id	sid	tid	token	lemma	pos
ajst-10387	1	1	academic	academic	ADJ
ajst-10387	1	2	journal	journal	NOUN
ajst-10387	1	3	of	of	ADP
ajst-10387	1	4	science	science	NOUN
ajst-10387	1	5	and	and	CCONJ
ajst-10387	1	6	technology	technology	NOUN
ajst-10387	1	7	issn	issn	NOUN
ajst-10387	1	8	:	:	PUNCT
ajst-10387	1	9	2771	2771	NUM
ajst-10387	1	10	-	-	SYM
ajst-10387	1	11	3032	3032	NUM
ajst-10387	1	12	|	|	NOUN
ajst-10387	1	13	vol	vol	NOUN
ajst-10387	1	14	.	.	PROPN
ajst-10387	2	1	6	6	NUM
ajst-10387	2	2	,	,	PUNCT
ajst-10387	2	3	no	no	INTJ
ajst-10387	2	4	.	.	NOUN
ajst-10387	2	5	3	3	NUM
ajst-10387	2	6	,	,	PUNCT
ajst-10387	2	7	2023	2023	NUM
ajst-10387	2	8	77	77	NUM
ajst-10387	2	9	short	short	ADJ
ajst-10387	2	10	term	term	NOUN
ajst-10387	2	11	wind	wind	NOUN
ajst-10387	2	12	power	power	NOUN
ajst-10387	2	13	prediction	prediction	NOUN
ajst-10387	2	14	based	base	VERB
ajst-10387	2	15	on	on	ADP
ajst-10387	2	16	ceemdan‐lstm	ceemdan‐lstm	PROPN
ajst-10387	2	17	congming	congming	PROPN
ajst-10387	2	18	zhang	zhang	PROPN
ajst-10387	2	19	,	,	PUNCT
ajst-10387	2	20	zicheng	zicheng	PROPN
ajst-10387	2	21	yang	yang	PROPN
ajst-10387	2	22	,	,	PUNCT
ajst-10387	2	23	shaofei	shaofei	PROPN
ajst-10387	2	24	gao	gao	PROPN
ajst-10387	2	25	department	department	PROPN
ajst-10387	2	26	of	of	ADP
ajst-10387	2	27	computer	computer	NOUN
ajst-10387	2	28	,	,	PUNCT
ajst-10387	2	29	north	north	PROPN
ajst-10387	2	30	china	china	PROPN
ajst-10387	2	31	electric	electric	PROPN
ajst-10387	2	32	power	power	PROPN
ajst-10387	2	33	university	university	PROPN
ajst-10387	2	34	,	,	PUNCT
ajst-10387	2	35	baoding	baoding	PROPN
ajst-10387	2	36	071000	071000	NUM
ajst-10387	2	37	,	,	PUNCT
ajst-10387	2	38	china	china	PROPN
ajst-10387	2	39	abstract	abstract	NOUN
ajst-10387	2	40	:	:	PUNCT
ajst-10387	2	41	to	to	PART
ajst-10387	2	42	improve	improve	VERB
ajst-10387	2	43	the	the	DET
ajst-10387	2	44	accuracy	accuracy	NOUN
ajst-10387	2	45	of	of	ADP
ajst-10387	2	46	wind	wind	NOUN
ajst-10387	2	47	power	power	NOUN
ajst-10387	2	48	prediction	prediction	NOUN
ajst-10387	2	49	,	,	PUNCT
ajst-10387	2	50	a	a	DET
ajst-10387	2	51	wind	wind	NOUN
ajst-10387	2	52	power	power	NOUN
ajst-10387	2	53	prediction	prediction	NOUN
ajst-10387	2	54	method	method	NOUN
ajst-10387	2	55	based	base	VERB
ajst-10387	2	56	on	on	ADP
ajst-10387	2	57	time	time	NOUN
ajst-10387	2	58	series	series	NOUN
ajst-10387	2	59	decomposition	decomposition	NOUN
ajst-10387	2	60	and	and	CCONJ
ajst-10387	2	61	error	error	NOUN
ajst-10387	2	62	correction	correction	NOUN
ajst-10387	2	63	is	be	AUX
ajst-10387	2	64	proposed	propose	VERB
ajst-10387	2	65	.	.	PUNCT
ajst-10387	3	1	firstly	firstly	ADV
ajst-10387	3	2	,	,	PUNCT
ajst-10387	3	3	the	the	DET
ajst-10387	3	4	maximum	maximum	ADJ
ajst-10387	3	5	information	information	NOUN
ajst-10387	3	6	coefficient	coefficient	NOUN
ajst-10387	3	7	(	(	PUNCT
ajst-10387	3	8	mic	mic	ADJ
ajst-10387	3	9	)	)	PUNCT
ajst-10387	3	10	method	method	NOUN
ajst-10387	3	11	was	be	AUX
ajst-10387	3	12	used	use	VERB
ajst-10387	3	13	to	to	PART
ajst-10387	3	14	select	select	VERB
ajst-10387	3	15	the	the	DET
ajst-10387	3	16	features	feature	NOUN
ajst-10387	3	17	that	that	PRON
ajst-10387	3	18	have	have	VERB
ajst-10387	3	19	strong	strong	ADJ
ajst-10387	3	20	correlation	correlation	NOUN
ajst-10387	3	21	with	with	ADP
ajst-10387	3	22	wind	wind	NOUN
ajst-10387	3	23	power	power	NOUN
ajst-10387	3	24	to	to	PART
ajst-10387	3	25	reduce	reduce	VERB
ajst-10387	3	26	the	the	DET
ajst-10387	3	27	complexity	complexity	NOUN
ajst-10387	3	28	of	of	ADP
ajst-10387	3	29	the	the	DET
ajst-10387	3	30	original	original	ADJ
ajst-10387	3	31	data	datum	NOUN
ajst-10387	3	32	;	;	PUNCT
ajst-10387	3	33	then	then	ADV
ajst-10387	3	34	,	,	PUNCT
ajst-10387	3	35	according	accord	VERB
ajst-10387	3	36	to	to	ADP
ajst-10387	3	37	the	the	DET
ajst-10387	3	38	non	non	ADJ
ajst-10387	3	39	-	-	ADJ
ajst-10387	3	40	stationary	stationary	ADJ
ajst-10387	3	41	characteristics	characteristic	NOUN
ajst-10387	3	42	of	of	ADP
ajst-10387	3	43	wind	wind	NOUN
ajst-10387	3	44	power	power	NOUN
ajst-10387	3	45	,	,	PUNCT
ajst-10387	3	46	complete	complete	ADJ
ajst-10387	3	47	ensemble	ensemble	ADJ
ajst-10387	3	48	empirical	empirical	ADJ
ajst-10387	3	49	mode	mode	NOUN
ajst-10387	3	50	decomposition	decomposition	NOUN
ajst-10387	3	51	with	with	ADP
ajst-10387	3	52	adaptive	adaptive	ADJ
ajst-10387	3	53	noise(ceemdan	noise(ceemdan	PROPN
ajst-10387	3	54	)	)	PUNCT
ajst-10387	3	55	was	be	AUX
ajst-10387	3	56	used	use	VERB
ajst-10387	3	57	to	to	PART
ajst-10387	3	58	decompose	decompose	VERB
ajst-10387	3	59	wind	wind	NOUN
ajst-10387	3	60	power	power	NOUN
ajst-10387	3	61	into	into	ADP
ajst-10387	3	62	several	several	ADJ
ajst-10387	3	63	stationary	stationary	ADJ
ajst-10387	3	64	subsequences	subsequence	NOUN
ajst-10387	3	65	;	;	PUNCT
ajst-10387	3	66	finally	finally	ADV
ajst-10387	3	67	,	,	PUNCT
ajst-10387	3	68	the	the	DET
ajst-10387	3	69	long	long	ADJ
ajst-10387	3	70	short	short	ADJ
ajst-10387	3	71	memory	memory	NOUN
ajst-10387	3	72	network	network	NOUN
ajst-10387	3	73	(	(	PUNCT
ajst-10387	3	74	lstm	lstm	PROPN
ajst-10387	3	75	)	)	PUNCT
ajst-10387	3	76	was	be	AUX
ajst-10387	3	77	used	use	VERB
ajst-10387	3	78	to	to	PART
ajst-10387	3	79	dynamically	dynamically	ADV
ajst-10387	3	80	model	model	VERB
ajst-10387	3	81	the	the	DET
ajst-10387	3	82	wind	wind	NOUN
ajst-10387	3	83	power	power	NOUN
ajst-10387	3	84	multivariable	multivariable	ADJ
ajst-10387	3	85	time	time	PROPN
ajst-10387	3	86	series	series	NOUN
ajst-10387	3	87	;	;	PUNCT
ajst-10387	3	88	add	add	VERB
ajst-10387	3	89	the	the	DET
ajst-10387	3	90	predicted	predict	VERB
ajst-10387	3	91	values	value	NOUN
ajst-10387	3	92	of	of	ADP
ajst-10387	3	93	each	each	DET
ajst-10387	3	94	subsequence	subsequence	NOUN
ajst-10387	3	95	to	to	PART
ajst-10387	3	96	get	get	VERB
ajst-10387	3	97	the	the	DET
ajst-10387	3	98	final	final	ADJ
ajst-10387	3	99	predicted	predict	VERB
ajst-10387	3	100	value	value	NOUN
ajst-10387	3	101	.	.	PUNCT
ajst-10387	4	1	combined	combine	VERB
ajst-10387	4	2	with	with	ADP
ajst-10387	4	3	the	the	DET
ajst-10387	4	4	measured	measured	ADJ
ajst-10387	4	5	data	datum	NOUN
ajst-10387	4	6	of	of	ADP
ajst-10387	4	7	a	a	DET
ajst-10387	4	8	domestic	domestic	ADJ
ajst-10387	4	9	wind	wind	NOUN
ajst-10387	4	10	farm	farm	NOUN
ajst-10387	4	11	,	,	PUNCT
ajst-10387	4	12	the	the	DET
ajst-10387	4	13	simulation	simulation	NOUN
ajst-10387	4	14	results	result	NOUN
ajst-10387	4	15	showed	show	VERB
ajst-10387	4	16	that	that	SCONJ
ajst-10387	4	17	the	the	DET
ajst-10387	4	18	proposed	propose	VERB
ajst-10387	4	19	method	method	NOUN
ajst-10387	4	20	had	have	VERB
ajst-10387	4	21	higher	high	ADJ
ajst-10387	4	22	short	short	ADJ
ajst-10387	4	23	-	-	PUNCT
ajst-10387	4	24	term	term	NOUN
ajst-10387	4	25	wind	wind	NOUN
ajst-10387	4	26	power	power	NOUN
ajst-10387	4	27	prediction	prediction	NOUN
ajst-10387	4	28	accuracy	accuracy	NOUN
ajst-10387	4	29	compared	compare	VERB
ajst-10387	4	30	with	with	ADP
ajst-10387	4	31	other	other	ADJ
ajst-10387	4	32	prediction	prediction	NOUN
ajst-10387	4	33	models	model	NOUN
ajst-10387	4	34	.	.	PUNCT
ajst-10387	5	1	keywords	keyword	NOUN
ajst-10387	5	2	:	:	PUNCT
ajst-10387	5	3	wind	wind	NOUN
ajst-10387	5	4	power	power	NOUN
ajst-10387	5	5	prediction	prediction	NOUN
ajst-10387	5	6	,	,	PUNCT
ajst-10387	5	7	ceemdan	ceemdan	ADJ
ajst-10387	5	8	,	,	PUNCT
ajst-10387	5	9	deep	deep	ADJ
ajst-10387	5	10	learning	learning	NOUN
ajst-10387	5	11	,	,	PUNCT
ajst-10387	5	12	lstm	lstm	ADJ
ajst-10387	5	13	;	;	PUNCT
ajst-10387	5	14	mic	mic	ADJ
ajst-10387	5	15	.	.	NOUN
ajst-10387	6	1	1	1	X
ajst-10387	6	2	.	.	X
ajst-10387	6	3	introduction	introduction	NOUN
ajst-10387	6	4	by	by	ADP
ajst-10387	6	5	the	the	DET
ajst-10387	6	6	end	end	NOUN
ajst-10387	6	7	of	of	ADP
ajst-10387	6	8	2020	2020	NUM
ajst-10387	6	9	,	,	PUNCT
ajst-10387	6	10	china	china	PROPN
ajst-10387	6	11	's	's	PART
ajst-10387	6	12	installed	instal	VERB
ajst-10387	6	13	wind	wind	NOUN
ajst-10387	6	14	power	power	NOUN
ajst-10387	6	15	capacity	capacity	NOUN
ajst-10387	6	16	was	be	AUX
ajst-10387	6	17	281	281	NUM
ajst-10387	6	18	million	million	NUM
ajst-10387	6	19	kilowatts	kilowatt	NOUN
ajst-10387	6	20	,	,	PUNCT
ajst-10387	6	21	and	and	CCONJ
ajst-10387	6	22	the	the	DET
ajst-10387	6	23	wind	wind	NOUN
ajst-10387	6	24	power	power	NOUN
ajst-10387	6	25	generation	generation	NOUN
ajst-10387	6	26	capacity	capacity	NOUN
ajst-10387	6	27	is	be	AUX
ajst-10387	6	28	466.5	466.5	NUM
ajst-10387	6	29	billion	billion	NUM
ajst-10387	6	30	kilowatt	kilowatt	NOUN
ajst-10387	6	31	hours	hour	NOUN
ajst-10387	6	32	,	,	PUNCT
ajst-10387	6	33	accounting	account	VERB
ajst-10387	6	34	for	for	ADP
ajst-10387	6	35	6.1	6.1	NUM
ajst-10387	6	36	%	%	NOUN
ajst-10387	6	37	of	of	ADP
ajst-10387	6	38	the	the	DET
ajst-10387	6	39	total	total	ADJ
ajst-10387	6	40	power	power	NOUN
ajst-10387	6	41	generation	generation	NOUN
ajst-10387	7	1	[	[	X
ajst-10387	7	2	1	1	NUM
ajst-10387	7	3	]	]	PUNCT
ajst-10387	7	4	.	.	PUNCT
ajst-10387	8	1	wind	wind	NOUN
ajst-10387	8	2	power	power	NOUN
ajst-10387	8	3	gradually	gradually	ADV
ajst-10387	8	4	changes	change	VERB
ajst-10387	8	5	from	from	ADP
ajst-10387	8	6	'	'	PUNCT
ajst-10387	8	7	auxiliary	auxiliary	ADJ
ajst-10387	8	8	power	power	NOUN
ajst-10387	8	9	supply	supply	NOUN
ajst-10387	8	10	'	'	PUNCT
ajst-10387	8	11	to	to	ADP
ajst-10387	8	12	'	'	PUNCT
ajst-10387	8	13	main	main	ADJ
ajst-10387	8	14	power	power	NOUN
ajst-10387	8	15	supply	supply	NOUN
ajst-10387	8	16	'	'	PART
ajst-10387	9	1	[	[	X
ajst-10387	9	2	2	2	NUM
ajst-10387	9	3	]	]	PUNCT
ajst-10387	9	4	.	.	PUNCT
ajst-10387	10	1	however	however	ADV
ajst-10387	10	2	,	,	PUNCT
ajst-10387	10	3	due	due	ADP
ajst-10387	10	4	to	to	ADP
ajst-10387	10	5	the	the	DET
ajst-10387	10	6	time	time	NOUN
ajst-10387	10	7	-	-	PUNCT
ajst-10387	10	8	varying	vary	VERB
ajst-10387	10	9	characteristics	characteristic	NOUN
ajst-10387	10	10	of	of	ADP
ajst-10387	10	11	wind	wind	NOUN
ajst-10387	10	12	,	,	PUNCT
ajst-10387	10	13	wind	wind	NOUN
ajst-10387	10	14	power	power	NOUN
ajst-10387	10	15	has	have	VERB
ajst-10387	10	16	strong	strong	ADJ
ajst-10387	10	17	randomness	randomness	NOUN
ajst-10387	10	18	,	,	PUNCT
ajst-10387	10	19	intermittency	intermittency	NOUN
ajst-10387	10	20	and	and	CCONJ
ajst-10387	10	21	uncertainty	uncertainty	NOUN
ajst-10387	10	22	[	[	X
ajst-10387	10	23	3	3	NUM
ajst-10387	10	24	]	]	PUNCT
ajst-10387	10	25	.	.	PUNCT
ajst-10387	11	1	therefore	therefore	ADV
ajst-10387	11	2	,	,	PUNCT
ajst-10387	11	3	accurate	accurate	ADJ
ajst-10387	11	4	prediction	prediction	NOUN
ajst-10387	11	5	of	of	ADP
ajst-10387	11	6	wind	wind	NOUN
ajst-10387	11	7	power	power	NOUN
ajst-10387	11	8	was	be	AUX
ajst-10387	11	9	of	of	ADP
ajst-10387	11	10	great	great	ADJ
ajst-10387	11	11	significance	significance	NOUN
ajst-10387	11	12	to	to	ADP
ajst-10387	11	13	the	the	DET
ajst-10387	11	14	safe	safe	ADJ
ajst-10387	11	15	and	and	CCONJ
ajst-10387	11	16	economic	economic	ADJ
ajst-10387	11	17	operation	operation	NOUN
ajst-10387	11	18	of	of	ADP
ajst-10387	11	19	power	power	NOUN
ajst-10387	11	20	system	system	NOUN
ajst-10387	11	21	[	[	X
ajst-10387	11	22	4	4	NUM
ajst-10387	11	23	]	]	PUNCT
ajst-10387	11	24	.	.	PUNCT
ajst-10387	12	1	wind	wind	NOUN
ajst-10387	12	2	power	power	PROPN
ajst-10387	12	3	time	time	PROPN
ajst-10387	12	4	series	series	PROPN
ajst-10387	12	5	data	data	PROPN
ajst-10387	12	6	were	be	AUX
ajst-10387	12	7	non	non	ADJ
ajst-10387	12	8	-	-	ADJ
ajst-10387	12	9	stationary	stationary	ADJ
ajst-10387	12	10	series	series	NOUN
ajst-10387	12	11	,	,	PUNCT
ajst-10387	12	12	and	and	CCONJ
ajst-10387	12	13	it	it	PRON
ajst-10387	12	14	was	be	AUX
ajst-10387	12	15	difficult	difficult	ADJ
ajst-10387	12	16	to	to	PART
ajst-10387	12	17	capture	capture	VERB
ajst-10387	12	18	their	their	PRON
ajst-10387	12	19	change	change	NOUN
ajst-10387	12	20	characteristics	characteristic	NOUN
ajst-10387	12	21	and	and	CCONJ
ajst-10387	12	22	autocorrelation	autocorrelation	NOUN
ajst-10387	12	23	in	in	ADP
ajst-10387	12	24	time	time	NOUN
ajst-10387	12	25	series	series	NOUN
ajst-10387	12	26	.	.	PUNCT
ajst-10387	13	1	therefore	therefore	ADV
ajst-10387	13	2	,	,	PUNCT
ajst-10387	13	3	the	the	DET
ajst-10387	13	4	direct	direct	ADJ
ajst-10387	13	5	prediction	prediction	NOUN
ajst-10387	13	6	of	of	ADP
ajst-10387	13	7	wind	wind	NOUN
ajst-10387	13	8	power	power	NOUN
ajst-10387	13	9	data	datum	NOUN
ajst-10387	13	10	is	be	AUX
ajst-10387	13	11	not	not	PART
ajst-10387	13	12	ideal[5	ideal[5	NOUN
ajst-10387	13	13	]	]	PUNCT
ajst-10387	13	14	.	.	PUNCT
ajst-10387	14	1	in	in	ADP
ajst-10387	14	2	view	view	NOUN
