ
cana-1628	274	9	network	network	NOUN
cana-1628	274	10	inspiration	inspiration	NOUN
cana-1628	274	11	and	and	CCONJ
cana-1628	274	12	lstm	lstm	NOUN
cana-1628	274	13	.	.	PUNCT
cana-1628	275	1	mathematics	mathematic	NOUN
cana-1628	275	2	and	and	CCONJ
cana-1628	275	3	computers	computer	NOUN
cana-1628	275	4	in	in	ADP
cana-1628	275	5	simulation	simulation	NOUN
cana-1628	275	6	,	,	PUNCT
cana-1628	275	7	190:97–109	190:97–109	NUM
cana-1628	275	8	,	,	PUNCT
cana-1628	275	9	2021	2021	NUM
cana-1628	275	10	.	.	PUNCT
cana-1628	276	1	[	[	X
cana-1628	276	2	25	25	NUM
cana-1628	276	3	]	]	PUNCT
cana-1628	276	4	myungjae	myungjae	ADJ
cana-1628	276	5	shin	shin	NOUN
cana-1628	276	6	,	,	PUNCT
cana-1628	276	7	david	david	PROPN
cana-1628	276	8	mohaisen	mohaisen	PROPN
cana-1628	276	9	,	,	PUNCT
cana-1628	276	10	and	and	CCONJ
cana-1628	276	11	joongheon	joongheon	PROPN
cana-1628	276	12	kim	kim	PROPN
cana-1628	276	13	.	.	PUNCT
cana-1628	277	1	bitcoin	bitcoin	PROPN
cana-1628	277	2	price	price	NOUN
cana-1628	277	3	forecasting	forecasting	NOUN
cana-1628	277	4	via	via	ADP
cana-1628	277	5	ensemble	ensemble	ADJ
cana-1628	277	6	-	-	PUNCT
cana-1628	277	7	based	base	VERB
cana-1628	277	8	lstm	lstm	NOUN
cana-1628	277	9	deep	deep	ADJ
cana-1628	277	10	learning	learning	NOUN
cana-1628	277	11	networks	network	NOUN
cana-1628	277	12	.	.	PUNCT
cana-1628	278	1	in	in	ADP
cana-1628	278	2	2021	2021	NUM
cana-1628	278	3	international	international	ADJ
cana-1628	278	4	conference	conference	NOUN
cana-1628	278	5	on	on	ADP
cana-1628	278	6	information	information	NOUN
cana-1628	278	7	networking	networking	NOUN
cana-1628	278	8	(	(	PUNCT
cana-1628	278	9	icoin	icoin	PROPN
cana-1628	278	10	)	)	PUNCT
cana-1628	278	11	,	,	PUNCT
cana-1628	278	12	pages	page	NOUN
cana-1628	278	13	603–608	603–608	NUM
cana-1628	278	14	.	.	PUNCT
cana-1628	279	1	ieee	ieee	NOUN
cana-1628	279	2	,	,	PUNCT
cana-1628	279	3	2021	2021	NUM
cana-1628	279	4	.	.	PUNCT
cana-1628	280	1	[	[	X
cana-1628	280	2	26	26	NUM
cana-1628	280	3	]	]	X
cana-1628	280	4	sn	sn	PROPN
cana-1628	280	5	singh	singh	PROPN
cana-1628	280	6	,	,	PUNCT
cana-1628	280	7	abheejeet	abheejeet	VERB
cana-1628	280	8	mohapatra	mohapatra	PROPN
cana-1628	280	9	,	,	PUNCT
cana-1628	280	10	et	et	PROPN
cana-1628	280	11	al	al	PROPN
cana-1628	280	12	.	.	PROPN
cana-1628	280	13	data	datum	NOUN
cana-1628	280	14	driven	drive	VERB
cana-1628	280	15	day	day	PROPN
cana-1628	280	16	-	-	PUNCT
cana-1628	280	17	ahead	ahead	NOUN
cana-1628	280	18	electrical	electrical	ADJ
cana-1628	280	19	load	load	NOUN
cana-1628	280	20	forecasting	forecast	VERB
cana-1628	280	21	through	through	ADP
cana-1628	280	22	repeated	repeat	VERB
cana-1628	280	23	wavelet	wavelet	NOUN
cana-1628	280	24	transform	transform	NOUN
