id	sid	tid	token	lemma	pos
ajst-32552	1	1	102	102	NUM
ajst-32552	1	2	solar	solar	ADJ
ajst-32552	1	3	energy	energy	NOUN
ajst-32552	1	4	forecasting	forecasting	NOUN
ajst-32552	1	5	in	in	ADP
ajst-32552	1	6	seattle	seattle	NOUN
ajst-32552	1	7	using	use	VERB
ajst-32552	1	8	machine	machine	NOUN
ajst-32552	1	9	learning	learning	NOUN
ajst-32552	1	10	models	model	NOUN
ajst-32552	1	11	tianshuo	tianshuo	PROPN
ajst-32552	1	12	wang	wang	PROPN
ajst-32552	1	13	university	university	PROPN
ajst-32552	1	14	of	of	ADP
ajst-32552	1	15	washington	washington	PROPN
ajst-32552	1	16	,	,	PUNCT
ajst-32552	1	17	seattle	seattle	PROPN
ajst-32552	1	18	,	,	PUNCT
ajst-32552	1	19	united	united	PROPN
ajst-32552	1	20	states	states	PROPN
ajst-32552	1	21	twang38@uw.edu	twang38@uw.edu	PROPN
ajst-32552	1	22	abstract	abstract	PROPN
ajst-32552	1	23	.	.	PUNCT
ajst-32552	2	1	seattle	seattle	PROPN
ajst-32552	2	2	faces	face	VERB
ajst-32552	2	3	challenging	challenge	VERB
ajst-32552	2	4	environmental	environmental	ADJ
ajst-32552	2	5	issues	issue	NOUN
ajst-32552	2	6	,	,	PUNCT
ajst-32552	2	7	which	which	PRON
ajst-32552	2	8	encourage	encourage	VERB
ajst-32552	2	9	people	people	NOUN
ajst-32552	2	10	to	to	PART
ajst-32552	2	11	explore	explore	VERB
ajst-32552	2	12	ways	way	NOUN
ajst-32552	2	13	of	of	ADP
ajst-32552	2	14	improving	improve	VERB
ajst-32552	2	15	the	the	DET
ajst-32552	2	16	utility	utility	NOUN
ajst-32552	2	17	of	of	ADP
ajst-32552	2	18	renewable	renewable	ADJ
ajst-32552	2	19	resources	resource	NOUN
ajst-32552	2	20	,	,	PUNCT
ajst-32552	2	21	such	such	ADJ
ajst-32552	2	22	as	as	ADP
ajst-32552	2	23	global	global	ADJ
ajst-32552	2	24	horizontal	horizontal	ADJ
ajst-32552	2	25	irradiance	irradiance	NOUN
ajst-32552	2	26	(	(	PUNCT
ajst-32552	2	27	ghi	ghi	PROPN
ajst-32552	2	28	)	)	PUNCT
ajst-32552	2	29	.	.	PUNCT
ajst-32552	3	1	this	this	DET
ajst-32552	3	2	research	research	NOUN
ajst-32552	3	3	consists	consist	VERB
ajst-32552	3	4	of	of	ADP
ajst-32552	3	5	methods	method	NOUN
ajst-32552	3	6	of	of	ADP
ajst-32552	3	7	data	datum	NOUN
ajst-32552	3	8	cleaning	cleaning	NOUN
ajst-32552	3	9	,	,	PUNCT
ajst-32552	3	10	data	data	NOUN
ajst-32552	3	11	visualization	visualization	NOUN
ajst-32552	3	12	,	,	PUNCT
ajst-32552	3	13	and	and	CCONJ
ajst-32552	3	14	designs	design	NOUN
ajst-32552	3	15	of	of	ADP
ajst-32552	3	16	machine	machine	NOUN
ajst-32552	3	17	learning	learning	NOUN
ajst-32552	3	18	models	model	NOUN
ajst-32552	3	19	,	,	PUNCT
ajst-32552	3	20	like	like	ADP
ajst-32552	3	21	linear	linear	PROPN
ajst-32552	3	22	regression	regression	NOUN
ajst-32552	3	23	,	,	PUNCT
ajst-32552	3	24	decision	decision	NOUN
ajst-32552	3	25	tree	tree	NOUN
ajst-32552	3	26	,	,	PUNCT
ajst-32552	3	27	and	and	CCONJ
ajst-32552	3	28	random	random	ADJ
ajst-32552	3	29	forest	forest	NOUN
ajst-32552	3	30	.	.	PUNCT
ajst-32552	4	1	based	base	VERB
ajst-32552	4	2	on	on	ADP
ajst-32552	4	3	the	the	DET
ajst-32552	4	4	results	result	NOUN
ajst-32552	4	5	of	of	ADP
ajst-32552	4	6	r2	r2	PROPN
ajst-32552	4	7	and	and	CCONJ
ajst-32552	4	8	rmse	rmse	ADJ
ajst-32552	4	9	values	value	NOUN
ajst-32552	4	10	,	,	PUNCT
ajst-32552	4	11	the	the	DET
ajst-32552	4	12	random	random	ADJ
ajst-32552	4	13	forest	forest	NOUN
ajst-32552	4	14	has	have	VERB
ajst-32552	4	15	the	the	DET
ajst-32552	4	16	best	good	ADJ
ajst-32552	4	17	performance	performance	NOUN
ajst-32552	4	18	among	among	ADP
ajst-32552	4	19	other	other	ADJ
ajst-32552	4	20	models	model	NOUN
ajst-32552	4	21	,	,	PUNCT
ajst-32552	4	22	with	with	ADP
ajst-32552	4	23	an	an	DET
ajst-32552	4	24	rmse≈of	rmse≈of	PROPN
ajst-32552	4	25	48.1	48.1	NUM
ajst-32552	4	26	and	and	CCONJ
ajst-32552	4	27	an	an	DET
ajst-32552	4	28	r²≈of	r²≈of	PROPN
ajst-32552	4	29	0.82	0.82	NUM
ajst-32552	4	30	.	.	PUNCT
ajst-32552	5	1	the	the	DET
ajst-32552	5	2	research	research	NOUN
ajst-32552	5	3	is	be	AUX
ajst-32552	5	4	dedicated	dedicate	VERB
ajst-32552	5	5	to	to	ADP
ajst-32552	5	6	providing	provide	VERB
ajst-32552	5	7	a	a	DET
ajst-32552	5	8	stable	stable	ADJ
ajst-32552	5	9	and	and	CCONJ
ajst-32552	5	10	reliable	reliable	ADJ
ajst-32552	5	11	prediction	prediction	NOUN
ajst-32552	5	12	of	of	ADP
ajst-32552	5	13	ghi	ghi	PROPN
ajst-32552	5	14	values	value	NOUN
ajst-32552	5	15	.	.	PUNCT
ajst-32552	6	1	with	with	ADP
ajst-32552	6	2	ghi	ghi	PROPN
ajst-32552	6	3	predictions	prediction	NOUN
ajst-32552	6	4	,	,	PUNCT
ajst-32552	6	5	city	city	NOUN
ajst-32552	6	6	planners	planner	NOUN
ajst-32552	6	7	and	and	CCONJ
ajst-32552	6	8	government	government	NOUN
ajst-32552	6	9	are	be	AUX
ajst-32552	6	10	able	able	ADJ
ajst-32552	6	11	to	to	PART
ajst-32552	6	12	efficiently	efficiently	ADV
ajst-32552	6	13	plan	plan	VERB
ajst-32552	6	14	and	and	CCONJ
ajst-32552	6	15	manage	manage	VERB
ajst-32552	6	16	solar	solar	ADJ
ajst-32552	6	17	resources	resource	NOUN
ajst-32552	6	18	,	,	PUNCT
ajst-32552	6	19	mitigate	mitigate	VERB
ajst-32552	6	20	instability	instability	NOUN
ajst-32552	6	21	risks	risk	NOUN
ajst-32552	6	22	,	,	PUNCT
ajst-32552	6	23	and	and	CCONJ
ajst-32552	6	24	enhance	enhance	VERB
ajst-32552	6	25	building	build	VERB
ajst-32552	6	26	energy	energy	NOUN
ajst-32552	6	27	performance	performance	NOUN
ajst-32552	6	28	through	through	ADP
ajst-32552	6	29	improved	improve	VERB
ajst-32552	6	30	control	control	NOUN
ajst-32552	6	31	of	of	ADP
ajst-32552	6	32	lighting	lighting	NOUN
ajst-32552	6	33	and	and	CCONJ
ajst-32552	6	34	shading	shading	NOUN
ajst-32552	6	35	systems	system	NOUN
ajst-32552	6	36	.	.	PUNCT
ajst-32552	7	1	moreover	moreover	ADV
ajst-32552	7	2	,	,	PUNCT
ajst-32552	7	3	the	the	DET
ajst-32552	7	4	application	application	NOUN
ajst-32552	7	5	of	of	ADP
ajst-32552	7	6	machine	machine	NOUN
ajst-32552	7	7	learning	learning	NOUN
ajst-32552	7	8	models	model	NOUN
ajst-32552	7	9	is	be	AUX
ajst-32552	7	10	a	a	DET
ajst-32552	7	11	good	good	ADJ
ajst-32552	7	12	start	start	NOUN
ajst-32552	7	13	in	in	ADP
ajst-32552	7	14	environmental	environmental	ADJ
ajst-32552	7	15	sciences	science	NOUN
ajst-32552	7	16	and	and	CCONJ
ajst-32552	7	17	gives	give	VERB
ajst-32552	7	18	a	a	DET
ajst-32552	7	19	solid	solid	ADJ
ajst-32552	7	20	foundation	foundation	NOUN
ajst-32552	7	21	in	in	ADP
ajst-32552	7	22	the	the	DET
ajst-32552	7	23	application	application	NOUN
ajst-32552	7	24	of	of	ADP
ajst-32552	7	25	data	datum	NOUN
ajst-32552	7	26	sciences	science	NOUN
ajst-32552	7	27	in	in	ADP
ajst-32552	7	28	real	real	ADJ
ajst-32552	7	29	life	life	NOUN
ajst-32552	7	30	.	.	PUNCT
ajst-32552	8	1	additionally	additionally	ADV
ajst-32552	8	2	,	,	PUNCT
ajst-32552	8	3	the	the	DET
ajst-32552	8	4	findings	finding	NOUN
ajst-32552	8	5	offer	offer	VERB
ajst-32552	8	6	a	a	DET
ajst-32552	8	7	reference	reference	NOUN
ajst-32552	8	8	framework	framework	NOUN
ajst-32552	8	9	for	for	ADP
ajst-32552	8	10	integrating	integrate	VERB
ajst-32552	8	11	predictive	predictive	ADJ
ajst-32552	8	12	solar	solar	ADJ
ajst-32552	8	13	energy	energy	NOUN
ajst-32552	8	14	data	datum	NOUN
ajst-32552	8	15	into	into	ADP
ajst-32552	8	16	urban	urban	ADJ
ajst-32552	8	17	planning	planning	NOUN
ajst-32552	8	18	and	and	CCONJ
ajst-32552	8	19	renewable	renewable	ADJ
ajst-32552	8	20	energy	energy	NOUN
ajst-32552	8	21	policy	policy	NOUN
ajst-32552	8	22	-	-	PUNCT
ajst-32552	8	23	making	making	NOUN
ajst-32552	8	24	.	.	PUNCT
ajst-32552	9	1	future	future	ADJ
ajst-32552	9	2	work	work	NOUN
ajst-32552	9	3	could	could	AUX
ajst-32552	9	4	extend	extend	VERB
ajst-32552	9	5	these	these	DET
ajst-32552	9	6	models	model	NOUN
ajst-32552	9	7	to	to	PART
ajst-32552	9	8	incorporate	incorporate	VERB
ajst-32552	9	9	real	real	ADJ
ajst-32552	9	10	-	-	PUNCT
ajst-32552	9	11	time	time	NOUN
ajst-32552	9	12	data	datum	NOUN
ajst-32552	9	13	for	for	ADP
ajst-32552	9	14	dynamic	dynamic	ADJ
ajst-32552	9	15	forecasting	forecasting	NOUN
ajst-32552	9	16	and	and	CCONJ
ajst-32552	9	17	decision	decision	NOUN
ajst-32552	9	18	support	support	NOUN
ajst-32552	9	19	.	.	PUNCT
ajst-32552	10	1	keywords	keyword	NOUN
ajst-32552	10	2	:	:	PUNCT
ajst-32552	10	3	global	global	ADJ
ajst-32552	10	4	horizontal	horizontal	ADJ
ajst-32552	10	5	irradiance	irradiance	NOUN
ajst-32552	10	6	(	(	PUNCT
ajst-32552	10	7	ghi	ghi	PROPN
ajst-32552	10	8	)	)	PUNCT
ajst-32552	10	9	;	;	PUNCT
ajst-32552	10	10	machine	machine	NOUN
ajst-32552	10	11	learning	learning	NOUN
ajst-32552	10	12	;	;	PUNCT
ajst-32552	10	13	solar	solar	ADJ
ajst-32552	10	14	energy	energy	NOUN
ajst-32552	10	15	forecasting	forecasting	NOUN
ajst-32552	10	16	;	;	PUNCT
ajst-32552	10	17	random	random	ADJ
ajst-32552	10	18	forest	forest	NOUN
ajst-32552	10	19	.	.	PUNCT
ajst-32552	11	1	1	1	X
ajst-32552	11	2	.	.	X
ajst-32552	11	3	introduction	introduction	NOUN
ajst-32552	11	4	with	with	ADP
ajst-32552	11	5	growing	grow	VERB
ajst-32552	11	6	emphasis	emphasis	NOUN
ajst-32552	11	7	on	on	ADP
ajst-32552	11	8	environmental	environmental	ADJ
ajst-32552	11	9	protection	protection	NOUN
ajst-32552	11	10	,	,	PUNCT
ajst-32552	11	11	increasing	increase	VERB
ajst-32552	11	12	attention	attention	NOUN
ajst-32552	11	13	is	be	AUX
ajst-32552	11	14	being	be	AUX
ajst-32552	11	15	paid	pay	VERB
ajst-32552	11	16	to	to	ADP
ajst-32552	11	17	solar	solar	ADJ
ajst-32552	11	18	energy	energy	NOUN
ajst-32552	11	19	and	and	CCONJ
ajst-32552	11	20	its	its	PRON
ajst-32552	11	21	applications	application	NOUN
ajst-32552	11	22	in	in	ADP
ajst-32552	11	23	daily	daily	ADJ
ajst-32552	11	24	life	life	NOUN
ajst-32552	11	25	.	.	PUNCT
ajst-32552	12	1	this	this	DET
ajst-32552	12	2	research	research	NOUN
ajst-32552	12	3	focuses	focus	VERB
ajst-32552	12	4	on	on	ADP
ajst-32552	12	5	the	the	DET
ajst-32552	12	6	prediction	prediction	NOUN
ajst-32552	12	7	of	of	ADP
ajst-32552	12	8	the	the	DET
ajst-32552	12	9	global	global	ADJ
ajst-32552	12	10	horizontal	horizontal	ADJ
ajst-32552	12	11	irradiation	irradiation	NOUN
ajst-32552	12	12	(	(	PUNCT
ajst-32552	12	13	ghi	ghi	PROPN
ajst-32552	12	14	)	)	PUNCT
ajst-32552	12	15	,	,	PUNCT
ajst-32552	12	16	defined	define	VERB
ajst-32552	12	17	as	as	ADP
ajst-32552	12	18	the	the	DET
ajst-32552	12	19	total	total	ADJ
ajst-32552	12	20	solar	solar	ADJ
ajst-32552	12	21	radiation	radiation	NOUN
ajst-32552	12	22	received	receive	VERB
ajst-32552	12	23	on	on	ADP
ajst-32552	12	24	a	a	DET
ajst-32552	12	25	horizontal	horizontal	ADJ
ajst-32552	12	26	surface	surface	NOUN
ajst-32552	12	27	at	at	ADP
ajst-32552	12	28	ground	ground	NOUN
ajst-32552	12	29	level	level	NOUN
ajst-32552	12	30	.	.	PUNCT
ajst-32552	13	1	the	the	DET
ajst-32552	13	2	applications	application	NOUN
ajst-32552	13	3	of	of	ADP
ajst-32552	13	4	ghi	ghi	PROPN
ajst-32552	13	5	include	include	VERB
ajst-32552	13	6	estimating	estimate	VERB
ajst-32552	13	7	electricity	electricity	NOUN
ajst-32552	13	8	generation	generation	NOUN
ajst-32552	13	9	from	from	ADP
ajst-32552	13	10	photovoltaic	photovoltaic	NOUN
ajst-32552	13	11	(	(	PUNCT
ajst-32552	13	12	pv	pv	NOUN
ajst-32552	13	13	)	)	PUNCT
ajst-32552	13	14	plants	plant	NOUN
ajst-32552	13	15	and	and	CCONJ
ajst-32552	13	16	improving	improve	VERB
ajst-32552	13	17	solar	solar	ADJ
ajst-32552	13	18	forecasting	forecasting	NOUN
ajst-32552	13	19	to	to	PART
ajst-32552	13	20	balance	balance	VERB
ajst-32552	13	21	supply	supply	NOUN
ajst-32552	13	22	and	and	CCONJ
ajst-32552	13	23	demand	demand	NOUN
ajst-32552	13	24	.	.	PUNCT
ajst-32552	14	1	these	these	DET
ajst-32552	14	2	functions	function	NOUN
ajst-32552	14	3	support	support	VERB
ajst-32552	14	4	governments	government	NOUN
ajst-32552	14	5	in	in	ADP
ajst-32552	14	6	managing	manage	VERB
ajst-32552	14	7	and	and	CCONJ
ajst-32552	14	8	planning	plan	VERB
ajst-32552	14	9	electricity	electricity	NOUN
ajst-32552	14	10	distribution	distribution	NOUN
ajst-32552	14	11	.	.	PUNCT
ajst-32552	15	1	the	the	DET
ajst-32552	15	2	study	study	NOUN
ajst-32552	15	3	aims	aim	VERB
ajst-32552	15	4	to	to	PART
ajst-32552	15	5	develop	develop	VERB
ajst-32552	15	6	accurate	accurate	ADJ
ajst-32552	15	7	ghi	ghi	PROPN
ajst-32552	15	8	predictions	prediction	NOUN
ajst-32552	15	9	using	use	VERB
ajst-32552	15	10	machine	machine	NOUN
ajst-32552	15	11	learning	learn	VERB
ajst-32552	15	12	techniques	technique	NOUN
ajst-32552	15	13	.	.	PUNCT
ajst-32552	16	1	by	by	ADP
ajst-32552	16	2	integrating	integrate	VERB
ajst-32552	16	3	time	time	NOUN
ajst-32552	16	4	series	series	PROPN
ajst-32552	16	5	analysis	analysis	NOUN
ajst-32552	16	6	with	with	ADP
ajst-32552	16	7	machine	machine	NOUN
ajst-32552	16	8	learning	learning	NOUN
ajst-32552	16	9	,	,	PUNCT
ajst-32552	16	10	it	it	PRON
ajst-32552	16	11	is	be	AUX
ajst-32552	16	12	possible	possible	ADJ
ajst-32552	16	13	to	to	PART
ajst-32552	16	14	obtain	obtain	VERB
ajst-32552	16	15	daily	daily	ADJ
ajst-32552	16	16	interactive	interactive	ADJ
ajst-32552	16	17	data	datum	NOUN
ajst-32552	16	18	and	and	CCONJ
ajst-32552	16	19	facilitate	facilitate	VERB
ajst-32552	16	20	operational	operational	ADJ
ajst-32552	16	21	decisions	decision	NOUN
ajst-32552	16	22	based	base	VERB
ajst-32552	16	23	on	on	ADP
ajst-32552	16	24	predictive	predictive	ADJ
ajst-32552	16	25	results	result	NOUN
ajst-32552	16	26	.	.	PUNCT
ajst-32552	17	1	seattle	seattle	PROPN
ajst-32552	17	2	’s	’s	PART
ajst-32552	17	3	climate	climate	NOUN
ajst-32552	17	4	,	,	PUNCT
ajst-32552	17	5	characterized	characterize	VERB
ajst-32552	17	6	by	by	ADP
ajst-32552	17	7	extensive	extensive	ADJ
ajst-32552	17	8	cloud	cloud	NOUN
ajst-32552	17	9	cover	cover	NOUN
ajst-32552	17	10	and	and	CCONJ
ajst-32552	17	11	rainfall	rainfall	NOUN
ajst-32552	17	12	,	,	PUNCT
ajst-32552	17	13	leads	lead	VERB
ajst-32552	17	14	to	to	ADP
ajst-32552	17	15	high	high	ADJ
ajst-32552	17	16	variability	variability	NOUN
ajst-32552	17	17	in	in	ADP
ajst-32552	17	18	ghi	ghi	PROPN
ajst-32552	17	19	.	.	PUNCT
ajst-32552	18	1	the	the	DET
ajst-32552	18	2	average	average	ADJ
ajst-32552	18	3	annual	annual	ADJ
ajst-32552	18	4	solar	solar	ADJ
ajst-32552	18	5	radiation	radiation	NOUN
ajst-32552	18	6	in	in	ADP
ajst-32552	18	7	seattle	seattle	PROPN
ajst-32552	18	8	is	be	AUX
ajst-32552	18	9	approximately	approximately	ADV
ajst-32552	18	10	4.12	4.12	NUM
ajst-32552	18	11	kwh	kwh	NOUN
ajst-32552	18	12	/	/	SYM
ajst-32552	18	13	m²/day	m²/day	PROPN
ajst-32552	18	14	,	,	PUNCT
ajst-32552	18	15	and	and	CCONJ
ajst-32552	18	16	the	the	DET
ajst-32552	18	17	average	average	ADJ
ajst-32552	18	18	monthly	monthly	ADJ
ajst-32552	18	19	ghi	ghi	PROPN
ajst-32552	18	20	is	be	AUX
ajst-32552	18	21	about	about	ADV
ajst-32552	18	22	3.46	3.46	NUM
ajst-32552	18	23	kwh	kwh	NOUN
ajst-32552	18	24	/	/	SYM
ajst-32552	18	25	m²/day	m²/day	PROPN
ajst-32552	18	26	(	(	PUNCT
ajst-32552	18	27	solar	solar	ADJ
ajst-32552	18	28	energy	energy	NOUN
ajst-32552	18	29	local	local	NOUN
ajst-32552	18	30	)	)	PUNCT
ajst-32552	19	1	[	[	X
ajst-32552	19	2	1	1	NUM
ajst-32552	19	3	]	]	PUNCT
ajst-32552	19	4	.	.	PUNCT
ajst-32552	20	1	however	however	ADV
ajst-32552	20	2	,	,	PUNCT
ajst-32552	20	3	phoenix	phoenix	PROPN
ajst-32552	20	4	,	,	PUNCT
ajst-32552	20	5	az	az	PROPN
ajst-32552	20	6	,	,	PUNCT
ajst-32552	20	7	averages	average	NOUN
ajst-32552	20	8	over	over	ADP
ajst-32552	20	9	6.5	6.5	NUM
ajst-32552	20	10	kwh	kwh	NOUN
ajst-32552	20	11	/	/	SYM
ajst-32552	20	12	m²/day	m²/day	PROPN
ajst-32552	20	13	.	.	PUNCT
ajst-32552	21	1	these	these	DET
ajst-32552	21	2	values	value	NOUN
ajst-32552	21	3	indicate	indicate	VERB
ajst-32552	21	4	both	both	CCONJ
ajst-32552	21	5	the	the	DET
ajst-32552	21	6	importance	importance	NOUN
ajst-32552	21	7	and	and	CCONJ
ajst-32552	21	8	the	the	DET
ajst-32552	21	9	difficulty	difficulty	NOUN
ajst-32552	21	10	of	of	ADP
ajst-32552	21	11	using	use	VERB
ajst-32552	21	12	solar	solar	ADJ
ajst-32552	21	13	energy	energy	NOUN
ajst-32552	21	14	in	in	ADP
ajst-32552	21	15	the	the	DET
ajst-32552	21	16	seattle	seattle	PROPN
ajst-32552	21	17	area	area	NOUN
ajst-32552	21	18	,	,	PUNCT
ajst-32552	21	19	emphasizing	emphasize	VERB
ajst-32552	21	20	the	the	DET
ajst-32552	21	21	need	need	NOUN
ajst-32552	21	22	for	for	ADP
ajst-32552	21	23	reliable	reliable	ADJ
ajst-32552	21	24	solar	solar	ADJ
ajst-32552	21	25	prediction	prediction	NOUN
ajst-32552	21	26	.	.	PUNCT
ajst-32552	22	1	seattle	seattle	PROPN
ajst-32552	22	2	city	city	PROPN
ajst-32552	22	3	light	light	PROPN
ajst-32552	22	4	,	,	PUNCT
ajst-32552	22	5	the	the	DET
ajst-32552	22	6	city	city	NOUN
ajst-32552	22	7	's	's	PART
ajst-32552	22	8	public	public	ADJ
ajst-32552	22	9	utility	utility	NOUN
ajst-32552	22	10	,	,	PUNCT
ajst-32552	22	11	sources	source	NOUN
ajst-32552	22	12	over	over	ADP
ajst-32552	22	13	88	88	NUM
ajst-32552	22	14	%	%	NOUN
ajst-32552	22	15	of	of	ADP
ajst-32552	22	16	its	its	PRON
ajst-32552	22	17	electricity	electricity	NOUN
ajst-32552	22	18	from	from	ADP
ajst-32552	22	19	renewable	renewable	ADJ
ajst-32552	22	20	hydroelectric	hydroelectric	ADJ
ajst-32552	22	21	power	power	NOUN
ajst-32552	22	22	.	.	PUNCT
ajst-32552	23	1	about	about	ADP
ajst-32552	23	2	40–50	40–50	NUM
ajst-32552	23	3	%	%	NOUN
ajst-32552	23	4	is	be	AUX
ajst-32552	23	5	generated	generate	VERB
ajst-32552	23	6	internally	internally	ADV
ajst-32552	23	7	,	,	PUNCT
ajst-32552	23	8	with	with	ADP
ajst-32552	23	9	the	the	DET
ajst-32552	23	10	remainder	remainder	NOUN
ajst-32552	23	11	supplied	supply	VERB