ajst-10387	14	3	of	of	ADP
ajst-10387	14	4	the	the	DET
ajst-10387	14	5	random	random	ADJ
ajst-10387	14	6	volatility	volatility	NOUN
ajst-10387	14	7	of	of	ADP
ajst-10387	14	8	wind	wind	NOUN
ajst-10387	14	9	power	power	NOUN
ajst-10387	14	10	data	datum	NOUN
ajst-10387	14	11	,	,	PUNCT
ajst-10387	14	12	the	the	DET
ajst-10387	14	13	literature[6	literature[6	NOUN
ajst-10387	14	14	]	]	PUNCT
ajst-10387	14	15	used	use	VERB
ajst-10387	14	16	the	the	DET
ajst-10387	14	17	complex	complex	ADJ
ajst-10387	14	18	ensemble	ensemble	ADJ
ajst-10387	14	19	empirical	empirical	ADJ
ajst-10387	14	20	model	model	NOUN
ajst-10387	14	21	decomposition	decomposition	NOUN
ajst-10387	14	22	(	(	PUNCT
ajst-10387	14	23	ceemd	ceemd	NOUN
ajst-10387	14	24	)	)	PUNCT
ajst-10387	14	25	to	to	PART
ajst-10387	14	26	decompose	decompose	VERB
ajst-10387	14	27	the	the	DET
ajst-10387	14	28	wind	wind	NOUN
ajst-10387	14	29	power	power	NOUN
ajst-10387	14	30	sequence	sequence	NOUN
ajst-10387	14	31	,	,	PUNCT
ajst-10387	14	32	and	and	CCONJ
ajst-10387	14	33	used	use	VERB
ajst-10387	14	34	the	the	DET
ajst-10387	14	35	satin	satin	NOUN
ajst-10387	14	36	gardener	gardener	NOUN
ajst-10387	14	37	algorithm	algorithm	NOUN
ajst-10387	14	38	to	to	PART
ajst-10387	14	39	optimize	optimize	VERB
ajst-10387	14	40	the	the	DET
ajst-10387	14	41	least	least	ADJ
ajst-10387	14	42	squares	square	NOUN
ajst-10387	14	43	support	support	VERB
ajst-10387	14	44	vector	vector	NOUN
ajst-10387	14	45	machine	machine	NOUN
ajst-10387	14	46	to	to	PART
ajst-10387	14	47	predict	predict	VERB
ajst-10387	14	48	the	the	DET
ajst-10387	14	49	wind	wind	NOUN
ajst-10387	14	50	power	power	NOUN
ajst-10387	14	51	.	.	PUNCT
ajst-10387	15	1	in	in	ADP
ajst-10387	15	2	reference[7	reference[7	NOUN
ajst-10387	15	3	]	]	PUNCT
ajst-10387	15	4	,	,	PUNCT
ajst-10387	15	5	the	the	DET
ajst-10387	15	6	serial	serial	ADJ
ajst-10387	15	7	information	information	NOUN
ajst-10387	15	8	such	such	ADJ
ajst-10387	15	9	as	as	ADP
ajst-10387	15	10	historical	historical	ADJ
ajst-10387	15	11	wind	wind	NOUN
ajst-10387	15	12	power	power	NOUN
ajst-10387	15	13	was	be	AUX
ajst-10387	15	14	decomposed	decompose	VERB
ajst-10387	15	15	into	into	ADP
ajst-10387	15	16	modal	modal	ADJ
ajst-10387	15	17	components	component	NOUN
ajst-10387	15	18	of	of	ADP
ajst-10387	15	19	specified	specified	ADJ
ajst-10387	15	20	layers	layer	NOUN
ajst-10387	15	21	by	by	ADP
ajst-10387	15	22	using	use	VERB
ajst-10387	15	23	variational	variational	ADJ
ajst-10387	15	24	mode	mode	NOUN
ajst-10387	15	25	decomposition(vmd).different	decomposition(vmd).different	NOUN
ajst-10387	15	26	modal	modal	NOUN
ajst-10387	15	27	components	component	NOUN
ajst-10387	15	28	represent	represent	VERB
ajst-10387	15	29	features	feature	NOUN
ajst-10387	15	30	of	of	ADP
ajst-10387	15	31	different	different	ADJ
ajst-10387	15	32	scales	scale	NOUN
ajst-10387	15	33	.	.	PUNCT
ajst-10387	16	1	weight	weight	NOUN
ajst-10387	16	2	sharing	sharing	NOUN
ajst-10387	16	3	gate	gate	PROPN
ajst-10387	16	4	recurrent	recurrent	PROPN
ajst-10387	16	5	unit	unit	NOUN
ajst-10387	16	6	(	(	PUNCT
ajst-10387	16	7	wsgru	wsgru	PROPN
ajst-10387	16	8	)	)	PUNCT
ajst-10387	16	9	was	be	AUX
ajst-10387	16	10	used	use	VERB
ajst-10387	16	11	for	for	ADP
ajst-10387	16	12	modeling	modeling	NOUN
ajst-10387	16	13	.	.	PUNCT
ajst-10387	17	1	finally	finally	ADV
ajst-10387	17	2	,	,	PUNCT
ajst-10387	17	3	ann	ann	PROPN
ajst-10387	17	4	was	be	AUX
ajst-10387	17	5	used	use	VERB
ajst-10387	17	6	to	to	PART
ajst-10387	17	7	correct	correct	VERB
ajst-10387	17	8	and	and	CCONJ
ajst-10387	17	9	obtain	obtain	VERB
ajst-10387	17	10	the	the	DET
ajst-10387	17	11	prediction	prediction	NOUN
ajst-10387	17	12	results	result	NOUN
ajst-10387	17	13	of	of	ADP
ajst-10387	17	14	wind	wind	NOUN
ajst-10387	17	15	power	power	NOUN
ajst-10387	17	16	.	.	PUNCT
ajst-10387	18	1	in	in	ADP
ajst-10387	18	2	reference[8	reference[8	NOUN
ajst-10387	18	3	]	]	PUNCT
ajst-10387	18	4	,	,	PUNCT
ajst-10387	18	5	for	for	ADP
ajst-10387	18	6	the	the	DET
ajst-10387	18	7	low	low	ADJ
ajst-10387	18	8	adaptability	adaptability	NOUN
ajst-10387	18	9	of	of	ADP
ajst-10387	18	10	vmd	vmd	PROPN
ajst-10387	18	11	method	method	PROPN
ajst-10387	18	12	,	,	PUNCT
ajst-10387	18	13	the	the	DET
ajst-10387	18	14	enhanced	enhanced	ADJ
ajst-10387	18	15	colliding	collide	VERB
ajst-10387	18	16	bodies	body	NOUN
ajst-10387	18	17	optimization	optimization	NOUN
ajst-10387	18	18	(	(	PUNCT
ajst-10387	18	19	ecbo	ecbo	NOUN
ajst-10387	18	20	)	)	PUNCT
ajst-10387	18	21	algorithm	algorithm	NOUN
ajst-10387	18	22	was	be	AUX
ajst-10387	18	23	used	use	VERB
ajst-10387	18	24	to	to	PART
ajst-10387	18	25	optimize	optimize	VERB
ajst-10387	18	26	the	the	DET
ajst-10387	18	27	core	core	NOUN
ajst-10387	18	28	parameters	parameter	NOUN
ajst-10387	18	29	of	of	ADP
ajst-10387	18	30	the	the	DET
ajst-10387	18	31	variational	variational	ADJ
ajst-10387	18	32	mode	mode	NOUN
ajst-10387	18	33	.	.	PUNCT
ajst-10387	19	1	after	after	ADP
ajst-10387	19	2	decomposition	decomposition	NOUN
ajst-10387	19	3	,	,	PUNCT
ajst-10387	19	4	the	the	DET
ajst-10387	19	5	wavelet	wavelet	NOUN
ajst-10387	19	6	kernel	kernel	PROPN
ajst-10387	19	7	limit	limit	NOUN
ajst-10387	19	8	learning	learn	VERB
ajst-10387	19	9	machine	machine	NOUN
ajst-10387	19	10	prediction	prediction	NOUN
ajst-10387	19	11	model	model	NOUN
ajst-10387	19	12	is	be	AUX
ajst-10387	19	13	used	use	VERB
ajst-10387	19	14	for	for	ADP
ajst-10387	19	15	prediction	prediction	NOUN
ajst-10387	19	16	.	.	PUNCT
ajst-10387	20	1	in	in	ADP
ajst-10387	20	2	order	order	NOUN
ajst-10387	20	3	to	to	PART
ajst-10387	20	4	improve	improve	VERB
ajst-10387	20	5	the	the	DET
ajst-10387	20	6	accuracy	accuracy	NOUN
ajst-10387	20	7	of	of	ADP
ajst-10387	20	8	wind	wind	NOUN
ajst-10387	20	9	power	power	NOUN
ajst-10387	20	10	prediction	prediction	NOUN
ajst-10387	20	11	,	,	PUNCT
ajst-10387	20	12	a	a	DET
ajst-10387	20	13	short	short	ADJ
ajst-10387	20	14	-	-	PUNCT
ajst-10387	20	15	term	term	NOUN
ajst-10387	20	16	wind	wind	NOUN
ajst-10387	20	17	power	power	NOUN
ajst-10387	20	18	prediction	prediction	NOUN
ajst-10387	20	19	method	method	NOUN
ajst-10387	20	20	based	base	VERB
ajst-10387	20	21	on	on	ADP
ajst-10387	20	22	fully	fully	ADV
ajst-10387	20	23	adaptive	adaptive	ADJ
ajst-10387	20	24	noise	noise	NOUN
ajst-10387	20	25	ensemble	ensemble	ADJ
ajst-10387	20	26	empirical	empirical	ADJ
ajst-10387	20	27	decomposition	decomposition	NOUN
ajst-10387	20	28	(	(	PUNCT
ajst-10387	20	29	ceemdan	ceemdan	ADJ
ajst-10387	20	30	)	)	PUNCT
ajst-10387	20	31	and	and	CCONJ
ajst-10387	20	32	long	long	ADJ
ajst-10387	20	33	short	short	ADJ
ajst-10387	20	34	memory	memory	NOUN
ajst-10387	20	35	network	network	NOUN
ajst-10387	20	36	(	(	PUNCT
ajst-10387	20	37	lstm	lstm	NOUN
ajst-10387	20	38	)	)	PUNCT
ajst-10387	20	39	prediction	prediction	NOUN
ajst-10387	20	40	was	be	AUX
ajst-10387	20	41	proposed	propose	VERB
ajst-10387	20	42	in	in	ADP
ajst-10387	20	43	this	this	DET
ajst-10387	20	44	paper	paper	NOUN
ajst-10387	20	45	.	.	PUNCT
ajst-10387	21	1	the	the	DET
ajst-10387	21	2	ceemdan	ceemdan	PROPN
ajst-10387	21	3	was	be	AUX
ajst-10387	21	4	used	use	VERB
ajst-10387	21	5	to	to	PART
ajst-10387	21	6	decompose	decompose	VERB
ajst-10387	21	7	the	the	DET
ajst-10387	21	8	wind	wind	NOUN
ajst-10387	21	9	power	power	NOUN
ajst-10387	21	10	into	into	ADP
ajst-10387	21	11	several	several	ADJ
ajst-10387	21	12	subsequences	subsequence	NOUN
ajst-10387	21	13	,	,	PUNCT
ajst-10387	21	14	establish	establish	VERB
ajst-10387	21	15	the	the	DET
ajst-10387	21	16	lstm	lstm	NOUN
ajst-10387	21	17	wind	wind	NOUN
ajst-10387	21	18	power	power	NOUN
ajst-10387	21	19	prediction	prediction	NOUN
ajst-10387	21	20	model	model	NOUN
ajst-10387	21	21	of	of	ADP
ajst-10387	21	22	subsequences	subsequence	NOUN
ajst-10387	21	23	,	,	PUNCT
ajst-10387	21	24	and	and	CCONJ
ajst-10387	21	25	stack	stack	VERB
ajst-10387	21	26	the	the	DET
ajst-10387	21	27	predicted	predict	VERB
ajst-10387	21	28	values	value	NOUN
ajst-10387	21	29	of	of	ADP
ajst-10387	21	30	subsequences	subsequence	NOUN
ajst-10387	21	31	to	to	PART
ajst-10387	21	32	obtain	obtain	VERB
ajst-10387	21	33	the	the	DET
ajst-10387	21	34	final	final	ADJ
ajst-10387	21	35	predicted	predict	VERB
ajst-10387	21	36	value	value	NOUN
ajst-10387	21	37	.	.	PUNCT
ajst-10387	22	1	the	the	DET
ajst-10387	22	2	proposed	propose	VERB
ajst-10387	22	3	method	method	NOUN
ajst-10387	22	4	was	be	AUX
ajst-10387	22	5	compared	compare	VERB
ajst-10387	22	6	with	with	ADP
ajst-10387	22	7	lstm	lstm	NOUN
ajst-10387	22	8	and	and	CCONJ
ajst-10387	22	9	emd	emd	PROPN
ajst-10387	22	10	-	-	PUNCT
ajst-10387	22	11	lstm	lstm	PROPN
ajst-10387	22	12	to	to	PART
ajst-10387	22	13	verify	verify	VERB
ajst-10387	22	14	that	that	SCONJ
ajst-10387	22	15	the	the	DET
ajst-10387	22	16	proposed	propose	VERB
ajst-10387	22	17	model	model	NOUN
ajst-10387	22	18	has	have	VERB
ajst-10387	22	19	higher	high	ADJ
ajst-10387	22	20	accuracy	accuracy	NOUN
ajst-10387	22	21	.	.	PUNCT
ajst-10387	23	1	2	2	X
ajst-10387	23	2	.	.	X
ajst-10387	23	3	data	datum	NOUN
ajst-10387	23	4	preprocessing	preprocesse	VERB
ajst-10387	23	5	2.1	2.1	NUM
ajst-10387	23	6	.	.	PUNCT
ajst-10387	24	1	mic	mic	ADJ
ajst-10387	24	2	feature	feature	NOUN
ajst-10387	24	3	selection	selection	NOUN
ajst-10387	24	4	reshef	reshef	NOUN
ajst-10387	24	5	et	et	PROPN
ajst-10387	24	6	al	al	PROPN
ajst-10387	24	7	.	.	PROPN
ajst-10387	24	8	proposed	propose	VERB
ajst-10387	24	9	the	the	DET
ajst-10387	24	10	maximum	maximum	ADJ
ajst-10387	24	11	information	information	NOUN
ajst-10387	24	12	coefficient	coefficient	NOUN
ajst-10387	24	13	(	(	PUNCT
ajst-10387	24	14	mic	mic	ADJ
ajst-10387	24	15	)	)	PUNCT
ajst-10387	24	16	method	method	NOUN
ajst-10387	24	17	basedon	basedon	NOUN
ajst-10387	24	18	mutual	mutual	ADJ
ajst-10387	24	19	information	information	NOUN
ajst-10387	25	1	[	[	X
ajst-10387	25	2	9	9	NUM
ajst-10387	25	3	]	]	PUNCT
ajst-10387	25	4	.	.	PUNCT
ajst-10387	26	1	mutual	mutual	ADJ
ajst-10387	26	2	information	information	NOUN
ajst-10387	26	3	is	be	AUX
ajst-10387	26	4	a	a	DET
ajst-10387	26	5	measure	measure	NOUN
ajst-10387	26	6	that	that	PRON
ajst-10387	26	7	describes	describe	VERB
ajst-10387	26	8	the	the	DET
ajst-10387	26	9	mutual	mutual	ADJ
ajst-10387	26	10	relationship	relationship	NOUN
ajst-10387	26	11	between	between	ADP
ajst-10387	26	12	two	two	NUM
ajst-10387	26	13	random	random	ADJ
ajst-10387	26	14	variables	variable	NOUN
ajst-10387	26	15	x	x	PUNCT
ajst-10387	26	16	and	and	CCONJ
ajst-10387	26	17	y.	y.	NOUN
ajst-10387	26	18	the	the	PRON
ajst-10387	26	19	greater	great	ADJ
ajst-10387	26	20	the	the	DET
ajst-10387	26	21	mutual	mutual	ADJ
ajst-10387	26	22	information	information	NOUN
ajst-10387	26	23	value	value	NOUN
ajst-10387	26	24	,	,	PUNCT
ajst-10387	26	25	the	the	PRON
ajst-10387	26	26	stronger	strong	ADJ
ajst-10387	26	27	the	the	DET
ajst-10387	26	28	correlation	correlation	NOUN
ajst-10387	26	29	between	between	ADP
ajst-10387	26	30	the	the	DET
ajst-10387	26	31	two	two	NUM
ajst-10387	26	32	variables	variable	NOUN
ajst-10387	26	33	.	.	PUNCT
ajst-10387	27	1	mutual	mutual	ADJ
ajst-10387	27	2	information	information	NOUN
ajst-10387	27	3	is	be	AUX
ajst-10387	27	4	defined	define	VERB
ajst-10387	27	5	as	as	ADP
ajst-10387	27	6	:	:	PUNCT
ajst-10387	27	7	(	(	PUNCT
ajst-10387	27	8	,	,	PUNCT
ajst-10387	27	9	)	)	PUNCT
ajst-10387	27	10	(	(	PUNCT
ajst-10387	27	11	,	,	PUNCT
ajst-10387	27	12	)	)	PUNCT
ajst-10387	27	13	(	(	PUNCT
ajst-10387	27	14	,	,	PUNCT
ajst-10387	27	15	)	)	PUNCT
ajst-10387	27	16	log	log	NOUN
ajst-10387	27	17	(	(	PUNCT
ajst-10387	27	18	)	)	PUNCT
ajst-10387	27	19	(	(	PUNCT
ajst-10387	27	20	)	)	PUNCT
ajst-10387	27	21	x	x	X
ajst-10387	28	1	x	x	PUNCT
ajst-10387	28	2	y	y	VERB
ajst-10387	28	3	y	y	PROPN
ajst-10387	28	4	p	p	NOUN
ajst-10387	28	5	x	x	X
ajst-10387	28	6	y	y	VERB
ajst-10387	28	7	i	i	NOUN
ajst-10387	28	8	x	x	PROPN
ajst-10387	28	9	y	y	NOUN
ajst-10387	28	10	p	p	X
ajst-10387	28	11	x	x	X
ajst-10387	28	12	y	y	NOUN
ajst-10387	28	13	p	p	NOUN
ajst-10387	28	14	x	x	X
ajst-10387	28	15	p	p	X
ajst-10387	28	16	y	y	PROPN
ajst-10387	28	17			NOUN
ajst-10387	28	18			X
ajst-10387	28	19	(	(	PUNCT
ajst-10387	28	20	1	1	X
ajst-10387	28	21	)	)	PUNCT
ajst-10387	28	22	in	in	ADP
ajst-10387	28	23	the	the	DET
ajst-10387	28	24	formula	formula	NOUN
ajst-10387	28	25	:	:	PUNCT
ajst-10387	28	26	p(x	p(x	PROPN
ajst-10387	28	27	,	,	PUNCT
ajst-10387	28	28	y	y	PROPN
ajst-10387	28	29	)	)	PUNCT
ajst-10387	28	30	is	be	AUX
ajst-10387	28	31	the	the	DET
ajst-10387	28	32	joint	joint	ADJ
ajst-10387	28	33	probability	probability	NOUN