cana-1628	280	25	assisted	assist	VERB
cana-1628	280	26	svm	svm	PROPN
cana-1628	280	27	model	model	NOUN
cana-1628	280	28	.	.	PUNCT
cana-1628	281	1	applied	apply	VERB
cana-1628	281	2	soft	soft	ADJ
cana-1628	281	3	computing	computing	NOUN
cana-1628	281	4	,	,	PUNCT
cana-1628	281	5	111:107730	111:107730	NUM
cana-1628	281	6	,	,	PUNCT
cana-1628	281	7	2021	2021	NUM
cana-1628	281	8	.	.	PUNCT
cana-1628	282	1	[	[	X
cana-1628	282	2	27	27	NUM
cana-1628	282	3	]	]	X
cana-1628	282	4	nivethitha	nivethitha	NOUN
cana-1628	282	5	somu	somu	NOUN
cana-1628	282	6	,	,	PUNCT
cana-1628	282	7	gauthama	gauthama	NOUN
cana-1628	282	8	raman	raman	NOUN
cana-1628	282	9	mr	mr	PROPN
cana-1628	282	10	,	,	PUNCT
cana-1628	282	11	and	and	CCONJ
cana-1628	282	12	krithi	krithi	PROPN
cana-1628	282	13	ramamritham	ramamritham	PROPN
cana-1628	282	14	.	.	PUNCT
cana-1628	283	1	a	a	DET
cana-1628	283	2	hybrid	hybrid	ADJ
cana-1628	283	3	model	model	NOUN
cana-1628	283	4	for	for	ADP
cana-1628	283	5	building	build	VERB
cana-1628	283	6	energy	energy	NOUN
cana-1628	283	7	consumption	consumption	NOUN
cana-1628	283	8	forecasting	forecasting	NOUN
cana-1628	283	9	using	use	VERB
cana-1628	283	10	long	long	ADJ
cana-1628	283	11	short	short	ADJ
cana-1628	283	12	term	term	NOUN
cana-1628	283	13	memory	memory	NOUN
cana-1628	283	14	networks	network	NOUN
cana-1628	283	15	.	.	PUNCT
cana-1628	284	1	applied	apply	VERB
cana-1628	284	2	energy	energy	NOUN
cana-1628	284	3	,	,	PUNCT
cana-1628	284	4	261:114131	261:114131	NUM
cana-1628	284	5	,	,	PUNCT
cana-1628	284	6	2020	2020	NUM
cana-1628	284	7	.	.	PUNCT
cana-1628	285	1	[	[	X
cana-1628	285	2	28	28	NUM
cana-1628	285	3	]	]	X
cana-1628	285	4	hui	hui	PROPN
cana-1628	285	5	song	song	PROPN
cana-1628	285	6	,	,	PUNCT
cana-1628	285	7	alex	alex	PROPN
cana-1628	285	8	kai	kai	PROPN
cana-1628	285	9	qin	qin	PROPN
cana-1628	285	10	,	,	PUNCT
cana-1628	285	11	and	and	CCONJ
cana-1628	285	12	flora	flora	NOUN
cana-1628	285	13	d	d	X
cana-1628	285	14	salim	salim	PROPN
cana-1628	285	15	.	.	PUNCT
cana-1628	286	1	evolutionary	evolutionary	ADJ
cana-1628	286	2	multi	multi	ADJ
cana-1628	286	3	-	-	ADJ
cana-1628	286	4	objective	objective	ADJ
cana-1628	286	5	ensemble	ensemble	ADJ
cana-1628	286	6	learning	learning	NOUN
cana-1628	286	7	for	for	ADP
cana-1628	286	8	multivariate	multivariate	NOUN
cana-1628	286	9	electricity	electricity	NOUN
cana-1628	286	10	consumption	consumption	NOUN
cana-1628	286	11	prediction	prediction	NOUN
cana-1628	286	12	.	.	PUNCT
cana-1628	287	1	in	in	ADP
cana-1628	287	2	2018	2018	NUM
cana-1628	287	3	international	international	ADJ
cana-1628	287	4	joint	joint	ADJ
cana-1628	287	5	conference	conference	NOUN
cana-1628	287	6	on	on	ADP
cana-1628	287	7	neural	neural	ADJ