ajst-32552	23	12	by	by	ADP
ajst-32552	23	13	the	the	DET
ajst-32552	23	14	bonneville	bonneville	PROPN
ajst-32552	23	15	power	power	PROPN
ajst-32552	23	16	administration	administration	PROPN
ajst-32552	23	17	(	(	PUNCT
ajst-32552	23	18	bpa	bpa	PROPN
ajst-32552	23	19	)	)	PUNCT
ajst-32552	23	20	and	and	CCONJ
ajst-32552	23	21	other	other	ADJ
ajst-32552	23	22	renewables	renewable	NOUN
ajst-32552	23	23	,	,	PUNCT
ajst-32552	23	24	including	include	VERB
ajst-32552	23	25	wind	wind	NOUN
ajst-32552	23	26	(	(	PUNCT
ajst-32552	23	27	5	5	NUM
ajst-32552	23	28	%	%	NOUN
ajst-32552	23	29	)	)	PUNCT
ajst-32552	23	30	,	,	PUNCT
ajst-32552	23	31	nuclear	nuclear	NOUN
ajst-32552	23	32	(	(	PUNCT
ajst-32552	23	33	4	4	NUM
ajst-32552	23	34	%	%	NOUN
ajst-32552	23	35	)	)	PUNCT
ajst-32552	23	36	,	,	PUNCT
ajst-32552	23	37	biogas	biogas	NOUN
ajst-32552	23	38	(	(	PUNCT
ajst-32552	23	39	1	1	NUM
ajst-32552	23	40	%	%	NOUN
ajst-32552	23	41	)	)	PUNCT
ajst-32552	23	42	,	,	PUNCT
ajst-32552	23	43	and	and	CCONJ
ajst-32552	23	44	other	other	ADJ
ajst-32552	23	45	sources	source	NOUN
ajst-32552	23	46	(	(	PUNCT
ajst-32552	23	47	2	2	NUM
ajst-32552	23	48	%	%	NOUN
ajst-32552	23	49	)	)	PUNCT
ajst-32552	24	1	[	[	X
ajst-32552	24	2	2	2	NUM
ajst-32552	24	3	]	]	PUNCT
ajst-32552	24	4	.	.	PUNCT
ajst-32552	25	1	the	the	DET
ajst-32552	25	2	clean	clean	PROPN
ajst-32552	25	3	energy	energy	PROPN
ajst-32552	25	4	institute	institute	PROPN
ajst-32552	25	5	(	(	PUNCT
ajst-32552	25	6	cei	cei	NOUN
ajst-32552	25	7	)	)	PUNCT
ajst-32552	25	8	at	at	ADP
ajst-32552	25	9	the	the	DET
ajst-32552	25	10	university	university	PROPN
ajst-32552	25	11	of	of	ADP
ajst-32552	25	12	washington	washington	PROPN
ajst-32552	25	13	,	,	PUNCT
ajst-32552	25	14	established	establish	VERB
ajst-32552	25	15	in	in	ADP
ajst-32552	25	16	2013	2013	NUM
ajst-32552	25	17	,	,	PUNCT
ajst-32552	25	18	conducts	conduct	VERB
ajst-32552	25	19	research	research	NOUN
ajst-32552	25	20	on	on	ADP
ajst-32552	25	21	solar	solar	ADJ
ajst-32552	25	22	batteries	battery	NOUN
ajst-32552	25	23	and	and	CCONJ
ajst-32552	25	24	grid	grid	NOUN
ajst-32552	25	25	systems	system	NOUN
ajst-32552	25	26	to	to	PART
ajst-32552	25	27	promote	promote	VERB
ajst-32552	25	28	renewable	renewable	ADJ
ajst-32552	25	29	energy	energy	NOUN
ajst-32552	25	30	adoption	adoption	NOUN
ajst-32552	25	31	and	and	CCONJ
ajst-32552	25	32	environmental	environmental	ADJ
ajst-32552	25	33	sustainability	sustainability	NOUN
ajst-32552	25	34	[	[	X
ajst-32552	25	35	2	2	NUM
ajst-32552	25	36	]	]	PUNCT
ajst-32552	25	37	.	.	PUNCT
ajst-32552	26	1	these	these	DET
ajst-32552	26	2	factors	factor	NOUN
ajst-32552	26	3	highlight	highlight	VERB
ajst-32552	26	4	the	the	DET
ajst-32552	26	5	importance	importance	NOUN
ajst-32552	26	6	of	of	ADP
ajst-32552	26	7	developing	develop	VERB
ajst-32552	26	8	prediction	prediction	NOUN
ajst-32552	26	9	algorithms	algorithm	NOUN
ajst-32552	26	10	to	to	PART
ajst-32552	26	11	enhance	enhance	VERB
ajst-32552	26	12	local	local	ADJ
ajst-32552	26	13	solar	solar	ADJ
ajst-32552	26	14	energy	energy	NOUN
ajst-32552	26	15	utilization	utilization	NOUN
ajst-32552	26	16	.	.	PUNCT
ajst-32552	27	1	ghi	ghi	PROPN
ajst-32552	27	2	serves	serve	VERB
ajst-32552	27	3	as	as	ADP
ajst-32552	27	4	a	a	DET
ajst-32552	27	5	fundamental	fundamental	ADJ
ajst-32552	27	6	indicator	indicator	NOUN
ajst-32552	27	7	for	for	ADP
ajst-32552	27	8	solar	solar	ADJ
ajst-32552	27	9	energy	energy	NOUN
ajst-32552	27	10	potential	potential	NOUN
ajst-32552	27	11	.	.	PUNCT
ajst-32552	28	1	it	it	PRON
ajst-32552	28	2	informs	inform	VERB
ajst-32552	28	3	site	site	NOUN
ajst-32552	28	4	selection	selection	NOUN
ajst-32552	28	5	,	,	PUNCT
ajst-32552	28	6	system	system	NOUN
ajst-32552	28	7	design	design	NOUN
ajst-32552	28	8	,	,	PUNCT
ajst-32552	28	9	and	and	CCONJ
ajst-32552	28	10	the	the	DET
ajst-32552	28	11	development	development	NOUN
ajst-32552	28	12	of	of	ADP
ajst-32552	28	13	solar	solar	ADJ
ajst-32552	28	14	energy	energy	NOUN
ajst-32552	28	15	facilities	facility	NOUN
ajst-32552	28	16	.	.	PUNCT
ajst-32552	29	1	accurate	accurate	PROPN
ajst-32552	29	2	ghi	ghi	PROPN
ajst-32552	29	3	prediction	prediction	PROPN
ajst-32552	29	4	aids	aids	PROPN
ajst-32552	29	5	grid	grid	NOUN
ajst-32552	29	6	operators	operator	NOUN
ajst-32552	29	7	in	in	ADP
ajst-32552	29	8	balancing	balance	VERB
ajst-32552	29	9	supply	supply	NOUN
ajst-32552	29	10	and	and	CCONJ
ajst-32552	29	11	demand	demand	NOUN
ajst-32552	29	12	,	,	PUNCT
ajst-32552	29	13	mitigating	mitigate	VERB
ajst-32552	29	14	instability	instability	NOUN
ajst-32552	29	15	risks	risk	NOUN
ajst-32552	29	16	,	,	PUNCT
ajst-32552	29	17	and	and	CCONJ
ajst-32552	29	18	enhancing	enhance	VERB
ajst-32552	29	19	building	building	NOUN
ajst-32552	29	20	energy	energy	NOUN
ajst-32552	29	21	performance	performance	NOUN
ajst-32552	29	22	through	through	ADP
ajst-32552	29	23	improved	improve	VERB
ajst-32552	29	24	control	control	NOUN
ajst-32552	29	25	of	of	ADP
ajst-32552	29	26	lighting	lighting	NOUN
ajst-32552	29	27	and	and	CCONJ
ajst-32552	29	28	shading	shading	NOUN
ajst-32552	29	29	systems	system	NOUN
ajst-32552	29	30	.	.	PUNCT
ajst-32552	30	1	with	with	ADP
ajst-32552	30	2	informed	informed	ADJ
ajst-32552	30	3	predictions	prediction	NOUN
ajst-32552	30	4	,	,	PUNCT
ajst-32552	30	5	governments	government	NOUN
ajst-32552	30	6	can	can	AUX
ajst-32552	30	7	establish	establish	VERB
ajst-32552	30	8	efficient	efficient	ADJ
ajst-32552	30	9	energy	energy	NOUN
ajst-32552	30	10	strategies	strategy	NOUN
ajst-32552	30	11	and	and	CCONJ
ajst-32552	30	12	distribution	distribution	NOUN
ajst-32552	30	13	plans	plan	NOUN
ajst-32552	30	14	that	that	PRON
ajst-32552	30	15	increase	increase	VERB
ajst-32552	30	16	supply	supply	NOUN
ajst-32552	30	17	without	without	ADP
ajst-32552	30	18	raising	raise	VERB
ajst-32552	30	19	carbon	carbon	NOUN
ajst-32552	30	20	emissions	emission	NOUN
ajst-32552	30	21	[	[	X
ajst-32552	30	22	2	2	NUM
ajst-32552	30	23	]	]	PUNCT
ajst-32552	30	24	.	.	PUNCT
ajst-32552	31	1	103	103	NUM
ajst-32552	31	2	2	2	NUM
ajst-32552	31	3	.	.	PUNCT
ajst-32552	31	4	literature	literature	NOUN
ajst-32552	31	5	review	review	VERB
ajst-32552	31	6	traditional	traditional	ADJ
ajst-32552	31	7	statistical	statistical	ADJ
ajst-32552	31	8	models	model	NOUN
ajst-32552	31	9	,	,	PUNCT
ajst-32552	31	10	such	such	ADJ
ajst-32552	31	11	as	as	ADP
ajst-32552	31	12	arima	arima	NOUN
ajst-32552	31	13	,	,	PUNCT
ajst-32552	31	14	exhibit	exhibit	VERB
ajst-32552	31	15	certain	certain	ADJ
ajst-32552	31	16	limitations	limitation	NOUN
ajst-32552	31	17	when	when	SCONJ
ajst-32552	31	18	applied	apply	VERB
ajst-32552	31	19	to	to	ADP
ajst-32552	31	20	ghi	ghi	PROPN
ajst-32552	31	21	prediction	prediction	NOUN
ajst-32552	32	1	[	[	X
ajst-32552	32	2	3	3	NUM
ajst-32552	32	3	]	]	PUNCT
ajst-32552	32	4	.	.	PUNCT
ajst-32552	33	1	as	as	ADP
ajst-32552	33	2	a	a	DET
ajst-32552	33	3	relatively	relatively	ADV
ajst-32552	33	4	simple	simple	ADJ
ajst-32552	33	5	and	and	CCONJ
ajst-32552	33	6	interpretable	interpretable	ADJ
ajst-32552	33	7	model	model	NOUN
ajst-32552	33	8	,	,	PUNCT
ajst-32552	33	9	arima	arima	PROPN
ajst-32552	33	10	assumes	assume	VERB
ajst-32552	33	11	that	that	SCONJ
ajst-32552	33	12	the	the	DET
ajst-32552	33	13	time	time	NOUN
ajst-32552	33	14	series	series	PROPN
ajst-32552	33	15	is	be	AUX
ajst-32552	33	16	stationary	stationary	ADJ
ajst-32552	33	17	and	and	CCONJ
ajst-32552	33	18	that	that	SCONJ
ajst-32552	33	19	variables	variable	NOUN
ajst-32552	33	20	are	be	AUX
ajst-32552	33	21	linearly	linearly	ADV
ajst-32552	33	22	dependent	dependent	ADJ
ajst-32552	33	23	.	.	PUNCT
ajst-32552	34	1	ghi	ghi	PROPN
ajst-32552	34	2	fluctuations	fluctuation	NOUN
ajst-32552	34	3	are	be	AUX
ajst-32552	34	4	influenced	influence	VERB
ajst-32552	34	5	by	by	ADP
ajst-32552	34	6	multiple	multiple	ADJ
ajst-32552	34	7	factors	factor	NOUN
ajst-32552	34	8	.	.	PUNCT
ajst-32552	35	1	notably	notably	ADV
ajst-32552	35	2	,	,	PUNCT
ajst-32552	35	3	daytime	daytime	ADJ
ajst-32552	35	4	and	and	CCONJ
ajst-32552	35	5	nighttime	nighttime	ADJ
ajst-32552	35	6	data	datum	NOUN
ajst-32552	35	7	display	display	VERB
ajst-32552	35	8	distinct	distinct	ADJ
ajst-32552	35	9	characteristics	characteristic	NOUN
ajst-32552	35	10	:	:	PUNCT
ajst-32552	35	11	daytime	daytime	PROPN
ajst-32552	35	12	ghi	ghi	PROPN
ajst-32552	35	13	shows	show	VERB
ajst-32552	35	14	significant	significant	ADJ
ajst-32552	35	15	variation	variation	NOUN
ajst-32552	35	16	due	due	ADP
ajst-32552	35	17	to	to	ADP
ajst-32552	35	18	solar	solar	ADJ
ajst-32552	35	19	activity	activity	NOUN
ajst-32552	35	20	,	,	PUNCT
ajst-32552	35	21	while	while	SCONJ
ajst-32552	35	22	nighttime	nighttime	ADJ
ajst-32552	35	23	data	datum	NOUN
ajst-32552	35	24	remain	remain	VERB
ajst-32552	35	25	relatively	relatively	ADV
ajst-32552	35	26	stable	stable	ADJ
ajst-32552	35	27	and	and	CCONJ
ajst-32552	35	28	stationary	stationary	ADJ
ajst-32552	35	29	.	.	PUNCT
ajst-32552	36	1	moreover	moreover	ADV
ajst-32552	36	2	,	,	PUNCT
ajst-32552	36	3	factors	factor	NOUN
ajst-32552	36	4	such	such	ADJ
ajst-32552	36	5	as	as	ADP
ajst-32552	36	6	temperature	temperature	NOUN
ajst-32552	36	7	and	and	CCONJ
ajst-32552	36	8	dew	dew	NOUN
ajst-32552	36	9	point	point	NOUN
ajst-32552	36	10	are	be	AUX
ajst-32552	36	11	not	not	PART
ajst-32552	36	12	necessarily	necessarily	ADV
ajst-32552	36	13	linearly	linearly	ADV
ajst-32552	36	14	correlated	correlate	VERB
ajst-32552	36	15	.	.	PUNCT
ajst-32552	37	1	as	as	ADP
ajst-32552	37	2	a	a	DET
ajst-32552	37	3	result	result	NOUN
ajst-32552	37	4	,	,	PUNCT
ajst-32552	37	5	traditional	traditional	ADJ
ajst-32552	37	6	statistical	statistical	ADJ
ajst-32552	37	7	models	model	NOUN
ajst-32552	37	8	are	be	AUX
ajst-32552	37	9	not	not	PART
ajst-32552	37	10	well	well	ADV
ajst-32552	37	11	-	-	PUNCT
ajst-32552	37	12	suited	suit	VERB
ajst-32552	37	13	for	for	ADP
ajst-32552	37	14	accurate	accurate	ADJ
ajst-32552	37	15	ghi	ghi	PROPN
ajst-32552	37	16	forecasting	forecasting	NOUN
ajst-32552	37	17	.	.	PUNCT
ajst-32552	38	1	given	give	VERB
ajst-32552	38	2	these	these	DET
ajst-32552	38	3	limitations	limitation	NOUN
ajst-32552	38	4	,	,	PUNCT
ajst-32552	38	5	nonlinear	nonlinear	ADJ
ajst-32552	38	6	models	model	NOUN
ajst-32552	38	7	such	such	ADJ
ajst-32552	38	8	as	as	ADP
ajst-32552	38	9	random	random	ADJ
ajst-32552	38	10	forests	forest	NOUN
ajst-32552	38	11	and	and	CCONJ
ajst-32552	38	12	decision	decision	NOUN
ajst-32552	38	13	trees	tree	NOUN
ajst-32552	38	14	offer	offer	VERB
ajst-32552	38	15	promising	promising	ADJ
ajst-32552	38	16	alternatives	alternative	NOUN
ajst-32552	39	1	[	[	X
ajst-32552	39	2	2	2	NUM
ajst-32552	39	3	,	,	PUNCT
ajst-32552	39	4	4	4	NUM
ajst-32552	39	5	]	]	PUNCT
ajst-32552	39	6	.	.	PUNCT
ajst-32552	40	1	these	these	DET
ajst-32552	40	2	machine	machine	NOUN
ajst-32552	40	3	learning	learn	VERB
ajst-32552	40	4	approaches	approach	NOUN
ajst-32552	40	5	can	can	AUX
ajst-32552	40	6	capture	capture	VERB
ajst-32552	40	7	complex	complex	ADJ
ajst-32552	40	8	relationships	relationship	NOUN
ajst-32552	40	9	without	without	ADP
ajst-32552	40	10	relying	rely	VERB
ajst-32552	40	11	on	on	ADP
ajst-32552	40	12	linear	linear	PROPN
ajst-32552	40	13	assumptions	assumption	NOUN
ajst-32552	40	14	,	,	PUNCT
ajst-32552	40	15	thereby	thereby	ADV
ajst-32552	40	16	improving	improve	VERB
ajst-32552	40	17	prediction	prediction	NOUN
ajst-32552	40	18	accuracy	accuracy	NOUN
ajst-32552	40	19	.	.	PUNCT
ajst-32552	41	1	existing	exist	VERB
ajst-32552	41	2	models	model	NOUN
ajst-32552	41	3	,	,	PUNCT
ajst-32552	41	4	such	such	ADJ
ajst-32552	41	5	as	as	ADP
ajst-32552	41	6	support	support	NOUN
ajst-32552	41	7	vector	vector	NOUN
ajst-32552	41	8	regression	regression	NOUN
ajst-32552	41	9	(	(	PUNCT
ajst-32552	41	10	svr	svr	PROPN
ajst-32552	41	11	)	)	PUNCT
ajst-32552	41	12	,	,	PUNCT
ajst-32552	41	13	are	be	AUX
ajst-32552	41	14	widely	widely	ADV
ajst-32552	41	15	used	use	VERB
ajst-32552	41	16	in	in	ADP
ajst-32552	41	17	short	short	ADJ
ajst-32552	41	18	-	-	PUNCT
ajst-32552	41	19	term	term	NOUN
ajst-32552	41	20	ghi	ghi	PROPN
ajst-32552	41	21	forecasting	forecasting	NOUN
ajst-32552	41	22	,	,	PUNCT
ajst-32552	41	23	while	while	SCONJ
ajst-32552	41	24	random	random	ADJ
ajst-32552	41	25	forest	forest	NOUN
ajst-32552	41	26	(	(	PUNCT
ajst-32552	41	27	rf	rf	NOUN
ajst-32552	41	28	)	)	PUNCT
ajst-32552	41	29	performs	perform	VERB
ajst-32552	41	30	effectively	effectively	ADV
ajst-32552	41	31	with	with	ADP
ajst-32552	41	32	multi	multi	ADJ
ajst-32552	41	33	-	-	ADJ
ajst-32552	41	34	variable	variable	ADJ
ajst-32552	41	35	weather	weather	NOUN
ajst-32552	41	36	data	datum	NOUN
ajst-32552	41	37	and	and	CCONJ
ajst-32552	41	38	short	short	ADJ
ajst-32552	41	39	-	-	PUNCT
ajst-32552	41	40	term	term	NOUN
ajst-32552	41	41	predictions	prediction	NOUN
ajst-32552	41	42	[	[	X
ajst-32552	41	43	2	2	NUM
ajst-32552	41	44	,	,	PUNCT
ajst-32552	41	45	5	5	NUM
ajst-32552	41	46	]	]	PUNCT
ajst-32552	41	47	.	.	PUNCT
ajst-32552	42	1	based	base	VERB
ajst-32552	42	2	on	on	ADP
ajst-32552	42	3	these	these	DET
ajst-32552	42	4	considerations	consideration	NOUN
ajst-32552	42	5	,	,	PUNCT
ajst-32552	42	6	this	this	DET
ajst-32552	42	7	study	study	NOUN
ajst-32552	42	8	will	will	AUX
ajst-32552	42	9	focus	focus	VERB
ajst-32552	42	10	on	on	ADP
ajst-32552	42	11	linear	linear	ADJ
ajst-32552	42	12	regression	regression	NOUN
ajst-32552	42	13	(	(	PUNCT
ajst-32552	42	14	accounting	account	VERB
ajst-32552	42	15	for	for	ADP
ajst-32552	42	16	potential	potential	ADJ
ajst-32552	42	17	linear	linear	ADJ
ajst-32552	42	18	relationships	relationship	NOUN
ajst-32552	42	19	)	)	PUNCT
ajst-32552	42	20	,	,	PUNCT
ajst-32552	42	21	decision	decision	NOUN
ajst-32552	42	22	trees	tree	NOUN
ajst-32552	42	23	,	,	PUNCT
ajst-32552	42	24	and	and	CCONJ
ajst-32552	42	25	random	random	ADJ
ajst-32552	42	26	forests	forest	NOUN
ajst-32552	42	27	(	(	PUNCT
ajst-32552	42	28	addressing	address	VERB
ajst-32552	42	29	nonlinear	nonlinear	ADJ
ajst-32552	42	30	variable	variable	ADJ
ajst-32552	42	31	interactions	interaction	NOUN
ajst-32552	42	32	)	)	PUNCT
ajst-32552	43	1	[	[	X
ajst-32552	43	2	2	2	NUM
ajst-32552	43	3	]	]	PUNCT
ajst-32552	43	4	.	.	PUNCT
ajst-32552	44	1	this	this	DET
ajst-32552	44	2	research	research	NOUN
ajst-32552	44	3	aims	aim	VERB
ajst-32552	44	4	to	to	PART
ajst-32552	44	5	provide	provide	VERB
ajst-32552	44	6	policymakers	policymaker	NOUN
ajst-32552	44	7	and	and	CCONJ
ajst-32552	44	8	research	research	NOUN
ajst-32552	44	9	institutions	institution	NOUN
ajst-32552	44	10	with	with	ADP
ajst-32552	44	11	valuable	valuable	ADJ
ajst-32552	44	12	references	reference	NOUN
ajst-32552	44	13	for	for	ADP
ajst-32552	44	14	urban	urban	ADJ
ajst-32552	44	15	energy	energy	NOUN
ajst-32552	44	16	infrastructure	infrastructure	NOUN
ajst-32552	44	17	planning	planning	NOUN
ajst-32552	44	18	,	,	PUNCT
ajst-32552	44	19	helping	help	VERB
ajst-32552	44	20	to	to	PART
ajst-32552	44	21	avoid	avoid	VERB
ajst-32552	44	22	underor	underor	NOUN
ajst-32552	44	23	over	over	ADP
ajst-32552	44	24	-	-	PUNCT
ajst-32552	44	25	investment	investment	NOUN
ajst-32552	44	26	in	in	ADP
ajst-32552	44	27	energy	energy	NOUN
ajst-32552	44	28	installations	installation	NOUN
ajst-32552	44	29	[	[	X
ajst-32552	44	30	3	3	NUM
ajst-32552	44	31	]	]	PUNCT
ajst-32552	44	32	.	.	PUNCT
ajst-32552	45	1	in	in	ADP
ajst-32552	45	2	addition	addition	NOUN
ajst-32552	45	3	,	,	PUNCT
ajst-32552	45	4	applied	apply	VERB
ajst-32552	45	5	g	g	NOUN
ajst-32552	45	6	prediction	prediction	NOUN
ajst-32552	45	7	supports	support	VERB
ajst-32552	45	8	carbon	carbon	NOUN
ajst-32552	45	9	reduction	reduction	NOUN
ajst-32552	45	10	efforts	effort	NOUN
ajst-32552	45	11	,	,	PUNCT
ajst-32552	45	12	a	a	DET
ajst-32552	45	13	topic	topic	NOUN
ajst-32552	45	14	of	of	ADP
ajst-32552	45	15	major	major	ADJ
ajst-32552	45	16	global	global	ADJ
ajst-32552	45	17	importance	importance	NOUN
ajst-32552	45	18	in	in	ADP
ajst-32552	45	19	recent	recent	ADJ
ajst-32552	45	20	years	year	NOUN
ajst-32552	46	1	[	[	X
ajst-32552	46	2	1	1	NUM
ajst-32552	46	3	]	]	PUNCT
ajst-32552	46	4	.	.	PUNCT
ajst-32552	47	1	it	it	PRON
ajst-32552	47	2	further	far	ADV
ajst-32552	47	3	promotes	promote	VERB
ajst-32552	47	4	interdisciplinary	interdisciplinary	ADJ
ajst-32552	47	5	research	research	NOUN
ajst-32552	47	6	integrating	integrate	VERB
ajst-32552	47	7	environmental	environmental	ADJ
ajst-32552	47	8	science	science	NOUN
ajst-32552	47	9	and	and	CCONJ
ajst-32552	47	10	artificial	artificial	ADJ
ajst-32552	47	11	intelligence	intelligence	NOUN
ajst-32552	47	12	.	.	PUNCT
ajst-32552	48	1	ai	ai	VERB
ajst-32552	48	2	-	-	PUNCT
ajst-32552	48	3	based	base	VERB
ajst-32552	48	4	models	model	NOUN
ajst-32552	48	5	facilitate	facilitate	VERB
ajst-32552	48	6	the	the	DET
ajst-32552	48	7	processing	processing	NOUN
ajst-32552	48	8	of	of	ADP
ajst-32552	48	9	largescale	largescale	NOUN
ajst-32552	48	10	datasets	dataset	NOUN
ajst-32552	48	11	,	,	PUNCT
ajst-32552	48	12	significantly	significantly	ADV
ajst-32552	48	13	reducing	reduce	VERB
ajst-32552	48	14	the	the	DET
ajst-32552	48	15	time	time	NOUN
ajst-32552	48	16	and	and	CCONJ
ajst-32552	48	17	effort	effort	NOUN