ajst-10387	28	34	density	density	NOUN
ajst-10387	28	35	function	function	NOUN
ajst-10387	28	36	of	of	ADP
ajst-10387	28	37	x	x	PROPN
ajst-10387	28	38	,	,	PUNCT
ajst-10387	28	39	y	y	PROPN
ajst-10387	28	40	;	;	PUNCT
ajst-10387	28	41	p(x	p(x	PROPN
ajst-10387	28	42	)	)	PUNCT
ajst-10387	28	43	and	and	CCONJ
ajst-10387	28	44	p	p	X
ajst-10387	28	45	(	(	PUNCT
ajst-10387	28	46	y	y	NOUN
ajst-10387	28	47	)	)	PUNCT
ajst-10387	28	48	are	be	AUX
ajst-10387	28	49	edge	edge	NOUN
ajst-10387	28	50	density	density	NOUN
ajst-10387	28	51	functions	function	NOUN
ajst-10387	28	52	of	of	ADP
ajst-10387	28	53	x	x	X
ajst-10387	28	54	and	and	CCONJ
ajst-10387	28	55	y	y	PROPN
ajst-10387	28	56	,	,	PUNCT
ajst-10387	28	57	respectively	respectively	ADV
ajst-10387	28	58	.	.	PUNCT
ajst-10387	29	1	the	the	DET
ajst-10387	29	2	maximum	maximum	ADJ
ajst-10387	29	3	information	information	NOUN
ajst-10387	29	4	coefficient	coefficient	NOUN
ajst-10387	29	5	is	be	AUX
ajst-10387	29	6	defined	define	VERB
ajst-10387	29	7	as	as	SCONJ
ajst-10387	29	8	follows	follow	VERB
ajst-10387	29	9	:	:	PUNCT
ajst-10387	29	10	assuming	assume	VERB
ajst-10387	29	11	that	that	SCONJ
ajst-10387	29	12	d(x	d(x	PROPN
ajst-10387	29	13	,	,	PUNCT
ajst-10387	29	14	y	y	NOUN
ajst-10387	29	15	)	)	PUNCT
ajst-10387	29	16	is	be	AUX
ajst-10387	29	17	a	a	DET
ajst-10387	29	18	finite	finite	ADJ
ajst-10387	29	19	two	two	NUM
ajst-10387	29	20	-	-	PUNCT
ajst-10387	29	21	dimensional	dimensional	ADJ
ajst-10387	29	22	data	datum	NOUN
ajst-10387	29	23	set	set	NOUN
ajst-10387	29	24	,	,	PUNCT
ajst-10387	29	25	the	the	DET
ajst-10387	29	26	current	current	ADJ
ajst-10387	29	27	two	two	NUM
ajst-10387	29	28	-	-	PUNCT
ajst-10387	29	29	dimensional	dimensional	ADJ
ajst-10387	29	30	space	space	NOUN
ajst-10387	29	31	was	be	AUX
ajst-10387	29	32	divided	divide	VERB
ajst-10387	29	33	into	into	ADP
ajst-10387	29	34	m	m	PROPN
ajst-10387	29	35	intervals	interval	NOUN
ajst-10387	29	36	and	and	CCONJ
ajst-10387	29	37	n	n	NOUN
ajst-10387	29	38	intervals	interval	NOUN
ajst-10387	29	39	in	in	ADP
ajst-10387	29	40	the	the	DET
ajst-10387	29	41	x	x	NOUN
ajst-10387	29	42	and	and	CCONJ
ajst-10387	29	43	y	y	PROPN
ajst-10387	29	44	directions	direction	NOUN
ajst-10387	29	45	,	,	PUNCT
ajst-10387	29	46	respectively	respectively	ADV
ajst-10387	29	47	,	,	PUNCT
ajst-10387	29	48	to	to	PART
ajst-10387	29	49	form	form	VERB
ajst-10387	29	50	a	a	DET
ajst-10387	29	51	grid	grid	NOUN
ajst-10387	29	52	of	of	ADP
ajst-10387	29	53	m	m	PROPN
ajst-10387	30	1	*	*	PUNCT
ajst-10387	30	2	n.	n.	NOUN
ajst-10387	30	3	there	there	PRON
ajst-10387	30	4	are	be	VERB
ajst-10387	30	5	multiple	multiple	ADJ
ajst-10387	30	6	ways	way	NOUN
ajst-10387	30	7	to	to	PART
ajst-10387	30	8	divide	divide	VERB
ajst-10387	30	9	grids	grid	NOUN
ajst-10387	30	10	with	with	ADP
ajst-10387	30	11	the	the	DET
ajst-10387	30	12	same	same	ADJ
ajst-10387	30	13	interval	interval	NOUN
ajst-10387	30	14	[	[	X
ajst-10387	30	15	9	9	NUM
ajst-10387	30	16	]	]	PUNCT
ajst-10387	30	17	.	.	PUNCT
ajst-10387	31	1	under	under	ADP
ajst-10387	31	2	grids	grid	NOUN
ajst-10387	31	3	of	of	ADP
ajst-10387	31	4	different	different	ADJ
ajst-10387	31	5	scales	scale	NOUN
ajst-10387	31	6	,	,	PUNCT
ajst-10387	31	7	the	the	DET
ajst-10387	31	8	maximum	maximum	ADJ
ajst-10387	31	9	mutual	mutual	ADJ
ajst-10387	31	10	information	information	NOUN
ajst-10387	31	11	value	value	NOUN
ajst-10387	31	12	was	be	AUX
ajst-10387	31	13	selected	select	VERB
ajst-10387	31	14	for	for	ADP
ajst-10387	31	15	calculation	calculation	NOUN
ajst-10387	31	16	,	,	PUNCT
ajst-10387	31	17	and	and	CCONJ
ajst-10387	31	18	the	the	DET
ajst-10387	31	19	maximum	maximum	ADJ
ajst-10387	31	20	information	information	NOUN
ajst-10387	31	21	coefficient	coefficient	NOUN
ajst-10387	31	22	is	be	AUX
ajst-10387	31	23	defined	define	VERB
ajst-10387	31	24	as	as	ADP
ajst-10387	31	25	:	:	PUNCT
ajst-10387	31	26	(	(	PUNCT
ajst-10387	31	27	,	,	PUNCT
ajst-10387	31	28	)	)	PUNCT
ajst-10387	31	29	(	(	PUNCT
ajst-10387	31	30	,	,	PUNCT
ajst-10387	31	31	)	)	PUNCT
ajst-10387	31	32	max	max	PROPN
ajst-10387	31	33	log(min	log(min	PROPN
ajst-10387	31	34	(	(	PUNCT
ajst-10387	31	35	,	,	PUNCT
ajst-10387	31	36	)	)	PUNCT
ajst-10387	31	37	)	)	PUNCT
ajst-10387	32	1	m	m	VERB
ajst-10387	32	2	n	n	NUM
ajst-10387	32	3	b	b	NOUN
ajst-10387	33	1	i	i	NOUN
ajst-10387	33	2	x	x	SYM
ajst-10387	33	3	y	y	PROPN
ajst-10387	33	4	mic	mic	NOUN
ajst-10387	33	5	x	x	VERB
ajst-10387	33	6	y	y	PROPN
ajst-10387	33	7	m	m	NOUN
ajst-10387	33	8	n	n	INTJ
ajst-10387	33	9			PROPN
ajst-10387	33	10			NUM
ajst-10387	33	11	(	(	PUNCT
ajst-10387	33	12	2	2	NUM
ajst-10387	33	13	)	)	PUNCT
ajst-10387	33	14	in	in	ADP
ajst-10387	33	15	the	the	DET
ajst-10387	33	16	formula	formula	NOUN
ajst-10387	33	17	,	,	PUNCT
ajst-10387	33	18	m*n	m*n	PROPN
ajst-10387	33	19	<	<	X
ajst-10387	33	20	b	b	X
ajst-10387	33	21	represents	represent	VERB
ajst-10387	33	22	the	the	DET
ajst-10387	33	23	constraint	constraint	NOUN
ajst-10387	33	24	condition	condition	NOUN
ajst-10387	33	25	of	of	ADP
ajst-10387	33	26	the	the	DET
ajst-10387	33	27	total	total	ADJ
ajst-10387	33	28	number	number	NOUN
ajst-10387	33	29	of	of	ADP
ajst-10387	33	30	grid	grid	NOUN
ajst-10387	33	31	divisions	division	NOUN
ajst-10387	33	32	.	.	PUNCT
ajst-10387	34	1	it	it	PRON
ajst-10387	34	2	can	can	AUX
ajst-10387	34	3	be	be	AUX
ajst-10387	34	4	seen	see	VERB
ajst-10387	34	5	from	from	ADP
ajst-10387	34	6	literature[10	literature[10	NOUN
ajst-10387	34	7	]	]	PUNCT
ajst-10387	34	8	that	that	SCONJ
ajst-10387	34	9	the	the	DET
ajst-10387	34	10	optimal	optimal	ADJ
ajst-10387	34	11	effect	effect	NOUN
ajst-10387	34	12	can	can	AUX
ajst-10387	34	13	be	be	AUX
ajst-10387	34	14	achieved	achieve	VERB
ajst-10387	34	15	when	when	SCONJ
ajst-10387	34	16	it	it	PRON
ajst-10387	34	17	is	be	AUX
ajst-10387	34	18	set	set	VERB
ajst-10387	34	19	to	to	ADP
ajst-10387	34	20	the	the	DET
ajst-10387	34	21	0.6th	0.6th	NUM
ajst-10387	34	22	power	power	NOUN
ajst-10387	34	23	of	of	ADP
ajst-10387	34	24	the	the	DET
ajst-10387	34	25	total	total	ADJ
ajst-10387	34	26	amount	amount	NOUN
ajst-10387	34	27	of	of	ADP
ajst-10387	34	28	data	datum	NOUN
ajst-10387	34	29	.	.	PUNCT
ajst-10387	35	1	therefore	therefore	ADV
ajst-10387	35	2	,	,	PUNCT
ajst-10387	35	3	this	this	DET
ajst-10387	35	4	setting	setting	NOUN
ajst-10387	35	5	method	method	NOUN
ajst-10387	35	6	was	be	AUX
ajst-10387	35	7	also	also	ADV
ajst-10387	35	8	used	use	VERB
ajst-10387	35	9	in	in	ADP
ajst-10387	35	10	this	this	DET
ajst-10387	35	11	paper	paper	NOUN
ajst-10387	35	12	.	.	PUNCT
ajst-10387	36	1	2.2	2.2	NUM
ajst-10387	36	2	.	.	PUNCT
ajst-10387	36	3	normalization	normalization	NOUN
ajst-10387	36	4	when	when	SCONJ
ajst-10387	36	5	using	use	VERB
ajst-10387	36	6	multivariate	multivariate	NOUN
ajst-10387	36	7	time	time	NOUN
ajst-10387	36	8	series	series	PROPN
ajst-10387	36	9	to	to	PART
ajst-10387	36	10	predict	predict	VERB
ajst-10387	36	11	wind	wind	NOUN
ajst-10387	36	12	power	power	NOUN
ajst-10387	36	13	,	,	PUNCT
ajst-10387	36	14	78	78	NUM
ajst-10387	36	15	the	the	DET
ajst-10387	36	16	dimensions	dimension	NOUN
ajst-10387	36	17	of	of	ADP
ajst-10387	36	18	different	different	ADJ
ajst-10387	36	19	variables	variable	NOUN
ajst-10387	36	20	are	be	AUX
ajst-10387	36	21	different	different	ADJ
ajst-10387	36	22	,	,	PUNCT
ajst-10387	36	23	and	and	CCONJ
ajst-10387	36	24	the	the	DET
ajst-10387	36	25	numerical	numerical	ADJ
ajst-10387	36	26	differences	difference	NOUN
ajst-10387	36	27	are	be	AUX
ajst-10387	36	28	also	also	ADV
ajst-10387	36	29	large	large	ADJ
ajst-10387	36	30	[	[	X
ajst-10387	36	31	11	11	NUM
ajst-10387	36	32	]	]	PUNCT
ajst-10387	36	33	.	.	PUNCT
ajst-10387	37	1	in	in	ADP
ajst-10387	37	2	orderto	orderto	NOUN
ajst-10387	37	3	make	make	VERB
ajst-10387	37	4	the	the	DET
ajst-10387	37	5	prediction	prediction	NOUN
ajst-10387	37	6	model	model	NOUN
ajst-10387	37	7	consider	consider	VERB
ajst-10387	37	8	the	the	DET
ajst-10387	37	9	effect	effect	NOUN
ajst-10387	37	10	of	of	ADP
ajst-10387	37	11	variables	variable	NOUN
ajst-10387	37	12	on	on	ADP
ajst-10387	37	13	wind	wind	NOUN
ajst-10387	37	14	power	power	NOUN
ajst-10387	37	15	equally	equally	ADV
ajst-10387	37	16	,	,	PUNCT
ajst-10387	37	17	the	the	DET
ajst-10387	37	18	wind	wind	NOUN
ajst-10387	37	19	speed	speed	NOUN
ajst-10387	37	20	,	,	PUNCT
ajst-10387	37	21	temperature	temperature	NOUN
ajst-10387	37	22	,	,	PUNCT
ajst-10387	37	23	wind	wind	NOUN
ajst-10387	37	24	direction	direction	NOUN
ajst-10387	37	25	angle	angle	NOUN
ajst-10387	37	26	and	and	CCONJ
ajst-10387	37	27	active	active	ADJ
ajst-10387	37	28	power	power	NOUN
ajst-10387	37	29	in	in	ADP
ajst-10387	37	30	the	the	DET
ajst-10387	37	31	wind	wind	NOUN
ajst-10387	37	32	power	power	NOUN
ajst-10387	37	33	data	datum	NOUN
ajst-10387	37	34	are	be	AUX
ajst-10387	37	35	normalized	normalize	VERB
ajst-10387	37	36	to	to	ADP
ajst-10387	37	37	the	the	DET
ajst-10387	37	38	[	[	X
ajst-10387	37	39	0,1	0,1	NUM
ajst-10387	37	40	]	]	PUNCT
ajst-10387	37	41	range	range	NOUN
ajst-10387	37	42	.	.	PUNCT
ajst-10387	38	1	min	min	PROPN
ajst-10387	38	2	max	max	PROPN
ajst-10387	38	3	min	min	PROPN
ajst-10387	38	4	norm	norm	VERB
ajst-10387	38	5	x	x	PUNCT
ajst-10387	38	6	x	x	PUNCT
ajst-10387	38	7	x	x	PUNCT
ajst-10387	38	8	x	x	PUNCT
ajst-10387	38	9	x	x	SYM
ajst-10387	38	10			PROPN
ajst-10387	38	11			NUM
ajst-10387	38	12			NOUN
ajst-10387	38	13	(	(	PUNCT
ajst-10387	38	14	3	3	NUM
ajst-10387	38	15	)	)	PUNCT
ajst-10387	38	16	in	in	ADP
ajst-10387	38	17	the	the	DET
ajst-10387	38	18	formula	formula	NOUN
ajst-10387	38	19	:	:	PUNCT
ajst-10387	38	20	xmin	xmin	PROPN
ajst-10387	38	21	and	and	CCONJ
ajst-10387	38	22	xmax	xmax	PROPN
ajst-10387	38	23	are	be	AUX
ajst-10387	38	24	the	the	DET
ajst-10387	38	25	minimum	minimum	ADJ
ajst-10387	38	26	and	and	CCONJ
ajst-10387	38	27	maximum	maximum	ADJ
ajst-10387	38	28	values	value	NOUN
ajst-10387	38	29	of	of	ADP
ajst-10387	38	30	variables	variable	NOUN
ajst-10387	38	31	respectively	respectively	ADV
ajst-10387	38	32	.	.	PUNCT
ajst-10387	39	1	3	3	X
ajst-10387	39	2	.	.	NOUN
ajst-10387	39	3	prediction	prediction	NOUN
ajst-10387	39	4	model	model	NOUN
ajst-10387	39	5	analysis	analysis	NOUN
ajst-10387	39	6	3.1	3.1	NUM
ajst-10387	39	7	.	.	PUNCT
ajst-10387	40	1	ceemdan	ceemdan	PROPN
ajst-10387	40	2	principle	principle	PROPN
ajst-10387	40	3	ceemdan	ceemdan	PROPN
ajst-10387	40	4	is	be	AUX
ajst-10387	40	5	based	base	VERB
ajst-10387	40	6	on	on	ADP
ajst-10387	40	7	emd	emd	PROPN
ajst-10387	40	8	decomposition	decomposition	NOUN
ajst-10387	40	9	.	.	PUNCT
ajst-10387	41	1	in	in	ADP
ajst-10387	41	2	the	the	DET
ajst-10387	41	3	process	process	NOUN
ajst-10387	41	4	of	of	ADP
ajst-10387	41	5	decomposing	decompose	VERB
ajst-10387	41	6	wind	wind	NOUN
ajst-10387	41	7	power	power	NOUN
ajst-10387	41	8	signals	signal	NOUN
ajst-10387	41	9	,	,	PUNCT
ajst-10387	41	10	it	it	PRON
ajst-10387	41	11	adaptively	adaptively	ADV
ajst-10387	41	12	adds	add	VERB
ajst-10387	41	13	white	white	ADJ
ajst-10387	41	14	noise	noise	NOUN
ajst-10387	41	15	to	to	PART
ajst-10387	41	16	weaken	weaken	VERB
ajst-10387	41	17	the	the	DET
ajst-10387	41	18	mode	mode	NOUN
ajst-10387	41	19	aliasing	aliasing	NOUN
ajst-10387	41	20	problem	problem	NOUN
ajst-10387	41	21	,	,	PUNCT
ajst-10387	41	22	and	and	CCONJ
ajst-10387	41	23	the	the	DET
ajst-10387	41	24	decomposition	decomposition	NOUN
ajst-10387	41	25	process	process	NOUN
ajst-10387	41	26	is	be	AUX
ajst-10387	41	27	characterized	characterize	VERB
ajst-10387	41	28	by	by	ADP
ajst-10387	41	29	integrity	integrity	NOUN
ajst-10387	41	30	and	and	CCONJ
ajst-10387	41	31	almost	almost	ADV
ajst-10387	41	32	no	no	PRON
ajst-10387	41	33	reconstruction	reconstruction	NOUN
ajst-10387	41	34	error	error	NOUN
ajst-10387	41	35	[	[	X
ajst-10387	41	36	12	12	NUM
ajst-10387	41	37	]	]	PUNCT
ajst-10387	41	38	.	.	PUNCT
ajst-10387	42	1	the	the	DET
ajst-10387	42	2	ceemdan	ceemdan	PROPN
ajst-10387	42	3	decomposition	decomposition	NOUN
ajst-10387	42	4	algorithm	algorithm	NOUN
ajst-10387	42	5	is	be	AUX
ajst-10387	42	6	implemented	implement	VERB
ajst-10387	42	7	as	as	SCONJ
ajst-10387	42	8	follows	follow	VERB
ajst-10387	42	9	:	:	PUNCT
ajst-10387	42	10	1	1	NUM
ajst-10387	42	11	)	)	PUNCT
ajst-10387	42	12	ceemdan	ceemdan	ADJ
ajst-10387	42	13	decomposition	decomposition	NOUN
ajst-10387	42	14	algorithm	algorithm	NOUN
ajst-10387	42	15	makes	make	VERB
ajst-10387	42	16	use	use	NOUN
ajst-10387	42	17	of	of	ADP
ajst-10387	42	18	emd	emd	PROPN
ajst-10387	42	19	for	for	ADP
ajst-10387	42	20	signal	signal	ADJ
ajst-10387	42	21	x(t)+εi	x(t)+εi	NOUN
ajst-10387	42	22	0n(t)is	0n(t)is	PROPN