cana-1628	287	8	networks	network	NOUN
cana-1628	287	9	(	(	PUNCT
cana-1628	287	10	ijcnn	ijcnn	PROPN
cana-1628	287	11	)	)	PUNCT
cana-1628	287	12	,	,	PUNCT
cana-1628	287	13	pages	page	NOUN
cana-1628	287	14	1	1	NUM
cana-1628	287	15	–	–	PUNCT
cana-1628	287	16	8	8	NUM
cana-1628	287	17	.	.	X
cana-1628	287	18	ieee	ieee	NOUN
cana-1628	287	19	,	,	PUNCT
cana-1628	287	20	2018	2018	NUM
cana-1628	287	21	.	.	PUNCT
cana-1628	288	1	[	[	X
cana-1628	288	2	29	29	NUM
cana-1628	288	3	]	]	PUNCT
cana-1628	288	4	zhiwei	zhiwei	PROPN
cana-1628	288	5	song	song	PROPN
cana-1628	288	6	,	,	PUNCT
cana-1628	288	7	zhaojing	zhaojing	NOUN
cana-1628	288	8	cao	cao	PROPN
cana-1628	288	9	,	,	PUNCT
cana-1628	288	10	can	can	AUX
cana-1628	288	11	wan	wan	PROPN
cana-1628	288	12	,	,	PUNCT
cana-1628	288	13	and	and	CCONJ
cana-1628	288	14	shenglan	shenglan	PROPN
cana-1628	288	15	xu	xu	PROPN
cana-1628	288	16	.	.	PUNCT
cana-1628	289	1	an	an	DET
cana-1628	289	2	ensemble	ensemble	ADJ
cana-1628	289	3	wavelet	wavelet	NOUN
cana-1628	289	4	deep	deep	ADJ
cana-1628	289	5	learning	learning	NOUN
cana-1628	289	6	approach	approach	NOUN
cana-1628	289	7	for	for	ADP
cana-1628	289	8	shortterm	shortterm	PROPN
cana-1628	289	9	load	load	NOUN
cana-1628	289	10	forecasting	forecasting	NOUN
cana-1628	289	11	.	.	PUNCT
cana-1628	290	1	in	in	ADP
cana-1628	290	2	2019	2019	NUM
cana-1628	290	3	ieee	ieee	NOUN
cana-1628	290	4	innovative	innovative	ADJ
cana-1628	290	5	smart	smart	ADJ
cana-1628	290	6	grid	grid	NOUN
cana-1628	290	7	technologies	technology	NOUN
cana-1628	290	8	-	-	PUNCT
cana-1628	290	9	asia	asia	PROPN
cana-1628	290	10	(	(	PUNCT
cana-1628	290	11	isgt	isgt	NOUN
cana-1628	290	12	asia	asia	PROPN
cana-1628	290	13	)	)	PUNCT
cana-1628	290	14	,	,	PUNCT
cana-1628	290	15	pages	page	NOUN
cana-1628	290	16	1205–1210	1205–1210	NUM
cana-1628	290	17	.	.	PUNCT
cana-1628	290	18	ieee	ieee	PROPN
cana-1628	290	19	,	,	PUNCT
cana-1628	290	20	2019.17	2019.17	NUM
cana-1628	290	21	[	[	X
cana-1628	290	22	30	30	NUM
cana-1628	290	23	]	]	X
cana-1628	290	24	mao	mao	NOUN
cana-1628	290	25	tan	tan	PROPN
cana-1628	290	26	,	,	PUNCT
cana-1628	290	27	siping	sip	VERB
cana-1628	290	28	yuan	yuan	NOUN
cana-1628	290	29	,	,	PUNCT
cana-1628	290	30	shuaihu	shuaihu	PROPN
cana-1628	290	31	li	li	PROPN
cana-1628	290	32	,	,	PUNCT
cana-1628	290	33	yongxin	yongxin	PROPN
cana-1628	290	34	su	su	PROPN
cana-1628	290	35	,	,	PUNCT
cana-1628	290	36	hui	hui	PROPN
cana-1628	290	37	li	li	PROPN
cana-1628	290	38	,	,	PUNCT
cana-1628	290	39	and	and	CCONJ
cana-1628	290	40	feng	feng	PROPN
cana-1628	290	41	he	he	PRON
cana-1628	290	42	.	.	PUNCT
cana-1628	291	1	ultra	ultra	ADJ
cana-1628	291	2	-	-	ADJ
cana-1628	291	3	short	short	ADJ
cana-1628	291	4	-	-	PUNCT
cana-1628	291	5	term	term	NOUN