ajst-32552	48	18	required	require	VERB
ajst-32552	48	19	for	for	ADP
ajst-32552	48	20	data	datum	NOUN
ajst-32552	48	21	cleaning	cleaning	NOUN
ajst-32552	48	22	and	and	CCONJ
ajst-32552	48	23	multidimensional	multidimensional	ADJ
ajst-32552	48	24	variable	variable	ADJ
ajst-32552	48	25	integration	integration	NOUN
ajst-32552	48	26	[	[	X
ajst-32552	48	27	2	2	NUM
ajst-32552	48	28	]	]	PUNCT
ajst-32552	48	29	.	.	PUNCT
ajst-32552	49	1	they	they	PRON
ajst-32552	49	2	also	also	ADV
ajst-32552	49	3	offer	offer	VERB
ajst-32552	49	4	robust	robust	ADJ
ajst-32552	49	5	and	and	CCONJ
ajst-32552	49	6	accurate	accurate	ADJ
ajst-32552	49	7	forecasting	forecasting	NOUN
ajst-32552	49	8	tools	tool	NOUN
ajst-32552	49	9	.	.	PUNCT
ajst-32552	50	1	moreover	moreover	ADV
ajst-32552	50	2	,	,	PUNCT
ajst-32552	50	3	ghi	ghi	PROPN
ajst-32552	50	4	prediction	prediction	NOUN
ajst-32552	50	5	enables	enable	VERB
ajst-32552	50	6	governments	government	NOUN
ajst-32552	50	7	to	to	PART
ajst-32552	50	8	formulate	formulate	VERB
ajst-32552	50	9	dynamic	dynamic	ADJ
ajst-32552	50	10	renewable	renewable	ADJ
ajst-32552	50	11	energy	energy	NOUN
ajst-32552	50	12	policies	policy	NOUN
ajst-32552	50	13	and	and	CCONJ
ajst-32552	50	14	implement	implement	VERB
ajst-32552	50	15	adaptive	adaptive	ADJ
ajst-32552	50	16	strategies	strategy	NOUN
ajst-32552	50	17	in	in	ADP
ajst-32552	50	18	real	real	ADJ
ajst-32552	50	19	time	time	NOUN
ajst-32552	50	20	.	.	PUNCT
ajst-32552	51	1	3	3	X
ajst-32552	51	2	.	.	X
ajst-32552	51	3	dataset	dataset	ADJ
ajst-32552	51	4	description	description	NOUN
ajst-32552	51	5	3.1	3.1	NUM
ajst-32552	51	6	data	datum	NOUN
ajst-32552	51	7	sources	source	NOUN
ajst-32552	51	8	based	base	VERB
ajst-32552	51	9	on	on	ADP
ajst-32552	51	10	all	all	DET
ajst-32552	51	11	the	the	DET
ajst-32552	51	12	references	reference	NOUN
ajst-32552	51	13	,	,	PUNCT
ajst-32552	51	14	the	the	DET
ajst-32552	51	15	data	data	NOUN
ajst-32552	51	16	is	be	AUX
ajst-32552	51	17	sourced	source	VERB
ajst-32552	51	18	from	from	ADP
ajst-32552	51	19	the	the	DET
ajst-32552	51	20	national	national	ADJ
ajst-32552	51	21	solar	solar	ADJ
ajst-32552	51	22	radiation	radiation	NOUN
ajst-32552	51	23	database	database	NOUN
ajst-32552	51	24	for	for	ADP
ajst-32552	51	25	seattle	seattle	PROPN
ajst-32552	51	26	2023	2023	NUM
ajst-32552	51	27	,	,	PUNCT
ajst-32552	51	28	which	which	PRON
ajst-32552	51	29	includes	include	VERB
ajst-32552	51	30	records	record	NOUN
ajst-32552	51	31	from	from	ADP
ajst-32552	51	32	1	1	NUM
ajst-32552	51	33	january	january	NOUN
ajst-32552	51	34	2023	2023	NUM
ajst-32552	51	35	to	to	ADP
ajst-32552	51	36	31	31	NUM
ajst-32552	51	37	december	december	PROPN
ajst-32552	51	38	2023	2023	NUM
ajst-32552	51	39	and	and	CCONJ
ajst-32552	51	40	contains	contain	VERB
ajst-32552	51	41	all	all	DET
ajst-32552	51	42	features	feature	NOUN
ajst-32552	51	43	necessary	necessary	ADJ
ajst-32552	51	44	for	for	ADP
ajst-32552	51	45	prediction	prediction	NOUN
ajst-32552	51	46	[	[	X
ajst-32552	51	47	2	2	NUM
ajst-32552	51	48	]	]	PUNCT
ajst-32552	51	49	.	.	PUNCT
ajst-32552	52	1	this	this	DET
ajst-32552	52	2	source	source	NOUN
ajst-32552	52	3	is	be	AUX
ajst-32552	52	4	reliable	reliable	ADJ
ajst-32552	52	5	and	and	CCONJ
ajst-32552	52	6	efficient	efficient	ADJ
ajst-32552	52	7	,	,	PUNCT
ajst-32552	52	8	as	as	SCONJ
ajst-32552	52	9	it	it	PRON
ajst-32552	52	10	provides	provide	VERB
ajst-32552	52	11	locationspecific	locationspecific	NOUN
ajst-32552	52	12	and	and	CCONJ
ajst-32552	52	13	timestamped	timestamped	ADJ
ajst-32552	52	14	data	datum	NOUN
ajst-32552	52	15	at	at	ADP
ajst-32552	52	16	regular	regular	ADJ
ajst-32552	52	17	intervals	interval	NOUN
ajst-32552	52	18	,	,	PUNCT
ajst-32552	52	19	making	make	VERB
ajst-32552	52	20	it	it	PRON
ajst-32552	52	21	suitable	suitable	ADJ
ajst-32552	52	22	for	for	ADP
ajst-32552	52	23	time	time	NOUN
ajst-32552	52	24	-	-	PUNCT
ajst-32552	52	25	series	series	NOUN
ajst-32552	52	26	analysis	analysis	NOUN
ajst-32552	52	27	.	.	PUNCT
ajst-32552	53	1	3.2	3.2	NUM
ajst-32552	53	2	target	target	NOUN
ajst-32552	53	3	variable	variable	NOUN
ajst-32552	53	4	:	:	PUNCT
ajst-32552	53	5	global	global	ADJ
ajst-32552	53	6	horizontal	horizontal	ADJ
ajst-32552	53	7	irradiance	irradiance	NOUN
ajst-32552	53	8	(	(	PUNCT
ajst-32552	53	9	ghi	ghi	PROPN
ajst-32552	53	10	)	)	PUNCT
ajst-32552	53	11	the	the	DET
ajst-32552	53	12	target	target	NOUN
ajst-32552	53	13	variable	variable	NOUN
ajst-32552	53	14	is	be	AUX
ajst-32552	53	15	global	global	ADJ
ajst-32552	53	16	horizontal	horizontal	ADJ
ajst-32552	53	17	irradiance	irradiance	NOUN
ajst-32552	53	18	(	(	PUNCT
ajst-32552	53	19	ghi	ghi	PROPN
ajst-32552	53	20	)	)	PUNCT
ajst-32552	53	21	,	,	PUNCT
ajst-32552	53	22	which	which	PRON
ajst-32552	53	23	measures	measure	VERB
ajst-32552	53	24	the	the	DET
ajst-32552	53	25	total	total	ADJ
ajst-32552	53	26	solar	solar	ADJ
ajst-32552	53	27	radiation	radiation	NOUN
ajst-32552	53	28	—	—	PUNCT
ajst-32552	53	29	including	include	VERB
ajst-32552	53	30	both	both	CCONJ
ajst-32552	53	31	direct	direct	ADJ
ajst-32552	53	32	and	and	CCONJ
ajst-32552	53	33	diffuse	diffuse	ADJ
ajst-32552	53	34	sunlight	sunlight	NOUN
ajst-32552	53	35	—	—	PUNCT
ajst-32552	53	36	incident	incident	NOUN
ajst-32552	53	37	on	on	ADP
ajst-32552	53	38	a	a	DET
ajst-32552	53	39	horizontal	horizontal	ADJ
ajst-32552	53	40	surface	surface	NOUN
ajst-32552	53	41	on	on	ADP
ajst-32552	53	42	the	the	DET
ajst-32552	53	43	earth	earth	NOUN
ajst-32552	53	44	.	.	PUNCT
ajst-32552	54	1	3.3	3.3	NUM
ajst-32552	54	2	auxiliary	auxiliary	ADJ
ajst-32552	54	3	data	datum	NOUN
ajst-32552	54	4	and	and	CCONJ
ajst-32552	54	5	splitting	splitting	NOUN
ajst-32552	54	6	strategies	strategy	NOUN
ajst-32552	54	7	3.3.1	3.3.1	NUM
ajst-32552	54	8	day	day	NOUN
ajst-32552	54	9	/	/	SYM
ajst-32552	54	10	night	night	NOUN
ajst-32552	54	11	dataset	dataset	NOUN
ajst-32552	54	12	splitting	split	VERB
ajst-32552	54	13	the	the	DET
ajst-32552	54	14	dataset	dataset	NOUN
ajst-32552	54	15	is	be	AUX
ajst-32552	54	16	divided	divide	VERB
ajst-32552	54	17	into	into	ADP
ajst-32552	54	18	daytime	daytime	NOUN
ajst-32552	54	19	and	and	CCONJ
ajst-32552	54	20	nighttime	nighttime	ADJ
ajst-32552	54	21	subsets	subset	NOUN
ajst-32552	54	22	based	base	VERB
ajst-32552	54	23	on	on	ADP
ajst-32552	54	24	sunrise	sunrise	NOUN
ajst-32552	54	25	and	and	CCONJ
ajst-32552	54	26	sunset	sunset	NOUN
ajst-32552	54	27	times	time	NOUN
ajst-32552	54	28	in	in	ADP
ajst-32552	54	29	seattle	seattle	NOUN
ajst-32552	54	30	for	for	ADP
ajst-32552	54	31	2023	2023	NUM
ajst-32552	54	32	,	,	PUNCT
ajst-32552	54	33	obtained	obtain	VERB
ajst-32552	54	34	via	via	ADP
ajst-32552	54	35	a	a	DET
ajst-32552	54	36	sunrise	sunrise	NOUN
ajst-32552	54	37	-	-	PUNCT
ajst-32552	54	38	sunset	sunset	NOUN
ajst-32552	54	39	api	api	NOUN
ajst-32552	54	40	.	.	PUNCT
ajst-32552	55	1	3.3.2	3.3.2	NUM
ajst-32552	55	2	train	train	NOUN
ajst-32552	55	3	and	and	CCONJ
ajst-32552	55	4	test	test	NOUN
ajst-32552	55	5	data	datum	NOUN
ajst-32552	55	6	split	split	VERB
ajst-32552	55	7	the	the	DET
ajst-32552	55	8	entire	entire	ADJ
ajst-32552	55	9	hourly	hourly	ADJ
ajst-32552	55	10	dataset	dataset	NOUN
ajst-32552	55	11	for	for	ADP
ajst-32552	55	12	2023	2023	NUM
ajst-32552	55	13	is	be	AUX
ajst-32552	55	14	partitioned	partition	VERB
ajst-32552	55	15	into	into	ADP
ajst-32552	55	16	80	80	NUM
ajst-32552	55	17	%	%	NOUN
ajst-32552	55	18	for	for	ADP
ajst-32552	55	19	training	training	NOUN
ajst-32552	55	20	(	(	PUNCT
ajst-32552	55	21	approximately	approximately	ADV
ajst-32552	55	22	7,000	7,000	NUM
ajst-32552	55	23	hours	hour	NOUN
ajst-32552	55	24	)	)	PUNCT
ajst-32552	55	25	and	and	CCONJ
ajst-32552	55	26	20	20	NUM
ajst-32552	55	27	%	%	NOUN
ajst-32552	55	28	for	for	ADP
ajst-32552	55	29	testing	testing	NOUN
ajst-32552	55	30	(	(	PUNCT
ajst-32552	55	31	approximately	approximately	ADV
ajst-32552	55	32	1,800	1,800	NUM
ajst-32552	55	33	hours	hour	NOUN
ajst-32552	55	34	)	)	PUNCT
ajst-32552	55	35	.	.	PUNCT
ajst-32552	56	1	this	this	PRON
ajst-32552	56	2	ensures	ensure	VERB
ajst-32552	56	3	sufficient	sufficient	ADJ
ajst-32552	56	4	data	datum	NOUN
ajst-32552	56	5	for	for	ADP
ajst-32552	56	6	model	model	NOUN
ajst-32552	56	7	development	development	NOUN
ajst-32552	56	8	while	while	SCONJ
ajst-32552	56	9	preserving	preserve	VERB
ajst-32552	56	10	an	an	DET
ajst-32552	56	11	independent	independent	ADJ
ajst-32552	56	12	test	test	NOUN
ajst-32552	56	13	set	set	VERB
ajst-32552	56	14	for	for	ADP
ajst-32552	56	15	performance	performance	NOUN
ajst-32552	56	16	evaluation	evaluation	NOUN
ajst-32552	56	17	.	.	PUNCT
ajst-32552	57	1	3.4	3.4	NUM
ajst-32552	57	2	data	datum	NOUN
ajst-32552	57	3	cleaning	cleaning	NOUN
ajst-32552	57	4	and	and	CCONJ
ajst-32552	57	5	visualization	visualization	NOUN
ajst-32552	57	6	all	all	PRON
ajst-32552	57	7	missing	miss	VERB
ajst-32552	57	8	and	and	CCONJ
ajst-32552	57	9	na	na	ADP
ajst-32552	57	10	values	value	NOUN
ajst-32552	57	11	were	be	AUX
ajst-32552	57	12	removed	remove	VERB
ajst-32552	57	13	,	,	PUNCT
ajst-32552	57	14	and	and	CCONJ
ajst-32552	57	15	feature	feature	NOUN
ajst-32552	57	16	values	value	NOUN
ajst-32552	57	17	were	be	AUX
ajst-32552	57	18	analyzed	analyze	VERB
ajst-32552	57	19	using	use	VERB
ajst-32552	57	20	consistent	consistent	ADJ
ajst-32552	57	21	units	unit	NOUN
ajst-32552	57	22	.	.	PUNCT
ajst-32552	58	1	the	the	DET
ajst-32552	58	2	dataset	dataset	NOUN
ajst-32552	58	3	comprises	comprise	VERB
ajst-32552	58	4	22	22	NUM
ajst-32552	58	5	features	feature	NOUN
ajst-32552	58	6	in	in	ADP
ajst-32552	58	7	total	total	ADJ
ajst-32552	58	8	:	:	PUNCT
ajst-32552	58	9	temperature	temperature	NOUN
ajst-32552	58	10	,	,	PUNCT
ajst-32552	58	11	alpha	alpha	PROPN
ajst-32552	58	12	,	,	PUNCT
ajst-32552	58	13	aod	aod	PROPN
ajst-32552	58	14	,	,	PUNCT
ajst-32552	58	15	asymmetry	asymmetry	NOUN
ajst-32552	58	16	,	,	PUNCT
ajst-32552	58	17	clearsky	clearsky	PROPN
ajst-32552	58	18	dhi	dhi	PROPN
ajst-32552	58	19	,	,	PUNCT
ajst-32552	58	20	104	104	NUM
ajst-32552	58	21	clearsky	clearsky	PROPN
ajst-32552	58	22	dni	dni	PROPN
ajst-32552	58	23	,	,	PUNCT
ajst-32552	58	24	clearsky	clearsky	PROPN
ajst-32552	58	25	ghi	ghi	PROPN
ajst-32552	58	26	,	,	PUNCT
ajst-32552	58	27	cloud	cloud	NOUN
ajst-32552	58	28	fill	fill	NOUN
ajst-32552	58	29	flag	flag	NOUN
ajst-32552	58	30	,	,	PUNCT
ajst-32552	58	31	cloud	cloud	NOUN
ajst-32552	58	32	type	type	NOUN
ajst-32552	58	33	,	,	PUNCT
ajst-32552	58	34	dew	dew	NOUN
ajst-32552	58	35	point	point	NOUN
ajst-32552	58	36	,	,	PUNCT
ajst-32552	58	37	dhi	dhi	PROPN
ajst-32552	58	38	,	,	PUNCT
ajst-32552	58	39	dni	dni	PROPN
ajst-32552	58	40	,	,	PUNCT
ajst-32552	58	41	fill	fill	VERB
ajst-32552	58	42	flag	flag	NOUN
ajst-32552	58	43	,	,	PUNCT
ajst-32552	58	44	ozone	ozone	NOUN
ajst-32552	58	45	,	,	PUNCT
ajst-32552	58	46	relative	relative	ADJ
ajst-32552	58	47	humidity	humidity	NOUN
ajst-32552	58	48	,	,	PUNCT
ajst-32552	58	49	solar	solar	ADJ
ajst-32552	58	50	zenith	zenith	NOUN
ajst-32552	58	51	angle	angle	NOUN
ajst-32552	58	52	,	,	PUNCT
ajst-32552	58	53	ssa	ssa	NOUN
ajst-32552	58	54	,	,	PUNCT
ajst-32552	58	55	surface	surface	NOUN
ajst-32552	58	56	albedo	albedo	NOUN
ajst-32552	58	57	,	,	PUNCT
ajst-32552	58	58	pressure	pressure	NOUN
ajst-32552	58	59	,	,	PUNCT
ajst-32552	58	60	precipitable	precipitable	ADJ
ajst-32552	58	61	water	water	NOUN
ajst-32552	58	62	,	,	PUNCT
ajst-32552	58	63	wind	wind	NOUN
ajst-32552	58	64	direction	direction	NOUN
ajst-32552	58	65	,	,	PUNCT
ajst-32552	58	66	wind	wind	NOUN
ajst-32552	58	67	speed	speed	NOUN
ajst-32552	58	68	.	.	PUNCT
ajst-32552	59	1	principal	principal	ADJ
ajst-32552	59	2	component	component	NOUN
ajst-32552	59	3	analysis	analysis	NOUN
ajst-32552	59	4	(	(	PUNCT
ajst-32552	59	5	pca	pca	NOUN
ajst-32552	59	6	)	)	PUNCT
ajst-32552	59	7	was	be	AUX
ajst-32552	59	8	initially	initially	ADV
ajst-32552	59	9	applied	apply	VERB
ajst-32552	59	10	for	for	ADP
ajst-32552	59	11	dimensionality	dimensionality	NOUN
ajst-32552	59	12	reduction	reduction	NOUN
ajst-32552	59	13	.	.	PUNCT
ajst-32552	60	1	however	however	ADV
ajst-32552	60	2	,	,	PUNCT
ajst-32552	60	3	the	the	DET
ajst-32552	60	4	result	result	NOUN
ajst-32552	60	5	retained	retain	VERB
ajst-32552	60	6	approximately	approximately	ADV
ajst-32552	60	7	18	18	NUM
ajst-32552	60	8	components	component	NOUN
ajst-32552	60	9	,	,	PUNCT
ajst-32552	60	10	indicating	indicate	VERB
ajst-32552	60	11	limited	limited	ADJ
ajst-32552	60	12	linear	linear	ADJ
ajst-32552	60	13	correlation	correlation	NOUN
ajst-32552	60	14	among	among	ADP
ajst-32552	60	15	variables	variable	NOUN
ajst-32552	60	16	and	and	CCONJ
ajst-32552	60	17	inefficacy	inefficacy	NOUN
ajst-32552	60	18	of	of	ADP
ajst-32552	60	19	pca	pca	PROPN
ajst-32552	60	20	for	for	ADP
ajst-32552	60	21	this	this	DET
ajst-32552	60	22	dataset	dataset	NOUN
ajst-32552	60	23	.	.	PUNCT
ajst-32552	61	1	this	this	PRON
ajst-32552	61	2	further	far	ADV
ajst-32552	61	3	underscores	underscore	VERB
ajst-32552	61	4	the	the	DET
ajst-32552	61	5	limitations	limitation	NOUN
ajst-32552	61	6	of	of	ADP
ajst-32552	61	7	traditional	traditional	ADJ
ajst-32552	61	8	statistical	statistical	ADJ
ajst-32552	61	9	models	model	NOUN
ajst-32552	61	10	and	and	CCONJ
ajst-32552	61	11	supports	support	VERB
ajst-32552	61	12	a	a	DET
ajst-32552	61	13	focused	focused	ADJ
ajst-32552	61	14	analysis	analysis	NOUN
ajst-32552	61	15	on	on	ADP
ajst-32552	61	16	ghi	ghi	PROPN
ajst-32552	61	17	.	.	PUNCT
ajst-32552	62	1	given	give	VERB
ajst-32552	62	2	the	the	DET
ajst-32552	62	3	significant	significant	ADJ
ajst-32552	62	4	difference	difference	NOUN
ajst-32552	62	5	between	between	ADP
ajst-32552	62	6	daytime	daytime	NOUN
ajst-32552	62	7	and	and	CCONJ
ajst-32552	62	8	nighttime	nighttime	ADJ
ajst-32552	62	9	ghi	ghi	PROPN
ajst-32552	62	10	patterns	pattern	NOUN
ajst-32552	62	11	,	,	PUNCT
ajst-32552	62	12	the	the	DET
ajst-32552	62	13	data	datum	NOUN
ajst-32552	62	14	were	be	AUX
ajst-32552	62	15	split	split	VERB
ajst-32552	62	16	accordingly	accordingly	ADV
ajst-32552	62	17	to	to	PART
ajst-32552	62	18	improve	improve	VERB
ajst-32552	62	19	modeling	modeling	NOUN
ajst-32552	62	20	.	.	PUNCT
ajst-32552	63	1	day	day	NOUN
ajst-32552	63	2	/	/	SYM
ajst-32552	63	3	night	night	NOUN
ajst-32552	63	4	splitting	splitting	NOUN
ajst-32552	63	5	was	be	AUX
ajst-32552	63	6	determined	determine	VERB
ajst-32552	63	7	using	use	VERB
ajst-32552	63	8	seattle	seattle	PROPN
ajst-32552	63	9	’s	’s	PART
ajst-32552	63	10	sunrise	sunrise	NOUN
ajst-32552	63	11	and	and	CCONJ
ajst-32552	63	12	sunset	sunset	NOUN
ajst-32552	63	13	times	time	NOUN
ajst-32552	63	14	for	for	ADP
ajst-32552	63	15	2023	2023	NUM
ajst-32552	63	16	.	.	PUNCT
ajst-32552	64	1	visualization	visualization	NOUN
ajst-32552	64	2	clearly	clearly	ADV
ajst-32552	64	3	shows	show	VERB
ajst-32552	64	4	that	that	SCONJ
ajst-32552	64	5	ghi	ghi	PROPN
ajst-32552	64	6	fluctuates	fluctuate	VERB
ajst-32552	64	7	actively	actively	ADV
ajst-32552	64	8	during	during	ADP
ajst-32552	64	9	the	the	DET
ajst-32552	64	10	daytime	daytime	NOUN
ajst-32552	64	11	and	and	CCONJ
ajst-32552	64	12	remains	remain	VERB
ajst-32552	64	13	near	near	ADP
ajst-32552	64	14	zero	zero	NUM
ajst-32552	64	15	at	at	ADP
ajst-32552	64	16	night	night	NOUN
ajst-32552	64	17	.	.	PUNCT
ajst-32552	65	1	figure	figure	NOUN
ajst-32552	65	2	1	1	NUM
ajst-32552	65	3	clearly	clearly	ADV
ajst-32552	65	4	shows	show	VERB
ajst-32552	65	5	that	that	SCONJ
ajst-32552	65	6	the	the	DET
ajst-32552	65	7	ghi	ghi	PROPN
ajst-32552	65	8	value	value	NOUN
ajst-32552	65	9	is	be	AUX
ajst-32552	65	10	actively	actively	ADV
ajst-32552	65	11	fluctuating	fluctuate	VERB
ajst-32552	65	12	during	during	ADP
ajst-32552	65	13	daytime	daytime	NOUN
ajst-32552	65	14	,	,	PUNCT
ajst-32552	65	15	and	and	CCONJ
ajst-32552	65	16	almost	almost	ADV
ajst-32552	65	17	at	at	ADP
ajst-32552	65	18	value	value	NOUN
ajst-32552	65	19	0	0	NUM
ajst-32552	65	20	during	during	ADP
ajst-32552	65	21	nighttime	nighttime	NOUN
ajst-32552	65	22	[	[	X
ajst-32552	65	23	7	7	NUM
ajst-32552	65	24	]	]	PUNCT
ajst-32552	65	25	.	.	PUNCT
ajst-32552	66	1	figure	figure	NOUN
ajst-32552	66	2	1	1	NUM
ajst-32552	66	3	.	.	PUNCT
ajst-32552	66	4	day	day	NOUN
ajst-32552	66	5	/	/	SYM
ajst-32552	66	6	night	night	NOUN
ajst-32552	66	7	classification	classification	NOUN
ajst-32552	66	8	3.4.1	3.4.1	NUM
ajst-32552	66	9	short	short	ADJ
ajst-32552	66	10	-	-	PUNCT
ajst-32552	66	11	term	term	NOUN
ajst-32552	66	12	ghi	ghi	PROPN
ajst-32552	66	13	prediction	prediction	NOUN
ajst-32552	66	14	using	use	VERB
ajst-32552	66	15	machine	machine	NOUN
ajst-32552	66	16	learning	learning	NOUN
ajst-32552	66	17	(	(	PUNCT
ajst-32552	66	18	10	10	NUM
ajst-32552	66	19	days	day	NOUN
ajst-32552	66	20	→	→	SYM
ajst-32552	66	21	1	1	NUM
ajst-32552	66	22	day	day	NOUN
ajst-32552	66	23	)	)	PUNCT
ajst-32552	66	24	a	a	DET
ajst-32552	66	25	sliding	slide	VERB
ajst-32552	66	26	window	window	NOUN
ajst-32552	66	27	method	method	NOUN
ajst-32552	66	28	was	be	AUX
ajst-32552	66	29	employed	employ	VERB
ajst-32552	66	30	,	,	PUNCT
ajst-32552	66	31	using	use	VERB
ajst-32552	66	32	the	the	DET
ajst-32552	66	33	past	past	ADJ
ajst-32552	66	34	10	10	NUM