ajst-10387	42	23	decomposed	decompose	VERB
ajst-10387	42	24	repeatedly	repeatedly	ADV
ajst-10387	42	25	for	for	ADP
ajst-10387	42	26	n	n	DET
ajst-10387	42	27	times	time	NOUN
ajst-10387	42	28	,	,	PUNCT
ajst-10387	42	29	and	and	CCONJ
ajst-10387	42	30	the	the	DET
ajst-10387	42	31	first	first	ADJ
ajst-10387	42	32	modal	modal	ADJ
ajst-10387	42	33	component	component	NOUN
ajst-10387	42	34	is	be	AUX
ajst-10387	42	35	obtained	obtain	VERB
ajst-10387	42	36	through	through	ADP
ajst-10387	42	37	mean	mean	ADJ
ajst-10387	42	38	value	value	NOUN
ajst-10387	42	39	calculation	calculation	NOUN
ajst-10387	42	40	:	:	PUNCT
ajst-10387	42	41	1	1	NUM
ajst-10387	42	42	1	1	NUM
ajst-10387	42	43	1	1	NUM
ajst-10387	42	44	1	1	NUM
ajst-10387	42	45	(	(	PUNCT
ajst-10387	42	46	t	t	NOUN
ajst-10387	42	47	)	)	PUNCT
ajst-10387	42	48	(	(	PUNCT
ajst-10387	42	49	)	)	PUNCT
ajst-10387	43	1	n	n	CCONJ
ajst-10387	43	2	i	i	PRON
ajst-10387	44	1	i	i	PRON
ajst-10387	44	2	imf	imf	PROPN
ajst-10387	44	3	imf	imf	PROPN
ajst-10387	44	4	t	t	PROPN
ajst-10387	45	1	n	n	PROPN
ajst-10387	45	2			NUM
ajst-10387	46	1			NOUN
ajst-10387	46	2			X
ajst-10387	46	3	(	(	PUNCT
ajst-10387	46	4	4	4	NUM
ajst-10387	46	5	)	)	PUNCT
ajst-10387	46	6	2	2	NUM
ajst-10387	46	7	)	)	PUNCT
ajst-10387	46	8	calculate	calculate	VERB
ajst-10387	46	9	the	the	DET
ajst-10387	46	10	first	first	ADJ
ajst-10387	46	11	residual	residual	ADJ
ajst-10387	46	12	signal	signal	NOUN
ajst-10387	46	13	r1(t	r1(t	PROPN
ajst-10387	46	14	)	)	PUNCT
ajst-10387	46	15	decomposed	decompose	VERB
ajst-10387	46	16	by	by	ADP
ajst-10387	46	17	ceemdan	ceemdan	PROPN
ajst-10387	46	18	as	as	SCONJ
ajst-10387	46	19	:	:	PUNCT
ajst-10387	46	20	1	1	NUM
ajst-10387	46	21	1	1	NUM
ajst-10387	46	22	(	(	PUNCT
ajst-10387	46	23	)	)	PUNCT
ajst-10387	46	24	(	(	PUNCT
ajst-10387	46	25	)	)	PUNCT
ajst-10387	46	26	(	(	PUNCT
ajst-10387	46	27	)	)	PUNCT
ajst-10387	46	28	r	r	NOUN
ajst-10387	46	29	t	t	NOUN
ajst-10387	46	30	x	x	SYM
ajst-10387	46	31	t	t	PROPN
ajst-10387	46	32	imf	imf	PROPN
ajst-10387	46	33	t	t	PROPN
ajst-10387	46	34			PROPN
ajst-10387	46	35	(	(	PUNCT
ajst-10387	46	36	5	5	NUM
ajst-10387	46	37	)	)	PUNCT
ajst-10387	46	38	3	3	NUM
ajst-10387	46	39	)	)	PUNCT
ajst-10387	46	40	the	the	DET
ajst-10387	46	41	signal	signal	NOUN
ajst-10387	46	42	1	1	NUM
ajst-10387	46	43	1	1	NUM
ajst-10387	46	44	1	1	NUM
ajst-10387	46	45	(	(	PUNCT
ajst-10387	46	46	)	)	PUNCT
ajst-10387	46	47	(	(	PUNCT
ajst-10387	46	48	(	(	PUNCT
ajst-10387	46	49	)	)	PUNCT
ajst-10387	46	50	)	)	PUNCT
ajst-10387	46	51	ir	ir	PROPN
ajst-10387	46	52	t	t	PROPN
ajst-10387	46	53	e	e	NOUN
ajst-10387	46	54	n	n	DET
ajst-10387	46	55	t	t	NOUN
ajst-10387	46	56	is	be	AUX
ajst-10387	46	57	decomposed	decompose	VERB
ajst-10387	46	58	repeatedly	repeatedly	ADV
ajst-10387	46	59	for	for	ADP
ajst-10387	46	60	n	n	PROPN
ajst-10387	46	61	times	time	NOUN
ajst-10387	46	62	to	to	PART
ajst-10387	46	63	obtain	obtain	VERB
ajst-10387	46	64	the	the	DET
ajst-10387	46	65	second	second	ADJ
ajst-10387	46	66	modal	modal	ADJ
ajst-10387	46	67	component	component	NOUN
ajst-10387	46	68	:	:	PUNCT
ajst-10387	46	69	2	2	NUM
ajst-10387	46	70	1	1	NUM
ajst-10387	46	71	1	1	NUM
ajst-10387	46	72	1	1	NUM
ajst-10387	46	73	1	1	NUM
ajst-10387	46	74	1	1	NUM
ajst-10387	46	75	1	1	NUM
ajst-10387	46	76	(	(	PUNCT
ajst-10387	46	77	)	)	PUNCT
ajst-10387	46	78	(	(	PUNCT
ajst-10387	46	79	(	(	PUNCT
ajst-10387	46	80	)	)	PUNCT
ajst-10387	46	81	(	(	PUNCT
ajst-10387	46	82	(	(	PUNCT
ajst-10387	46	83	)	)	PUNCT
ajst-10387	46	84	)	)	PUNCT
ajst-10387	46	85	)	)	PUNCT
ajst-10387	47	1	n	n	CCONJ
ajst-10387	48	1	i	i	PRON
ajst-10387	48	2	i	i	PRON
ajst-10387	48	3	imf	imf	INTJ
ajst-10387	48	4	t	t	NOUN
ajst-10387	48	5	e	e	NOUN
ajst-10387	48	6	r	r	NOUN
ajst-10387	48	7	t	t	PROPN
ajst-10387	48	8	e	e	NOUN
ajst-10387	48	9	n	n	ADP
ajst-10387	48	10	t	t	PROPN
ajst-10387	48	11	n	n	CCONJ
ajst-10387	48	12			PROPN
ajst-10387	49	1			PROPN
ajst-10387	49	2			PROPN
ajst-10387	49	3			NOUN
ajst-10387	49	4	(	(	PUNCT
ajst-10387	49	5	6	6	NUM
ajst-10387	49	6	)	)	PUNCT
ajst-10387	49	7	4	4	NUM
ajst-10387	49	8	)	)	PUNCT
ajst-10387	49	9	for	for	ADP
ajst-10387	49	10	k=2,	k=2,	PROPN
ajst-10387	49	11	...	...	PUNCT
ajst-10387	49	12	,k	,k	PUNCT
ajst-10387	49	13	,	,	PUNCT
ajst-10387	49	14	calculate	calculate	VERB
ajst-10387	49	15	the	the	DET
ajst-10387	49	16	kth	kth	PROPN
ajst-10387	49	17	residual	residual	ADJ
ajst-10387	49	18	signal	signal	NOUN
ajst-10387	49	19	:	:	PUNCT
ajst-10387	49	20	1	1	NUM
ajst-10387	49	21	(	(	PUNCT
ajst-10387	49	22	)	)	PUNCT
ajst-10387	49	23	k	k	PROPN
ajst-10387	50	1	k	k	PROPN
ajst-10387	50	2	kr	kr	PROPN
ajst-10387	50	3	r	r	PROPN
ajst-10387	51	1	i	i	PRON
ajst-10387	51	2	m	m	VERB
ajst-10387	51	3	f	f	VERB
ajst-10387	51	4	t	t	PROPN
ajst-10387	51	5			NOUN
ajst-10387	51	6	(	(	PUNCT
ajst-10387	51	7	7	7	NUM
ajst-10387	51	8	)	)	SYM
ajst-10387	51	9	5	5	NUM
ajst-10387	51	10	)	)	PUNCT
ajst-10387	51	11	repeat	repeat	VERB
ajst-10387	51	12	the	the	DET
ajst-10387	51	13	calculation	calculation	NOUN
ajst-10387	51	14	process	process	NOUN
ajst-10387	51	15	in	in	ADP
ajst-10387	51	16	step	step	NOUN
ajst-10387	51	17	3	3	NUM
ajst-10387	51	18	)	)	PUNCT
ajst-10387	51	19	to	to	PART
ajst-10387	51	20	obtain	obtain	VERB
ajst-10387	51	21	the	the	DET
ajst-10387	51	22	k+1	k+1	X
ajst-10387	51	23	modal	modal	ADJ
ajst-10387	51	24	function	function	NOUN
ajst-10387	51	25	:	:	PUNCT
ajst-10387	51	26	1	1	NUM
ajst-10387	51	27	1	1	NUM
ajst-10387	51	28	1	1	NUM
ajst-10387	51	29	1	1	NUM
ajst-10387	51	30	(	(	PUNCT
ajst-10387	51	31	)	)	PUNCT
ajst-10387	51	32	(	(	PUNCT
ajst-10387	51	33	(	(	PUNCT
ajst-10387	51	34	)	)	PUNCT
ajst-10387	51	35	(	(	PUNCT
ajst-10387	51	36	(	(	PUNCT
ajst-10387	51	37	)	)	PUNCT
ajst-10387	51	38	)	)	PUNCT
ajst-10387	51	39	)	)	PUNCT
ajst-10387	52	1	n	n	CCONJ
ajst-10387	53	1	i	i	PRON
ajst-10387	53	2	k	k	PROPN
ajst-10387	54	1	k	k	PROPN
ajst-10387	54	2	k	k	PROPN
ajst-10387	55	1	k	k	PROPN
ajst-10387	55	2	i	i	PRON
ajst-10387	55	3	imf	imf	INTJ
ajst-10387	55	4	t	t	X
ajst-10387	55	5	e	e	NOUN
ajst-10387	55	6	r	r	NOUN
ajst-10387	55	7	t	t	PROPN
ajst-10387	55	8	e	e	NOUN
ajst-10387	55	9	n	n	ADP
ajst-10387	55	10	t	t	PROPN
ajst-10387	56	1	n	n	CCONJ
ajst-10387	56	2			PROPN
ajst-10387	56	3			PROPN
ajst-10387	56	4			PROPN
ajst-10387	56	5			NOUN
ajst-10387	56	6	(	(	PUNCT
ajst-10387	56	7	8)	8)	NUM
ajst-10387	56	8	6	6	NUM
ajst-10387	56	9	)	)	PUNCT
ajst-10387	56	10	repeat	repeat	NOUN
ajst-10387	56	11	steps	step	NOUN
ajst-10387	56	12	4	4	NUM
ajst-10387	56	13	)	)	PUNCT
ajst-10387	56	14	and	and	CCONJ
ajst-10387	56	15	5	5	NUM
ajst-10387	56	16	)	)	PUNCT
ajst-10387	56	17	until	until	SCONJ
ajst-10387	56	18	the	the	DET
ajst-10387	56	19	residual	residual	ADJ
ajst-10387	56	20	signal	signal	NOUN
ajst-10387	56	21	meets	meet	VERB
ajst-10387	56	22	the	the	DET
ajst-10387	56	23	termination	termination	NOUN
ajst-10387	56	24	condition	condition	NOUN
ajst-10387	56	25	of	of	ADP
ajst-10387	56	26	decomposition	decomposition	NOUN
ajst-10387	56	27	,	,	PUNCT
ajst-10387	56	28	and	and	CCONJ
ajst-10387	56	29	finally	finally	ADV
ajst-10387	56	30	obtain	obtain	VERB
ajst-10387	56	31	k	k	PROPN
ajst-10387	56	32	modal	modal	ADJ
ajst-10387	56	33	components	component	NOUN
ajst-10387	56	34	.	.	PUNCT
ajst-10387	57	1	the	the	DET
ajst-10387	57	2	final	final	ADJ
ajst-10387	57	3	residual	residual	ADJ
ajst-10387	57	4	signal	signal	NOUN
ajst-10387	57	5	decomposed	decomposed	NOUN
ajst-10387	57	6	is	be	AUX
ajst-10387	57	7	:	:	PUNCT
ajst-10387	57	8	1	1	NUM
ajst-10387	57	9	(	(	PUNCT
ajst-10387	57	10	)	)	PUNCT
ajst-10387	57	11	(	(	PUNCT
ajst-10387	57	12	)	)	PUNCT
ajst-10387	57	13	(	(	PUNCT
ajst-10387	57	14	)	)	PUNCT
ajst-10387	58	1	k	k	PROPN
ajst-10387	58	2	k	k	X
ajst-10387	58	3	k	k	PROPN
ajst-10387	58	4	r	r	NOUN
ajst-10387	58	5	t	t	PROPN
ajst-10387	58	6	x	x	SYM
ajst-10387	58	7	t	t	PROPN
ajst-10387	58	8	imf	imf	PROPN
ajst-10387	58	9	t	t	PROPN
ajst-10387	58	10			PROPN
ajst-10387	58	11			NUM
ajst-10387	59	1			X
ajst-10387	59	2	(	(	PUNCT
ajst-10387	59	3	9	9	NUM
ajst-10387	59	4	)	)	PUNCT
ajst-10387	59	5	the	the	DET
ajst-10387	59	6	original	original	ADJ
ajst-10387	59	7	signal	signal	NOUN
ajst-10387	59	8	can	can	AUX
ajst-10387	59	9	be	be	AUX
ajst-10387	59	10	decomposed	decompose	VERB
ajst-10387	59	11	into	into	ADP
ajst-10387	59	12	:	:	SYM
ajst-10387	59	13	1	1	NUM
ajst-10387	59	14	(	(	PUNCT
ajst-10387	59	15	)	)	PUNCT
ajst-10387	59	16	(	(	PUNCT
ajst-10387	59	17	)	)	PUNCT
ajst-10387	59	18	(	(	PUNCT
ajst-10387	59	19	)	)	PUNCT
ajst-10387	60	1	k	k	PROPN
ajst-10387	61	1	k	k	PROPN
ajst-10387	61	2	k	k	PROPN
ajst-10387	61	3	x	x	PUNCT
ajst-10387	61	4	t	t	NOUN
ajst-10387	61	5	r	r	NOUN
ajst-10387	61	6	t	t	PROPN
ajst-10387	61	7	imf	imf	PROPN
ajst-10387	61	8	t	t	PROPN
ajst-10387	61	9			PROPN
ajst-10387	61	10			PROPN
ajst-10387	61	11			NOUN
ajst-10387	61	12	(	(	PUNCT
ajst-10387	61	13	10	10	NUM
ajst-10387	61	14	)	)	PUNCT
ajst-10387	61	15	3.2	3.2	NUM
ajst-10387	61	16	.	.	PUNCT
ajst-10387	62	1	lstm	lstm	PROPN
ajst-10387	62	2	network	network	PROPN
ajst-10387	62	3	model	model	NOUN
ajst-10387	62	4	lstm	lstm	PROPN
ajst-10387	62	5	is	be	AUX
ajst-10387	62	6	a	a	DET
ajst-10387	62	7	variant	variant	NOUN
ajst-10387	62	8	of	of	ADP
ajst-10387	62	9	the	the	DET
ajst-10387	62	10	cyclic	cyclic	ADJ
ajst-10387	62	11	neural	neural	ADJ
ajst-10387	62	12	network	network	NOUN
ajst-10387	62	13	,	,	PUNCT
ajst-10387	62	14	which	which	PRON
ajst-10387	62	15	can	can	AUX
ajst-10387	62	16	effectively	effectively	ADV
ajst-10387	62	17	capture	capture	VERB
ajst-10387	62	18	the	the	DET
ajst-10387	62	19	dependencies	dependency	NOUN
ajst-10387	62	20	in	in	ADP
ajst-10387	62	21	the	the	DET
ajst-10387	62	22	time	time	NOUN
ajst-10387	62	23	series	series	NOUN
ajst-10387	62	24	,	,	PUNCT
ajst-10387	62	25	and	and	CCONJ
ajst-10387	62	26	is	be	AUX
ajst-10387	62	27	suitable	suitable	ADJ
ajst-10387	62	28	for	for	ADP
ajst-10387	62	29	modeling	model	VERB
ajst-10387	62	30	the	the	DET
ajst-10387	62	31	wind	wind	NOUN
ajst-10387	62	32	power	power	NOUN
ajst-10387	62	33	time	time	PROPN
ajst-10387	62	34	series	series	PROPN
ajst-10387	62	35	data[13	data[13	PROPN
ajst-10387	62	36	]	]	X
ajst-10387	62	37	.	.	PUNCT
ajst-10387	63	1	its	its	PRON
ajst-10387	63	2	basic	basic	ADJ
ajst-10387	63	3	unit	unit	NOUN
ajst-10387	63	4	structure	structure	NOUN
ajst-10387	63	5	is	be	AUX
ajst-10387	63	6	shown	show	VERB
ajst-10387	63	7	in	in	ADP
ajst-10387	63	8	figure	figure	NOUN
ajst-10387	63	9	1	1	NUM
ajst-10387	63	10	.	.	PUNCT
ajst-10387	63	11	figure	figure	NOUN
ajst-10387	63	12	1	1	NUM
ajst-10387	63	13	.	.	PUNCT
ajst-10387	64	1	lstm	lstm	PROPN
ajst-10387	64	2	basic	basic	ADJ
ajst-10387	64	3	unit	unit	NOUN
ajst-10387	64	4	structure	structure	NOUN
ajst-10387	64	5	diagram	diagram	NOUN
ajst-10387	64	6	as	as	SCONJ
ajst-10387	64	7	shown	show	VERB
ajst-10387	64	8	in	in	ADP
ajst-10387	64	9	the	the	DET
ajst-10387	64	10	figure	figure	NOUN
ajst-10387	64	11	above	above	ADV
ajst-10387	64	12	,	,	PUNCT
ajst-10387	64	13	the	the	DET
ajst-10387	64	14	calculation	calculation	NOUN
ajst-10387	64	15	formula	formula	NOUN
ajst-10387	64	16	is	be	AUX
ajst-10387	64	17	as	as	SCONJ
ajst-10387	64	18	follows	follow	VERB
ajst-10387	64	19	:	:	PUNCT
ajst-10387	64	20	1	1	NUM
ajst-10387	64	21	(	(	PUNCT
ajst-10387	64	22	)	)	PUNCT
ajst-10387	64	23	t	t	PROPN
ajst-10387	64	24	fx	fx	PROPN
ajst-10387	64	25	t	t	PROPN
ajst-10387	64	26	fh	fh	PROPN
ajst-10387	64	27	t	t	PROPN
ajst-10387	64	28	ff	ff	NOUN
ajst-10387	64	29	w	w	PROPN
ajst-10387	64	30	x	x	PROPN
ajst-10387	64	31	w	w	PROPN
ajst-10387	64	32	h	h	PROPN
ajst-10387	64	33	b	b	PROPN
ajst-10387	64	34			PROPN
ajst-10387	64	35			PUNCT
ajst-10387	64	36			X
ajst-10387	64	37	(	(	PUNCT
ajst-10387	64	38	11	11	NUM
ajst-10387	64	39	)	)	SYM
ajst-10387	64	40	1	1	NUM
ajst-10387	64	41	(	(	PUNCT
ajst-10387	64	42	)	)	PUNCT
ajst-10387	64	43	t	t	PROPN
ajst-10387	64	44	ix	ix	ADP
ajst-10387	64	45	t	t	PROPN
ajst-10387	64	46	ih	ih	PROPN
ajst-10387	65	1	t	t	PROPN
ajst-10387	65	2	ii	ii	PROPN
ajst-10387	65	3	w	w	PROPN
ajst-10387	65	4	x	x	PROPN
ajst-10387	65	5	w	w	PROPN
ajst-10387	65	6	h	h	PROPN
ajst-10387	65	7	b	b	PROPN
ajst-10387	65	8			PROPN
ajst-10387	65	9			PUNCT
ajst-10387	65	10			X
ajst-10387	65	11	(	(	PUNCT
ajst-10387	65	12	12	12	NUM
ajst-10387	65	13	)	)	PUNCT
ajst-10387	65	14	1	1	NUM
ajst-10387	65	15	(	(	PUNCT
ajst-10387	65	16	)	)	PUNCT
ajst-10387	65	17	t	t	PROPN
ajst-10387	65	18	gx	gx	PROPN
ajst-10387	65	19	t	t	PROPN
ajst-10387	65	20	gh	gh	PROPN
ajst-10387	65	21	t	t	PROPN
ajst-10387	65	22	gg	gg	PROPN
ajst-10387	65	23	w	w	PROPN
ajst-10387	65	24	x	x	PROPN
ajst-10387	65	25	w	w	PROPN