cana-1628	291	6	industrial	industrial	ADJ
cana-1628	291	7	power	power	NOUN
cana-1628	291	8	demand	demand	NOUN
cana-1628	291	9	forecasting	forecasting	NOUN
cana-1628	291	10	using	use	VERB
cana-1628	291	11	lstm	lstm	NOUN
cana-1628	291	12	based	base	VERB
cana-1628	291	13	hybrid	hybrid	ADJ
cana-1628	291	14	ensemble	ensemble	ADJ
cana-1628	291	15	learning	learning	NOUN
cana-1628	291	16	.	.	PUNCT
cana-1628	292	1	ieee	ieee	NOUN
cana-1628	292	2	transactions	transaction	NOUN
cana-1628	292	3	on	on	ADP
cana-1628	292	4	power	power	NOUN
cana-1628	292	5	systems	system	NOUN
cana-1628	292	6	,	,	PUNCT
cana-1628	292	7	35(4):2937–2948	35(4):2937–2948	NUM
cana-1628	292	8	,	,	PUNCT
cana-1628	292	9	2019	2019	NUM
cana-1628	292	10	.	.	PUNCT
cana-1628	293	1	[	[	X
cana-1628	293	2	31	31	NUM
cana-1628	293	3	]	]	PUNCT
cana-1628	293	4	j.	j.	PROPN
cana-1628	293	5	f.	f.	PROPN
cana-1628	293	6	torres	torres	PROPN
cana-1628	293	7	,	,	PUNCT
cana-1628	293	8	f.	f.	PROPN
cana-1628	293	9	mart´ınez	mart´ınez	PROPN
cana-1628	293	10	-	-	PUNCT
cana-1628	293	11	alvarez	alvarez	PROPN
cana-1628	293	12	,	,	PUNCT
cana-1628	293	13	and	and	CCONJ
cana-1628	293	14	a.	a.	NOUN
cana-1628	293	15	troncoso	troncoso	PROPN
cana-1628	293	16	.	.	PUNCT
cana-1628	294	1	a	a	DET
cana-1628	294	2	deep	deep	ADJ
cana-1628	294	3	lstm	lstm	NOUN
cana-1628	294	4	network	network	NOUN
cana-1628	294	5	for	for	ADP
cana-1628	294	6	the	the	DET
cana-1628	294	7	spanish	spanish	ADJ
cana-1628	294	8	´	´	NOUN
cana-1628	294	9	electricity	electricity	NOUN
cana-1628	294	10	consumption	consumption	NOUN
cana-1628	294	11	forecasting	forecasting	NOUN
cana-1628	294	12	.	.	PUNCT
cana-1628	295	1	neural	neural	ADJ
cana-1628	295	2	computing	computing	NOUN
cana-1628	295	3	and	and	CCONJ
cana-1628	295	4	applications	application	NOUN
cana-1628	295	5	,	,	PUNCT
cana-1628	295	6	34(13):10533	34(13):10533	NUM
cana-1628	295	7	–	–	PUNCT
cana-1628	295	8	10545	10545	NUM
cana-1628	295	9	,	,	PUNCT
cana-1628	295	10	2022	2022	NUM
cana-1628	295	11	.	.	PUNCT
cana-1628	296	1	[	[	X
cana-1628	296	2	32	32	NUM
cana-1628	296	3	]	]	X
cana-1628	296	4	amin	amin	PROPN
cana-1628	296	5	ullah	ullah	PROPN
cana-1628	296	6	,	,	PUNCT
cana-1628	296	7	kilichbek	kilichbek	PROPN
cana-1628	296	8	haydarov	haydarov	PROPN
cana-1628	296	9	,	,	PUNCT
cana-1628	296	10	ijaz	ijaz	PROPN
cana-1628	296	11	ul	ul	PROPN
cana-1628	296	12	haq	haq	PROPN
cana-1628	296	13	,	,	PUNCT
cana-1628	296	14	khan	khan	PROPN
cana-1628	296	15	muhammad	muhammad	PROPN
cana-1628	296	16	,	,	PUNCT
cana-1628	296	17	seungmin	seungmin	PROPN
cana-1628	296	18	rho	rho	PROPN
cana-1628	296	19	,	,	PUNCT
cana-1628	296	20	miyoung	miyoung	PROPN
cana-1628	296	21	lee	lee	PROPN
cana-1628	296	22	,	,	PUNCT