ajst-32552	66	35	days	day	NOUN
ajst-32552	66	36	(	(	PUNCT
ajst-32552	66	37	240	240	NUM
ajst-32552	66	38	hours	hour	NOUN
ajst-32552	66	39	)	)	PUNCT
ajst-32552	66	40	of	of	ADP
ajst-32552	66	41	data	datum	NOUN
ajst-32552	66	42	to	to	PART
ajst-32552	66	43	predict	predict	VERB
ajst-32552	66	44	ghi	ghi	PROPN
ajst-32552	66	45	values	value	NOUN
ajst-32552	66	46	for	for	ADP
ajst-32552	66	47	the	the	DET
ajst-32552	66	48	next	next	ADJ
ajst-32552	66	49	24	24	NUM
ajst-32552	66	50	hours	hour	NOUN
ajst-32552	66	51	.	.	PUNCT
ajst-32552	67	1	this	this	DET
ajst-32552	67	2	approach	approach	NOUN
ajst-32552	67	3	helps	help	VERB
ajst-32552	67	4	temporal	temporal	ADJ
ajst-32552	67	5	pattern	pattern	NOUN
ajst-32552	67	6	recognition	recognition	NOUN
ajst-32552	67	7	in	in	ADP
ajst-32552	67	8	machine	machine	NOUN
ajst-32552	67	9	learning	learning	NOUN
ajst-32552	67	10	models	model	NOUN
ajst-32552	67	11	.	.	PUNCT
ajst-32552	68	1	3.4.2	3.4.2	NUM
ajst-32552	68	2	model	model	NOUN
ajst-32552	68	3	comparison	comparison	NOUN
ajst-32552	68	4	and	and	CCONJ
ajst-32552	68	5	performance	performance	NOUN
ajst-32552	68	6	evaluation	evaluation	NOUN
ajst-32552	68	7	the	the	DET
ajst-32552	68	8	following	follow	VERB
ajst-32552	68	9	information	information	NOUN
ajst-32552	68	10	summarizes	summarize	VERB
ajst-32552	68	11	the	the	DET
ajst-32552	68	12	performance	performance	NOUN
ajst-32552	68	13	of	of	ADP
ajst-32552	68	14	different	different	ADJ
ajst-32552	68	15	models	model	NOUN
ajst-32552	68	16	under	under	ADP
ajst-32552	68	17	day	day	NOUN
ajst-32552	68	18	/	/	SYM
ajst-32552	68	19	night	night	NOUN
ajst-32552	68	20	splitting	splitting	NOUN
ajst-32552	68	21	and	and	CCONJ
ajst-32552	68	22	unified	unified	ADJ
ajst-32552	68	23	dataset	dataset	NOUN
ajst-32552	68	24	conditions	condition	NOUN
ajst-32552	68	25	:	:	PUNCT
ajst-32552	68	26			ADJ
ajst-32552	68	27	linear	linear	ADJ
ajst-32552	68	28	regression	regression	NOUN
ajst-32552	68	29	performed	perform	VERB
ajst-32552	68	30	best	well	ADV
ajst-32552	68	31	with	with	ADP
ajst-32552	68	32	day	day	NOUN
ajst-32552	68	33	/	/	SYM
ajst-32552	68	34	night	night	NOUN
ajst-32552	68	35	split	split	NOUN
ajst-32552	68	36	models	model	NOUN
ajst-32552	68	37	.	.	PUNCT
ajst-32552	69	1			ADJ
ajst-32552	69	2	decision	decision	NOUN
ajst-32552	69	3	trees	tree	NOUN
ajst-32552	69	4	also	also	ADV
ajst-32552	69	5	achieved	achieve	VERB
ajst-32552	69	6	the	the	DET
ajst-32552	69	7	best	good	ADJ
ajst-32552	69	8	performance	performance	NOUN
ajst-32552	69	9	under	under	ADP
ajst-32552	69	10	day	day	NOUN
ajst-32552	69	11	/	/	SYM
ajst-32552	69	12	night	night	NOUN
ajst-32552	69	13	models	model	NOUN
ajst-32552	69	14	.	.	PUNCT
ajst-32552	70	1			ADJ
ajst-32552	70	2	random	random	ADJ
ajst-32552	70	3	forest	forest	NOUN
ajst-32552	70	4	yielded	yield	VERB
ajst-32552	70	5	the	the	DET
ajst-32552	70	6	best	good	ADJ
ajst-32552	70	7	results	result	NOUN
ajst-32552	70	8	using	use	VERB
ajst-32552	70	9	the	the	DET
ajst-32552	70	10	unified	unified	ADJ
ajst-32552	70	11	dataset	dataset	NOUN
ajst-32552	70	12	(	(	PUNCT
ajst-32552	70	13	without	without	ADP
ajst-32552	70	14	day	day	NOUN
ajst-32552	70	15	/	/	SYM
ajst-32552	70	16	night	night	NOUN
ajst-32552	70	17	split	split	NOUN
ajst-32552	70	18	)	)	PUNCT
ajst-32552	70	19	.	.	PUNCT
ajst-32552	71	1	note	note	VERB
ajst-32552	71	2	:	:	PUNCT
ajst-32552	71	3	“	"	PUNCT
ajst-32552	71	4	unified	unified	ADJ
ajst-32552	71	5	model	model	NOUN
ajst-32552	71	6	”	"	PUNCT
ajst-32552	71	7	refers	refer	VERB
ajst-32552	71	8	to	to	ADP
ajst-32552	71	9	the	the	DET
ajst-32552	71	10	approach	approach	NOUN
ajst-32552	71	11	where	where	SCONJ
ajst-32552	71	12	the	the	DET
ajst-32552	71	13	dataset	dataset	NOUN
ajst-32552	71	14	is	be	AUX
ajst-32552	71	15	not	not	PART
ajst-32552	71	16	split	split	VERB
ajst-32552	71	17	into	into	ADP
ajst-32552	71	18	daytime	daytime	NOUN
ajst-32552	71	19	and	and	CCONJ
ajst-32552	71	20	nighttime	nighttime	ADJ
ajst-32552	71	21	subsets	subset	NOUN
ajst-32552	71	22	.	.	PUNCT
ajst-32552	72	1	4	4	X
ajst-32552	72	2	.	.	X
ajst-32552	72	3	methodology	methodology	NOUN
ajst-32552	72	4	4.1	4.1	NUM
ajst-32552	72	5	regression	regression	NOUN
ajst-32552	72	6	models	model	NOUN
ajst-32552	72	7	three	three	NUM
ajst-32552	72	8	regression	regression	NOUN
ajst-32552	72	9	approaches	approach	NOUN
ajst-32552	72	10	were	be	AUX
ajst-32552	72	11	applied	apply	VERB
ajst-32552	72	12	to	to	ADP
ajst-32552	72	13	model	model	PROPN
ajst-32552	72	14	ghi	ghi	PROPN
ajst-32552	72	15	:	:	PUNCT
ajst-32552	72	16	linear	linear	ADJ
ajst-32552	72	17	regression	regression	NOUN
ajst-32552	72	18	,	,	PUNCT
ajst-32552	72	19	decision	decision	NOUN
ajst-32552	72	20	tree	tree	NOUN
ajst-32552	72	21	,	,	PUNCT
ajst-32552	72	22	and	and	CCONJ
ajst-32552	72	23	random	random	ADJ
ajst-32552	72	24	forest	forest	NOUN
ajst-32552	72	25	.	.	PUNCT
ajst-32552	73	1	4.1.1	4.1.1	NUM
ajst-32552	73	2	linear	linear	ADJ
ajst-32552	73	3	regression	regression	NOUN
ajst-32552	73	4	linear	linear	PROPN
ajst-32552	73	5	regression	regression	NOUN
ajst-32552	73	6	model	model	NOUN
ajst-32552	73	7	serves	serve	VERB
ajst-32552	73	8	as	as	ADP
ajst-32552	73	9	a	a	DET
ajst-32552	73	10	baseline	baseline	NOUN
ajst-32552	73	11	,	,	PUNCT
ajst-32552	73	12	providing	provide	VERB
ajst-32552	73	13	interpretable	interpretable	ADJ
ajst-32552	73	14	results	result	NOUN
ajst-32552	73	15	but	but	CCONJ
ajst-32552	73	16	limited	limited	ADJ
ajst-32552	73	17	capacity	capacity	NOUN
ajst-32552	73	18	to	to	PART
ajst-32552	73	19	capture	capture	VERB
ajst-32552	73	20	the	the	DET
ajst-32552	73	21	nonlinear	nonlinear	ADJ
ajst-32552	73	22	variability	variability	NOUN
ajst-32552	73	23	of	of	ADP
ajst-32552	73	24	ghi	ghi	PROPN
ajst-32552	73	25	.	.	PUNCT
ajst-32552	73	26			ADJ
ajst-32552	73	27	ordinary	ordinary	ADJ
ajst-32552	73	28	least	least	ADJ
ajst-32552	73	29	squares	square	NOUN
ajst-32552	73	30	were	be	AUX
ajst-32552	73	31	used	use	VERB
ajst-32552	73	32	(	(	PUNCT
ajst-32552	73	33	no	no	DET
ajst-32552	73	34	regularization	regularization	NOUN
ajst-32552	73	35	)	)	PUNCT
ajst-32552	73	36	.	.	PUNCT
ajst-32552	74	1	105	105	NUM
ajst-32552	74	2			PROPN
ajst-32552	74	3	ridge	ridge	NOUN
ajst-32552	74	4	(	(	PUNCT
ajst-32552	74	5	α	α	PROPN
ajst-32552	74	6	∈	∈	PROPN
ajst-32552	74	7	{	{	PUNCT
ajst-32552	74	8	0.1	0.1	NUM
ajst-32552	74	9	,	,	PUNCT
ajst-32552	74	10	1	1	NUM
ajst-32552	74	11	,	,	PUNCT
ajst-32552	74	12	10	10	NUM
ajst-32552	74	13	}	}	PUNCT
ajst-32552	74	14	)	)	PUNCT
ajst-32552	74	15	and	and	CCONJ
ajst-32552	74	16	lasso	lasso	NOUN
ajst-32552	74	17	(	(	PUNCT
ajst-32552	74	18	α	α	NOUN
ajst-32552	74	19	∈	∈	PROPN
ajst-32552	74	20	{	{	PUNCT
ajst-32552	74	21	0.01	0.01	NUM
ajst-32552	74	22	,	,	PUNCT
ajst-32552	74	23	0.1	0.1	NUM
ajst-32552	74	24	}	}	PUNCT
ajst-32552	74	25	)	)	PUNCT
ajst-32552	74	26	were	be	AUX
ajst-32552	74	27	also	also	ADV
ajst-32552	74	28	tried	try	VERB
ajst-32552	74	29	,	,	PUNCT
ajst-32552	74	30	but	but	CCONJ
ajst-32552	74	31	no	no	DET
ajst-32552	74	32	improvement	improvement	NOUN
ajst-32552	74	33	over	over	ADP
ajst-32552	74	34	ols	ol	NOUN
ajst-32552	74	35	was	be	AUX
ajst-32552	74	36	observed	observe	VERB
ajst-32552	74	37	.	.	PUNCT
ajst-32552	75	1			ADJ
ajst-32552	75	2	thus	thus	ADV
ajst-32552	75	3	,	,	PUNCT
ajst-32552	75	4	regularization	regularization	NOUN
ajst-32552	75	5	was	be	AUX
ajst-32552	75	6	not	not	PART
ajst-32552	75	7	useful	useful	ADJ
ajst-32552	75	8	in	in	ADP
ajst-32552	75	9	this	this	DET
ajst-32552	75	10	case	case	NOUN
ajst-32552	75	11	,	,	PUNCT
ajst-32552	75	12	and	and	CCONJ
ajst-32552	75	13	the	the	DET
ajst-32552	75	14	ordinary	ordinary	ADJ
ajst-32552	75	15	least	least	ADJ
ajst-32552	75	16	squares	square	NOUN
ajst-32552	75	17	(	(	PUNCT
ajst-32552	75	18	ols	ol	NOUN
ajst-32552	75	19	)	)	PUNCT
ajst-32552	75	20	method	method	NOUN
ajst-32552	75	21	was	be	AUX
ajst-32552	75	22	applied	apply	VERB
ajst-32552	75	23	.	.	PUNCT
ajst-32552	76	1	4.1.2	4.1.2	NUM
ajst-32552	76	2	decision	decision	NOUN
ajst-32552	76	3	tree	tree	NOUN
ajst-32552	76	4	decision	decision	NOUN
ajst-32552	76	5	tree	tree	NOUN
ajst-32552	76	6	model	model	NOUN
ajst-32552	76	7	captures	capture	VERB
ajst-32552	76	8	nonlinear	nonlinear	ADJ
ajst-32552	76	9	relationships	relationship	NOUN
ajst-32552	76	10	in	in	ADP
ajst-32552	76	11	ghi	ghi	PROPN
ajst-32552	76	12	data	data	PROPN
ajst-32552	76	13	,	,	PUNCT
ajst-32552	76	14	offering	offer	VERB
ajst-32552	76	15	flexibility	flexibility	NOUN
ajst-32552	76	16	,	,	PUNCT
ajst-32552	76	17	though	though	SCONJ
ajst-32552	76	18	it	it	PRON
ajst-32552	76	19	may	may	AUX
ajst-32552	76	20	suffer	suffer	VERB
ajst-32552	76	21	from	from	ADP
ajst-32552	76	22	overfitting	overfitte	VERB
ajst-32552	76	23	on	on	ADP
ajst-32552	76	24	noisy	noisy	ADJ
ajst-32552	76	25	patterns	pattern	NOUN
ajst-32552	76	26	.	.	PUNCT
ajst-32552	77	1			ADJ
ajst-32552	77	2	use	use	NOUN
ajst-32552	77	3	decisiontreeregressor	decisiontreeregressor	NOUN
ajst-32552	77	4	(	(	PUNCT
ajst-32552	77	5	max_depth=10	max_depth=10	PROPN
ajst-32552	77	6	,	,	PUNCT
ajst-32552	77	7	random_state=42	random_state=42	NOUN
ajst-32552	77	8	)	)	PUNCT
ajst-32552	77	9	to	to	PART
ajst-32552	77	10	model	model	VERB
ajst-32552	77	11	ghi	ghi	PROPN
ajst-32552	77	12	as	as	ADP
ajst-32552	77	13	a	a	DET
ajst-32552	77	14	function	function	NOUN
ajst-32552	77	15	of	of	ADP
ajst-32552	77	16	the	the	DET
ajst-32552	77	17	other	other	ADJ
ajst-32552	77	18	weather	weather	NOUN
ajst-32552	77	19	features	feature	NOUN
ajst-32552	77	20	.	.	PUNCT
ajst-32552	78	1			VERB
ajst-32552	78	2	after	after	ADP
ajst-32552	78	3	importing	import	VERB
ajst-32552	78	4	and	and	CCONJ
ajst-32552	78	5	instantiating	instantiate	VERB
ajst-32552	78	6	the	the	DET
ajst-32552	78	7	regressor	regressor	NOUN
ajst-32552	78	8	with	with	ADP
ajst-32552	78	9	a	a	DET
ajst-32552	78	10	fixed	fix	VERB
ajst-32552	78	11	random_state	random_state	NOUN
ajst-32552	78	12	for	for	ADP
ajst-32552	78	13	reproducibility	reproducibility	NOUN
ajst-32552	78	14	,	,	PUNCT
ajst-32552	78	15	we	we	PRON
ajst-32552	78	16	call	call	VERB
ajst-32552	78	17	.	.	PUNCT
ajst-32552	79	1	fit	fit	ADJ
ajst-32552	79	2	(	(	PUNCT
ajst-32552	79	3	)	)	PUNCT
ajst-32552	79	4	to	to	PART
ajst-32552	79	5	train	train	VERB
ajst-32552	79	6	two	two	NUM
ajst-32552	79	7	separate	separate	ADJ
ajst-32552	79	8	trees	tree	NOUN
ajst-32552	79	9	on	on	ADP
ajst-32552	79	10	daytime	daytime	NOUN
ajst-32552	79	11	and	and	CCONJ
ajst-32552	79	12	nighttime	nighttime	ADJ
ajst-32552	79	13	data	datum	NOUN
ajst-32552	79	14	.	.	PUNCT
ajst-32552	80	1	4.1.3	4.1.3	NUM
ajst-32552	80	2	random	random	ADJ
ajst-32552	80	3	forest	forest	NOUN
ajst-32552	80	4	random	random	ADJ
ajst-32552	80	5	forest	forest	NOUN
ajst-32552	80	6	aggregates	aggregate	VERB
ajst-32552	80	7	multiple	multiple	ADJ
ajst-32552	80	8	decision	decision	NOUN
ajst-32552	80	9	trees	tree	NOUN
ajst-32552	80	10	,	,	PUNCT
ajst-32552	80	11	reducing	reduce	VERB
ajst-32552	80	12	overfitting	overfitte	VERB
ajst-32552	80	13	and	and	CCONJ
ajst-32552	80	14	achieving	achieve	VERB
ajst-32552	80	15	higher	high	ADJ
ajst-32552	80	16	accuracy	accuracy	NOUN
ajst-32552	80	17	in	in	ADP
ajst-32552	80	18	predicting	predict	VERB
ajst-32552	80	19	complex	complex	ADJ
ajst-32552	80	20	ghi	ghi	PROPN
ajst-32552	80	21	fluctuations	fluctuations	PROPN
ajst-32552	80	22	.	.	PUNCT
ajst-32552	81	1			PROPN
ajst-32552	81	2	randomforestregressor	randomforestregressor	PROPN
ajst-32552	81	3	(	(	PUNCT
ajst-32552	81	4	n_estimators=100	n_estimators=100	PROPN
ajst-32552	81	5	,	,	PUNCT
ajst-32552	81	6	max_depth=10	max_depth=10	PROPN
ajst-32552	81	7	)	)	PUNCT
ajst-32552	81	8	on	on	ADP
ajst-32552	81	9	all	all	PRON
ajst-32552	81	10	-	-	PUNCT
ajst-32552	81	11	hours	hour	NOUN
ajst-32552	81	12	weather	weather	NOUN
ajst-32552	81	13	data	datum	NOUN
ajst-32552	81	14	→	→	PUNCT
ajst-32552	81	15	predicting	predict	VERB
ajst-32552	81	16	continuous	continuous	ADJ
ajst-32552	81	17	ghi	ghi	PROPN
ajst-32552	81	18	(	(	PUNCT
ajst-32552	81	19	train	train	NOUN
ajst-32552	81	20	+	+	CCONJ
ajst-32552	81	21	validation	validation	NOUN
ajst-32552	81	22	rmse	rmse	NOUN
ajst-32552	81	23	&	&	CCONJ
ajst-32552	81	24	r²	r²	PROPN
ajst-32552	81	25	)	)	PUNCT
ajst-32552	81	26	.	.	PUNCT
ajst-32552	82	1			ADJ
ajst-32552	82	2	on	on	ADP
ajst-32552	82	3	the	the	DET
ajst-32552	82	4	same	same	ADJ
ajst-32552	82	5	features	feature	NOUN
ajst-32552	82	6	→	→	PUNCT
ajst-32552	82	7	predicting	predict	VERB
ajst-32552	82	8	the	the	DET
ajst-32552	82	9	binary	binary	ADJ
ajst-32552	82	10	is_daylabel	is_daylabel	PROPN
ajst-32552	82	11	(	(	PUNCT
ajst-32552	82	12	train	train	NOUN
ajst-32552	82	13	/	/	SYM
ajst-32552	82	14	validation	validation	NOUN
ajst-32552	82	15	accuracy	accuracy	NOUN
ajst-32552	82	16	)	)	PUNCT
ajst-32552	82	17	.	.	PUNCT
ajst-32552	83	1	4.2	4.2	NUM
ajst-32552	83	2	time	time	NOUN
ajst-32552	83	3	-	-	PUNCT
ajst-32552	83	4	series	series	NOUN
ajst-32552	83	5	forecasting	forecasting	NOUN
ajst-32552	83	6	via	via	ADP
ajst-32552	83	7	sliding	slide	VERB
ajst-32552	83	8	window	window	NOUN
ajst-32552	83	9	to	to	PART
ajst-32552	83	10	capture	capture	VERB
ajst-32552	83	11	temporal	temporal	ADJ
ajst-32552	83	12	dependencies	dependency	NOUN
ajst-32552	83	13	in	in	ADP
ajst-32552	83	14	ghi	ghi	PROPN
ajst-32552	83	15	,	,	PUNCT
ajst-32552	83	16	a	a	DET
ajst-32552	83	17	sliding	slide	VERB
ajst-32552	83	18	window	window	NOUN
ajst-32552	83	19	approach	approach	NOUN
ajst-32552	83	20	was	be	AUX
ajst-32552	83	21	applied	apply	VERB
ajst-32552	83	22	:	:	PUNCT
ajst-32552	83	23			ADJ
ajst-32552	83	24	a	a	DET
ajst-32552	83	25	sliding	slide	VERB
ajst-32552	83	26	window	window	NOUN
ajst-32552	83	27	approach	approach	NOUN
ajst-32552	83	28	was	be	AUX
ajst-32552	83	29	applied	apply	VERB
ajst-32552	83	30	using	use	VERB
ajst-32552	83	31	240	240	NUM
ajst-32552	83	32	hours	hour	NOUN
ajst-32552	83	33	of	of	ADP
ajst-32552	83	34	historical	historical	ADJ
ajst-32552	83	35	data	datum	NOUN
ajst-32552	83	36	to	to	PART
ajst-32552	83	37	predict	predict	VERB
ajst-32552	83	38	the	the	DET
ajst-32552	83	39	next	next	ADJ
ajst-32552	83	40	24	24	NUM
ajst-32552	83	41	hours	hour	NOUN
ajst-32552	83	42	of	of	ADP
ajst-32552	83	43	ghi	ghi	PROPN
ajst-32552	83	44	values	value	NOUN
ajst-32552	83	45	.	.	PUNCT
ajst-32552	84	1			VERB
ajst-32552	84	2	the	the	DET
ajst-32552	84	3	2d	2d	NUM
ajst-32552	84	4	input	input	NOUN
ajst-32552	84	5	array	array	NOUN
ajst-32552	84	6	(	(	PUNCT
ajst-32552	84	7	240	240	NUM
ajst-32552	84	8	×	×	NOUN
ajst-32552	84	9	number	number	NOUN
ajst-32552	84	10	of	of	ADP
ajst-32552	84	11	features	feature	NOUN
ajst-32552	84	12	)	)	PUNCT
ajst-32552	84	13	was	be	AUX
ajst-32552	84	14	flattened	flatten	VERB
ajst-32552	84	15	into	into	ADP
ajst-32552	84	16	a	a	DET
ajst-32552	84	17	1d	1d	NUM
ajst-32552	84	18	vector	vector	NOUN
ajst-32552	84	19	to	to	PART
ajst-32552	84	20	be	be	AUX
ajst-32552	84	21	used	use	VERB
ajst-32552	84	22	as	as	ADP
ajst-32552	84	23	input	input	NOUN
ajst-32552	84	24	for	for	ADP
ajst-32552	84	25	machine	machine	NOUN
ajst-32552	84	26	learning	learning	NOUN
ajst-32552	84	27	models	model	NOUN
ajst-32552	84	28	.	.	PUNCT
ajst-32552	85	1			ADJ
ajst-32552	85	2	this	this	DET
ajst-32552	85	3	method	method	NOUN
ajst-32552	85	4	uses	use	VERB
ajst-32552	85	5	past	past	ADJ
ajst-32552	85	6	data	datum	NOUN
ajst-32552	85	7	points	point	NOUN
ajst-32552	85	8	as	as	ADP
ajst-32552	85	9	input	input	NOUN
ajst-32552	85	10	to	to	PART
ajst-32552	85	11	predict	predict	VERB
ajst-32552	85	12	future	future	ADJ
ajst-32552	85	13	values	value	NOUN
ajst-32552	85	14	,	,	PUNCT
ajst-32552	85	15	capturing	capture	VERB
ajst-32552	85	16	temporal	temporal	ADJ
ajst-32552	85	17	patterns	pattern	NOUN
ajst-32552	85	18	through	through	ADP
ajst-32552	85	19	sliding	slide	VERB
ajst-32552	85	20	windows	window	NOUN
ajst-32552	85	21	in	in	ADP
ajst-32552	85	22	machine	machine	NOUN
ajst-32552	85	23	learning	learning	NOUN
ajst-32552	85	24	models	model	NOUN
ajst-32552	85	25	.	.	PUNCT
ajst-32552	86	1	the	the	DET
ajst-32552	86	2	sliding	slide	VERB
ajst-32552	86	3	window	window	NOUN
ajst-32552	86	4	method	method	NOUN
ajst-32552	86	5	provides	provide	VERB
ajst-32552	86	6	an	an	DET
ajst-32552	86	7	effective	effective	ADJ
ajst-32552	86	8	way	way	NOUN
ajst-32552	86	9	to	to	PART
ajst-32552	86	10	transform	transform	VERB
ajst-32552	86	11	sequential	sequential	ADJ
ajst-32552	86	12	ghi	ghi	PROPN
ajst-32552	86	13	data	datum	NOUN
ajst-32552	86	14	into	into	ADP
ajst-32552	86	15	a	a	DET
ajst-32552	86	16	supervised	supervised	ADJ
ajst-32552	86	17	learning	learning	NOUN
ajst-32552	86	18	format	format	NOUN
ajst-32552	86	19	.	.	PUNCT
ajst-32552	87	1	by	by	ADP
ajst-32552	87	2	using	use	VERB
ajst-32552	87	3	240	240	NUM
ajst-32552	87	4	hours	hour	NOUN
ajst-32552	87	5	of	of	ADP
ajst-32552	87	6	past	past	ADJ
ajst-32552	87	7	observations	observation	NOUN
ajst-32552	87	8	as	as	ADP
ajst-32552	87	9	input	input	NOUN
ajst-32552	87	10	to	to	PART
ajst-32552	87	11	predict	predict	VERB
ajst-32552	87	12	the	the	DET
ajst-32552	87	13	subsequent	subsequent	ADJ
ajst-32552	87	14	24	24	NUM
ajst-32552	87	15	-	-	PUNCT