ajst-10387	65	26	h	h	NOUN
ajst-10387	65	27	b	b	PUNCT
ajst-10387	65	28			ADV
ajst-10387	65	29			X
ajst-10387	65	30	(	(	PUNCT
ajst-10387	65	31	13	13	NUM
ajst-10387	65	32	)	)	SYM
ajst-10387	65	33	1	1	NUM
ajst-10387	65	34	(	(	PUNCT
ajst-10387	65	35	)	)	PUNCT
ajst-10387	65	36	t	t	PROPN
ajst-10387	65	37	ox	ox	NOUN
ajst-10387	65	38	t	t	PROPN
ajst-10387	66	1	oh	oh	INTJ
ajst-10387	66	2	t	t	INTJ
ajst-10387	66	3	oo	oo	INTJ
ajst-10387	66	4	w	w	PROPN
ajst-10387	66	5	x	x	X
ajst-10387	66	6	w	w	PROPN
ajst-10387	66	7	h	h	PROPN
ajst-10387	66	8	b	b	PROPN
ajst-10387	66	9			PROPN
ajst-10387	66	10			PUNCT
ajst-10387	66	11			X
ajst-10387	66	12	(	(	PUNCT
ajst-10387	66	13	14	14	NUM
ajst-10387	66	14	)	)	PUNCT
ajst-10387	66	15	1	1	NUM
ajst-10387	66	16	t	t	NOUN
ajst-10387	66	17	t	t	NOUN
ajst-10387	66	18	t	t	PROPN
ajst-10387	66	19	t	t	NOUN
ajst-10387	66	20	ts	ts	ADP
ajst-10387	66	21	g	g	PROPN
ajst-10387	67	1	i	i	PRON
ajst-10387	67	2	s	s	PROPN
ajst-10387	67	3	f	f	PROPN
ajst-10387	67	4			PROPN
ajst-10387	67	5	�	�	PROPN
ajst-10387	67	6	�	�	PROPN
ajst-10387	67	7	(	(	PUNCT
ajst-10387	67	8	15	15	NUM
ajst-10387	67	9	)	)	PUNCT
ajst-10387	67	10	(	(	PUNCT
ajst-10387	67	11	)	)	PUNCT
ajst-10387	67	12	t	t	NOUN
ajst-10387	67	13	t	t	PROPN
ajst-10387	67	14	th	th	X
ajst-10387	67	15	s	s	VERB
ajst-10387	67	16	o	o	PUNCT
ajst-10387	67	17			PROPN
ajst-10387	67	18	�	�	PROPN
ajst-10387	67	19	(	(	PUNCT
ajst-10387	67	20	16	16	NUM
ajst-10387	67	21	)	)	PUNCT
ajst-10387	67	22	in	in	ADP
ajst-10387	67	23	the	the	DET
ajst-10387	67	24	formula	formula	NOUN
ajst-10387	67	25	:	:	PUNCT
ajst-10387	67	26	ft、it、gt、ot、st、ht	ft、it、gt、ot、st、ht	ADV
ajst-10387	67	27	are	be	AUX
ajst-10387	67	28	the	the	DET
ajst-10387	67	29	states	state	NOUN
ajst-10387	67	30	of	of	ADP
ajst-10387	67	31	forgetting	forget	VERB
ajst-10387	67	32	gate	gate	NOUN
ajst-10387	67	33	,	,	PUNCT
ajst-10387	67	34	input	input	NOUN
ajst-10387	67	35	gate	gate	NOUN
ajst-10387	67	36	,	,	PUNCT
ajst-10387	67	37	input	input	NOUN
ajst-10387	67	38	node	node	NOUN
ajst-10387	67	39	,	,	PUNCT
ajst-10387	67	40	output	output	NOUN
ajst-10387	67	41	gate	gate	NOUN
ajst-10387	67	42	,	,	PUNCT
ajst-10387	67	43	intermediate	intermediate	ADJ
ajst-10387	67	44	output	output	NOUN
ajst-10387	67	45	and	and	CCONJ
ajst-10387	67	46	state	state	NOUN
ajst-10387	67	47	unit	unit	NOUN
ajst-10387	67	48	respectively	respectively	ADV
ajst-10387	67	49	;	;	PUNCT
ajst-10387	67	50	s	s	X
ajst-10387	67	51	is	be	AUX
ajst-10387	67	52	the	the	DET
ajst-10387	67	53	activation	activation	NOUN
ajst-10387	67	54	function	function	NOUN
ajst-10387	67	55	sigmoid	sigmoid	NOUN
ajst-10387	67	56	;	;	PUNCT
ajst-10387	67	57	wfx、wtx、wgx、wox	wfx、wtx、wgx、wox	NOUN
ajst-10387	67	58	are	be	AUX
ajst-10387	67	59	weight	weight	NOUN
ajst-10387	67	60	matrices	matrix	NOUN
ajst-10387	67	61	;	;	PUNCT
ajst-10387	67	62	bf、bi、bg	bf、bi、bg	PROPN
ajst-10387	67	63	、	、	PROPN
ajst-10387	67	64	bo	bo	PROPN
ajst-10387	67	65	offset	offset	VERB
ajst-10387	67	66	terms	term	NOUN
ajst-10387	67	67	;	;	PUNCT
ajst-10387	67	68	f	f	PROPN
ajst-10387	67	69	is	be	AUX
ajst-10387	67	70	the	the	DET
ajst-10387	67	71	change	change	NOUN
ajst-10387	67	72	of	of	ADP
ajst-10387	67	73	tanh	tanh	PROPN
ajst-10387	67	74	function	function	NOUN
ajst-10387	67	75	.	.	PUNCT
ajst-10387	68	1	4	4	X
ajst-10387	68	2	.	.	X
ajst-10387	68	3	wind	wind	NOUN
ajst-10387	68	4	power	power	NOUN
ajst-10387	68	5	prediction	prediction	NOUN
ajst-10387	68	6	model	model	NOUN
ajst-10387	68	7	4.1	4.1	NUM
ajst-10387	68	8	.	.	PUNCT
ajst-10387	68	9	forecast	forecast	NOUN
ajst-10387	68	10	process	process	NOUN
ajst-10387	68	11	the	the	DET
ajst-10387	68	12	flow	flow	NOUN
ajst-10387	68	13	of	of	ADP
ajst-10387	68	14	short	short	ADJ
ajst-10387	68	15	-	-	PUNCT
ajst-10387	68	16	term	term	NOUN
ajst-10387	68	17	wind	wind	NOUN
ajst-10387	68	18	power	power	NOUN
ajst-10387	68	19	prediction	prediction	NOUN
ajst-10387	68	20	method	method	NOUN
ajst-10387	68	21	proposed	propose	VERB
ajst-10387	68	22	in	in	ADP
ajst-10387	68	23	this	this	DET
ajst-10387	68	24	paper	paper	NOUN
ajst-10387	68	25	is	be	AUX
ajst-10387	68	26	as	as	SCONJ
ajst-10387	68	27	follows	follow	VERB
ajst-10387	68	28	:	:	PUNCT
ajst-10387	68	29	(	(	PUNCT
ajst-10387	68	30	1	1	X
ajst-10387	68	31	)	)	PUNCT
ajst-10387	68	32	mic	mic	ADJ
ajst-10387	68	33	method	method	NOUN
ajst-10387	68	34	was	be	AUX
ajst-10387	68	35	used	use	VERB
ajst-10387	68	36	to	to	PART
ajst-10387	68	37	select	select	VERB
ajst-10387	68	38	the	the	DET
ajst-10387	68	39	features	feature	NOUN
ajst-10387	68	40	of	of	ADP
ajst-10387	68	41	wind	wind	NOUN
ajst-10387	68	42	power	power	NOUN
ajst-10387	68	43	data	datum	NOUN
ajst-10387	68	44	,	,	PUNCT
ajst-10387	68	45	and	and	CCONJ
ajst-10387	68	46	the	the	DET
ajst-10387	68	47	features	feature	NOUN
ajst-10387	68	48	with	with	ADP
ajst-10387	68	49	strong	strong	ADJ
ajst-10387	68	50	correlation	correlation	NOUN
ajst-10387	68	51	with	with	ADP
ajst-10387	68	52	wind	wind	NOUN
ajst-10387	68	53	power	power	NOUN
ajst-10387	68	54	were	be	AUX
ajst-10387	68	55	selected	select	VERB
ajst-10387	68	56	to	to	PART
ajst-10387	68	57	accelerate	accelerate	VERB
ajst-10387	68	58	network	network	NOUN
ajst-10387	68	59	training	training	NOUN
ajst-10387	68	60	;	;	PUNCT
ajst-10387	68	61	(	(	PUNCT
ajst-10387	68	62	2	2	X
ajst-10387	68	63	)	)	PUNCT
ajst-10387	68	64	based	base	VERB
ajst-10387	68	65	on	on	ADP
ajst-10387	68	66	ceemdan	ceemdan	ADJ
ajst-10387	68	67	decomposition	decomposition	NOUN
ajst-10387	68	68	algorithm	algorithm	NOUN
ajst-10387	68	69	,	,	PUNCT
ajst-10387	68	70	wind	wind	NOUN
ajst-10387	68	71	power	power	NOUN
ajst-10387	68	72	time	time	PROPN
ajst-10387	68	73	series	series	PROPN
ajst-10387	68	74	data	data	PROPN
ajst-10387	68	75	was	be	AUX
ajst-10387	68	76	decomposed	decompose	VERB
ajst-10387	68	77	to	to	PART
ajst-10387	68	78	obtain	obtain	VERB
ajst-10387	68	79	several	several	ADJ
ajst-10387	68	80	stable	stable	ADJ
ajst-10387	68	81	sub	sub	NOUN
ajst-10387	68	82	sequences	sequence	NOUN
ajst-10387	68	83	;	;	PUNCT
ajst-10387	68	84	(	(	PUNCT
ajst-10387	68	85	3	3	X
ajst-10387	68	86	)	)	PUNCT
ajst-10387	68	87	lstm	lstm	NOUN
ajst-10387	68	88	network	network	NOUN
ajst-10387	68	89	prediction	prediction	NOUN
ajst-10387	68	90	model	model	NOUN
ajst-10387	68	91	is	be	AUX
ajst-10387	68	92	established	establish	VERB
ajst-10387	68	93	for	for	ADP
ajst-10387	68	94	each	each	DET
ajst-10387	68	95	subsequence	subsequence	NOUN
ajst-10387	68	96	.	.	PUNCT
ajst-10387	69	1	after	after	ADP
ajst-10387	69	2	training	training	NOUN
ajst-10387	69	3	,	,	PUNCT
ajst-10387	69	4	the	the	DET
ajst-10387	69	5	prediction	prediction	NOUN
ajst-10387	69	6	results	result	NOUN
ajst-10387	69	7	of	of	ADP
ajst-10387	69	8	each	each	DET
ajst-10387	69	9	79	79	NUM
ajst-10387	69	10	subsequence	subsequence	NOUN
ajst-10387	69	11	were	be	AUX
ajst-10387	69	12	obtained	obtain	VERB
ajst-10387	69	13	,	,	PUNCT
ajst-10387	69	14	and	and	CCONJ
ajst-10387	69	15	the	the	DET
ajst-10387	69	16	prediction	prediction	NOUN
ajst-10387	69	17	values	value	NOUN
ajst-10387	69	18	were	be	AUX
ajst-10387	69	19	obtained	obtain	VERB
ajst-10387	69	20	after	after	ADP
ajst-10387	69	21	superposition	superposition	NOUN
ajst-10387	69	22	.	.	PUNCT
ajst-10387	70	1	the	the	DET
ajst-10387	70	2	flow	flow	NOUN
ajst-10387	70	3	chart	chart	NOUN
ajst-10387	70	4	was	be	AUX
ajst-10387	70	5	shown	show	VERB
ajst-10387	70	6	in	in	ADP
ajst-10387	70	7	figure	figure	NOUN
ajst-10387	70	8	2	2	NUM
ajst-10387	70	9	:	:	PUNCT
ajst-10387	70	10	figure	figure	NOUN
ajst-10387	70	11	2	2	NUM
ajst-10387	70	12	.	.	PUNCT
ajst-10387	70	13	algorithm	algorithm	NOUN
ajst-10387	70	14	flow	flow	NOUN
ajst-10387	70	15	chart	chart	NOUN
ajst-10387	70	16	4.2	4.2	NUM
ajst-10387	70	17	.	.	PUNCT
ajst-10387	71	1	evaluation	evaluation	NOUN
ajst-10387	71	2	index	index	NOUN
ajst-10387	71	3	in	in	ADP
ajst-10387	71	4	this	this	DET
ajst-10387	71	5	paper	paper	NOUN
ajst-10387	71	6	,	,	PUNCT
ajst-10387	71	7	three	three	NUM
ajst-10387	71	8	evaluation	evaluation	NOUN
ajst-10387	71	9	indicators	indicator	NOUN
ajst-10387	71	10	,	,	PUNCT
ajst-10387	71	11	mean	mean	VERB
ajst-10387	71	12	absolute	absolute	ADJ
ajst-10387	71	13	error	error	NOUN
ajst-10387	71	14	(	(	PUNCT
ajst-10387	71	15	mae	mae	PROPN
ajst-10387	71	16	)	)	PUNCT
ajst-10387	71	17	,	,	PUNCT
ajst-10387	71	18	mean	mean	VERB
ajst-10387	71	19	relative	relative	ADJ
ajst-10387	71	20	error	error	NOUN
ajst-10387	71	21	(	(	PUNCT
ajst-10387	71	22	mre	mre	NOUN
ajst-10387	71	23	)	)	PUNCT
ajst-10387	71	24	and	and	CCONJ
ajst-10387	71	25	root	root	NOUN
ajst-10387	71	26	mean	mean	ADJ
ajst-10387	71	27	square	square	ADJ
ajst-10387	71	28	error	error	NOUN
ajst-10387	71	29	(	(	PUNCT
ajst-10387	71	30	rmse	rmse	NOUN
ajst-10387	71	31	)	)	PUNCT
ajst-10387	71	32	,	,	PUNCT
ajst-10387	71	33	are	be	AUX
ajst-10387	71	34	used	use	VERB
ajst-10387	71	35	to	to	PART
ajst-10387	71	36	analyze	analyze	VERB
ajst-10387	71	37	the	the	DET
ajst-10387	71	38	wind	wind	NOUN
ajst-10387	71	39	power	power	NOUN
ajst-10387	71	40	prediction	prediction	NOUN
ajst-10387	71	41	results	result	NOUN
ajst-10387	71	42	.	.	PUNCT
ajst-10387	72	1	the	the	DET
ajst-10387	72	2	evaluation	evaluation	NOUN
ajst-10387	72	3	indicators	indicator	NOUN
ajst-10387	72	4	are	be	AUX
ajst-10387	72	5	as	as	SCONJ
ajst-10387	72	6	follows	follow	VERB
ajst-10387	72	7	:	:	PUNCT
ajst-10387	72	8	1	1	NUM
ajst-10387	72	9	ˆn	ˆn	ADP
ajst-10387	72	10	t	t	PROPN
ajst-10387	72	11	t	t	PROPN
ajst-10387	72	12	t	t	PROPN
ajst-10387	73	1	n	n	PROPN
ajst-10387	73	2	p	p	PROPN
ajst-10387	73	3	p	p	PROPN
ajst-10387	73	4	mae	mae	PROPN
ajst-10387	73	5	np	np	PROPN
ajst-10387	73	6			PROPN
ajst-10387	73	7			PRON
ajst-10387	73	8			X
ajst-10387	73	9	(	(	PUNCT
ajst-10387	73	10	17	17	NUM
ajst-10387	73	11	)	)	SYM
ajst-10387	73	12	1	1	NUM
ajst-10387	73	13	ˆn	ˆn	ADP
ajst-10387	73	14	t	t	PROPN
ajst-10387	73	15	t	t	PROPN
ajst-10387	73	16	t	t	PROPN
ajst-10387	73	17	t	t	PROPN
ajst-10387	74	1	p	p	X
ajst-10387	74	2	p	p	PROPN
ajst-10387	74	3	mre	mre	PROPN
ajst-10387	74	4	np	np	PROPN
ajst-10387	74	5			PROPN
ajst-10387	74	6			PRON
ajst-10387	74	7			X
ajst-10387	74	8	(	(	PUNCT
ajst-10387	74	9	18	18	NUM
ajst-10387	74	10	)	)	PUNCT
ajst-10387	74	11			NOUN
ajst-10387	74	12	2	2	NOUN
ajst-10387	74	13	1	1	NUM
ajst-10387	74	14	ˆ1	ˆ1	NOUN
ajst-10387	74	15	n	n	ADP
ajst-10387	74	16	t	t	NOUN
ajst-10387	74	17	t	t	X
ajst-10387	74	18	tn	tn	PROPN
ajst-10387	75	1	p	p	X
ajst-10387	75	2	p	p	PROPN
ajst-10387	75	3	rmse	rmse	NOUN
ajst-10387	76	1	p	p	X
ajst-10387	76	2	n	n	PROPN
ajst-10387	76	3			PROPN
ajst-10387	76	4			NOUN
ajst-10387	76	5			X
ajst-10387	76	6	(	(	PUNCT
ajst-10387	76	7	19	19	NUM
ajst-10387	76	8	)	)	PUNCT
ajst-10387	76	9	in	in	ADP
ajst-10387	76	10	the	the	DET
ajst-10387	76	11	formula	formula	NOUN
ajst-10387	76	12	:	:	PUNCT
ajst-10387	76	13	n	n	PRON
ajst-10387	76	14	is	be	AUX
ajst-10387	76	15	the	the	DET
ajst-10387	76	16	total	total	ADJ
ajst-10387	76	17	number	number	NOUN
ajst-10387	76	18	of	of	ADP
ajst-10387	76	19	forecasts	forecast	NOUN
ajst-10387	76	20	;	;	PUNCT
ajst-10387	76	21	pn	pn	PROPN
ajst-10387	76	22	is	be	AUX
ajst-10387	76	23	the	the	DET
ajst-10387	76	24	capacity	capacity	NOUN
ajst-10387	76	25	of	of	ADP
ajst-10387	76	26	wind	wind	NOUN
ajst-10387	76	27	turbine	turbine	NOUN
ajst-10387	76	28	;	;	PUNCT
ajst-10387	76	29	pt	pt	X
ajst-10387	76	30	is	be	AUX
ajst-10387	76	31	the	the	DET
ajst-10387	76	32	actual	actual	ADJ
ajst-10387	76	33	power	power	NOUN
ajst-10387	76	34	of	of	ADP
ajst-10387	76	35	wind	wind	NOUN
ajst-10387	76	36	turbine	turbine	NOUN
ajst-10387	76	37	;	;	PUNCT
ajst-10387	76	38	ˆ	ˆ	PRON
ajst-10387	76	39	tp	tp	NOUN
ajst-10387	76	40	is	be	AUX
ajst-10387	76	41	the	the	DET
ajst-10387	76	42	predictive	predictive	ADJ
ajst-10387	76	43	value	value	NOUN
ajst-10387	76	44	of	of	ADP
ajst-10387	76	45	wind	wind	NOUN
ajst-10387	76	46	turbine	turbine	NOUN
ajst-10387	76	47	power	power	NOUN
ajst-10387	76	48	.	.	PUNCT
ajst-10387	77	1	5	5	NUM
ajst-10387	77	2	.	.	NOUN
ajst-10387	77	3	example	example	NOUN
ajst-10387	77	4	analysis	analysis	NOUN
ajst-10387	77	5	5.1	5.1	NUM
ajst-10387	77	6	.	.	PUNCT
ajst-10387	78	1	data	datum	NOUN
ajst-10387	78	2	preparation	preparation	NOUN
ajst-10387	78	3	the	the	DET
ajst-10387	78	4	measured	measure	VERB
ajst-10387	78	5	data	datum	NOUN
ajst-10387	78	6	of	of	ADP
ajst-10387	78	7	a	a	DET
ajst-10387	78	8	domestic	domestic	ADJ
ajst-10387	78	9	wind	wind	NOUN
ajst-10387	78	10	farm	farm	NOUN
ajst-10387	78	11	was	be	AUX
ajst-10387	78	12	used	use	VERB
ajst-10387	78	13	for	for	ADP
ajst-10387	78	14	simulation	simulation	NOUN