cana-1628	296	23	and	and	CCONJ
cana-1628	296	24	sung	sung	PROPN
cana-1628	296	25	wook	wook	NOUN
cana-1628	296	26	baik	baik	PROPN
cana-1628	296	27	.	.	PUNCT
cana-1628	297	1	deep	deep	ADJ
cana-1628	297	2	learning	learning	NOUN
cana-1628	297	3	assisted	assist	VERB
cana-1628	297	4	buildings	building	NOUN
cana-1628	297	5	energy	energy	NOUN
cana-1628	297	6	consumption	consumption	NOUN
cana-1628	297	7	profiling	profiling	NOUN
cana-1628	297	8	using	use	VERB
cana-1628	297	9	smart	smart	ADJ
cana-1628	297	10	meter	meter	NOUN
cana-1628	297	11	data	datum	NOUN
cana-1628	297	12	.	.	PUNCT
cana-1628	298	1	sensors	sensor	NOUN
cana-1628	298	2	,	,	PUNCT
cana-1628	298	3	20(3):873	20(3):873	NOUN
cana-1628	298	4	,	,	PUNCT
cana-1628	298	5	2020	2020	NUM
cana-1628	298	6	.	.	PUNCT
cana-1628	299	1	[	[	X
cana-1628	299	2	33	33	NUM
cana-1628	299	3	]	]	PUNCT
cana-1628	299	4	lingxiao	lingxiao	NOUN
cana-1628	299	5	wang	wang	PROPN
cana-1628	299	6	,	,	PUNCT
cana-1628	299	7	shiwen	shiwen	PROPN
cana-1628	299	8	mao	mao	PROPN
cana-1628	299	9	,	,	PUNCT
cana-1628	299	10	and	and	CCONJ
cana-1628	299	11	bogdan	bogdan	PROPN
cana-1628	299	12	wilamowski	wilamowski	PROPN
cana-1628	299	13	.	.	PUNCT
cana-1628	300	1	short	short	ADJ
cana-1628	300	2	-	-	PUNCT
cana-1628	300	3	term	term	NOUN
cana-1628	300	4	load	load	NOUN
cana-1628	300	5	forecasting	forecasting	NOUN
cana-1628	300	6	with	with	ADP
cana-1628	300	7	lstm	lstm	NOUN
cana-1628	300	8	based	base	VERB
cana-1628	300	9	ensemble	ensemble	ADJ
cana-1628	300	10	learning	learning	NOUN
cana-1628	300	11	.	.	PUNCT
cana-1628	301	1	in	in	ADP
cana-1628	301	2	2019	2019	NUM
cana-1628	301	3	international	international	ADJ
cana-1628	301	4	conference	conference	NOUN
cana-1628	301	5	on	on	ADP
cana-1628	301	6	internet	internet	NOUN
cana-1628	301	7	of	of	ADP
cana-1628	301	8	things	thing	NOUN
cana-1628	301	9	(	(	PUNCT
cana-1628	301	10	ithings	ithing	NOUN
cana-1628	301	11	)	)	PUNCT
cana-1628	301	12	and	and	CCONJ
cana-1628	301	13	ieee	ieee	NOUN
cana-1628	301	14	green	green	PROPN
cana-1628	301	15	computing	computing	PROPN
cana-1628	301	16	and	and	CCONJ
cana-1628	301	17	communications	communication	NOUN
cana-1628	301	18	(	(	PUNCT
cana-1628	301	19	greencom	greencom	PROPN
cana-1628	301	20	)	)	PUNCT
cana-1628	301	21	and	and	CCONJ
cana-1628	301	22	ieee	ieee	PROPN
cana-1628	301	23	cyber	cyber	NOUN
cana-1628	301	24	,	,	PUNCT
cana-1628	301	25	physical	physical	ADJ
cana-1628	301	26	and	and	CCONJ
cana-1628	301	27	social	social	ADJ
cana-1628	301	28	computing	computing	NOUN
cana-1628	301	29	(	(	PUNCT
cana-1628	301	30	cpscom	cpscom	PROPN
cana-1628	301	31	)	)	PUNCT
cana-1628	301	32	and	and	CCONJ
cana-1628	301	33	ieee	ieee	VERB
cana-1628	301	34	smart	smart	ADJ