ajst-32552	87	16	hour	hour	NOUN
ajst-32552	87	17	output	output	NOUN
ajst-32552	87	18	,	,	PUNCT
ajst-32552	87	19	the	the	DET
ajst-32552	87	20	model	model	NOUN
ajst-32552	87	21	captures	capture	VERB
ajst-32552	87	22	short	short	ADJ
ajst-32552	87	23	-	-	PUNCT
ajst-32552	87	24	term	term	NOUN
ajst-32552	87	25	temporal	temporal	ADJ
ajst-32552	87	26	dependencies	dependency	NOUN
ajst-32552	87	27	in	in	ADP
ajst-32552	87	28	solar	solar	ADJ
ajst-32552	87	29	radiation	radiation	NOUN
ajst-32552	87	30	.	.	PUNCT
ajst-32552	88	1	in	in	ADP
ajst-32552	88	2	this	this	DET
ajst-32552	88	3	study	study	NOUN
ajst-32552	88	4	,	,	PUNCT
ajst-32552	88	5	the	the	DET
ajst-32552	88	6	approach	approach	NOUN
ajst-32552	88	7	is	be	AUX
ajst-32552	88	8	applied	apply	VERB
ajst-32552	88	9	with	with	ADP
ajst-32552	88	10	linear	linear	PROPN
ajst-32552	88	11	regression	regression	NOUN
ajst-32552	88	12	,	,	PUNCT
ajst-32552	88	13	random	random	ADJ
ajst-32552	88	14	forest	forest	NOUN
ajst-32552	88	15	,	,	PUNCT
ajst-32552	88	16	and	and	CCONJ
ajst-32552	88	17	decision	decision	NOUN
ajst-32552	88	18	tree	tree	NOUN
ajst-32552	88	19	models	model	NOUN
ajst-32552	88	20	.	.	PUNCT
ajst-32552	89	1	linear	linear	ADJ
ajst-32552	89	2	regression	regression	NOUN
ajst-32552	89	3	provides	provide	VERB
ajst-32552	89	4	a	a	DET
ajst-32552	89	5	simple	simple	ADJ
ajst-32552	89	6	baseline	baseline	NOUN
ajst-32552	89	7	but	but	CCONJ
ajst-32552	89	8	struggles	struggle	VERB
ajst-32552	89	9	with	with	ADP
ajst-32552	89	10	nonlinear	nonlinear	ADJ
ajst-32552	89	11	dynamics	dynamic	NOUN
ajst-32552	89	12	,	,	PUNCT
ajst-32552	89	13	while	while	SCONJ
ajst-32552	89	14	decision	decision	NOUN
ajst-32552	89	15	trees	tree	NOUN
ajst-32552	89	16	and	and	CCONJ
ajst-32552	89	17	random	random	ADJ
ajst-32552	89	18	forests	forest	NOUN
ajst-32552	89	19	capture	capture	VERB
ajst-32552	89	20	more	more	ADV
ajst-32552	89	21	complex	complex	ADJ
ajst-32552	89	22	patterns	pattern	NOUN
ajst-32552	89	23	.	.	PUNCT
ajst-32552	90	1	the	the	DET
ajst-32552	90	2	data	datum	NOUN
ajst-32552	90	3	,	,	PUNCT
ajst-32552	90	4	provided	provide	VERB
ajst-32552	90	5	in	in	ADP
ajst-32552	90	6	hourly	hourly	ADJ
ajst-32552	90	7	resolution	resolution	NOUN
ajst-32552	90	8	and	and	CCONJ
ajst-32552	90	9	preprocessed	preprocesse	VERB
ajst-32552	90	10	by	by	ADP
ajst-32552	90	11	the	the	DET
ajst-32552	90	12	source	source	NOUN
ajst-32552	90	13	database	database	NOUN
ajst-32552	90	14	,	,	PUNCT
ajst-32552	90	15	was	be	AUX
ajst-32552	90	16	used	use	VERB
ajst-32552	90	17	without	without	ADP
ajst-32552	90	18	further	further	ADJ
ajst-32552	90	19	standardization	standardization	NOUN
ajst-32552	90	20	.	.	PUNCT
ajst-32552	91	1	overall	overall	ADV
ajst-32552	91	2	,	,	PUNCT
ajst-32552	91	3	the	the	DET
ajst-32552	91	4	sliding	slide	VERB
ajst-32552	91	5	window	window	NOUN
ajst-32552	91	6	enables	enable	VERB
ajst-32552	91	7	effective	effective	ADJ
ajst-32552	91	8	model	model	NOUN
ajst-32552	91	9	training	training	NOUN
ajst-32552	91	10	by	by	ADP
ajst-32552	91	11	converting	convert	VERB
ajst-32552	91	12	time	time	NOUN
ajst-32552	91	13	-	-	PUNCT
ajst-32552	91	14	series	series	NOUN
ajst-32552	91	15	ghi	ghi	PROPN
ajst-32552	91	16	data	data	PROPN
ajst-32552	91	17	into	into	ADP
ajst-32552	91	18	structured	structured	ADJ
ajst-32552	91	19	inputoutput	inputoutput	NOUN
ajst-32552	91	20	samples	sample	NOUN
ajst-32552	91	21	.	.	PUNCT
ajst-32552	92	1	4.3	4.3	NUM
ajst-32552	92	2	evaluation	evaluation	NOUN
ajst-32552	92	3	metrics	metric	NOUN
ajst-32552	92	4	the	the	DET
ajst-32552	92	5	performance	performance	NOUN
ajst-32552	92	6	of	of	ADP
ajst-32552	92	7	models	model	NOUN
ajst-32552	92	8	was	be	AUX
ajst-32552	92	9	evaluated	evaluate	VERB
ajst-32552	92	10	using	use	VERB
ajst-32552	92	11	root	root	NOUN
ajst-32552	92	12	mean	mean	ADJ
ajst-32552	92	13	square	square	ADJ
ajst-32552	92	14	error	error	NOUN
ajst-32552	92	15	(	(	PUNCT
ajst-32552	92	16	rmse	rmse	NOUN
ajst-32552	92	17	)	)	PUNCT
ajst-32552	92	18	and	and	CCONJ
ajst-32552	92	19	coefficient	coefficient	NOUN
ajst-32552	92	20	of	of	ADP
ajst-32552	92	21	determination	determination	NOUN
ajst-32552	92	22	(	(	PUNCT
ajst-32552	92	23	r2	r2	PROPN
ajst-32552	92	24	)	)	PUNCT
ajst-32552	92	25	.	.	PUNCT
ajst-32552	93	1	rmse=	rmse=	ADV
ajst-32552	93	2	1	1	NUM
ajst-32552	93	3	n	n	NUM
ajst-32552	93	4	∑	∑	ADP
ajst-32552	93	5	yt−yt	yt−yt	DET
ajst-32552	93	6	2n	2n	NUM
ajst-32552	93	7	t=1	t=1	ADV
ajst-32552	93	8	(	(	PUNCT
ajst-32552	93	9	1	1	X
ajst-32552	93	10	)	)	PUNCT
ajst-32552	93	11	r2=1−	r2=1−	PROPN
ajst-32552	93	12	∑	∑	PUNCT
ajst-32552	93	13	yt−yt	yt−yt	DET
ajst-32552	93	14	2n	2n	NUM
ajst-32552	93	15	t=1	t=1	PUNCT
ajst-32552	93	16	∑	∑	PUNCT
ajst-32552	93	17	yt−y	yt−y	NOUN
ajst-32552	93	18	2n	2n	NUM
ajst-32552	93	19	t=1	t=1	ADV
ajst-32552	93	20	(	(	PUNCT
ajst-32552	93	21	2	2	X
ajst-32552	93	22	)	)	PUNCT
ajst-32552	93	23	yt	yt	NOUN
ajst-32552	93	24	    	    	SPACE
ajst-32552	93	25	=	=	SYM
ajst-32552	93	26	actual	actual	ADJ
ajst-32552	93	27	observed	observe	VERB
ajst-32552	93	28	value	value	NOUN
ajst-32552	93	29	at	at	ADP
ajst-32552	93	30	time	time	NOUN
ajst-32552	93	31	t	t	PROPN
ajst-32552	93	32	ŷt	ŷt	NOUN
ajst-32552	93	33	     	     	SPACE
ajst-32552	93	34	=	=	NOUN
ajst-32552	93	35	predicted	predict	VERB
ajst-32552	93	36	value	value	NOUN
ajst-32552	93	37	at	at	ADP
ajst-32552	93	38	time	time	NOUN
ajst-32552	93	39	t	t	PROPN
ajst-32552	93	40	ȳ	ȳ	PROPN
ajst-32552	93	41	       	       	SPACE
ajst-32552	93	42	=	=	PUNCT
ajst-32552	94	1	mean	mean	NOUN
ajst-32552	94	2	of	of	ADP
ajst-32552	94	3	the	the	DET
ajst-32552	94	4	observed	observed	ADJ
ajst-32552	94	5	values	value	NOUN
ajst-32552	94	6	n	n	NOUN
ajst-32552	94	7	=	=	NUM
ajst-32552	94	8	total	total	ADJ
ajst-32552	94	9	number	number	NOUN
ajst-32552	94	10	of	of	ADP
ajst-32552	94	11	observations	observation	NOUN
ajst-32552	94	12	.	.	PUNCT
ajst-32552	95	1	106	106	NUM
ajst-32552	95	2	5	5	NUM
ajst-32552	95	3	.	.	PUNCT
ajst-32552	95	4	results	result	VERB
ajst-32552	95	5	5.1	5.1	NUM
ajst-32552	95	6	hourly	hourly	ADJ
ajst-32552	95	7	forecasting	forecasting	NOUN
ajst-32552	95	8	results	result	NOUN
ajst-32552	95	9	(	(	PUNCT
ajst-32552	95	10	day	day	NOUN
ajst-32552	95	11	,	,	PUNCT
ajst-32552	95	12	night	night	NOUN
ajst-32552	95	13	,	,	PUNCT
ajst-32552	95	14	unified	unified	ADJ
ajst-32552	95	15	datasets	dataset	NOUN
ajst-32552	95	16	)	)	PUNCT
ajst-32552	95	17	the	the	DET
ajst-32552	95	18	experimental	experimental	ADJ
ajst-32552	95	19	results	result	NOUN
ajst-32552	95	20	,	,	PUNCT
ajst-32552	95	21	summarized	summarize	VERB
ajst-32552	95	22	in	in	ADP
ajst-32552	95	23	table	table	NOUN
ajst-32552	95	24	1	1	NUM
ajst-32552	95	25	,	,	PUNCT
ajst-32552	95	26	demonstrate	demonstrate	VERB
ajst-32552	95	27	clear	clear	ADJ
ajst-32552	95	28	differences	difference	NOUN
ajst-32552	95	29	in	in	ADP
ajst-32552	95	30	the	the	DET
ajst-32552	95	31	performance	performance	NOUN
ajst-32552	95	32	of	of	ADP
ajst-32552	95	33	linear	linear	PROPN
ajst-32552	95	34	regression	regression	NOUN
ajst-32552	95	35	,	,	PUNCT
ajst-32552	95	36	decision	decision	NOUN
ajst-32552	95	37	tree	tree	NOUN
ajst-32552	95	38	,	,	PUNCT
ajst-32552	95	39	and	and	CCONJ
ajst-32552	95	40	random	random	ADJ
ajst-32552	95	41	forests	forest	NOUN
ajst-32552	95	42	across	across	ADP
ajst-32552	95	43	day	day	NOUN
ajst-32552	95	44	,	,	PUNCT
ajst-32552	95	45	night	night	NOUN
ajst-32552	95	46	,	,	PUNCT
ajst-32552	95	47	and	and	CCONJ
ajst-32552	95	48	unified	unified	ADJ
ajst-32552	95	49	datasets	dataset	NOUN
ajst-32552	95	50	.	.	PUNCT
ajst-32552	96	1	table	table	NOUN
ajst-32552	96	2	1	1	NUM
ajst-32552	96	3	.	.	PUNCT
ajst-32552	96	4	performance	performance	NOUN
ajst-32552	96	5	of	of	ADP
ajst-32552	96	6	linear	linear	PROPN
ajst-32552	96	7	regression	regression	NOUN
ajst-32552	96	8	,	,	PUNCT
ajst-32552	96	9	decision	decision	NOUN
ajst-32552	96	10	tree	tree	NOUN
ajst-32552	96	11	,	,	PUNCT
ajst-32552	96	12	and	and	CCONJ
ajst-32552	96	13	random	random	ADJ
ajst-32552	96	14	forest	forest	NOUN
ajst-32552	96	15	on	on	ADP
ajst-32552	96	16	day	day	NOUN
ajst-32552	96	17	,	,	PUNCT
ajst-32552	96	18	night	night	NOUN
ajst-32552	96	19	,	,	PUNCT
ajst-32552	96	20	and	and	CCONJ
ajst-32552	96	21	unified	unified	ADJ
ajst-32552	96	22	data	datum	NOUN
ajst-32552	96	23	the	the	DET
ajst-32552	96	24	experimental	experimental	ADJ
ajst-32552	96	25	results	result	NOUN
ajst-32552	96	26	demonstrate	demonstrate	VERB
ajst-32552	96	27	clear	clear	ADJ
ajst-32552	96	28	differences	difference	NOUN
ajst-32552	96	29	in	in	ADP
ajst-32552	96	30	the	the	DET
ajst-32552	96	31	performance	performance	NOUN
ajst-32552	96	32	of	of	ADP
ajst-32552	96	33	linear	linear	PROPN
ajst-32552	96	34	regression	regression	NOUN
ajst-32552	96	35	,	,	PUNCT
ajst-32552	96	36	decision	decision	NOUN
ajst-32552	96	37	tree	tree	NOUN
ajst-32552	96	38	,	,	PUNCT
ajst-32552	96	39	and	and	CCONJ
ajst-32552	96	40	random	random	ADJ
ajst-32552	96	41	forests	forest	NOUN
ajst-32552	96	42	under	under	ADP
ajst-32552	96	43	day	day	NOUN
ajst-32552	96	44	,	,	PUNCT
ajst-32552	96	45	night	night	NOUN
ajst-32552	96	46	,	,	PUNCT
ajst-32552	96	47	and	and	CCONJ
ajst-32552	96	48	unified	unified	ADJ
ajst-32552	96	49	datasets	dataset	NOUN
ajst-32552	96	50	.	.	PUNCT
ajst-32552	97	1	linear	linear	ADJ
ajst-32552	97	2	regression	regression	NOUN
ajst-32552	97	3	serves	serve	VERB
ajst-32552	97	4	as	as	ADP
ajst-32552	97	5	a	a	DET
ajst-32552	97	6	simple	simple	ADJ
ajst-32552	97	7	baseline	baseline	NOUN
ajst-32552	97	8	but	but	CCONJ
ajst-32552	97	9	shows	show	VERB
ajst-32552	97	10	limitations	limitation	NOUN
ajst-32552	97	11	in	in	ADP
ajst-32552	97	12	capturing	capture	VERB
ajst-32552	97	13	nonlinear	nonlinear	ADJ
ajst-32552	97	14	patterns	pattern	NOUN
ajst-32552	97	15	of	of	ADP
ajst-32552	97	16	ghi	ghi	PROPN
ajst-32552	97	17	.	.	PUNCT
ajst-32552	98	1	its	its	PRON
ajst-32552	98	2	daytime	daytime	ADJ
ajst-32552	98	3	errors	error	NOUN
ajst-32552	98	4	are	be	AUX
ajst-32552	98	5	high	high	ADJ
ajst-32552	98	6	(	(	PUNCT
ajst-32552	98	7	test	test	NOUN
ajst-32552	98	8	rmse	rmse	NOUN
ajst-32552	99	1	=	=	PROPN
ajst-32552	99	2	48.61	48.61	NUM
ajst-32552	99	3	,	,	PUNCT
ajst-32552	99	4	r2	r2	PROPN
ajst-32552	99	5	=	=	SYM
ajst-32552	99	6	0.9657	0.9657	NUM
ajst-32552	99	7	)	)	PUNCT
ajst-32552	99	8	,	,	PUNCT
ajst-32552	99	9	while	while	SCONJ
ajst-32552	99	10	nighttime	nighttime	ADJ
ajst-32552	99	11	performance	performance	NOUN
ajst-32552	99	12	improves	improve	VERB
ajst-32552	99	13	significantly	significantly	ADV
ajst-32552	99	14	(	(	PUNCT
ajst-32552	99	15	test	test	NOUN
ajst-32552	99	16	rmse	rmse	NOUN
ajst-32552	99	17	=	=	SYM
ajst-32552	99	18	0.46	0.46	NUM
ajst-32552	99	19	,	,	PUNCT
ajst-32552	99	20	r2	r2	PROPN
ajst-32552	99	21	=	=	SYM
ajst-32552	99	22	0.9954	0.9954	NUM
ajst-32552	99	23	)	)	PUNCT
ajst-32552	99	24	due	due	ADP
ajst-32552	99	25	to	to	ADP
ajst-32552	99	26	the	the	DET
ajst-32552	99	27	stable	stable	ADJ
ajst-32552	99	28	irradiance	irradiance	NOUN
ajst-32552	99	29	conditions	condition	NOUN
ajst-32552	99	30	.	.	PUNCT
ajst-32552	100	1	the	the	DET
ajst-32552	100	2	unified	unified	ADJ
ajst-32552	100	3	model	model	NOUN
ajst-32552	100	4	reduces	reduce	VERB
ajst-32552	100	5	errors	error	NOUN
ajst-32552	100	6	compared	compare	VERB
ajst-32552	100	7	to	to	ADP
ajst-32552	100	8	the	the	DET
ajst-32552	100	9	day	day	NOUN
ajst-32552	100	10	case	case	NOUN
ajst-32552	100	11	but	but	CCONJ
ajst-32552	100	12	remains	remain	VERB
ajst-32552	100	13	less	less	ADV
ajst-32552	100	14	competitive	competitive	ADJ
ajst-32552	100	15	than	than	ADP
ajst-32552	100	16	tree	tree	NOUN
ajst-32552	100	17	-	-	PUNCT
ajst-32552	100	18	based	base	VERB
ajst-32552	100	19	approaches	approach	NOUN
ajst-32552	100	20	.	.	PUNCT
ajst-32552	101	1	the	the	DET
ajst-32552	101	2	decision	decision	NOUN
ajst-32552	101	3	tree	tree	NOUN
ajst-32552	101	4	model	model	NOUN
ajst-32552	101	5	performs	perform	VERB
ajst-32552	101	6	better	well	ADV
ajst-32552	101	7	at	at	ADP
ajst-32552	101	8	learning	learn	VERB
ajst-32552	101	9	nonlinear	nonlinear	ADJ
ajst-32552	101	10	relationships	relationship	NOUN
ajst-32552	101	11	,	,	PUNCT
ajst-32552	101	12	with	with	ADP
ajst-32552	101	13	higher	high	ADJ
ajst-32552	101	14	r^2	r^2	NOUN
ajst-32552	101	15	values	value	NOUN
ajst-32552	101	16	across	across	ADP
ajst-32552	101	17	all	all	DET
ajst-32552	101	18	scenarios	scenario	NOUN
ajst-32552	101	19	.	.	PUNCT
ajst-32552	102	1	however	however	ADV
ajst-32552	102	2	,	,	PUNCT
ajst-32552	102	3	it	it	PRON
ajst-32552	102	4	shows	show	VERB
ajst-32552	102	5	signs	sign	NOUN
ajst-32552	102	6	of	of	ADP
ajst-32552	102	7	overfitting	overfitte	VERB
ajst-32552	102	8	:	:	PUNCT
ajst-32552	102	9	training	training	NOUN
ajst-32552	102	10	errors	error	NOUN
ajst-32552	102	11	are	be	AUX
ajst-32552	102	12	very	very	ADV
ajst-32552	102	13	low	low	ADJ
ajst-32552	102	14	,	,	PUNCT
ajst-32552	102	15	yet	yet	CCONJ
ajst-32552	102	16	test	test	NOUN
ajst-32552	102	17	errors	error	NOUN
ajst-32552	102	18	remain	remain	VERB
ajst-32552	102	19	relatively	relatively	ADV
ajst-32552	102	20	large	large	ADJ
ajst-32552	102	21	(	(	PUNCT
ajst-32552	102	22	e.g.	e.g.	ADJ
ajst-32552	102	23	,	,	PUNCT
ajst-32552	102	24	day	day	NOUN
ajst-32552	102	25	test	test	NOUN
ajst-32552	102	26	rmse	rmse	NOUN
ajst-32552	102	27	=	=	PROPN
ajst-32552	102	28	45.18	45.18	NUM
ajst-32552	102	29	)	)	PUNCT
ajst-32552	102	30	.	.	PUNCT
ajst-32552	103	1	this	this	PRON
ajst-32552	103	2	indicates	indicate	VERB
ajst-32552	103	3	limited	limited	ADJ
ajst-32552	103	4	generalization	generalization	NOUN
ajst-32552	103	5	despite	despite	SCONJ
ajst-32552	103	6	a	a	DET
ajst-32552	103	7	strong	strong	ADJ
ajst-32552	103	8	fit	fit	NOUN
ajst-32552	103	9	on	on	ADP
ajst-32552	103	10	training	training	NOUN
ajst-32552	103	11	data	datum	NOUN
ajst-32552	103	12	.	.	PUNCT
ajst-32552	104	1	as	as	SCONJ
ajst-32552	104	2	shown	show	VERB
ajst-32552	104	3	in	in	ADP
ajst-32552	104	4	table	table	NOUN
ajst-32552	104	5	1	1	NUM
ajst-32552	104	6	,	,	PUNCT
ajst-32552	104	7	random	random	ADJ
ajst-32552	104	8	forest	forest	NOUN
ajst-32552	104	9	consistently	consistently	ADV
ajst-32552	104	10	outperforms	outperform	VERB
ajst-32552	104	11	the	the	DET
ajst-32552	104	12	other	other	ADJ
ajst-32552	104	13	models	model	NOUN
ajst-32552	104	14	.	.	PUNCT
ajst-32552	105	1	by	by	ADP
ajst-32552	105	2	combining	combine	VERB
ajst-32552	105	3	multiple	multiple	ADJ
ajst-32552	105	4	decision	decision	NOUN
ajst-32552	105	5	trees	tree	NOUN
ajst-32552	105	6	,	,	PUNCT
ajst-32552	105	7	it	it	PRON
ajst-32552	105	8	achieves	achieve	VERB
ajst-32552	105	9	the	the	DET
ajst-32552	105	10	lowest	low	ADJ
ajst-32552	105	11	test	test	NOUN
ajst-32552	105	12	errors	error	NOUN
ajst-32552	105	13	(	(	PUNCT
ajst-32552	105	14	day	day	NOUN
ajst-32552	105	15	=	=	SYM
ajst-32552	105	16	8.88	8.88	NUM
ajst-32552	105	17	,	,	PUNCT
ajst-32552	105	18	night	night	NOUN
ajst-32552	105	19	=	=	SYM
ajst-32552	105	20	0.53	0.53	NUM
ajst-32552	105	21	,	,	PUNCT
ajst-32552	105	22	unified	unified	ADJ
ajst-32552	105	23	=	=	NOUN
ajst-32552	105	24	6.04	6.04	NUM
ajst-32552	105	25	)	)	PUNCT
ajst-32552	105	26	and	and	CCONJ
ajst-32552	105	27	the	the	DET
ajst-32552	105	28	highest	high	ADJ
ajst-32552	105	29	r2	r2	NOUN
ajst-32552	105	30	values	value	NOUN
ajst-32552	105	31	(	(	PUNCT
ajst-32552	105	32	>	>	NOUN
ajst-32552	105	33	0.99	0.99	NUM
ajst-32552	105	34	)	)	PUNCT
ajst-32552	105	35	,	,	PUNCT
ajst-32552	105	36	demonstrating	demonstrate	VERB
ajst-32552	105	37	both	both	DET
ajst-32552	105	38	accuracy	accuracy	NOUN
ajst-32552	105	39	and	and	CCONJ
ajst-32552	105	40	robustness	robustness	NOUN
ajst-32552	105	41	.	.	PUNCT
ajst-32552	106	1	overall	overall	ADJ
ajst-32552	106	2	,	,	PUNCT
ajst-32552	106	3	random	random	ADJ
ajst-32552	106	4	forest	forest	NOUN
ajst-32552	106	5	proves	prove	VERB
ajst-32552	106	6	to	to	PART
ajst-32552	106	7	be	be	AUX
ajst-32552	106	8	the	the	DET
ajst-32552	106	9	most	most	ADV
ajst-32552	106	10	reliable	reliable	ADJ
ajst-32552	106	11	model	model	NOUN
ajst-32552	106	12	for	for	ADP
ajst-32552	106	13	ghi	ghi	PROPN
ajst-32552	106	14	prediction	prediction	NOUN
ajst-32552	106	15	,	,	PUNCT
ajst-32552	106	16	especially	especially	ADV
ajst-32552	106	17	in	in	ADP
ajst-32552	106	18	handling	handle	VERB
ajst-32552	106	19	the	the	DET
ajst-32552	106	20	variability	variability	NOUN
ajst-32552	106	21	of	of	ADP
ajst-32552	106	22	daytime	daytime	ADJ
ajst-32552	106	23	data	datum	NOUN
ajst-32552	106	24	while	while	SCONJ
ajst-32552	106	25	maintaining	maintain	VERB
ajst-32552	106	26	stable	stable	ADJ
ajst-32552	106	27	nighttime	nighttime	ADJ
ajst-32552	106	28	performance	performance	NOUN
ajst-32552	106	29	.	.	PUNCT
ajst-32552	107	1	5.2	5.2	NUM
ajst-32552	107	2	comparative	comparative	ADJ
ajst-32552	107	3	model	model	NOUN
ajst-32552	107	4	analysis	analysis	NOUN
ajst-32552	107	5	table	table	NOUN
ajst-32552	107	6	2	2	NUM
ajst-32552	107	7	.	.	PUNCT
ajst-32552	107	8	comparative	comparative	ADJ
ajst-32552	107	9	model	model	NOUN
ajst-32552	107	10	analysis	analysis	NOUN
ajst-32552	107	11	.	.	PUNCT
ajst-32552	108	1	table	table	NOUN