ajst-10387	78	15	experiments	experiment	NOUN
ajst-10387	78	16	.	.	PUNCT
ajst-10387	79	1	two	two	NUM
ajst-10387	79	2	adjacent	adjacent	ADJ
ajst-10387	79	3	wind	wind	NOUN
ajst-10387	79	4	turbines	turbine	NOUN
ajst-10387	79	5	in	in	ADP
ajst-10387	79	6	the	the	DET
ajst-10387	79	7	wind	wind	NOUN
ajst-10387	79	8	farm	farm	NOUN
ajst-10387	79	9	are	be	AUX
ajst-10387	79	10	selected	select	VERB
ajst-10387	79	11	and	and	CCONJ
ajst-10387	79	12	divided	divide	VERB
ajst-10387	79	13	into	into	ADP
ajst-10387	79	14	wind	wind	NOUN
ajst-10387	79	15	turbine	turbine	NOUN
ajst-10387	79	16	a	a	DET
ajst-10387	79	17	and	and	CCONJ
ajst-10387	79	18	wind	wind	NOUN
ajst-10387	79	19	turbine	turbine	NOUN
ajst-10387	79	20	b.	b.	PROPN
ajst-10387	80	1	the	the	DET
ajst-10387	80	2	data	datum	NOUN
ajst-10387	80	3	collected	collect	VERB
ajst-10387	80	4	in	in	ADP
ajst-10387	80	5	the	the	DET
ajst-10387	80	6	wind	wind	NOUN
ajst-10387	80	7	farm	farm	NOUN
ajst-10387	80	8	include	include	VERB
ajst-10387	80	9	wind	wind	NOUN
ajst-10387	80	10	turbine	turbine	NOUN
ajst-10387	80	11	i	i	PROPN
ajst-10387	80	12	d	d	PROPN
ajst-10387	80	13	,	,	PUNCT
ajst-10387	80	14	wind	wind	NOUN
ajst-10387	80	15	speed	speed	NOUN
ajst-10387	80	16	,	,	PUNCT
ajst-10387	80	17	wind	wind	NOUN
ajst-10387	80	18	direction	direction	NOUN
ajst-10387	80	19	angle	angle	NOUN
ajst-10387	80	20	and	and	CCONJ
ajst-10387	80	21	ambient	ambient	ADJ
ajst-10387	80	22	temperature	temperature	NOUN
ajst-10387	80	23	,	,	PUNCT
ajst-10387	80	24	with	with	ADP
ajst-10387	80	25	a	a	DET
ajst-10387	80	26	sampling	sample	VERB
ajst-10387	80	27	period	period	NOUN
ajst-10387	80	28	of	of	ADP
ajst-10387	80	29	10	10	NUM
ajst-10387	80	30	minutes	minute	NOUN
ajst-10387	80	31	.	.	PUNCT
ajst-10387	81	1	the	the	DET
ajst-10387	81	2	15	15	NUM
ajst-10387	81	3	day	day	NOUN
ajst-10387	81	4	continuous	continuous	ADJ
ajst-10387	81	5	scada	scada	PROPN
ajst-10387	81	6	data	data	PROPN
ajst-10387	81	7	of	of	ADP
ajst-10387	81	8	wind	wind	NOUN
ajst-10387	81	9	turbine	turbine	NOUN
ajst-10387	81	10	in	in	ADP
ajst-10387	81	11	september	september	PROPN
ajst-10387	81	12	2015	2015	NUM
ajst-10387	81	13	was	be	AUX
ajst-10387	81	14	used	use	VERB
ajst-10387	81	15	to	to	PART
ajst-10387	81	16	predict	predict	VERB
ajst-10387	81	17	the	the	DET
ajst-10387	81	18	wind	wind	NOUN
ajst-10387	81	19	power	power	NOUN
ajst-10387	81	20	of	of	ADP
ajst-10387	81	21	wind	wind	NOUN
ajst-10387	81	22	turbine	turbine	NOUN
ajst-10387	81	23	a	a	PRON
ajst-10387	81	24	on	on	ADP
ajst-10387	81	25	the	the	DET
ajst-10387	81	26	15th	15th	ADJ
ajst-10387	81	27	day	day	NOUN
ajst-10387	81	28	.	.	PUNCT
ajst-10387	82	1	the	the	DET
ajst-10387	82	2	raw	raw	ADJ
ajst-10387	82	3	data	datum	NOUN
ajst-10387	82	4	of	of	ADP
ajst-10387	82	5	wind	wind	NOUN
ajst-10387	82	6	turbine	turbine	NOUN
ajst-10387	82	7	a	a	PRON
ajst-10387	82	8	was	be	AUX
ajst-10387	82	9	shown	show	VERB
ajst-10387	82	10	in	in	ADP
ajst-10387	82	11	figure	figure	NOUN
ajst-10387	82	12	3	3	NUM
ajst-10387	82	13	.	.	PUNCT
ajst-10387	82	14	figure	figure	NOUN
ajst-10387	82	15	3	3	NUM
ajst-10387	82	16	.	.	PUNCT
ajst-10387	82	17	raw	raw	ADJ
ajst-10387	82	18	data	datum	NOUN
ajst-10387	82	19	5.2	5.2	NUM
ajst-10387	82	20	.	.	PUNCT
ajst-10387	83	1	mic	mic	ADJ
ajst-10387	83	2	feature	feature	NOUN
ajst-10387	83	3	selection	selection	NOUN
ajst-10387	83	4	there	there	PRON
ajst-10387	83	5	are	be	VERB
ajst-10387	83	6	many	many	ADJ
ajst-10387	83	7	types	type	NOUN
ajst-10387	83	8	of	of	ADP
ajst-10387	83	9	parameters	parameter	NOUN
ajst-10387	83	10	of	of	ADP
ajst-10387	83	11	wind	wind	NOUN
ajst-10387	83	12	power	power	PROPN
ajst-10387	83	13	scada	scada	PROPN
ajst-10387	83	14	data	data	PROPN
ajst-10387	83	15	setted	sette	VERB
ajst-10387	83	16	recording	recording	NOUN
ajst-10387	83	17	units	unit	NOUN
ajst-10387	83	18	.	.	PUNCT
ajst-10387	84	1	if	if	SCONJ
ajst-10387	84	2	all	all	DET
ajst-10387	84	3	dimensions	dimension	NOUN
ajst-10387	84	4	of	of	ADP
ajst-10387	84	5	the	the	DET
ajst-10387	84	6	data	datum	NOUN
ajst-10387	84	7	setted	sette	VERB
ajst-10387	84	8	are	be	AUX
ajst-10387	84	9	input	input	NOUN
ajst-10387	84	10	into	into	ADP
ajst-10387	84	11	the	the	DET
ajst-10387	84	12	prediction	prediction	NOUN
ajst-10387	84	13	model	model	NOUN
ajst-10387	84	14	,	,	PUNCT
ajst-10387	84	15	a	a	DET
ajst-10387	84	16	large	large	ADJ
ajst-10387	84	17	number	number	NOUN
ajst-10387	84	18	of	of	ADP
ajst-10387	84	19	parameters	parameter	NOUN
ajst-10387	84	20	will	will	AUX
ajst-10387	84	21	be	be	AUX
ajst-10387	84	22	generated	generate	VERB
ajst-10387	84	23	in	in	ADP
ajst-10387	84	24	the	the	DET
ajst-10387	84	25	network	network	NOUN
ajst-10387	84	26	model	model	NOUN
ajst-10387	84	27	,	,	PUNCT
ajst-10387	84	28	and	and	CCONJ
ajst-10387	84	29	the	the	DET
ajst-10387	84	30	training	training	NOUN
ajst-10387	84	31	speed	speed	NOUN
ajst-10387	84	32	will	will	AUX
ajst-10387	84	33	be	be	AUX
ajst-10387	84	34	slow	slow	ADJ
ajst-10387	84	35	,	,	PUNCT
ajst-10387	84	36	affecting	affect	VERB
ajst-10387	84	37	the	the	DET
ajst-10387	84	38	prediction	prediction	NOUN
ajst-10387	84	39	accuracy	accuracy	NOUN
ajst-10387	84	40	.	.	PUNCT
ajst-10387	85	1	the	the	DET
ajst-10387	85	2	maximum	maximum	ADJ
ajst-10387	85	3	information	information	NOUN
ajst-10387	85	4	coefficient	coefficient	NOUN
ajst-10387	85	5	method	method	NOUN
ajst-10387	85	6	was	be	AUX
ajst-10387	85	7	used	use	VERB
ajst-10387	85	8	for	for	ADP
ajst-10387	85	9	feature	feature	NOUN
ajst-10387	85	10	selection	selection	NOUN
ajst-10387	85	11	of	of	ADP
ajst-10387	85	12	wind	wind	NOUN
ajst-10387	85	13	turbine	turbine	NOUN
ajst-10387	85	14	multivariable	multivariable	ADJ
ajst-10387	85	15	time	time	NOUN
ajst-10387	85	16	seriesto	seriesto	NOUN
ajst-10387	85	17	reduce	reduce	VERB
ajst-10387	85	18	the	the	DET
ajst-10387	85	19	data	datum	NOUN
ajst-10387	85	20	scale	scale	NOUN
ajst-10387	85	21	and	and	CCONJ
ajst-10387	85	22	complexity	complexity	NOUN
ajst-10387	85	23	.	.	PUNCT
ajst-10387	86	1	table	table	NOUN
ajst-10387	86	2	1	1	NUM
ajst-10387	86	3	.	.	PUNCT
ajst-10387	87	1	mic	mic	ADJ
ajst-10387	87	2	feature	feature	NOUN
ajst-10387	87	3	selection	selection	NOUN
ajst-10387	87	4	results	result	VERB
ajst-10387	87	5	influence	influence	NOUN
ajst-10387	87	6	factor	factor	NOUN
ajst-10387	87	7	ws	ws	NOUN
ajst-10387	87	8	gs	gs	INTJ
ajst-10387	88	1	rs	rs	INTJ
ajst-10387	88	2	wd	wd	PROPN
ajst-10387	88	3	gt	gt	PROPN
ajst-10387	88	4	t	t	PROPN
ajst-10387	88	5	mic	mic	ADJ
ajst-10387	88	6	coefficient	coefficient	NOUN
ajst-10387	88	7	0.982	0.982	NUM
ajst-10387	88	8	0.965	0.965	NUM
ajst-10387	88	9	0.971	0.971	NUM
ajst-10387	88	10	0.250	0.250	NUM
ajst-10387	88	11	0.810	0.810	NUM
ajst-10387	88	12	0.143	0.143	NUM
ajst-10387	88	13	in	in	ADP
ajst-10387	88	14	table	table	NOUN
ajst-10387	88	15	1	1	NUM
ajst-10387	88	16	,	,	PUNCT
ajst-10387	88	17	six	six	NUM
ajst-10387	88	18	variables	variable	NOUN
ajst-10387	88	19	with	with	ADP
ajst-10387	88	20	the	the	DET
ajst-10387	88	21	strongest	strong	ADJ
ajst-10387	88	22	correlation	correlation	NOUN
ajst-10387	88	23	with	with	ADP
ajst-10387	88	24	output	output	NOUN
ajst-10387	88	25	power	power	NOUN
ajst-10387	88	26	were	be	AUX
ajst-10387	88	27	selected	select	VERB
ajst-10387	88	28	by	by	ADP
ajst-10387	88	29	using	use	VERB
ajst-10387	88	30	the	the	DET
ajst-10387	88	31	maximum	maximum	ADJ
ajst-10387	88	32	information	information	NOUN
ajst-10387	88	33	coefficient	coefficient	NOUN
ajst-10387	88	34	method	method	NOUN
ajst-10387	88	35	for	for	ADP
ajst-10387	88	36	feature	feature	NOUN
ajst-10387	88	37	selection	selection	NOUN
ajst-10387	88	38	.	.	PUNCT
ajst-10387	89	1	ws	ws	PROPN
ajst-10387	89	2	represented	represent	VERB
ajst-10387	89	3	wind	wind	NOUN
ajst-10387	89	4	speed	speed	NOUN
ajst-10387	89	5	;	;	PUNCT
ajst-10387	89	6	gs	gs	PROPN
ajst-10387	89	7	represented	represent	VERB
ajst-10387	89	8	the	the	DET
ajst-10387	89	9	generator	generator	NOUN
ajst-10387	89	10	speed	speed	NOUN
ajst-10387	89	11	;	;	PUNCT
ajst-10387	89	12	rs	rs	ADV
ajst-10387	89	13	stands	stand	VERB
ajst-10387	89	14	for	for	ADP
ajst-10387	89	15	impeller	impeller	NOUN
ajst-10387	89	16	speed	speed	NOUN
ajst-10387	89	17	;	;	PUNCT
ajst-10387	89	18	wd	wd	PROPN
ajst-10387	89	19	represented	represent	VERB
ajst-10387	89	20	wind	wind	NOUN
ajst-10387	89	21	direction	direction	NOUN
ajst-10387	89	22	angle	angle	NOUN
ajst-10387	89	23	;	;	PUNCT
ajst-10387	89	24	gt	gt	PROPN
ajst-10387	89	25	standed	stand	VERB
ajst-10387	89	26	for	for	ADP
ajst-10387	89	27	gearbox	gearbox	NOUN
ajst-10387	89	28	temperature	temperature	NOUN
ajst-10387	89	29	;	;	PUNCT
ajst-10387	89	30	t	t	PROPN
ajst-10387	89	31	were	be	AUX
ajst-10387	89	32	the	the	DET
ajst-10387	89	33	ambient	ambient	ADJ
ajst-10387	89	34	temperature	temperature	NOUN
ajst-10387	89	35	.	.	PUNCT
ajst-10387	90	1	5.3	5.3	NUM
ajst-10387	90	2	.	.	PUNCT
ajst-10387	90	3	ceemdan	ceemdan	ADJ
ajst-10387	90	4	decomposition	decomposition	NOUN
ajst-10387	90	5	results	result	NOUN
ajst-10387	90	6	in	in	ADP
ajst-10387	90	7	this	this	DET
ajst-10387	90	8	paper	paper	NOUN
ajst-10387	90	9	,	,	PUNCT
ajst-10387	90	10	ceemdan	ceemdan	PROPN
ajst-10387	90	11	algorithm	algorithm	PROPN
ajst-10387	90	12	was	be	AUX
ajst-10387	90	13	used	use	VERB
ajst-10387	90	14	to	to	PART
ajst-10387	90	15	decompose	decompose	VERB
ajst-10387	90	16	the	the	DET
ajst-10387	90	17	wind	wind	NOUN
ajst-10387	90	18	power	power	NOUN
ajst-10387	90	19	time	time	PROPN
ajst-10387	90	20	series	series	PROPN
ajst-10387	90	21	data	data	PROPN
ajst-10387	90	22	.	.	PUNCT
ajst-10387	91	1	the	the	DET
ajst-10387	91	2	white	white	PROPN
ajst-10387	91	3	noise	noise	PROPN
ajst-10387	91	4	group	group	NOUN
ajst-10387	91	5	number	number	NOUN
ajst-10387	91	6	nr=100	nr=100	PROPN
ajst-10387	91	7	,	,	PUNCT
ajst-10387	91	8	the	the	DET
ajst-10387	91	9	noise	noise	NOUN
ajst-10387	91	10	standard	standard	ADJ
ajst-10387	91	11	deviation	deviation	NOUN
ajst-10387	91	12	nstd=0.05	nstd=0.05	NOUN
ajst-10387	91	13	,	,	PUNCT
ajst-10387	91	14	and	and	CCONJ
ajst-10387	91	15	the	the	DET
ajst-10387	91	16	maximum	maximum	ADJ
ajst-10387	91	17	iteration	iteration	NOUN
ajst-10387	91	18	number	number	NOUN
ajst-10387	91	19	maxiter=300	maxiter=300	NOUN
ajst-10387	91	20	were	be	AUX
ajst-10387	91	21	added	add	VERB
ajst-10387	91	22	.	.	PUNCT
ajst-10387	92	1	nine	nine	NUM
ajst-10387	92	2	subsequences	subsequence	NOUN
ajst-10387	92	3	were	be	AUX
ajst-10387	92	4	obtained	obtain	VERB
ajst-10387	92	5	from	from	ADP
ajst-10387	92	6	the	the	DET
ajst-10387	92	7	decomposition	decomposition	NOUN
ajst-10387	92	8	shown	show	VERB
ajst-10387	92	9	in	in	ADP
ajst-10387	92	10	figure	figure	NOUN
ajst-10387	92	11	4	4	NUM
ajst-10387	92	12	.	.	SYM
ajst-10387	92	13	80	80	NUM
ajst-10387	92	14	figure	figure	NOUN
ajst-10387	92	15	4	4	NUM
ajst-10387	92	16	.	.	PUNCT
ajst-10387	92	17	ceemdan	ceemdan	ADJ
ajst-10387	92	18	decomposition	decomposition	NOUN
ajst-10387	92	19	sequence	sequence	NOUN
ajst-10387	92	20	5.4	5.4	NUM
ajst-10387	92	21	.	.	PUNCT
ajst-10387	93	1	results	result	NOUN
ajst-10387	93	2	figure	figure	VERB
ajst-10387	93	3	5	5	NUM
ajst-10387	93	4	showed	show	VERB
ajst-10387	93	5	the	the	DET
ajst-10387	93	6	power	power	NOUN
ajst-10387	93	7	change	change	NOUN
ajst-10387	93	8	curve	curve	NOUN
ajst-10387	93	9	of	of	ADP
ajst-10387	93	10	wind	wind	NOUN
ajst-10387	93	11	turbine	turbine	NOUN
ajst-10387	93	12	a	a	DET
ajst-10387	93	13	predicted	predict	VERB
ajst-10387	93	14	by	by	ADP
ajst-10387	93	15	each	each	DET
ajst-10387	93	16	model	model	NOUN
ajst-10387	93	17	on	on	ADP
ajst-10387	93	18	september	september	PROPN
ajst-10387	93	19	15	15	NUM
ajst-10387	93	20	,	,	PUNCT
ajst-10387	93	21	with	with	ADP
ajst-10387	93	22	a	a	DET
ajst-10387	93	23	prediction	prediction	NOUN
ajst-10387	93	24	resolution	resolution	NOUN
ajst-10387	93	25	of	of	ADP
ajst-10387	93	26	10	10	NUM
ajst-10387	93	27	minutes	minute	NOUN
ajst-10387	93	28	and	and	CCONJ
ajst-10387	93	29	144	144	NUM
ajst-10387	93	30	data	datum	NOUN
ajst-10387	93	31	points	point	NOUN
ajst-10387	93	32	in	in	ADP
ajst-10387	93	33	total	total	NOUN
ajst-10387	93	34	.	.	PUNCT
ajst-10387	94	1	figure	figure	NOUN
ajst-10387	94	2	5	5	NUM
ajst-10387	94	3	.	.	PUNCT
ajst-10387	95	1	the	the	DET
ajst-10387	95	2	effect	effect	NOUN
ajst-10387	95	3	of	of	ADP
ajst-10387	95	4	each	each	DET
ajst-10387	95	5	prediction	prediction	NOUN
ajst-10387	95	6	model	model	NOUN
ajst-10387	95	7	the	the	DET
ajst-10387	95	8	mae	mae	PROPN
ajst-10387	95	9	,	,	PUNCT
ajst-10387	95	10	mre	mre	NOUN
ajst-10387	95	11	and	and	CCONJ