cana-1628	301	35	data	datum	NOUN
cana-1628	301	36	(	(	PUNCT
cana-1628	301	37	smartdata	smartdata	PROPN
cana-1628	301	38	)	)	PUNCT
cana-1628	301	39	,	,	PUNCT
cana-1628	301	40	pages	page	NOUN
cana-1628	301	41	793–800	793–800	NUM
cana-1628	301	42	.	.	PUNCT
cana-1628	302	1	ieee	ieee	NOUN
cana-1628	302	2	,	,	PUNCT
cana-1628	302	3	2019	2019	NUM
cana-1628	302	4	.	.	PUNCT
cana-1628	303	1	[	[	X
cana-1628	303	2	34	34	NUM
cana-1628	303	3	]	]	X
cana-1628	303	4	yi	yi	PROPN
cana-1628	303	5	wang	wang	PROPN
cana-1628	303	6	,	,	PUNCT
cana-1628	303	7	dahua	dahua	PROPN
cana-1628	303	8	gan	gan	PROPN
cana-1628	303	9	,	,	PUNCT
cana-1628	303	10	mingyang	mingyang	PROPN
cana-1628	303	11	sun	sun	PROPN
cana-1628	303	12	,	,	PUNCT
cana-1628	303	13	ning	ning	PROPN
cana-1628	303	14	zhang	zhang	PROPN
cana-1628	303	15	,	,	PUNCT
cana-1628	303	16	zongxiang	zongxiang	PROPN
cana-1628	303	17	lu	lu	PROPN
cana-1628	303	18	,	,	PUNCT
cana-1628	303	19	and	and	CCONJ
cana-1628	303	20	chongqing	chongqing	PROPN
cana-1628	303	21	kang	kang	PROPN
cana-1628	303	22	.	.	PUNCT
cana-1628	304	1	probabilistic	probabilistic	ADJ
cana-1628	304	2	individual	individual	ADJ
cana-1628	304	3	load	load	NOUN
cana-1628	304	4	forecasting	forecasting	NOUN
cana-1628	304	5	using	use	VERB
cana-1628	304	6	pinball	pinball	NOUN
cana-1628	304	7	loss	loss	NOUN
cana-1628	304	8	guided	guide	VERB
cana-1628	304	9	lstm	lstm	NOUN
cana-1628	304	10	.	.	PUNCT
cana-1628	305	1	applied	apply	VERB
cana-1628	305	2	energy	energy	NOUN
cana-1628	305	3	,	,	PUNCT
cana-1628	305	4	235:10–20	235:10–20	NUM
cana-1628	305	5	,	,	PUNCT
cana-1628	305	6	2019	2019	NUM
cana-1628	305	7	.	.	PUNCT
cana-1628	306	1	[	[	X
cana-1628	306	2	35	35	NUM
cana-1628	306	3	]	]	X
cana-1628	306	4	lulu	lulu	PROPN
cana-1628	306	5	wen	wen	PROPN
cana-1628	306	6	,	,	PUNCT
cana-1628	306	7	kaile	kaile	PROPN
cana-1628	306	8	zhou	zhou	PROPN
cana-1628	306	9	,	,	PUNCT
cana-1628	306	10	and	and	CCONJ
cana-1628	306	11	shanlin	shanlin	PROPN
cana-1628	306	12	yang	yang	PROPN
cana-1628	306	13	.	.	PROPN
cana-1628	307	1	load	load	PROPN
cana-1628	307	2	demand	demand	NOUN
cana-1628	307	3	forecasting	forecasting	NOUN
cana-1628	307	4	of	of	ADP
cana-1628	307	5	residential	residential	ADJ
cana-1628	307	6	buildings	building	NOUN
cana-1628	307	7	using	use	VERB
cana-1628	307	8	a	a	DET
cana-1628	307	9	deep	deep	ADJ
cana-1628	307	10	learning	learning	NOUN
cana-1628	307	11	model	model	NOUN
cana-1628	307	12	.	.	PUNCT
cana-1628	308	1	electric	electric	PROPN
cana-1628	308	2	power	power	PROPN
cana-1628	308	3	systems	systems	PROPN
cana-1628	308	4	research	research	NOUN
cana-1628	308	5	,	,	PUNCT
cana-1628	308	6	179:106073	179:106073	NUM
cana-1628	308	7	,	,	PUNCT
cana-1628	308	8	2020	2020	NUM
cana-1628	308	9	.	.	PUNCT