ajst-32552	108	2	2	2	NUM
ajst-32552	108	3	shows	show	VERB
ajst-32552	108	4	the	the	DET
ajst-32552	108	5	order	order	NOUN
ajst-32552	108	6	of	of	ADP
ajst-32552	108	7	the	the	DET
ajst-32552	108	8	values	value	NOUN
ajst-32552	108	9	of	of	ADP
ajst-32552	108	10	our	our	PRON
ajst-32552	108	11	models	model	NOUN
ajst-32552	108	12	,	,	PUNCT
ajst-32552	108	13	and	and	CCONJ
ajst-32552	108	14	we	we	PRON
ajst-32552	108	15	can	can	AUX
ajst-32552	108	16	notice	notice	VERB
ajst-32552	108	17	that	that	SCONJ
ajst-32552	108	18	if	if	SCONJ
ajst-32552	108	19	we	we	PRON
ajst-32552	108	20	have	have	VERB
ajst-32552	108	21	the	the	DET
ajst-32552	108	22	lower	low	ADJ
ajst-32552	108	23	rmse	rmse	NOUN
ajst-32552	108	24	values	value	NOUN
ajst-32552	108	25	,	,	PUNCT
ajst-32552	108	26	the	the	DET
ajst-32552	108	27	results	result	NOUN
ajst-32552	108	28	are	be	AUX
ajst-32552	108	29	better	well	ADJ
ajst-32552	108	30	,	,	PUNCT
ajst-32552	108	31	and	and	CCONJ
ajst-32552	108	32	if	if	SCONJ
ajst-32552	108	33	we	we	PRON
ajst-32552	108	34	have	have	VERB
ajst-32552	108	35	the	the	DET
ajst-32552	108	36	higher	high	ADJ
ajst-32552	108	37	r2	r2	NOUN
ajst-32552	108	38	scores	score	NOUN
ajst-32552	108	39	,	,	PUNCT
ajst-32552	108	40	the	the	DET
ajst-32552	108	41	results	result	NOUN
ajst-32552	108	42	are	be	AUX
ajst-32552	108	43	better	well	ADJ
ajst-32552	108	44	.	.	PUNCT
ajst-32552	109	1	by	by	ADP
ajst-32552	109	2	comparison	comparison	NOUN
ajst-32552	109	3	,	,	PUNCT
ajst-32552	109	4	we	we	PRON
ajst-32552	109	5	can	can	AUX
ajst-32552	109	6	figure	figure	VERB
ajst-32552	109	7	out	out	ADP
ajst-32552	109	8	that	that	SCONJ
ajst-32552	109	9	our	our	PRON
ajst-32552	109	10	random	random	ADJ
ajst-32552	109	11	forests	forest	NOUN
ajst-32552	109	12	work	work	VERB
ajst-32552	109	13	best	well	ADV
ajst-32552	109	14	.	.	PUNCT
ajst-32552	110	1	107	107	NUM
ajst-32552	110	2	5.3	5.3	NUM
ajst-32552	110	3	time	time	NOUN
ajst-32552	110	4	-	-	PUNCT
ajst-32552	110	5	series	series	NOUN
ajst-32552	110	6	forecasting	forecasting	NOUN
ajst-32552	110	7	results	result	NOUN
ajst-32552	110	8	(	(	PUNCT
ajst-32552	110	9	10	10	NUM
ajst-32552	110	10	-	-	PUNCT
ajst-32552	110	11	day	day	NOUN
ajst-32552	110	12	→	→	SYM
ajst-32552	110	13	1	1	NUM
ajst-32552	110	14	-	-	PUNCT
ajst-32552	110	15	day	day	NOUN
ajst-32552	110	16	prediction	prediction	NOUN
ajst-32552	110	17	)	)	PUNCT
ajst-32552	110	18	figure	figure	NOUN
ajst-32552	110	19	2	2	NUM
ajst-32552	110	20	.	.	PUNCT
ajst-32552	111	1	linear	linear	PROPN
ajst-32552	111	2	regression	regression	NOUN
ajst-32552	111	3	model	model	NOUN
ajst-32552	111	4	with	with	ADP
ajst-32552	111	5	ghi	ghi	PROPN
ajst-32552	111	6	forecasting	forecasting	PROPN
ajst-32552	111	7	.	.	PUNCT
ajst-32552	112	1	figure	figure	NOUN
ajst-32552	112	2	3	3	NUM
ajst-32552	112	3	.	.	PUNCT
ajst-32552	112	4	decision	decision	NOUN
ajst-32552	112	5	tree	tree	NOUN
ajst-32552	112	6	model	model	NOUN
ajst-32552	112	7	with	with	ADP
ajst-32552	112	8	ghi	ghi	PROPN
ajst-32552	112	9	forecasting	forecasting	PROPN
ajst-32552	112	10	.	.	PUNCT
ajst-32552	113	1	figure	figure	NOUN
ajst-32552	113	2	4	4	NUM
ajst-32552	113	3	.	.	PUNCT
ajst-32552	113	4	random	random	ADJ
ajst-32552	113	5	forest	forest	NOUN
ajst-32552	113	6	with	with	ADP
ajst-32552	113	7	ghi	ghi	PROPN
ajst-32552	113	8	forecasting	forecasting	PROPN
ajst-32552	113	9	.	.	PUNCT
ajst-32552	114	1	table	table	NOUN
ajst-32552	114	2	3	3	NUM
ajst-32552	114	3	.	.	PUNCT
ajst-32552	114	4	short	short	ADJ
ajst-32552	114	5	-	-	PUNCT
ajst-32552	114	6	term	term	NOUN
ajst-32552	114	7	ghi	ghi	PROPN
ajst-32552	114	8	forecasting	forecasting	NOUN
ajst-32552	114	9	performance	performance	NOUN
ajst-32552	114	10	of	of	ADP
ajst-32552	114	11	different	different	ADJ
ajst-32552	114	12	models	model	NOUN
ajst-32552	114	13	.	.	PUNCT
ajst-32552	115	1	model	model	NOUN
ajst-32552	115	2	rmse	rmse	PROPN
ajst-32552	115	3	r²	r²	NOUN
ajst-32552	115	4	linear	linear	NOUN
ajst-32552	115	5	regression	regression	VERB
ajst-32552	115	6	147.87	147.87	NUM
ajst-32552	115	7	0.5913	0.5913	NUM
ajst-32552	115	8	decision	decision	NOUN
ajst-32552	115	9	tree	tree	NOUN
ajst-32552	115	10	60.13	60.13	NUM
ajst-32552	115	11	0.7147	0.7147	NUM
ajst-32552	115	12	random	random	ADJ
ajst-32552	115	13	forest	forest	NOUN
ajst-32552	115	14	48.10	48.10	NUM
ajst-32552	115	15	0.8229	0.8229	NUM
ajst-32552	115	16	figure	figure	NOUN
ajst-32552	115	17	5	5	NUM
ajst-32552	115	18	.	.	PUNCT
ajst-32552	115	19	ghi	ghi	PROPN
ajst-32552	115	20	time	time	PROPN
ajst-32552	115	21	series	series	PROPN
ajst-32552	115	22	:	:	PUNCT
ajst-32552	115	23	actual	actual	ADJ
ajst-32552	115	24	vs	vs	ADP
ajst-32552	115	25	predicted	predict	VERB
ajst-32552	115	26	(	(	PUNCT
ajst-32552	115	27	whole	whole	ADJ
ajst-32552	115	28	year	year	NOUN
ajst-32552	115	29	)	)	PUNCT
ajst-32552	115	30	.	.	PUNCT
ajst-32552	116	1	108	108	NUM
ajst-32552	116	2	as	as	SCONJ
ajst-32552	116	3	shown	show	VERB
ajst-32552	116	4	in	in	ADP
ajst-32552	116	5	table	table	NOUN
ajst-32552	116	6	3	3	NUM
ajst-32552	116	7	,	,	PUNCT
ajst-32552	116	8	linear	linear	ADJ
ajst-32552	116	9	regression	regression	NOUN
ajst-32552	116	10	model	model	NOUN
ajst-32552	116	11	for	for	ADP
ajst-32552	116	12	ghi	ghi	PROPN
ajst-32552	116	13	forecasting	forecasting	NOUN
ajst-32552	116	14	yielded	yield	VERB
ajst-32552	116	15	an	an	DET
ajst-32552	116	16	rmse	rmse	NOUN
ajst-32552	116	17	of	of	ADP
ajst-32552	116	18	147.87	147.87	NUM
ajst-32552	116	19	and	and	CCONJ
ajst-32552	116	20	an	an	DET
ajst-32552	116	21	r²	r²	NOUN
ajst-32552	116	22	of	of	ADP
ajst-32552	116	23	0.5913	0.5913	NUM
ajst-32552	116	24	.	.	PUNCT
ajst-32552	117	1	decision	decision	NOUN
ajst-32552	117	2	tree	tree	NOUN
ajst-32552	117	3	demonstrated	demonstrate	VERB
ajst-32552	117	4	improved	improved	ADJ
ajst-32552	117	5	performance	performance	NOUN
ajst-32552	117	6	metrics	metric	NOUN
ajst-32552	117	7	over	over	ADP
ajst-32552	117	8	the	the	DET
ajst-32552	117	9	linear	linear	PROPN
ajst-32552	117	10	regression	regression	NOUN
ajst-32552	117	11	model	model	NOUN
ajst-32552	117	12	.	.	PUNCT
ajst-32552	118	1	random	random	ADJ
ajst-32552	118	2	forest	forest	NOUN
ajst-32552	118	3	achieved	achieve	VERB
ajst-32552	118	4	the	the	DET
ajst-32552	118	5	highest	high	ADJ
ajst-32552	118	6	predictive	predictive	ADJ
ajst-32552	118	7	accuracy	accuracy	NOUN
ajst-32552	118	8	among	among	ADP
ajst-32552	118	9	all	all	DET
ajst-32552	118	10	models	model	NOUN
ajst-32552	118	11	tested	test	VERB
ajst-32552	118	12	.	.	PUNCT
ajst-32552	119	1	a	a	DET
ajst-32552	119	2	full	full	ADJ
ajst-32552	119	3	-	-	PUNCT
ajst-32552	119	4	year	year	NOUN
ajst-32552	119	5	prediction	prediction	NOUN
ajst-32552	119	6	was	be	AUX
ajst-32552	119	7	generated	generate	VERB
ajst-32552	119	8	using	use	VERB
ajst-32552	119	9	the	the	DET
ajst-32552	119	10	random	random	ADJ
ajst-32552	119	11	forest	forest	NOUN
ajst-32552	119	12	.	.	PUNCT
ajst-32552	120	1	using	use	VERB
ajst-32552	120	2	the	the	DET
ajst-32552	120	3	random	random	ADJ
ajst-32552	120	4	forest	forest	NOUN
ajst-32552	120	5	to	to	PART
ajst-32552	120	6	get	get	VERB
ajst-32552	120	7	the	the	DET
ajst-32552	120	8	whole	whole	ADJ
ajst-32552	120	9	year	year	NOUN
ajst-32552	120	10	prediction	prediction	NOUN
ajst-32552	120	11	:	:	PUNCT
ajst-32552	120	12	6	6	NUM
ajst-32552	120	13	.	.	X
ajst-32552	120	14	discussion	discussion	NOUN
ajst-32552	120	15	6.1	6.1	NUM
ajst-32552	120	16	main	main	ADJ
ajst-32552	120	17	findings	finding	NOUN
ajst-32552	120	18	based	base	VERB
ajst-32552	120	19	on	on	ADP
ajst-32552	120	20	all	all	DET
ajst-32552	120	21	the	the	DET
ajst-32552	120	22	discussions	discussion	NOUN
ajst-32552	120	23	and	and	CCONJ
ajst-32552	120	24	research	research	NOUN
ajst-32552	120	25	,	,	PUNCT
ajst-32552	120	26	we	we	PRON
ajst-32552	120	27	can	can	AUX
ajst-32552	120	28	figure	figure	VERB
ajst-32552	120	29	out	out	ADP
ajst-32552	120	30	that	that	SCONJ
ajst-32552	120	31	our	our	PRON
ajst-32552	120	32	machine	machine	NOUN
ajst-32552	120	33	learning	learning	NOUN
ajst-32552	120	34	models	model	NOUN
ajst-32552	120	35	are	be	AUX
ajst-32552	120	36	good	good	ADJ
ajst-32552	120	37	for	for	ADP
ajst-32552	120	38	ghi	ghi	PROPN
ajst-32552	120	39	prediction	prediction	NOUN
ajst-32552	120	40	,	,	PUNCT
ajst-32552	120	41	and	and	CCONJ
ajst-32552	120	42	they	they	PRON
ajst-32552	120	43	have	have	VERB
ajst-32552	120	44	huge	huge	ADJ
ajst-32552	120	45	potential	potential	NOUN
ajst-32552	120	46	to	to	PART
ajst-32552	120	47	improve	improve	VERB
ajst-32552	120	48	the	the	DET
ajst-32552	120	49	strategies	strategy	NOUN
ajst-32552	120	50	.	.	PUNCT
ajst-32552	121	1	the	the	DET
ajst-32552	121	2	performance	performance	NOUN
ajst-32552	121	3	of	of	ADP
ajst-32552	121	4	the	the	DET
ajst-32552	121	5	linear	linear	PROPN
ajst-32552	121	6	regression	regression	NOUN
ajst-32552	121	7	model	model	NOUN
ajst-32552	121	8	suggests	suggest	VERB
ajst-32552	121	9	that	that	SCONJ
ajst-32552	121	10	the	the	DET
ajst-32552	121	11	variables	variable	NOUN
ajst-32552	121	12	may	may	AUX
ajst-32552	121	13	not	not	PART
ajst-32552	121	14	have	have	VERB
ajst-32552	121	15	strong	strong	ADJ
ajst-32552	121	16	linear	linear	ADJ
ajst-32552	121	17	relationships	relationship	NOUN
ajst-32552	121	18	with	with	ADP
ajst-32552	121	19	the	the	DET
ajst-32552	121	20	target	target	NOUN
ajst-32552	121	21	.	.	PUNCT
ajst-32552	122	1	the	the	DET
ajst-32552	122	2	attempt	attempt	NOUN
ajst-32552	122	3	to	to	PART
ajst-32552	122	4	use	use	VERB
ajst-32552	122	5	pca	pca	NOUN
ajst-32552	122	6	for	for	ADP
ajst-32552	122	7	dimensionality	dimensionality	NOUN
ajst-32552	122	8	reduction	reduction	NOUN
ajst-32552	122	9	was	be	AUX
ajst-32552	122	10	not	not	PART
ajst-32552	122	11	successful	successful	ADJ
ajst-32552	122	12	,	,	PUNCT
ajst-32552	122	13	indicating	indicate	VERB
ajst-32552	122	14	that	that	SCONJ
ajst-32552	122	15	the	the	DET
ajst-32552	122	16	variables	variable	NOUN
ajst-32552	122	17	are	be	AUX
ajst-32552	122	18	not	not	PART
ajst-32552	122	19	necessarily	necessarily	ADV
ajst-32552	122	20	interdependent	interdependent	ADJ
ajst-32552	122	21	and	and	CCONJ
ajst-32552	122	22	may	may	AUX
ajst-32552	122	23	be	be	AUX
ajst-32552	122	24	largely	largely	ADV
ajst-32552	122	25	independent	independent	ADJ
ajst-32552	122	26	.	.	PUNCT
ajst-32552	123	1	while	while	SCONJ
ajst-32552	123	2	the	the	DET
ajst-32552	123	3	decision	decision	NOUN
ajst-32552	123	4	tree	tree	NOUN
ajst-32552	123	5	model	model	NOUN
ajst-32552	123	6	performed	perform	VERB
ajst-32552	123	7	better	well	ADV
ajst-32552	123	8	than	than	ADP
ajst-32552	123	9	linear	linear	PROPN
ajst-32552	123	10	regression	regression	NOUN
ajst-32552	123	11	,	,	PUNCT
ajst-32552	123	12	which	which	PRON
ajst-32552	123	13	is	be	AUX
ajst-32552	123	14	suitable	suitable	ADJ
ajst-32552	123	15	for	for	ADP
ajst-32552	123	16	capturing	capture	VERB
ajst-32552	123	17	nonlinear	nonlinear	ADJ
ajst-32552	123	18	relationships	relationship	NOUN
ajst-32552	123	19	,	,	PUNCT
ajst-32552	123	20	its	its	PRON
ajst-32552	123	21	performance	performance	NOUN
ajst-32552	123	22	on	on	ADP
ajst-32552	123	23	unseen	unseen	ADJ
ajst-32552	123	24	data	datum	NOUN
ajst-32552	123	25	suggests	suggest	VERB
ajst-32552	123	26	potential	potential	ADJ
ajst-32552	123	27	overfitting	overfitting	NOUN
ajst-32552	123	28	and	and	CCONJ
ajst-32552	123	29	associated	associated	ADJ
ajst-32552	123	30	uncertainties	uncertainty	NOUN
ajst-32552	123	31	.	.	PUNCT
ajst-32552	124	1	the	the	DET
ajst-32552	124	2	superior	superior	ADJ
ajst-32552	124	3	performance	performance	NOUN
ajst-32552	124	4	of	of	ADP
ajst-32552	124	5	the	the	DET
ajst-32552	124	6	random	random	ADJ
ajst-32552	124	7	forest	forest	NOUN
ajst-32552	124	8	is	be	AUX
ajst-32552	124	9	attributed	attribute	VERB
ajst-32552	124	10	to	to	ADP
ajst-32552	124	11	its	its	PRON
ajst-32552	124	12	inherent	inherent	ADJ
ajst-32552	124	13	mechanism	mechanism	NOUN
ajst-32552	124	14	of	of	ADP
ajst-32552	124	15	averaging	average	VERB
ajst-32552	124	16	multiple	multiple	ADJ
ajst-32552	124	17	decision	decision	NOUN
ajst-32552	124	18	trees	tree	NOUN
ajst-32552	124	19	,	,	PUNCT
ajst-32552	124	20	which	which	PRON
ajst-32552	124	21	mitigates	mitigate	VERB
ajst-32552	124	22	overfitting	overfitte	VERB
ajst-32552	124	23	and	and	CCONJ
ajst-32552	124	24	improves	improve	VERB
ajst-32552	124	25	generalization	generalization	NOUN
ajst-32552	124	26	.	.	PUNCT
ajst-32552	125	1	this	this	DET
ajst-32552	125	2	model	model	NOUN
ajst-32552	125	3	is	be	AUX
ajst-32552	125	4	recommended	recommend	VERB
ajst-32552	125	5	for	for	ADP
ajst-32552	125	6	applications	application	NOUN
ajst-32552	125	7	where	where	SCONJ
ajst-32552	125	8	computational	computational	ADJ
ajst-32552	125	9	resources	resource	NOUN
ajst-32552	125	10	are	be	AUX
ajst-32552	125	11	sufficient	sufficient	ADJ
ajst-32552	125	12	.	.	PUNCT
ajst-32552	126	1	however	however	ADV
ajst-32552	126	2	,	,	PUNCT
ajst-32552	126	3	for	for	ADP
ajst-32552	126	4	very	very	ADV
ajst-32552	126	5	large	large	ADJ
ajst-32552	126	6	datasets	dataset	NOUN
ajst-32552	126	7	,	,	PUNCT
ajst-32552	126	8	the	the	DET
ajst-32552	126	9	computational	computational	ADJ
ajst-32552	126	10	demands	demand	NOUN
ajst-32552	126	11	and	and	CCONJ
ajst-32552	126	12	longer	long	ADJ
ajst-32552	126	13	processing	processing	NOUN
ajst-32552	126	14	times	time	NOUN
ajst-32552	126	15	of	of	ADP
ajst-32552	126	16	the	the	DET
ajst-32552	126	17	random	random	ADJ
ajst-32552	126	18	forest	forest	NOUN
ajst-32552	126	19	may	may	AUX
ajst-32552	126	20	present	present	VERB
ajst-32552	126	21	practical	practical	ADJ
ajst-32552	126	22	limitations	limitation	NOUN
ajst-32552	126	23	.	.	PUNCT
ajst-32552	127	1	6.2	6.2	NUM
ajst-32552	127	2	comparison	comparison	NOUN
ajst-32552	127	3	with	with	ADP
ajst-32552	127	4	state	state	NOUN
ajst-32552	127	5	-	-	PUNCT
ajst-32552	127	6	of	of	ADP
ajst-32552	127	7	-	-	PUNCT
ajst-32552	127	8	the	the	DET
ajst-32552	127	9	-	-	PUNCT
ajst-32552	127	10	art	art	NOUN
ajst-32552	127	11	approaches	approach	NOUN
ajst-32552	127	12	(	(	PUNCT
ajst-32552	127	13	e.g.	e.g.	ADV
ajst-32552	127	14	,	,	PUNCT
ajst-32552	127	15	lstm	lstm	ADJ
ajst-32552	127	16	,	,	PUNCT
ajst-32552	127	17	sarima	sarima	NOUN
ajst-32552	127	18	)	)	PUNCT
ajst-32552	127	19	with	with	ADP
ajst-32552	127	20	the	the	DET
ajst-32552	127	21	current	current	ADJ
ajst-32552	127	22	discussions	discussion	NOUN
ajst-32552	127	23	and	and	CCONJ
ajst-32552	127	24	research	research	NOUN
ajst-32552	127	25	about	about	ADP
ajst-32552	127	26	the	the	DET
ajst-32552	127	27	prediction	prediction	NOUN
ajst-32552	127	28	of	of	ADP
ajst-32552	127	29	ghi	ghi	PROPN
ajst-32552	127	30	values	value	NOUN
ajst-32552	127	31	,	,	PUNCT
ajst-32552	127	32	we	we	PRON
ajst-32552	127	33	can	can	AUX
ajst-32552	127	34	try	try	VERB
ajst-32552	127	35	to	to	PART
ajst-32552	127	36	figure	figure	VERB
ajst-32552	127	37	out	out	ADP
ajst-32552	127	38	how	how	SCONJ
ajst-32552	127	39	people	people	NOUN
ajst-32552	127	40	use	use	VERB
ajst-32552	127	41	deep	deep	ADJ
ajst-32552	127	42	learning	learning	NOUN
ajst-32552	127	43	and	and	CCONJ
ajst-32552	127	44	other	other	ADJ
ajst-32552	127	45	models	model	NOUN
ajst-32552	127	46	like	like	ADP
ajst-32552	127	47	lstm	lstm	NOUN
ajst-32552	127	48	.	.	PUNCT
ajst-32552	128	1	they	they	PRON
ajst-32552	128	2	combine	combine	VERB
ajst-32552	128	3	them	they	PRON
ajst-32552	128	4	to	to	PART
ajst-32552	128	5	analyze	analyze	VERB
ajst-32552	128	6	massive	massive	ADJ
ajst-32552	128	7	datasets	dataset	NOUN
ajst-32552	128	8	and	and	CCONJ
ajst-32552	128	9	get	get	VERB
ajst-32552	128	10	more	more	ADV
ajst-32552	128	11	precise	precise	ADJ
ajst-32552	128	12	results	result	NOUN
ajst-32552	128	13	.	.	PUNCT
ajst-32552	129	1	our	our	PRON
ajst-32552	129	2	models	model	NOUN
ajst-32552	129	3	are	be	AUX
ajst-32552	129	4	not	not	PART
ajst-32552	129	5	really	really	ADV
ajst-32552	129	6	good	good	ADJ
ajst-32552	129	7	enough	enough	ADV
ajst-32552	129	8	.	.	PUNCT
ajst-32552	130	1	our	our	PRON
ajst-32552	130	2	data	data	NOUN
ajst-32552	130	3	is	be	AUX
ajst-32552	130	4	not	not	PART
ajst-32552	130	5	large	large	ADJ
ajst-32552	130	6	,	,	PUNCT
ajst-32552	130	7	which	which	PRON
ajst-32552	130	8	is	be	AUX
ajst-32552	130	9	only	only	ADV
ajst-32552	130	10	about	about	ADV
ajst-32552	130	11	one	one	NUM
ajst-32552	130	12	year	year	NOUN
ajst-32552	130	13	of	of	ADP
ajst-32552	130	14	data	datum	NOUN
ajst-32552	130	15	,	,	PUNCT
ajst-32552	130	16	and	and	CCONJ
ajst-32552	130	17	is	be	AUX
ajst-32552	130	18	not	not	PART
ajst-32552	130	19	sufficient	sufficient	ADJ
ajst-32552	130	20	for	for	SCONJ
ajst-32552	130	21	us	we	PRON
ajst-32552	130	22	to	to	PART
ajst-32552	130	23	make	make	VERB
ajst-32552	130	24	a	a	DET
ajst-32552	130	25	decision	decision	NOUN
ajst-32552	130	26	.	.	PUNCT
ajst-32552	131	1	and	and	CCONJ
ajst-32552	131	2	our	our	PRON
ajst-32552	131	3	models	model	NOUN
ajst-32552	131	4	are	be	AUX
ajst-32552	131	5	sensitive	sensitive	ADJ
ajst-32552	131	6	to	to	ADP
ajst-32552	131	7	noise	noise	NOUN
ajst-32552	131	8	,	,	PUNCT
ajst-32552	131	9	so	so	SCONJ
ajst-32552	131	10	that	that	SCONJ
ajst-32552	131	11	our	our	PRON