ajst-10387	95	12	rmse	rmse	ADJ
ajst-10387	95	13	indexes	index	NOUN
ajst-10387	95	14	of	of	ADP
ajst-10387	95	15	each	each	DET
ajst-10387	95	16	model	model	NOUN
ajst-10387	95	17	were	be	AUX
ajst-10387	95	18	calculated	calculate	VERB
ajst-10387	95	19	for	for	ADP
ajst-10387	95	20	evaluation	evaluation	NOUN
ajst-10387	95	21	and	and	CCONJ
ajst-10387	95	22	analysis	analysis	NOUN
ajst-10387	95	23	,	,	PUNCT
ajst-10387	95	24	as	as	SCONJ
ajst-10387	95	25	shown	show	VERB
ajst-10387	95	26	in	in	ADP
ajst-10387	95	27	table	table	NOUN
ajst-10387	95	28	3	3	NUM
ajst-10387	95	29	.	.	PUNCT
ajst-10387	95	30	table	table	NOUN
ajst-10387	95	31	2	2	NUM
ajst-10387	95	32	.	.	PUNCT
ajst-10387	95	33	evaluation	evaluation	NOUN
ajst-10387	95	34	index	index	NOUN
ajst-10387	95	35	of	of	ADP
ajst-10387	95	36	each	each	DET
ajst-10387	95	37	prediction	prediction	NOUN
ajst-10387	95	38	model	model	NOUN
ajst-10387	95	39	model	model	NOUN
ajst-10387	95	40	name	name	NOUN
ajst-10387	95	41	mae/%	mae/%	NOUN
ajst-10387	95	42	mre/%	mre/%	NOUN
ajst-10387	95	43	rmse/%	rmse/%	NOUN
ajst-10387	95	44	rnn	rnn	VERB
ajst-10387	95	45	7.94	7.94	NUM
ajst-10387	95	46	8.99	8.99	NUM
ajst-10387	95	47	9.96	9.96	NUM
ajst-10387	95	48	lstm	lstm	NOUN
ajst-10387	95	49	7.13	7.13	NUM
ajst-10387	95	50	8.63	8.63	NUM
ajst-10387	95	51	9.37	9.37	NUM
ajst-10387	95	52	emd	emd	PROPN
ajst-10387	95	53	-	-	PUNCT
ajst-10387	95	54	lstm	lstm	NOUN
ajst-10387	95	55	6.94	6.94	NUM
ajst-10387	95	56	7.44	7.44	NUM
ajst-10387	95	57	8.21	8.21	NUM
ajst-10387	95	58	ceemdan	ceemdan	ADJ
ajst-10387	95	59	-	-	PUNCT
ajst-10387	95	60	lstm	lstm	NOUN
ajst-10387	95	61	5.54	5.54	NUM
ajst-10387	95	62	5.88	5.88	NUM
ajst-10387	95	63	5.53	5.53	NUM
ajst-10387	95	64	after	after	ADP
ajst-10387	95	65	training	train	VERB
ajst-10387	95	66	the	the	DET
ajst-10387	95	67	model	model	NOUN
ajst-10387	95	68	with	with	ADP
ajst-10387	95	69	the	the	DET
ajst-10387	95	70	same	same	ADJ
ajst-10387	95	71	data	datum	NOUN
ajst-10387	95	72	,	,	PUNCT
ajst-10387	95	73	it	it	PRON
ajst-10387	95	74	can	can	AUX
ajst-10387	95	75	be	be	AUX
ajst-10387	95	76	seen	see	VERB
ajst-10387	95	77	from	from	ADP
ajst-10387	95	78	table	table	NOUN
ajst-10387	95	79	2	2	NUM
ajst-10387	95	80	that	that	PRON
ajst-10387	95	81	the	the	DET
ajst-10387	95	82	time	time	NOUN
ajst-10387	95	83	series	series	PROPN
ajst-10387	95	84	decomposition	decomposition	NOUN
ajst-10387	95	85	algorithm	algorithm	NOUN
ajst-10387	95	86	can	can	AUX
ajst-10387	95	87	effectively	effectively	ADV
ajst-10387	95	88	reduce	reduce	VERB
ajst-10387	95	89	the	the	DET
ajst-10387	95	90	impact	impact	NOUN
ajst-10387	95	91	of	of	ADP
ajst-10387	95	92	wind	wind	NOUN
ajst-10387	95	93	power	power	NOUN
ajst-10387	95	94	uncertainty	uncertainty	NOUN
ajst-10387	95	95	.	.	PUNCT
ajst-10387	96	1	after	after	ADP
ajst-10387	96	2	decomposition	decomposition	NOUN
ajst-10387	96	3	,	,	PUNCT
ajst-10387	96	4	the	the	DET
ajst-10387	96	5	prediction	prediction	NOUN
ajst-10387	96	6	accuracy	accuracy	NOUN
ajst-10387	96	7	becomes	become	VERB
ajst-10387	96	8	higher	high	ADJ
ajst-10387	96	9	.	.	PUNCT
ajst-10387	97	1	with	with	ADP
ajst-10387	97	2	lstm	lstm	NOUN
ajst-10387	97	3	as	as	ADP
ajst-10387	97	4	the	the	DET
ajst-10387	97	5	benchmark	benchmark	NOUN
ajst-10387	97	6	model	model	NOUN
ajst-10387	97	7	,	,	PUNCT
ajst-10387	97	8	ceemdan	ceemdan	PROPN
ajst-10387	97	9	has	have	VERB
ajst-10387	97	10	a	a	DET
ajst-10387	97	11	better	well	ADJ
ajst-10387	97	12	prediction	prediction	NOUN
ajst-10387	97	13	effect	effect	NOUN
ajst-10387	97	14	than	than	SCONJ
ajst-10387	97	15	emd	emd	PROPN
ajst-10387	97	16	.	.	PROPN
ajst-10387	97	17	mae	mae	PROPN
ajst-10387	97	18	,	,	PUNCT
ajst-10387	97	19	mre	mre	PROPN
ajst-10387	97	20	and	and	CCONJ
ajst-10387	97	21	rmse	rmse	PROPN
ajst-10387	97	22	have	have	AUX
ajst-10387	97	23	decreased	decrease	VERB
ajst-10387	97	24	by	by	ADP
ajst-10387	97	25	1.40	1.40	NUM
ajst-10387	97	26	%	%	NOUN
ajst-10387	97	27	,	,	PUNCT
ajst-10387	97	28	1.56	1.56	NUM
ajst-10387	97	29	%	%	NOUN
ajst-10387	97	30	and	and	CCONJ
ajst-10387	97	31	2.68	2.68	NUM
ajst-10387	97	32	%	%	NOUN
ajst-10387	97	33	respectively	respectively	ADV
ajst-10387	97	34	.	.	PUNCT
ajst-10387	98	1	the	the	DET
ajst-10387	98	2	evaluation	evaluation	NOUN
ajst-10387	98	3	index	index	NOUN
ajst-10387	98	4	showed	show	VERB
ajst-10387	98	5	that	that	SCONJ
ajst-10387	98	6	the	the	DET
ajst-10387	98	7	method	method	NOUN
ajst-10387	98	8	in	in	ADP
ajst-10387	98	9	this	this	DET
ajst-10387	98	10	paper	paper	NOUN
ajst-10387	98	11	has	have	VERB
ajst-10387	98	12	higher	high	ADJ
ajst-10387	98	13	prediction	prediction	NOUN
ajst-10387	98	14	accuracy	accuracy	NOUN
ajst-10387	98	15	compared	compare	VERB
ajst-10387	98	16	with	with	ADP
ajst-10387	98	17	lstm	lstm	PROPN
ajst-10387	98	18	,	,	PUNCT
ajst-10387	98	19	rnn	rnn	NOUN
ajst-10387	98	20	and	and	CCONJ
ajst-10387	98	21	emd	emd	PROPN
ajst-10387	98	22	-	-	PUNCT
ajst-10387	98	23	lstm	lstm	ADJ
ajst-10387	98	24	models	model	NOUN
ajst-10387	98	25	.	.	PUNCT
ajst-10387	99	1	6	6	X
ajst-10387	99	2	.	.	X
ajst-10387	99	3	conclusion	conclusion	NOUN
ajst-10387	99	4	a	a	DET
ajst-10387	99	5	wind	wind	NOUN
ajst-10387	99	6	power	power	NOUN
ajst-10387	99	7	prediction	prediction	NOUN
ajst-10387	99	8	model	model	NOUN
ajst-10387	99	9	based	base	VERB
ajst-10387	99	10	on	on	ADP
ajst-10387	99	11	ceemdanlstm	ceemdanlstm	NOUN
ajst-10387	99	12	was	be	AUX
ajst-10387	99	13	proposed	propose	VERB
ajst-10387	99	14	.	.	PUNCT
ajst-10387	100	1	through	through	ADP
ajst-10387	100	2	the	the	DET
ajst-10387	100	3	verification	verification	NOUN
ajst-10387	100	4	of	of	ADP
ajst-10387	100	5	the	the	DET
ajst-10387	100	6	measured	measured	ADJ
ajst-10387	100	7	data	datum	NOUN
ajst-10387	100	8	of	of	ADP
ajst-10387	100	9	a	a	DET
ajst-10387	100	10	domestic	domestic	ADJ
ajst-10387	100	11	wind	wind	NOUN
ajst-10387	100	12	farm	farm	NOUN
ajst-10387	100	13	,	,	PUNCT
ajst-10387	100	14	the	the	DET
ajst-10387	100	15	proposed	propose	VERB
ajst-10387	100	16	model	model	NOUN
ajst-10387	100	17	had	have	VERB
ajst-10387	100	18	a	a	DET
ajst-10387	100	19	more	more	ADV
ajst-10387	100	20	accurate	accurate	ADJ
ajst-10387	100	21	prediction	prediction	NOUN
ajst-10387	100	22	effect	effect	NOUN
ajst-10387	100	23	.	.	PUNCT
ajst-10387	101	1	the	the	DET
ajst-10387	101	2	main	main	ADJ
ajst-10387	101	3	conclusions	conclusion	NOUN
ajst-10387	101	4	were	be	AUX
ajst-10387	101	5	as	as	SCONJ
ajst-10387	101	6	follows	follow	VERB
ajst-10387	101	7	:	:	PUNCT
ajst-10387	101	8	(	(	PUNCT
ajst-10387	101	9	1	1	X
ajst-10387	101	10	)	)	PUNCT
ajst-10387	101	11	according	accord	VERB
ajst-10387	101	12	to	to	ADP
ajst-10387	101	13	the	the	DET
ajst-10387	101	14	fluctuation	fluctuation	NOUN
ajst-10387	101	15	characteristics	characteristic	NOUN
ajst-10387	101	16	of	of	ADP
ajst-10387	101	17	wind	wind	NOUN
ajst-10387	101	18	power	power	NOUN
ajst-10387	101	19	,	,	PUNCT
ajst-10387	101	20	ceemdan	ceemdan	PROPN
ajst-10387	101	21	algorithm	algorithm	PROPN
ajst-10387	101	22	was	be	AUX
ajst-10387	101	23	used	use	VERB
ajst-10387	101	24	to	to	PART
ajst-10387	101	25	decompose	decompose	VERB
ajst-10387	101	26	the	the	DET
ajst-10387	101	27	wind	wind	NOUN
ajst-10387	101	28	power	power	NOUN
ajst-10387	101	29	time	time	PROPN
ajst-10387	101	30	series	series	PROPN
ajst-10387	101	31	data	datum	NOUN
ajst-10387	101	32	into	into	ADP
ajst-10387	101	33	relatively	relatively	ADV
ajst-10387	101	34	stable	stable	ADJ
ajst-10387	101	35	subsequences	subsequence	NOUN
ajst-10387	101	36	,	,	PUNCT
ajst-10387	101	37	which	which	PRON
ajst-10387	101	38	can	can	AUX
ajst-10387	101	39	effectively	effectively	ADV
ajst-10387	101	40	improve	improve	VERB
ajst-10387	101	41	the	the	DET
ajst-10387	101	42	stability	stability	NOUN
ajst-10387	101	43	of	of	ADP
ajst-10387	101	44	prediction	prediction	NOUN
ajst-10387	101	45	data	datum	NOUN
ajst-10387	101	46	and	and	CCONJ
ajst-10387	101	47	reduce	reduce	VERB
ajst-10387	101	48	the	the	DET
ajst-10387	101	49	difficulty	difficulty	NOUN
ajst-10387	101	50	of	of	ADP
ajst-10387	101	51	prediction	prediction	NOUN
ajst-10387	101	52	.	.	PUNCT
ajst-10387	102	1	(	(	PUNCT
ajst-10387	102	2	2	2	X
ajst-10387	102	3	)	)	PUNCT
ajst-10387	102	4	the	the	DET
ajst-10387	102	5	forgetting	forget	VERB
ajst-10387	102	6	gate	gate	NOUN
ajst-10387	102	7	in	in	ADP
ajst-10387	102	8	lstm	lstm	NOUN
ajst-10387	102	9	can	can	AUX
ajst-10387	102	10	update	update	VERB
ajst-10387	102	11	the	the	DET
ajst-10387	102	12	effective	effective	ADJ
ajst-10387	102	13	information	information	NOUN
ajst-10387	102	14	in	in	ADP
ajst-10387	102	15	the	the	DET
ajst-10387	102	16	time	time	NOUN
ajst-10387	102	17	series	series	PROPN
ajst-10387	102	18	,	,	PUNCT
ajst-10387	102	19	enhanced	enhance	VERB
ajst-10387	102	20	the	the	DET
ajst-10387	102	21	ability	ability	NOUN
ajst-10387	102	22	to	to	PART
ajst-10387	102	23	capture	capture	VERB
ajst-10387	102	24	the	the	DET
ajst-10387	102	25	nonlinear	nonlinear	ADJ
ajst-10387	102	26	dependencies	dependency	NOUN
ajst-10387	102	27	in	in	ADP
ajst-10387	102	28	the	the	DET
ajst-10387	102	29	wind	wind	NOUN
ajst-10387	102	30	power	power	NOUN
ajst-10387	102	31	time	time	PROPN
ajst-10387	102	32	series	series	PROPN
ajst-10387	102	33	data	data	PROPN
ajst-10387	102	34	,	,	PUNCT
ajst-10387	102	35	and	and	CCONJ
ajst-10387	102	36	had	have	VERB
ajst-10387	102	37	higher	high	ADJ
ajst-10387	102	38	prediction	prediction	NOUN
ajst-10387	102	39	accuracy	accuracy	NOUN
ajst-10387	102	40	than	than	ADP
ajst-10387	102	41	rnn	rnn	PROPN
ajst-10387	102	42	.	.	PUNCT
ajst-10387	103	1	(	(	PUNCT
ajst-10387	103	2	3	3	X
ajst-10387	103	3	)	)	PUNCT
ajst-10387	103	4	the	the	DET
ajst-10387	103	5	time	time	NOUN
ajst-10387	103	6	series	series	PROPN
ajst-10387	103	7	modeling	modeling	NOUN
ajst-10387	103	8	method	method	NOUN
ajst-10387	103	9	based	base	VERB
ajst-10387	103	10	on	on	ADP
ajst-10387	103	11	ceemdan	ceemdan	ADJ
ajst-10387	103	12	-	-	PUNCT
ajst-10387	103	13	lstm	lstm	NOUN
ajst-10387	103	14	proposed	propose	VERB
ajst-10387	103	15	in	in	ADP
ajst-10387	103	16	this	this	DET
ajst-10387	103	17	paper	paper	NOUN
ajst-10387	103	18	had	have	VERB
ajst-10387	103	19	higher	high	ADJ
ajst-10387	103	20	prediction	prediction	NOUN
ajst-10387	103	21	accuracy	accuracy	NOUN
ajst-10387	103	22	,	,	PUNCT
ajst-10387	103	23	had	have	VERB
ajst-10387	103	24	some	some	DET
ajst-10387	103	25	room	room	NOUN
ajst-10387	103	26	for	for	ADP
ajst-10387	103	27	improvement	improvement	NOUN
ajst-10387	103	28	,	,	PUNCT
ajst-10387	103	29	and	and	CCONJ
ajst-10387	103	30	can	can	AUX
ajst-10387	103	31	be	be	AUX
ajst-10387	103	32	applied	apply	VERB
ajst-10387	103	33	to	to	ADP
ajst-10387	103	34	other	other	ADJ
ajst-10387	103	35	time	time	NOUN
ajst-10387	103	36	series	series	NOUN
ajst-10387	103	37	prediction	prediction	NOUN
ajst-10387	103	38	scenarios	scenario	NOUN
ajst-10387	103	39	.	.	PUNCT
ajst-10387	104	1	references	reference	NOUN
ajst-10387	104	2	[	[	X
ajst-10387	104	3	1	1	NUM
ajst-10387	104	4	]	]	X
ajst-10387	104	5	national	national	ADJ
ajst-10387	104	6	renewable	renewable	ADJ
ajst-10387	104	7	energy	energy	NOUN
ajst-10387	104	8	power	power	NOUN
ajst-10387	104	9	development	development	NOUN
ajst-10387	104	10	monitoring	monitoring	NOUN
ajst-10387	104	11	and	and	CCONJ
ajst-10387	104	12	evaluation	evaluation	NOUN
ajst-10387	104	13	report	report	NOUN
ajst-10387	104	14	of	of	ADP
ajst-10387	104	15	the	the	DET
ajst-10387	104	16	national	national	PROPN
ajst-10387	104	17	energy	energy	PROPN
ajst-10387	104	18	administration	administration	NOUN
ajst-10387	104	19	in	in	ADP
ajst-10387	104	20	2020	2020	NUM
ajst-10387	104	21	[	[	X
ajst-10387	104	22	2021	2021	NUM
ajst-10387	104	23	-	-	SYM
ajst-10387	104	24	06	06	NUM
ajst-10387	104	25	-	-	SYM
ajst-10387	104	26	20	20	NUM
ajst-10387	104	27	]	]	PUNCT
ajst-10387	104	28	http://zfxxgk.nea.gov.cn/202106/20/c_1310039970.htm	http://zfxxgk.nea.gov.cn/202106/20/c_1310039970.htm	PROPN
ajst-10387	104	29	81	81	NUM
ajst-10387	105	1	[	[	SYM
ajst-10387	105	2	2	2	NUM
ajst-10387	105	3	]	]	X
ajst-10387	105	4	sun	sun	NOUN
ajst-10387	105	5	rongfu	rongfu	PROPN
ajst-10387	105	6	,	,	PUNCT
ajst-10387	105	7	zhang	zhang	PROPN
ajst-10387	105	8	tao	tao	PROPN
ajst-10387	105	9	,	,	PUNCT
ajst-10387	105	10	et	et	PROPN
ajst-10387	105	11	al	al	PROPN
ajst-10387	105	12	.	.	PROPN
ajst-10387	105	13	overview	overview	NOUN
ajst-10387	105	14	of	of	ADP
ajst-10387	105	15	key	key	ADJ
ajst-10387	105	16	technologies	technology	NOUN
ajst-10387	105	17	and	and	CCONJ
ajst-10387	105	18	applications	application	NOUN
ajst-10387	105	19	of	of	ADP