cana-1628	309	1	[	[	X
cana-1628	309	2	36	36	NUM
cana-1628	309	3	]	]	PUNCT
cana-1628	309	4	yu	yu	PROPN
cana-1628	309	5	yang	yang	PROPN
cana-1628	309	6	,	,	PUNCT
cana-1628	309	7	fan	fan	PROPN
cana-1628	309	8	jinfu	jinfu	PROPN
cana-1628	309	9	,	,	PUNCT
cana-1628	309	10	wang	wang	PROPN
cana-1628	309	11	zhongjie	zhongjie	PROPN
cana-1628	309	12	,	,	PUNCT
cana-1628	309	13	zhu	zhu	PROPN
cana-1628	309	14	zheng	zheng	PROPN
cana-1628	309	15	,	,	PUNCT
cana-1628	309	16	and	and	CCONJ
cana-1628	309	17	xu	xu	PROPN
cana-1628	309	18	yukun	yukun	PROPN
cana-1628	309	19	.	.	PUNCT
cana-1628	310	1	a	a	DET
cana-1628	310	2	dynamic	dynamic	ADJ
cana-1628	310	3	ensemble	ensemble	ADJ
cana-1628	310	4	method	method	NOUN
cana-1628	310	5	for	for	ADP
cana-1628	310	6	residential	residential	ADJ
cana-1628	310	7	shortterm	shortterm	NOUN
cana-1628	310	8	load	load	NOUN
cana-1628	310	9	forecasting	forecasting	NOUN
cana-1628	310	10	.	.	PUNCT
cana-1628	311	1	alexandria	alexandria	PROPN
cana-1628	311	2	engineering	engineering	PROPN
cana-1628	311	3	journal	journal	PROPN
cana-1628	311	4	,	,	PUNCT
cana-1628	311	5	63:75–88	63:75–88	NUM
cana-1628	311	6	,	,	PUNCT
cana-1628	311	7	2023	2023	NUM
cana-1628	311	8	.	.	PUNCT
cana-1628	312	1	[	[	X
cana-1628	312	2	37	37	NUM
cana-1628	312	3	]	]	PUNCT
cana-1628	312	4	shuai	shuai	PROPN
cana-1628	312	5	zhang	zhang	PROPN
cana-1628	312	6	,	,	PUNCT
cana-1628	312	7	yong	yong	PROPN
cana-1628	312	8	chen	chen	PROPN
cana-1628	312	9	,	,	PUNCT
cana-1628	312	10	wenyu	wenyu	PROPN
cana-1628	312	11	zhang	zhang	PROPN
cana-1628	312	12	,	,	PUNCT
cana-1628	312	13	and	and	CCONJ
cana-1628	312	14	ruijun	ruijun	PROPN
cana-1628	312	15	feng	feng	PROPN
cana-1628	312	16	.	.	PUNCT
cana-1628	313	1	a	a	DET
cana-1628	313	2	novel	novel	NOUN
cana-1628	313	3	ensemble	ensemble	ADJ
cana-1628	313	4	deep	deep	ADJ
cana-1628	313	5	learning	learning	NOUN
cana-1628	313	6	model	model	NOUN
cana-1628	313	7	with	with	ADP
cana-1628	313	8	dynamic	dynamic	ADJ
cana-1628	313	9	error	error	NOUN
cana-1628	313	10	correction	correction	NOUN
cana-1628	313	11	and	and	CCONJ
cana-1628	313	12	multi	multi	ADJ
cana-1628	313	13	-	-	ADJ
cana-1628	313	14	objective	objective	ADJ
cana-1628	313	15	ensemble	ensemble	ADJ
cana-1628	313	16	pruning	pruning	NOUN
cana-1628	313	17	for	for	ADP
cana-1628	313	18	time	time	NOUN
cana-1628	313	19	series	series	PROPN
cana-1628	313	20	forecasting	forecasting	PROPN
cana-1628	313	21	.	.	PUNCT
cana-1628	314	1	information	information	NOUN
cana-1628	314	2	sciences	sciences	PROPN
cana-1628	314	3	,	,	PUNCT
cana-1628	314	4	544:427	544:427	NUM
cana-1628	314	5	–	–	PUNCT
cana-1628	314	6	445	445	NUM
cana-1628	314	7	,	,	PUNCT
cana-1628	314	8	2021	2021	NUM
cana-1628	314	9	.	.	PUNCT