ajst-32552	131	12	real	real	ADJ
ajst-32552	131	13	-	-	PUNCT
ajst-32552	131	14	world	world	NOUN
ajst-32552	131	15	data	datum	NOUN
ajst-32552	131	16	will	will	AUX
ajst-32552	131	17	affect	affect	VERB
ajst-32552	131	18	the	the	DET
ajst-32552	131	19	accuracy	accuracy	NOUN
ajst-32552	131	20	of	of	ADP
ajst-32552	131	21	results	result	NOUN
ajst-32552	131	22	,	,	PUNCT
ajst-32552	131	23	since	since	SCONJ
ajst-32552	131	24	realworld	realworld	PROPN
ajst-32552	131	25	data	data	PROPN
ajst-32552	131	26	is	be	AUX
ajst-32552	131	27	random	random	ADJ
ajst-32552	131	28	and	and	CCONJ
ajst-32552	131	29	not	not	PART
ajst-32552	131	30	ideal	ideal	ADJ
ajst-32552	131	31	as	as	SCONJ
ajst-32552	131	32	we	we	PRON
ajst-32552	131	33	expected	expect	VERB
ajst-32552	131	34	.	.	PUNCT
ajst-32552	132	1	for	for	ADP
ajst-32552	132	2	the	the	DET
ajst-32552	132	3	strong	strong	ADJ
ajst-32552	132	4	time	time	NOUN
ajst-32552	132	5	sequence	sequence	NOUN
ajst-32552	132	6	characteristics	characteristic	NOUN
ajst-32552	132	7	(	(	PUNCT
ajst-32552	132	8	such	such	ADJ
ajst-32552	132	9	as	as	ADP
ajst-32552	132	10	self	self	NOUN
ajst-32552	132	11	-	-	PUNCT
ajst-32552	132	12	correlation	correlation	NOUN
ajst-32552	132	13	,	,	PUNCT
ajst-32552	132	14	periodicity	periodicity	NOUN
ajst-32552	132	15	)	)	PUNCT
ajst-32552	132	16	and	and	CCONJ
ajst-32552	132	17	spatial	spatial	ADJ
ajst-32552	132	18	dependence	dependence	NOUN
ajst-32552	132	19	(	(	PUNCT
ajst-32552	132	20	such	such	ADJ
ajst-32552	132	21	as	as	ADP
ajst-32552	132	22	the	the	DET
ajst-32552	132	23	influence	influence	NOUN
ajst-32552	132	24	of	of	ADP
ajst-32552	132	25	peripheral	peripheral	ADJ
ajst-32552	132	26	sites	site	NOUN
ajst-32552	132	27	)	)	PUNCT
ajst-32552	132	28	in	in	ADP
ajst-32552	132	29	ghi	ghi	PROPN
ajst-32552	132	30	prediction	prediction	NOUN
ajst-32552	132	31	,	,	PUNCT
ajst-32552	132	32	the	the	DET
ajst-32552	132	33	ability	ability	NOUN
ajst-32552	132	34	to	to	PART
ajst-32552	132	35	directly	directly	ADV
ajst-32552	132	36	capture	capture	VERB
ajst-32552	132	37	standard	standard	ADJ
ajst-32552	132	38	decision	decision	NOUN
ajst-32552	132	39	trees	tree	NOUN
ajst-32552	132	40	and	and	CCONJ
ajst-32552	132	41	random	random	ADJ
ajst-32552	132	42	forests	forest	NOUN
ajst-32552	132	43	is	be	AUX
ajst-32552	132	44	not	not	PART
ajst-32552	132	45	as	as	ADV
ajst-32552	132	46	good	good	ADJ
ajst-32552	132	47	as	as	ADP
ajst-32552	132	48	models	model	NOUN
ajst-32552	132	49	specially	specially	ADV
ajst-32552	132	50	designed	design	VERB
ajst-32552	132	51	for	for	ADP
ajst-32552	132	52	sequence	sequence	NOUN
ajst-32552	132	53	modeling	modeling	NOUN
ajst-32552	132	54	(	(	PUNCT
ajst-32552	132	55	such	such	ADJ
ajst-32552	132	56	as	as	ADP
ajst-32552	132	57	lstm	lstm	NOUN
ajst-32552	132	58	,	,	PUNCT
ajst-32552	132	59	sarima	sarima	NOUN
ajst-32552	132	60	)	)	PUNCT
ajst-32552	132	61	or	or	CCONJ
ajst-32552	132	62	models	model	NOUN
ajst-32552	132	63	that	that	PRON
ajst-32552	132	64	consider	consider	VERB
ajst-32552	132	65	spatial	spatial	ADJ
ajst-32552	132	66	relationships	relationship	NOUN
ajst-32552	132	67	[	[	X
ajst-32552	132	68	6	6	NUM
ajst-32552	132	69	]	]	PUNCT
ajst-32552	132	70	.	.	PUNCT
ajst-32552	133	1	we	we	PRON
ajst-32552	133	2	want	want	VERB
ajst-32552	133	3	to	to	PART
ajst-32552	133	4	consider	consider	VERB
ajst-32552	133	5	including	include	VERB
ajst-32552	133	6	more	more	ADJ
ajst-32552	133	7	models	model	NOUN
ajst-32552	133	8	and	and	CCONJ
ajst-32552	133	9	combining	combine	VERB
ajst-32552	133	10	the	the	DET
ajst-32552	133	11	advantages	advantage	NOUN
ajst-32552	133	12	and	and	CCONJ
ajst-32552	133	13	avoiding	avoid	VERB
ajst-32552	133	14	the	the	DET
ajst-32552	133	15	disadvantages	disadvantage	NOUN
ajst-32552	133	16	with	with	ADP
ajst-32552	133	17	different	different	ADJ
ajst-32552	133	18	models	model	NOUN
ajst-32552	133	19	,	,	PUNCT
ajst-32552	133	20	and	and	CCONJ
ajst-32552	133	21	consider	consider	VERB
ajst-32552	133	22	more	more	ADJ
ajst-32552	133	23	situations	situation	NOUN
ajst-32552	133	24	together	together	ADV
ajst-32552	133	25	to	to	PART
ajst-32552	133	26	get	get	VERB
ajst-32552	133	27	a	a	DET
ajst-32552	133	28	more	more	ADV
ajst-32552	133	29	accurate	accurate	ADJ
ajst-32552	133	30	result	result	NOUN
ajst-32552	133	31	.	.	PUNCT
ajst-32552	134	1	6.3	6.3	NUM
ajst-32552	134	2	research	research	NOUN
ajst-32552	134	3	limitations	limitation	NOUN
ajst-32552	134	4	no	no	ADV
ajst-32552	134	5	matter	matter	ADV
ajst-32552	134	6	how	how	SCONJ
ajst-32552	134	7	we	we	PRON
ajst-32552	134	8	develop	develop	VERB
ajst-32552	134	9	our	our	PRON
ajst-32552	134	10	models	model	NOUN
ajst-32552	134	11	and	and	CCONJ
ajst-32552	134	12	the	the	DET
ajst-32552	134	13	strategy	strategy	NOUN
ajst-32552	134	14	we	we	PRON
ajst-32552	134	15	want	want	VERB
ajst-32552	134	16	to	to	PART
ajst-32552	134	17	combine	combine	VERB
ajst-32552	134	18	with	with	ADP
ajst-32552	134	19	,	,	PUNCT
ajst-32552	134	20	we	we	PRON
ajst-32552	134	21	usually	usually	ADV
ajst-32552	134	22	face	face	VERB
ajst-32552	134	23	the	the	DET
ajst-32552	134	24	challenging	challenging	ADJ
ajst-32552	134	25	facts	fact	NOUN
ajst-32552	134	26	that	that	SCONJ
ajst-32552	134	27	operating	operating	NOUN
ajst-32552	134	28	models	model	NOUN
ajst-32552	134	29	need	need	VERB
ajst-32552	134	30	huge	huge	ADJ
ajst-32552	134	31	computational	computational	ADJ
ajst-32552	134	32	resources	resource	NOUN
ajst-32552	134	33	and	and	CCONJ
ajst-32552	134	34	require	require	VERB
ajst-32552	134	35	highly	highly	ADV
ajst-32552	134	36	calculating	calculate	VERB
ajst-32552	134	37	performance	performance	NOUN
ajst-32552	134	38	,	,	PUNCT
ajst-32552	134	39	which	which	PRON
ajst-32552	134	40	will	will	AUX
ajst-32552	134	41	cost	cost	VERB
ajst-32552	134	42	a	a	DET
ajst-32552	134	43	lot	lot	NOUN
ajst-32552	134	44	of	of	ADP
ajst-32552	134	45	time	time	NOUN
ajst-32552	134	46	and	and	CCONJ
ajst-32552	134	47	effort	effort	NOUN
ajst-32552	134	48	.	.	PUNCT
ajst-32552	135	1	the	the	DET
ajst-32552	135	2	strategy	strategy	NOUN
ajst-32552	135	3	we	we	PRON
ajst-32552	135	4	try	try	VERB
ajst-32552	135	5	to	to	PART
ajst-32552	135	6	use	use	VERB
ajst-32552	135	7	will	will	AUX
ajst-32552	135	8	still	still	ADV
ajst-32552	135	9	need	need	VERB
ajst-32552	135	10	to	to	PART
ajst-32552	135	11	consider	consider	VERB
ajst-32552	135	12	the	the	DET
ajst-32552	135	13	abilities	ability	NOUN
ajst-32552	135	14	of	of	ADP
ajst-32552	135	15	calculations	calculation	NOUN
ajst-32552	135	16	and	and	CCONJ
ajst-32552	135	17	the	the	DET
ajst-32552	135	18	performance	performance	NOUN
ajst-32552	135	19	of	of	ADP
ajst-32552	135	20	the	the	DET
ajst-32552	135	21	machines	machine	NOUN
ajst-32552	135	22	.	.	PUNCT
ajst-32552	136	1	during	during	ADP
ajst-32552	136	2	the	the	DET
ajst-32552	136	3	selection	selection	NOUN
ajst-32552	136	4	process	process	NOUN
ajst-32552	136	5	,	,	PUNCT
ajst-32552	136	6	choosing	choose	VERB
ajst-32552	136	7	an	an	DET
ajst-32552	136	8	accessible	accessible	ADJ
ajst-32552	136	9	model	model	NOUN
ajst-32552	136	10	is	be	AUX
ajst-32552	136	11	one	one	NUM
ajst-32552	136	12	factor	factor	NOUN
ajst-32552	136	13	to	to	PART
ajst-32552	136	14	consider	consider	VERB
ajst-32552	136	15	.	.	PUNCT
ajst-32552	137	1	7	7	X
ajst-32552	137	2	.	.	X
ajst-32552	137	3	future	future	ADJ
ajst-32552	137	4	work	work	NOUN
ajst-32552	137	5	based	base	VERB
ajst-32552	137	6	on	on	ADP
ajst-32552	137	7	the	the	DET
ajst-32552	137	8	current	current	ADJ
ajst-32552	137	9	study	study	NOUN
ajst-32552	137	10	,	,	PUNCT
ajst-32552	137	11	we	we	PRON
ajst-32552	137	12	plan	plan	VERB
ajst-32552	137	13	to	to	PART
ajst-32552	137	14	explore	explore	VERB
ajst-32552	137	15	more	more	ADV
ajst-32552	137	16	advanced	advanced	ADJ
ajst-32552	137	17	models	model	NOUN
ajst-32552	137	18	,	,	PUNCT
ajst-32552	137	19	such	such	ADJ
ajst-32552	137	20	as	as	ADP
ajst-32552	137	21	deep	deep	ADJ
ajst-32552	137	22	learning	learning	NOUN
ajst-32552	137	23	,	,	PUNCT
ajst-32552	137	24	svm	svm	PROPN
ajst-32552	137	25	,	,	PUNCT
ajst-32552	137	26	lstm	lstm	ADJ
ajst-32552	137	27	,	,	PUNCT
ajst-32552	137	28	etc	etc	X
ajst-32552	137	29	.	.	X
ajst-32552	137	30	deep	deep	ADJ
ajst-32552	137	31	learning	learning	NOUN
ajst-32552	137	32	models	model	NOUN
ajst-32552	137	33	will	will	AUX
ajst-32552	137	34	represent	represent	VERB
ajst-32552	137	35	the	the	DET
ajst-32552	137	36	most	most	ADV
ajst-32552	137	37	advanced	advanced	ADJ
ajst-32552	137	38	level	level	NOUN
ajst-32552	137	39	of	of	ADP
ajst-32552	137	40	current	current	ADJ
ajst-32552	137	41	ghi	ghi	PROPN
ajst-32552	137	42	prediction	prediction	NOUN
ajst-32552	137	43	,	,	PUNCT
ajst-32552	137	44	which	which	PRON
ajst-32552	137	45	can	can	AUX
ajst-32552	137	46	capture	capture	VERB
ajst-32552	137	47	space	space	NOUN
ajst-32552	137	48	-	-	PUNCT
ajst-32552	137	49	time	time	NOUN
ajst-32552	137	50	features	feature	NOUN
ajst-32552	137	51	at	at	ADP
ajst-32552	137	52	the	the	DET
ajst-32552	137	53	same	same	ADJ
ajst-32552	137	54	time	time	NOUN
ajst-32552	137	55	,	,	PUNCT
ajst-32552	137	56	greatly	greatly	ADV
ajst-32552	137	57	improving	improve	VERB
ajst-32552	137	58	the	the	DET
ajst-32552	137	59	accuracy	accuracy	NOUN
ajst-32552	137	60	of	of	ADP
ajst-32552	137	61	prediction	prediction	NOUN
ajst-32552	137	62	,	,	PUNCT
ajst-32552	137	63	especially	especially	ADV
ajst-32552	137	64	short	short	ADJ
ajst-32552	137	65	-	-	PUNCT
ajst-32552	137	66	term	term	NOUN
ajst-32552	137	67	and	and	CCONJ
ajst-32552	137	68	ultra	ultra	ADJ
ajst-32552	137	69	-	-	ADJ
ajst-32552	137	70	short	short	ADJ
ajst-32552	137	71	-	-	PUNCT
ajst-32552	137	72	term	term	NOUN
ajst-32552	137	73	forecasts	forecast	NOUN
ajst-32552	137	74	.	.	PUNCT
ajst-32552	138	1	for	for	ADP
ajst-32552	138	2	svm	svm	ADJ
ajst-32552	138	3	models	model	NOUN
ajst-32552	138	4	,	,	PUNCT
ajst-32552	138	5	it	it	PRON
ajst-32552	138	6	efficiently	efficiently	ADV
ajst-32552	138	7	deals	deal	VERB
ajst-32552	138	8	with	with	ADP
ajst-32552	138	9	nonlinear	nonlinear	ADJ
ajst-32552	138	10	problems	problem	NOUN
ajst-32552	138	11	:	:	PUNCT
ajst-32552	138	12	by	by	ADP
ajst-32552	138	13	selecting	select	VERB
ajst-32552	138	14	appropriate	appropriate	ADJ
ajst-32552	138	15	nuclear	nuclear	ADJ
ajst-32552	138	16	functions	function	NOUN
ajst-32552	138	17	(	(	PUNCT
ajst-32552	138	18	such	such	ADJ
ajst-32552	138	19	as	as	ADP
ajst-32552	138	20	rbf	rbf	PROPN
ajst-32552	138	21	)	)	PUNCT
ajst-32552	138	22	,	,	PUNCT
ajst-32552	138	23	svm	svm	PROPN
ajst-32552	138	24	can	can	AUX
ajst-32552	138	25	very	very	ADV
ajst-32552	138	26	effectively	effectively	ADV
ajst-32552	138	27	capture	capture	VERB
ajst-32552	138	28	the	the	DET
ajst-32552	138	29	complex	complex	ADJ
ajst-32552	138	30	nonlinear	nonlinear	ADJ
ajst-32552	138	31	relationship	relationship	NOUN
ajst-32552	138	32	between	between	ADP
ajst-32552	138	33	ghi	ghi	PROPN
ajst-32552	138	34	and	and	CCONJ
ajst-32552	138	35	meteorological	meteorological	ADJ
ajst-32552	138	36	factors	factor	NOUN
ajst-32552	138	37	109	109	NUM
ajst-32552	138	38	(	(	PUNCT
ajst-32552	138	39	such	such	ADJ
ajst-32552	138	40	as	as	ADP
ajst-32552	138	41	cloud	cloud	NOUN
ajst-32552	138	42	,	,	PUNCT
ajst-32552	138	43	temperature	temperature	NOUN
ajst-32552	138	44	,	,	PUNCT
ajst-32552	138	45	humidity	humidity	NOUN
ajst-32552	138	46	)	)	PUNCT
ajst-32552	138	47	without	without	ADP
ajst-32552	138	48	manual	manual	ADJ
ajst-32552	138	49	feature	feature	NOUN
ajst-32552	138	50	transformation	transformation	NOUN
ajst-32552	138	51	.	.	PUNCT
ajst-32552	139	1	for	for	ADP
ajst-32552	139	2	the	the	DET
ajst-32552	139	3	lstm	lstm	PROPN
ajst-32552	139	4	model	model	NOUN
ajst-32552	139	5	,	,	PUNCT
ajst-32552	139	6	with	with	ADP
ajst-32552	139	7	the	the	DET
ajst-32552	139	8	support	support	NOUN
ajst-32552	139	9	of	of	ADP
ajst-32552	139	10	sufficient	sufficient	ADJ
ajst-32552	139	11	data	datum	NOUN
ajst-32552	139	12	,	,	PUNCT
ajst-32552	139	13	lstm	lstm	NOUN
ajst-32552	139	14	can	can	AUX
ajst-32552	139	15	usually	usually	ADV
ajst-32552	139	16	achieve	achieve	VERB
ajst-32552	139	17	higher	high	ADJ
ajst-32552	139	18	prediction	prediction	NOUN
ajst-32552	139	19	accuracy	accuracy	NOUN
ajst-32552	139	20	than	than	ADP
ajst-32552	139	21	svm	svm	ADJ
ajst-32552	139	22	and	and	CCONJ
ajst-32552	139	23	traditional	traditional	ADJ
ajst-32552	139	24	machine	machine	NOUN
ajst-32552	139	25	learning	learning	NOUN
ajst-32552	139	26	models	model	NOUN
ajst-32552	139	27	,	,	PUNCT
ajst-32552	139	28	especially	especially	ADV
ajst-32552	139	29	when	when	SCONJ
ajst-32552	139	30	predicting	predict	VERB
ajst-32552	139	31	and	and	CCONJ
ajst-32552	139	32	capturing	capture	VERB
ajst-32552	139	33	complex	complex	ADJ
ajst-32552	139	34	dynamic	dynamic	ADJ
ajst-32552	139	35	changes	change	NOUN
ajst-32552	139	36	in	in	ADP
ajst-32552	139	37	multiple	multiple	ADJ
ajst-32552	139	38	steps	step	NOUN
ajst-32552	139	39	.	.	PUNCT
ajst-32552	140	1	finally	finally	ADV
ajst-32552	140	2	,	,	PUNCT
ajst-32552	140	3	we	we	PRON
ajst-32552	140	4	intend	intend	VERB
ajst-32552	140	5	to	to	PART
ajst-32552	140	6	extend	extend	VERB
ajst-32552	140	7	the	the	DET
ajst-32552	140	8	prediction	prediction	NOUN
ajst-32552	140	9	horizon	horizon	NOUN
ajst-32552	140	10	beyond	beyond	ADP
ajst-32552	140	11	24	24	NUM
ajst-32552	140	12	hours	hour	NOUN
ajst-32552	140	13	,	,	PUNCT
ajst-32552	140	14	possibly	possibly	ADV
ajst-32552	140	15	forecasting	forecast	VERB
ajst-32552	140	16	up	up	ADP
ajst-32552	140	17	to	to	ADP
ajst-32552	140	18	7	7	NUM
ajst-32552	140	19	or	or	CCONJ
ajst-32552	140	20	10	10	NUM
ajst-32552	140	21	days	day	NOUN
ajst-32552	140	22	.	.	PUNCT
ajst-32552	141	1	we	we	PRON
ajst-32552	141	2	hope	hope	VERB
ajst-32552	141	3	to	to	PART
ajst-32552	141	4	develop	develop	VERB
ajst-32552	141	5	a	a	DET
ajst-32552	141	6	lightweight	lightweight	ADJ
ajst-32552	141	7	web	web	NOUN
ajst-32552	141	8	interface	interface	NOUN
ajst-32552	141	9	to	to	PART
ajst-32552	141	10	visualize	visualize	VERB
ajst-32552	141	11	predictions	prediction	NOUN
ajst-32552	141	12	and	and	CCONJ
ajst-32552	141	13	support	support	VERB
ajst-32552	141	14	real	real	ADJ
ajst-32552	141	15	-	-	PUNCT
ajst-32552	141	16	time	time	NOUN
ajst-32552	141	17	solar	solar	ADJ
ajst-32552	141	18	energy	energy	NOUN
ajst-32552	141	19	planning	planning	NOUN
ajst-32552	141	20	,	,	PUNCT
ajst-32552	141	21	since	since	SCONJ
ajst-32552	141	22	our	our	PRON
ajst-32552	141	23	goal	goal	NOUN
ajst-32552	141	24	is	be	AUX
ajst-32552	141	25	to	to	PART
ajst-32552	141	26	help	help	VERB
ajst-32552	141	27	people	people	NOUN
ajst-32552	141	28	and	and	CCONJ
ajst-32552	141	29	the	the	DET
ajst-32552	141	30	government	government	NOUN
ajst-32552	141	31	plan	plan	NOUN
ajst-32552	141	32	and	and	CCONJ
ajst-32552	141	33	develop	develop	VERB
ajst-32552	141	34	strategies	strategy	NOUN
ajst-32552	141	35	for	for	ADP
ajst-32552	141	36	energy	energy	NOUN
ajst-32552	141	37	resources	resource	NOUN
ajst-32552	141	38	.	.	PUNCT
ajst-32552	142	1	so	so	ADV
ajst-32552	142	2	,	,	PUNCT
ajst-32552	142	3	for	for	ADP
ajst-32552	142	4	people	people	NOUN
ajst-32552	142	5	who	who	PRON
ajst-32552	142	6	have	have	VERB
ajst-32552	142	7	no	no	DET
ajst-32552	142	8	idea	idea	NOUN
ajst-32552	142	9	about	about	ADP
ajst-32552	142	10	how	how	SCONJ
ajst-32552	142	11	the	the	DET
ajst-32552	142	12	models	model	NOUN
ajst-32552	142	13	perform	perform	VERB
ajst-32552	142	14	and	and	CCONJ
ajst-32552	142	15	how	how	SCONJ
ajst-32552	142	16	we	we	PRON
ajst-32552	142	17	can	can	AUX
ajst-32552	142	18	build	build	VERB
ajst-32552	142	19	these	these	DET
ajst-32552	142	20	models	model	NOUN
ajst-32552	142	21	,	,	PUNCT
ajst-32552	142	22	but	but	CCONJ
ajst-32552	142	23	they	they	PRON
ajst-32552	142	24	can	can	AUX
ajst-32552	142	25	still	still	ADV
ajst-32552	142	26	experience	experience	VERB
ajst-32552	142	27	and	and	CCONJ
ajst-32552	142	28	understand	understand	VERB
ajst-32552	142	29	the	the	DET
ajst-32552	142	30	ghi	ghi	PROPN
ajst-32552	142	31	and	and	CCONJ
ajst-32552	142	32	how	how	SCONJ
ajst-32552	142	33	they	they	PRON
ajst-32552	142	34	can	can	AUX
ajst-32552	142	35	use	use	VERB
ajst-32552	142	36	the	the	DET
ajst-32552	142	37	data	datum	NOUN
ajst-32552	142	38	from	from	ADP
ajst-32552	142	39	the	the	DET
ajst-32552	142	40	ghi	ghi	PROPN
ajst-32552	142	41	to	to	PART
ajst-32552	142	42	plan	plan	VERB
ajst-32552	142	43	their	their	PRON
ajst-32552	142	44	life	life	NOUN
ajst-32552	142	45	,	,	PUNCT
ajst-32552	142	46	which	which	PRON
ajst-32552	142	47	is	be	AUX
ajst-32552	142	48	our	our	PRON
ajst-32552	142	49	goal	goal	NOUN
ajst-32552	142	50	to	to	PART
ajst-32552	142	51	help	help	VERB
ajst-32552	142	52	others	other	NOUN
ajst-32552	142	53	,	,	PUNCT
ajst-32552	142	54	even	even	ADV
ajst-32552	142	55	though	though	SCONJ
ajst-32552	142	56	they	they	PRON
ajst-32552	142	57	have	have	VERB
ajst-32552	142	58	no	no	DET
ajst-32552	142	59	background	background	NOUN
ajst-32552	142	60	in	in	ADP
ajst-32552	142	61	coding	code	VERB
ajst-32552	142	62	and	and	CCONJ
ajst-32552	142	63	machine	machine	NOUN
ajst-32552	142	64	learning	learning	NOUN