ajst-10387	105	20	wind	wind	NOUN
ajst-10387	105	21	power	power	NOUN
ajst-10387	105	22	prediction[j	prediction[j	PROPN
ajst-10387	105	23	]	]	PUNCT
ajst-10387	105	24	.	.	PUNCT
ajst-10387	106	1	high	high	ADJ
ajst-10387	106	2	voltage	voltage	NOUN
ajst-10387	106	3	technology,2021,47(04):1129	technology,2021,47(04):1129	PROPN
ajst-10387	106	4	-	-	PUNCT
ajst-10387	106	5	1143	1143	NUM
ajst-10387	106	6	.	.	PUNCT
ajst-10387	107	1	[	[	X
ajst-10387	107	2	3	3	X
ajst-10387	107	3	]	]	X
ajst-10387	107	4	zhang	zhang	PROPN
ajst-10387	107	5	yachao	yachao	PROPN
ajst-10387	107	6	,	,	PUNCT
ajst-10387	107	7	liu	liu	PROPN
ajst-10387	107	8	kaipei	kaipei	PROPN
ajst-10387	107	9	,	,	PUNCT
ajst-10387	107	10	et	et	PROPN
ajst-10387	107	11	al	al	PROPN
ajst-10387	107	12	.	.	PROPN
ajst-10387	107	13	study	study	PROPN
ajst-10387	107	14	on	on	ADP
ajst-10387	107	15	multi	multi	ADJ
ajst-10387	107	16	time	time	NOUN
ajst-10387	107	17	scale	scale	NOUN
ajst-10387	107	18	source	source	NOUN
ajst-10387	107	19	load	load	NOUN
ajst-10387	107	20	coordinated	coordinate	VERB
ajst-10387	107	21	dispatching	dispatch	VERB
ajst-10387	107	22	model	model	NOUN
ajst-10387	107	23	of	of	ADP
ajst-10387	107	24	power	power	NOUN
ajst-10387	107	25	system	system	NOUN
ajst-10387	107	26	with	with	ADP
ajst-10387	107	27	large	large	ADJ
ajst-10387	107	28	scale	scale	NOUN
ajst-10387	107	29	wind	wind	NOUN
ajst-10387	107	30	power[j	power[j	NOUN
ajst-10387	107	31	]	]	PUNCT
ajst-10387	107	32	.	.	PUNCT
ajst-10387	108	1	high	high	ADJ
ajst-10387	108	2	voltage	voltage	NOUN
ajst-10387	108	3	technology	technology	NOUN
ajst-10387	108	4	,	,	PUNCT
ajst-10387	108	5	2019,45(02):600	2019,45(02):600	NOUN
ajst-10387	108	6	-	-	PUNCT
ajst-10387	108	7	608	608	NUM
ajst-10387	108	8	.	.	PUNCT
ajst-10387	109	1	[	[	X
ajst-10387	109	2	4	4	NUM
ajst-10387	109	3	]	]	X
ajst-10387	109	4	tao	tao	PROPN
ajst-10387	109	5	yubo	yubo	PROPN
ajst-10387	109	6	,	,	PUNCT
ajst-10387	109	7	chen	chen	PROPN
ajst-10387	109	8	hao	hao	PROPN
ajst-10387	109	9	,	,	PUNCT
ajst-10387	109	10	et	et	PROPN
ajst-10387	109	11	al	al	PROPN
ajst-10387	109	12	.	.	PUNCT
ajst-10387	110	1	short	short	ADJ
ajst-10387	110	2	term	term	NOUN
ajst-10387	110	3	wind	wind	NOUN
ajst-10387	110	4	power	power	NOUN
ajst-10387	110	5	prediction	prediction	NOUN
ajst-10387	110	6	concept	concept	NOUN
ajst-10387	110	7	,	,	PUNCT
ajst-10387	110	8	model	model	NOUN
ajst-10387	110	9	and	and	CCONJ
ajst-10387	110	10	method[j	method[j	NOUN
ajst-10387	110	11	]	]	PUNCT
ajst-10387	110	12	.	.	PUNCT
ajst-10387	111	1	power	power	NOUN
ajst-10387	111	2	engineering	engineering	NOUN
ajst-10387	111	3	technology	technology	NOUN
ajst-10387	111	4	,	,	PUNCT
ajst-10387	111	5	2018	2018	NUM
ajst-10387	111	6	,	,	PUNCT
ajst-10387	111	7	37(05):7	37(05):7	NUM
ajst-10387	111	8	-	-	SYM
ajst-10387	111	9	13	13	NUM
ajst-10387	111	10	.	.	PUNCT
ajst-10387	112	1	[	[	X
ajst-10387	112	2	5	5	NUM
ajst-10387	112	3	]	]	X
ajst-10387	112	4	zhao	zhao	PROPN
ajst-10387	112	5	dongmei	dongmei	PROPN
ajst-10387	112	6	,	,	PUNCT
ajst-10387	112	7	du	du	PROPN
ajst-10387	112	8	gang	gang	PROPN
ajst-10387	112	9	,	,	PUNCT
ajst-10387	112	10	et	et	PROPN
ajst-10387	112	11	al	al	PROPN
ajst-10387	112	12	.	.	PUNCT
ajst-10387	113	1	wind	wind	NOUN
ajst-10387	113	2	power	power	NOUN
ajst-10387	113	3	combination	combination	NOUN
ajst-10387	113	4	prediction	prediction	NOUN
ajst-10387	113	5	model	model	NOUN
ajst-10387	113	6	based	base	VERB
ajst-10387	113	7	on	on	ADP
ajst-10387	113	8	time	time	NOUN
ajst-10387	113	9	series	series	NOUN
ajst-10387	113	10	decomposition	decomposition	NOUN
ajst-10387	113	11	and	and	CCONJ
ajst-10387	113	12	machine	machine	NOUN
ajst-10387	113	13	learning[j	learning[j	PROPN
ajst-10387	113	14	/	/	SYM
ajst-10387	113	15	ol	ol	PROPN
ajst-10387	113	16	]	]	PUNCT
ajst-10387	113	17	.	.	PUNCT
ajst-10387	114	1	modern	modern	ADJ
ajst-10387	114	2	power:1	power:1	NOUN
ajst-10387	114	3	-	-	PUNCT
ajst-10387	114	4	11[2021	11[2021	NUM
ajst-10387	114	5	-	-	PUNCT
ajst-10387	114	6	10	10	NUM
ajst-10387	114	7	-	-	SYM
ajst-10387	114	8	21	21	NUM
ajst-10387	114	9	]	]	PUNCT
ajst-10387	115	1	[	[	X
ajst-10387	115	2	6	6	NUM
ajst-10387	115	3	]	]	X
ajst-10387	115	4	zhou	zhou	PROPN
ajst-10387	115	5	xiaolin	xiaolin	PROPN
ajst-10387	115	6	,	,	PUNCT
ajst-10387	115	7	tong	tong	PROPN
ajst-10387	115	8	xiaoyang	xiaoyang	PROPN
ajst-10387	115	9	.	.	PUNCT
ajst-10387	116	1	ultra	ultra	ADJ
ajst-10387	116	2	short	short	ADJ
ajst-10387	116	3	term	term	NOUN
ajst-10387	116	4	wind	wind	NOUN
ajst-10387	116	5	power	power	NOUN
ajst-10387	116	6	combination	combination	NOUN
ajst-10387	116	7	prediction	prediction	NOUN
ajst-10387	116	8	based	base	VERB
ajst-10387	116	9	on	on	ADP
ajst-10387	116	10	ceemd	ceemd	PROPN
ajst-10387	116	11	-	-	PUNCT
ajst-10387	116	12	sbo	sbo	NOUN
ajst-10387	116	13	-	-	ADJ
ajst-10387	116	14	lssvr[j	lssvr[j	NOUN
ajst-10387	116	15	]	]	PUNCT
ajst-10387	116	16	.	.	PUNCT
ajst-10387	117	1	power	power	NOUN
ajst-10387	117	2	grid	grid	NOUN
ajst-10387	117	3	technology,2021,45(03):855	technology,2021,45(03):855	PROPN
ajst-10387	117	4	-	-	PUNCT
ajst-10387	117	5	864	864	NUM
ajst-10387	117	6	.	.	PUNCT
ajst-10387	118	1	[	[	X
ajst-10387	118	2	7	7	X
ajst-10387	118	3	]	]	X
ajst-10387	118	4	sheng	sheng	PROPN
ajst-10387	118	5	siqing	siqing	PROPN
ajst-10387	118	6	,	,	PUNCT
ajst-10387	118	7	jin	jin	PROPN
ajst-10387	118	8	hang	hang	PROPN
ajst-10387	118	9	,	,	PUNCT
ajst-10387	118	10	et	et	PROPN
ajst-10387	118	11	al	al	PROPN
ajst-10387	118	12	.	.	PROPN
ajst-10387	119	1	short	short	ADJ
ajst-10387	119	2	and	and	CCONJ
ajst-10387	119	3	medium	medium	ADJ
ajst-10387	119	4	term	term	NOUN
ajst-10387	119	5	prediction	prediction	NOUN
ajst-10387	119	6	of	of	ADP
ajst-10387	119	7	wind	wind	NOUN
ajst-10387	119	8	farm	farm	NOUN
ajst-10387	119	9	power	power	NOUN
ajst-10387	119	10	generation	generation	NOUN
ajst-10387	119	11	based	base	VERB
ajst-10387	119	12	on	on	ADP
ajst-10387	119	13	vmdwsgru[j	vmdwsgru[j	NOUN
ajst-10387	119	14	/	/	SYM
ajst-10387	119	15	ol	ol	PROPN
ajst-10387	119	16	]	]	PUNCT
ajst-10387	119	17	.	.	PUNCT
ajst-10387	120	1	grid	grid	VERB
ajst-10387	120	2	technology:1	technology:1	NOUN
ajst-10387	120	3	-	-	PUNCT
ajst-10387	120	4	8[2021	8[2021	NUM
ajst-10387	120	5	-	-	PUNCT
ajst-10387	120	6	10	10	NUM
ajst-10387	120	7	-	-	NUM
ajst-10387	120	8	27	27	NUM
ajst-10387	120	9	]	]	PUNCT
ajst-10387	121	1	[	[	X
ajst-10387	121	2	8	8	NUM
ajst-10387	121	3	]	]	X
ajst-10387	121	4	li	li	PROPN
ajst-10387	121	5	qing	qing	PROPN
ajst-10387	121	6	,	,	PUNCT
ajst-10387	121	7	zhang	zhang	PROPN
ajst-10387	121	8	xinyan	xinyan	PROPN
ajst-10387	121	9	,	,	PUNCT
ajst-10387	121	10	et	et	PROPN
ajst-10387	121	11	al	al	PROPN
ajst-10387	121	12	.	.	PUNCT
ajst-10387	122	1	ultra	ultra	ADJ
ajst-10387	122	2	short	short	ADJ
ajst-10387	122	3	term	term	NOUN
ajst-10387	122	4	multi	multi	ADJ
ajst-10387	122	5	-	-	ADJ
ajst-10387	122	6	step	step	ADJ
ajst-10387	122	7	prediction	prediction	NOUN
ajst-10387	122	8	of	of	ADP
ajst-10387	122	9	wind	wind	NOUN
ajst-10387	122	10	power	power	NOUN
ajst-10387	122	11	based	base	VERB
ajst-10387	122	12	on	on	ADP
ajst-10387	122	13	ecbo	ecbo	NOUN
ajst-10387	122	14	-	-	PUNCT
ajst-10387	122	15	vmdwkelm[j	vmdwkelm[j	NOUN
ajst-10387	122	16	/	/	SYM
ajst-10387	122	17	ol	ol	ADJ
ajst-10387	122	18	]	]	PUNCT
ajst-10387	122	19	.	.	PUNCT
ajst-10387	123	1	power	power	NOUN
ajst-10387	123	2	grid	grid	PROPN
ajst-10387	123	3	technology,{3},{4}{5}:1	technology,{3},{4}{5}:1	PROPN
ajst-10387	123	4	-	-	SYM
ajst-10387	123	5	14	14	NUM
ajst-10387	124	1	[	[	SYM
ajst-10387	124	2	2021	2021	NUM
ajst-10387	124	3	-	-	SYM
ajst-10387	124	4	07	07	NUM
ajst-10387	124	5	-	-	PUNCT
ajst-10387	124	6	14	14	NUM
ajst-10387	124	7	]	]	PUNCT
ajst-10387	125	1	[	[	X
ajst-10387	125	2	9	9	NUM
ajst-10387	125	3	]	]	X
ajst-10387	125	4	reshef	reshef	NOUN
ajst-10387	125	5	d	d	PROPN
ajst-10387	125	6	n	n	CCONJ
ajst-10387	125	7	,	,	PUNCT
ajst-10387	125	8	reshef	reshef	PROPN
ajst-10387	125	9	y	y	PROPN
ajst-10387	125	10	a	a	PROPN
ajst-10387	125	11	,	,	PUNCT
ajst-10387	125	12	et	et	PROPN
ajst-10387	125	13	al	al	PROPN
ajst-10387	125	14	.	.	PUNCT
ajst-10387	126	1	detecting	detect	VERB
ajst-10387	126	2	novel	novel	ADJ
ajst-10387	126	3	associations	association	NOUN
ajst-10387	126	4	in	in	ADP
ajst-10387	126	5	large	large	ADJ
ajst-10387	126	6	data	datum	NOUN
ajst-10387	126	7	sets[j].sicence,2011,334(6062):1518	sets[j].sicence,2011,334(6062):1518	NOUN
ajst-10387	126	8	-	-	NOUN
ajst-10387	126	9	1524	1524	NUM
ajst-10387	126	10	.	.	PUNCT
ajst-10387	127	1	[	[	X
ajst-10387	127	2	10	10	NUM
ajst-10387	127	3	]	]	X
ajst-10387	127	4	zheng	zheng	PROPN
ajst-10387	127	5	ruicheng	ruicheng	PROPN
ajst-10387	127	6	,	,	PUNCT
ajst-10387	127	7	gu	gu	PROPN
ajst-10387	127	8	jie	jie	PROPN
ajst-10387	127	9	,	,	PUNCT
ajst-10387	127	10	et	et	PROPN
ajst-10387	127	11	al	al	PROPN
ajst-10387	127	12	.	.	PROPN
ajst-10387	127	13	research	research	NOUN
ajst-10387	127	14	on	on	ADP
ajst-10387	127	15	input	input	NOUN
ajst-10387	127	16	variable	variable	ADJ
ajst-10387	127	17	selection	selection	NOUN
ajst-10387	127	18	method	method	NOUN
ajst-10387	127	19	of	of	ADP
ajst-10387	127	20	short	short	ADJ
ajst-10387	127	21	-	-	PUNCT
ajst-10387	127	22	term	term	NOUN
ajst-10387	127	23	load	load	NOUN
ajst-10387	127	24	forecasting	forecasting	NOUN
ajst-10387	127	25	based	base	VERB
ajst-10387	127	26	on	on	ADP
ajst-10387	127	27	data	datum	NOUN
ajst-10387	127	28	driven	drive	VERB
ajst-10387	127	29	and	and	CCONJ
ajst-10387	127	30	prediction	prediction	NOUN
ajst-10387	127	31	error	error	NOUN
ajst-10387	127	32	driven	drive	VERB
ajst-10387	127	33	integration[j].chinese	integration[j].chinese	ADJ
ajst-10387	127	34	journal	journal	NOUN
ajst-10387	127	35	of	of	ADP
ajst-10387	127	36	electrical	electrical	ADJ
ajst-10387	127	37	engineering,2020,40(02):487	engineering,2020,40(02):487	PROPN
ajst-10387	127	38	-	-	PUNCT
ajst-10387	127	39	500	500	NUM
ajst-10387	127	40	.	.	PUNCT
ajst-10387	128	1	[	[	X
ajst-10387	128	2	11	11	NUM
ajst-10387	128	3	]	]	PUNCT
ajst-10387	128	4	power	power	NOUN
ajst-10387	128	5	grid	grid	NOUN
ajst-10387	128	6	technology,2017,41	technology,2017,41	ADP
ajst-10387	128	7	(	(	PUNCT
ajst-10387	128	8	12):3797	12):3797	NUM
ajst-10387	128	9	-	-	SYM
ajst-10387	128	10	3802	3802	NUM
ajst-10387	128	11	.	.	PUNCT
ajst-10387	129	1	[	[	X
ajst-10387	129	2	12	12	NUM
ajst-10387	129	3	]	]	X
ajst-10387	129	4	shu	shu	PROPN
ajst-10387	129	5	chang	chang	PROPN
ajst-10387	129	6	,	,	PUNCT
ajst-10387	129	7	jin	jin	PROPN
ajst-10387	129	8	xiao	xiao	PROPN
ajst-10387	129	9	,	,	PUNCT
ajst-10387	129	10	et	et	PROPN
ajst-10387	129	11	al	al	PROPN
ajst-10387	129	12	.	.	PROPN
ajst-10387	129	13	ceemdan	ceemdan	PROPN
ajst-10387	129	14	based	base	VERB
ajst-10387	129	15	noise	noise	NOUN
ajst-10387	129	16	diagnosis	diagnosis	NOUN
ajst-10387	129	17	method	method	NOUN
ajst-10387	129	18	for	for	ADP
ajst-10387	129	19	distribution	distribution	NOUN
ajst-10387	129	20	transformer	transformer	NOUN
ajst-10387	129	21	discharge	discharge	NOUN
ajst-10387	129	22	fault	fault	NOUN
ajst-10387	129	23	[	[	X
ajst-10387	129	24	j	j	X
ajst-10387	129	25	]	]	X
ajst-10387	129	26	.	.	PUNCT
ajst-10387	130	1	high	high	ADJ
ajst-10387	130	2	voltage	voltage	NOUN
ajst-10387	130	3	technology,2018,44(08):2603	technology,2018,44(08):2603	PROPN
ajst-10387	130	4	-	-	PUNCT
ajst-10387	130	5	2611	2611	NUM
ajst-10387	130	6	.	.	PUNCT
ajst-10387	131	1	[	[	X
ajst-10387	131	2	13	13	NUM
ajst-10387	131	3	]	]	X
ajst-10387	131	4	lu	lu	PROPN
ajst-10387	131	5	jixiang	jixiang	PROPN
ajst-10387	131	6	,	,	PUNCT
ajst-10387	131	7	zhang	zhang	PROPN
ajst-10387	131	8	qipei	qipei	PROPN
ajst-10387	131	9	,	,	PUNCT
ajst-10387	131	10	et	et	PROPN
ajst-10387	131	11	al	al	PROPN
ajst-10387	131	12	.	.	PUNCT
ajst-10387	131	13	short	short	ADJ
ajst-10387	131	14	term	term	NOUN
ajst-10387	131	15	load	load	NOUN
ajst-10387	131	16	forecasting	forecasting	NOUN
ajst-10387	131	17	method	method	NOUN
ajst-10387	131	18	based	base	VERB
ajst-10387	131	19	on	on	ADP
ajst-10387	131	20	cnn	cnn	PROPN
ajst-10387	131	21	-	-	PUNCT
ajst-10387	131	22	lstm	lstm	ADJ
ajst-10387	131	23	hybrid	hybrid	ADJ
ajst-10387	131	24	neural	neural	ADJ
ajst-10387	131	25	network	network	NOUN
ajst-10387	131	26	model[j	model[j	PROPN
ajst-10387	131	27	]	]	PUNCT
ajst-10387	131	28	.	.	PUNCT
ajst-10387	132	1	power	power	NOUN
ajst-10387	132	2	system	system	NOUN
ajst-10387	132	3	automation	automation	NOUN
ajst-10387	132	4	,	,	PUNCT
ajst-10387	132	5	2019,43(08):131	2019,43(08):131	NUM
ajst-10387	132	6	-	-	SYM
ajst-10387	132	7	137	137	NUM
ajst-10387	132	8	.	.	PUNCT