ajst-32552	142	65	.	.	PUNCT
ajst-32552	143	1	8	8	X
ajst-32552	143	2	.	.	X
ajst-32552	143	3	conclusion	conclusion	NOUN
ajst-32552	143	4	this	this	DET
ajst-32552	143	5	study	study	NOUN
ajst-32552	143	6	evaluates	evaluate	VERB
ajst-32552	143	7	the	the	DET
ajst-32552	143	8	performance	performance	NOUN
ajst-32552	143	9	of	of	ADP
ajst-32552	143	10	various	various	ADJ
ajst-32552	143	11	machine	machine	NOUN
ajst-32552	143	12	learning	learning	NOUN
ajst-32552	143	13	models	model	NOUN
ajst-32552	143	14	for	for	ADP
ajst-32552	143	15	predicting	predict	VERB
ajst-32552	143	16	global	global	ADJ
ajst-32552	143	17	horizontal	horizontal	ADJ
ajst-32552	143	18	irradiance	irradiance	NOUN
ajst-32552	143	19	(	(	PUNCT
ajst-32552	143	20	ghi	ghi	PROPN
ajst-32552	143	21	)	)	PUNCT
ajst-32552	143	22	.	.	PUNCT
ajst-32552	144	1	among	among	ADP
ajst-32552	144	2	linear	linear	PROPN
ajst-32552	144	3	regression	regression	NOUN
ajst-32552	144	4	,	,	PUNCT
ajst-32552	144	5	decision	decision	NOUN
ajst-32552	144	6	tree	tree	NOUN
ajst-32552	144	7	,	,	PUNCT
ajst-32552	144	8	and	and	CCONJ
ajst-32552	144	9	random	random	ADJ
ajst-32552	144	10	forests	forest	NOUN
ajst-32552	144	11	,	,	PUNCT
ajst-32552	144	12	the	the	DET
ajst-32552	144	13	random	random	ADJ
ajst-32552	144	14	forest	forest	NOUN
ajst-32552	144	15	achieved	achieve	VERB
ajst-32552	144	16	the	the	DET
ajst-32552	144	17	best	good	ADJ
ajst-32552	144	18	performance	performance	NOUN
ajst-32552	144	19	,	,	PUNCT
ajst-32552	144	20	with	with	ADP
ajst-32552	144	21	the	the	DET
ajst-32552	144	22	lowest	low	ADJ
ajst-32552	144	23	rmse	rmse	NOUN
ajst-32552	144	24	(	(	PUNCT
ajst-32552	144	25	6.04	6.04	NUM
ajst-32552	144	26	w	w	NOUN
ajst-32552	144	27	/	/	SYM
ajst-32552	144	28	m²	m²	PROPN
ajst-32552	144	29	)	)	PUNCT
ajst-32552	144	30	and	and	CCONJ
ajst-32552	144	31	the	the	DET
ajst-32552	144	32	highest	high	ADJ
ajst-32552	144	33	r²	r²	NOUN
ajst-32552	144	34	score	score	NOUN
ajst-32552	144	35	(	(	PUNCT
ajst-32552	144	36	0.9994	0.9994	NUM
ajst-32552	144	37	)	)	PUNCT
ajst-32552	144	38	on	on	ADP
ajst-32552	144	39	the	the	DET
ajst-32552	144	40	test	test	NOUN
ajst-32552	144	41	set	set	NOUN
ajst-32552	144	42	,	,	PUNCT
ajst-32552	144	43	demonstrating	demonstrate	VERB
ajst-32552	144	44	exceptionally	exceptionally	ADV
ajst-32552	144	45	high	high	ADJ
ajst-32552	144	46	predictive	predictive	ADJ
ajst-32552	144	47	accuracy	accuracy	NOUN
ajst-32552	144	48	and	and	CCONJ
ajst-32552	144	49	strong	strong	ADJ
ajst-32552	144	50	generalization	generalization	NOUN
ajst-32552	144	51	capability	capability	NOUN
ajst-32552	144	52	.	.	PUNCT
ajst-32552	145	1	although	although	SCONJ
ajst-32552	145	2	the	the	DET
ajst-32552	145	3	decision	decision	NOUN
ajst-32552	145	4	tree	tree	NOUN
ajst-32552	145	5	model	model	NOUN
ajst-32552	145	6	exhibited	exhibit	VERB
ajst-32552	145	7	very	very	ADV
ajst-32552	145	8	high	high	ADJ
ajst-32552	145	9	training	training	NOUN
ajst-32552	145	10	accuracy	accuracy	NOUN
ajst-32552	145	11	,	,	PUNCT
ajst-32552	145	12	significant	significant	ADJ
ajst-32552	145	13	overfitting	overfitting	NOUN
ajst-32552	145	14	was	be	AUX
ajst-32552	145	15	observed	observe	VERB
ajst-32552	145	16	,	,	PUNCT
ajst-32552	145	17	indicating	indicate	VERB
ajst-32552	145	18	limited	limited	ADJ
ajst-32552	145	19	generalization	generalization	NOUN
ajst-32552	145	20	ability	ability	NOUN
ajst-32552	145	21	.	.	PUNCT
ajst-32552	146	1	the	the	DET
ajst-32552	146	2	linear	linear	PROPN
ajst-32552	146	3	regression	regression	NOUN
ajst-32552	146	4	model	model	NOUN
ajst-32552	146	5	performed	perform	VERB
ajst-32552	146	6	the	the	DET
ajst-32552	146	7	worst	bad	ADJ
ajst-32552	146	8	,	,	PUNCT
ajst-32552	146	9	confirming	confirm	VERB
ajst-32552	146	10	that	that	SCONJ
ajst-32552	146	11	the	the	DET
ajst-32552	146	12	relationship	relationship	NOUN
ajst-32552	146	13	between	between	ADP
ajst-32552	146	14	ghi	ghi	PROPN
ajst-32552	146	15	and	and	CCONJ
ajst-32552	146	16	meteorological	meteorological	ADJ
ajst-32552	146	17	features	feature	NOUN
ajst-32552	146	18	is	be	AUX
ajst-32552	146	19	complex	complex	ADJ
ajst-32552	146	20	and	and	CCONJ
ajst-32552	146	21	nonlinear	nonlinear	ADJ
ajst-32552	146	22	,	,	PUNCT
ajst-32552	146	23	and	and	CCONJ
ajst-32552	146	24	can	can	AUX
ajst-32552	146	25	not	not	PART
ajst-32552	146	26	be	be	AUX
ajst-32552	146	27	adequately	adequately	ADV
ajst-32552	146	28	captured	capture	VERB
ajst-32552	146	29	by	by	ADP
ajst-32552	146	30	a	a	DET
ajst-32552	146	31	simple	simple	ADJ
ajst-32552	146	32	linear	linear	NOUN
ajst-32552	146	33	approach	approach	NOUN
ajst-32552	146	34	.	.	PUNCT
ajst-32552	147	1	furthermore	furthermore	ADV
ajst-32552	147	2	,	,	PUNCT
ajst-32552	147	3	the	the	DET
ajst-32552	147	4	study	study	NOUN
ajst-32552	147	5	revealed	reveal	VERB
ajst-32552	147	6	that	that	SCONJ
ajst-32552	147	7	training	train	VERB
ajst-32552	147	8	a	a	DET
ajst-32552	147	9	unified	unified	ADJ
ajst-32552	147	10	model	model	NOUN
ajst-32552	147	11	on	on	ADP
ajst-32552	147	12	full	full	ADJ
ajst-32552	147	13	-	-	PUNCT
ajst-32552	147	14	day	day	NOUN
ajst-32552	147	15	data	datum	NOUN
ajst-32552	147	16	yields	yield	VERB
ajst-32552	147	17	satisfactory	satisfactory	ADJ
ajst-32552	147	18	predictions	prediction	NOUN
ajst-32552	147	19	,	,	PUNCT
ajst-32552	147	20	making	make	VERB
ajst-32552	147	21	it	it	PRON
ajst-32552	147	22	unnecessary	unnecessary	ADJ
ajst-32552	147	23	to	to	PART
ajst-32552	147	24	separate	separate	VERB
ajst-32552	147	25	daytime	daytime	NOUN
ajst-32552	147	26	and	and	CCONJ
ajst-32552	147	27	nighttime	nighttime	ADJ
ajst-32552	147	28	data	datum	NOUN
ajst-32552	147	29	.	.	PUNCT
ajst-32552	148	1	these	these	DET
ajst-32552	148	2	findings	finding	NOUN
ajst-32552	148	3	highlight	highlight	VERB
ajst-32552	148	4	the	the	DET
ajst-32552	148	5	random	random	ADJ
ajst-32552	148	6	forest	forest	NOUN
ajst-32552	148	7	as	as	ADP
ajst-32552	148	8	an	an	DET
ajst-32552	148	9	efficient	efficient	ADJ
ajst-32552	148	10	and	and	CCONJ
ajst-32552	148	11	reliable	reliable	ADJ
ajst-32552	148	12	tool	tool	NOUN
ajst-32552	148	13	for	for	ADP
ajst-32552	148	14	ghi	ghi	PROPN
ajst-32552	148	15	prediction	prediction	NOUN
ajst-32552	148	16	.	.	PUNCT
ajst-32552	149	1	its	its	PRON
ajst-32552	149	2	ensemble	ensemble	ADJ
ajst-32552	149	3	learning	learning	NOUN
ajst-32552	149	4	mechanism	mechanism	NOUN
ajst-32552	149	5	effectively	effectively	ADV
ajst-32552	149	6	captures	capture	VERB
ajst-32552	149	7	complex	complex	ADJ
ajst-32552	149	8	nonlinear	nonlinear	ADJ
ajst-32552	149	9	relationships	relationship	NOUN
ajst-32552	149	10	,	,	PUNCT
ajst-32552	149	11	offering	offer	VERB
ajst-32552	149	12	a	a	DET
ajst-32552	149	13	practical	practical	ADJ
ajst-32552	149	14	solution	solution	NOUN
ajst-32552	149	15	for	for	ADP
ajst-32552	149	16	forecasting	forecast	VERB
ajst-32552	149	17	solar	solar	ADJ
ajst-32552	149	18	power	power	NOUN
ajst-32552	149	19	generation	generation	NOUN
ajst-32552	149	20	.	.	PUNCT
ajst-32552	150	1	references	reference	NOUN
ajst-32552	150	2	[	[	X
ajst-32552	150	3	1	1	NUM
ajst-32552	150	4	]	]	X
ajst-32552	150	5	singhal	singhal	PROPN
ajst-32552	150	6	,	,	PUNCT
ajst-32552	150	7	r.	r.	PROPN
ajst-32552	150	8	,	,	PUNCT
ajst-32552	150	9	singhal	singhal	PROPN
ajst-32552	150	10	,	,	PUNCT
ajst-32552	150	11	p.	p.	PROPN
ajst-32552	150	12	,	,	PUNCT
ajst-32552	150	13	&	&	CCONJ
ajst-32552	150	14	gupta	gupta	PROPN
ajst-32552	150	15	,	,	PUNCT
ajst-32552	150	16	s.	s.	PROPN
ajst-32552	150	17	(	(	PUNCT
ajst-32552	150	18	2022	2022	NUM
ajst-32552	150	19	)	)	PUNCT
ajst-32552	150	20	.	.	PUNCT
ajst-32552	151	1	solar	solar	ADJ
ajst-32552	151	2	-	-	PUNCT
ajst-32552	151	3	cast	cast	NOUN
ajst-32552	151	4	:	:	PUNCT
ajst-32552	151	5	solar	solar	ADJ
ajst-32552	151	6	power	power	NOUN
ajst-32552	151	7	generation	generation	NOUN
ajst-32552	151	8	prediction	prediction	NOUN
ajst-32552	151	9	from	from	ADP
ajst-32552	151	10	weather	weather	NOUN
ajst-32552	151	11	forecasts	forecast	NOUN
ajst-32552	151	12	using	use	VERB
ajst-32552	151	13	machine	machine	NOUN
ajst-32552	151	14	learning	learning	NOUN
ajst-32552	151	15	.	.	PUNCT
ajst-32552	152	1	2022	2022	NUM
ajst-32552	152	2	ieee	ieee	PROPN
ajst-32552	152	3	10th	10th	ADJ
ajst-32552	152	4	power	power	PROPN
ajst-32552	152	5	india	india	PROPN
ajst-32552	152	6	international	international	PROPN
ajst-32552	152	7	conference	conference	PROPN
ajst-32552	152	8	(	(	PUNCT
ajst-32552	152	9	piicon	piicon	NOUN
ajst-32552	152	10	)	)	PUNCT
ajst-32552	152	11	,	,	PUNCT
ajst-32552	152	12	1–6	1–6	NUM
ajst-32552	152	13	.	.	PUNCT
ajst-32552	152	14	https://doi.org/10.1109/piicon56320.2022.10045237	https://doi.org/10.1109/piicon56320.2022.10045237	PROPN
ajst-32552	152	15	.	.	PUNCT
ajst-32552	153	1	[	[	X
ajst-32552	153	2	2	2	X
ajst-32552	153	3	]	]	X
ajst-32552	153	4	seattle	seattle	PROPN
ajst-32552	153	5	city	city	PROPN
ajst-32552	153	6	light	light	NOUN
ajst-32552	153	7	.	.	PUNCT
ajst-32552	154	1	(	(	PUNCT
ajst-32552	154	2	2025	2025	NUM
ajst-32552	154	3	,	,	PUNCT
ajst-32552	154	4	march	march	PROPN
ajst-32552	154	5	21	21	NUM
ajst-32552	154	6	)	)	PUNCT
ajst-32552	154	7	.	.	PUNCT
ajst-32552	155	1	celebrating	celebrate	VERB
ajst-32552	155	2	renewable	renewable	ADJ
ajst-32552	155	3	energy	energy	NOUN
ajst-32552	155	4	.	.	PUNCT
ajst-32552	156	1	powerlines	powerline	NOUN
ajst-32552	156	2	.	.	PUNCT
ajst-32552	157	1	https://	https://	NOUN
ajst-32552	157	2	powerlines	powerline	NOUN
ajst-32552	157	3	.	.	PUNCT
ajst-32552	158	1	seattle	seattle	PROPN
ajst-32552	158	2	.	.	PUNCT
ajst-32552	158	3	gov/2025/03/21	gov/2025/03/21	PROPN
ajst-32552	158	4	/	/	SYM
ajst-32552	158	5	celebrating	celebrate	VERB
ajst-32552	158	6	-	-	PUNCT
ajst-32552	158	7	renewable	renewable	ADJ
ajst-32552	158	8	-	-	PUNCT
ajst-32552	158	9	energy/.	energy/.	NOUN
ajst-32552	158	10	[	[	X
ajst-32552	158	11	3	3	NUM
ajst-32552	158	12	]	]	X
ajst-32552	158	13	zeng	zeng	PROPN
ajst-32552	158	14	,	,	PUNCT
ajst-32552	158	15	j.	j.	PROPN
ajst-32552	158	16	w.	w.	PROPN
ajst-32552	158	17	,	,	PUNCT
ajst-32552	158	18	&	&	CCONJ
ajst-32552	158	19	qiao	qiao	PROPN
ajst-32552	158	20	,	,	PUNCT
ajst-32552	158	21	w.	w.	PROPN
ajst-32552	158	22	(	(	PUNCT
ajst-32552	158	23	2013	2013	NUM
ajst-32552	158	24	)	)	PUNCT
ajst-32552	158	25	.	.	PUNCT
ajst-32552	159	1	short	short	ADJ
ajst-32552	159	2	-	-	PUNCT
ajst-32552	159	3	term	term	NOUN
ajst-32552	159	4	solar	solar	ADJ
ajst-32552	159	5	power	power	NOUN
ajst-32552	159	6	prediction	prediction	NOUN
ajst-32552	159	7	using	use	VERB
ajst-32552	159	8	a	a	DET
ajst-32552	159	9	support	support	NOUN
ajst-32552	159	10	vector	vector	NOUN
ajst-32552	159	11	machine	machine	NOUN
ajst-32552	159	12	.	.	PUNCT
ajst-32552	160	1	renewable	renewable	ADJ
ajst-32552	160	2	energy	energy	NOUN
ajst-32552	160	3	,	,	PUNCT
ajst-32552	160	4	52	52	NUM
ajst-32552	160	5	,	,	PUNCT
ajst-32552	160	6	118–127	118–127	NUM
ajst-32552	160	7	.	.	PUNCT
ajst-32552	160	8	https://doi.org/10.1016/j.renene.2012.10.009	https://doi.org/10.1016/j.renene.2012.10.009	NUM
ajst-32552	160	9	.	.	PUNCT
ajst-32552	161	1	[	[	X
ajst-32552	161	2	4	4	NUM
ajst-32552	161	3	]	]	X
ajst-32552	161	4	hiremath	hiremath	NOUN
ajst-32552	161	5	,	,	PUNCT
ajst-32552	161	6	v.	v.	PROPN
ajst-32552	161	7	,	,	PUNCT
ajst-32552	161	8	naik	naik	PROPN
ajst-32552	161	9	,	,	PUNCT
ajst-32552	161	10	r.	r.	PROPN
ajst-32552	161	11	,	,	PUNCT
ajst-32552	161	12	naik	naik	PROPN
ajst-32552	161	13	,	,	PUNCT
ajst-32552	161	14	s.	s.	PROPN
ajst-32552	161	15	,	,	PUNCT
ajst-32552	161	16	shettar	shettar	NOUN
ajst-32552	161	17	,	,	PUNCT
ajst-32552	161	18	s.	s.	PROPN
ajst-32552	161	19	,	,	PUNCT
ajst-32552	161	20	&	&	CCONJ
ajst-32552	161	21	chachadi	chachadi	PROPN
ajst-32552	161	22	,	,	PUNCT
ajst-32552	161	23	k.	k.	PROPN
ajst-32552	161	24	(	(	PUNCT
ajst-32552	161	25	2024	2024	NUM
ajst-32552	161	26	)	)	PUNCT
ajst-32552	161	27	.	.	PUNCT
ajst-32552	162	1	ghi	ghi	PROPN
ajst-32552	162	2	prediction	prediction	NOUN
ajst-32552	162	3	based	base	VERB
ajst-32552	162	4	on	on	ADP
ajst-32552	162	5	weather	weather	NOUN
ajst-32552	162	6	data	datum	NOUN
ajst-32552	162	7	using	use	VERB
ajst-32552	162	8	machine	machine	NOUN
ajst-32552	162	9	learning	learning	NOUN
ajst-32552	162	10	.	.	PUNCT
ajst-32552	163	1	2024	2024	NUM
ajst-32552	163	2	ieee	ieee	PROPN
ajst-32552	163	3	international	international	ADJ
ajst-32552	163	4	conference	conference	NOUN
ajst-32552	163	5	on	on	ADP
ajst-32552	163	6	information	information	NOUN
ajst-32552	163	7	technology	technology	NOUN
ajst-32552	163	8	,	,	PUNCT
ajst-32552	163	9	electronics	electronic	NOUN
ajst-32552	163	10	and	and	CCONJ
ajst-32552	163	11	intelligent	intelligent	ADJ
ajst-32552	163	12	communication	communication	NOUN
ajst-32552	163	13	systems	system	NOUN
ajst-32552	163	14	(	(	PUNCT
ajst-32552	163	15	iciteics	iciteic	NOUN
ajst-32552	163	16	)	)	PUNCT
ajst-32552	163	17	,	,	PUNCT
ajst-32552	163	18	1–6	1–6	X
ajst-32552	163	19	.	.	PUNCT
ajst-32552	163	20	https://doi.org/10	https://doi.org/10	PROPN
ajst-32552	163	21	.	.	PUNCT
ajst-32552	164	1	1109/	1109/	NUM
ajst-32552	164	2	iciteics	iciteic	NOUN
ajst-32552	164	3	61368	61368	NUM
ajst-32552	164	4	.	.	PUNCT
ajst-32552	165	1	2024.10624921	2024.10624921	X
ajst-32552	165	2	.	.	PUNCT
ajst-32552	166	1	[	[	X
ajst-32552	166	2	5	5	NUM
ajst-32552	166	3	]	]	PUNCT
ajst-32552	166	4	narváez	narváez	PROPN
ajst-32552	166	5	,	,	PUNCT
ajst-32552	166	6	g.	g.	PROPN
ajst-32552	166	7	,	,	PUNCT
ajst-32552	166	8	giraldo	giraldo	PROPN
ajst-32552	166	9	,	,	PUNCT
ajst-32552	166	10	l.	l.	PROPN
ajst-32552	166	11	f.	f.	PROPN
ajst-32552	166	12	,	,	PUNCT
ajst-32552	166	13	bressan	bressan	NOUN
ajst-32552	166	14	,	,	PUNCT
ajst-32552	166	15	m.	m.	NOUN
ajst-32552	166	16	,	,	PUNCT
ajst-32552	166	17	&	&	CCONJ
ajst-32552	166	18	pantoja	pantoja	PROPN
ajst-32552	166	19	,	,	PUNCT
ajst-32552	166	20	a.	a.	NOUN
ajst-32552	166	21	(	(	PUNCT
ajst-32552	166	22	2021	2021	NUM
ajst-32552	166	23	)	)	PUNCT
ajst-32552	166	24	.	.	PUNCT
ajst-32552	167	1	machine	machine	NOUN
ajst-32552	167	2	learning	learn	VERB
ajst-32552	167	3	for	for	ADP
ajst-32552	167	4	site	site	NOUN
ajst-32552	167	5	-	-	PUNCT
ajst-32552	167	6	adaptation	adaptation	NOUN
ajst-32552	167	7	and	and	CCONJ
ajst-32552	167	8	solar	solar	ADJ
ajst-32552	167	9	radiation	radiation	NOUN
ajst-32552	167	10	forecasting	forecasting	NOUN
ajst-32552	167	11	.	.	PUNCT
ajst-32552	168	1	renewable	renewable	ADJ
ajst-32552	168	2	energy	energy	NOUN
ajst-32552	168	3	,	,	PUNCT
ajst-32552	168	4	167	167	NUM
ajst-32552	168	5	,	,	PUNCT
ajst-32552	168	6	333–342	333–342	NUM
ajst-32552	168	7	.	.	PUNCT
ajst-32552	169	1	https://doi.org/	https://doi.org/	NOUN
ajst-32552	169	2	10.1016/	10.1016/	NUM
ajst-32552	169	3	j.renene	j.renene	NOUN
ajst-32552	169	4	.	.	PUNCT
ajst-32552	170	1	2020	2020	NUM
ajst-32552	170	2	.	.	PUNCT
ajst-32552	171	1	11	11	NUM
ajst-32552	171	2	.	.	NOUN
ajst-32552	171	3	089	089	NUM
ajst-32552	171	4	.	.	PUNCT
ajst-32552	172	1	[	[	X
ajst-32552	172	2	6	6	NUM
ajst-32552	172	3	]	]	SYM
ajst-32552	172	4	yu	yu	PROPN
ajst-32552	172	5	,	,	PUNCT
ajst-32552	172	6	y.	y.	PROPN
ajst-32552	172	7	,	,	PUNCT
ajst-32552	172	8	cao	cao	PROPN
ajst-32552	172	9	,	,	PUNCT
ajst-32552	172	10	j.	j.	PROPN
ajst-32552	172	11	,	,	PUNCT
ajst-32552	172	12	&	&	CCONJ
ajst-32552	172	13	zhu	zhu	PROPN
ajst-32552	172	14	,	,	PUNCT
ajst-32552	172	15	j.	j.	PROPN
ajst-32552	172	16	(	(	PUNCT
ajst-32552	172	17	2019	2019	NUM
ajst-32552	172	18	)	)	PUNCT
ajst-32552	172	19	.	.	PUNCT
ajst-32552	173	1	an	an	DET
ajst-32552	173	2	lstm	lstm	ADJ
ajst-32552	173	3	short	short	ADJ
ajst-32552	173	4	-	-	PUNCT
ajst-32552	173	5	term	term	NOUN
ajst-32552	173	6	solar	solar	ADJ
ajst-32552	173	7	irradiance	irradiance	NOUN
ajst-32552	173	8	forecasting	forecasting	NOUN
ajst-32552	173	9	under	under	ADP
ajst-32552	173	10	complicated	complicated	ADJ
ajst-32552	173	11	weather	weather	NOUN
ajst-32552	173	12	conditions	condition	NOUN
ajst-32552	173	13	.	.	PUNCT
ajst-32552	174	1	ieee	ieee	NOUN
ajst-32552	174	2	access	access	NOUN
ajst-32552	174	3	,	,	PUNCT
ajst-32552	174	4	7	7	NUM
ajst-32552	174	5	,	,	PUNCT
ajst-32552	174	6	145651–145666	145651–145666	NUM
ajst-32552	174	7	.	.	PUNCT
ajst-32552	175	1	https://doi.org/10.1109/access.2019.2946057	https://doi.org/10.1109/access.2019.2946057	PROPN
ajst-32552	176	1	[	[	X
ajst-32552	176	2	7	7	X
ajst-32552	176	3	]	]	ADJ
ajst-32552	176	4	sunrise	sunrise	NOUN
ajst-32552	176	5	-	-	PUNCT
ajst-32552	176	6	sunset	sunset	NOUN
ajst-32552	176	7	api	api	NOUN
ajst-32552	176	8	.	.	PUNCT
ajst-32552	177	1	sunrise	sunrise	NOUN
ajst-32552	177	2	and	and	CCONJ
ajst-32552	177	3	sunset	sunset	NOUN
ajst-32552	177	4	times	time	NOUN
ajst-32552	177	5	for	for	ADP
ajst-32552	177	6	specific	specific	ADJ
ajst-32552	177	7	coordinates	coordinate	NOUN
ajst-32552	177	8	.	.	PUNCT
ajst-32552	178	1	https://api.sunrise-sunset.org/	https://api.sunrise-sunset.org/	PRON
ajst-32552	178	2	json	json	PROPN
ajst-32552	178	3	?	?	PUNCT
ajst-32552	179	1	lat={lat}&lng={lon}&date={date_str	lat={lat}&lng={lon}&date={date_str	NOUN
ajst-32552	179	2	}	}	PUNCT
ajst-32552	179	3	&	&	CCONJ
ajst-32552	179	4	formatted=0	formatted=0	PROPN
ajst-32552	179	5	.	.	PUNCT