气候变化领域集成服务门户(CCPortal): A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data

CCPortal

DOI	10.1016/j.rser.2019.109327
	A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data
	Jiang, Hou; Lu, Ning; Qin, Jun; Tang, Wenjun; Yao, Ling
通讯作者	Lu, N (通讯作者)
发表日期	2019
ISSN	1364-0321
EISSN	1879-0690
卷号	114
英文摘要	To apply deep learning technique for estimating hourly global solar radiation (GSR) from geostationary satellite observations, a hybrid deep network is proposed, relying on convolutional neural network (CNN) to extract spatial pattern from satellite imagery, multi-layer perceptron (MLP) to link the abstract patterns and additional time/location information to target hourly GSR. Its representative advantage lies in the ability to characterize changeable cloud morphology and simulate complex non-linear relationships. The deep network is trained using ground measured GSR values at 90 Chinese radiation stations in 2008 as well as the radiative transfer model simulation at the top of Mt. Everest which serves as constraints of extrapolation for high elevation regions. The extensibility of trained network is validated at 5 independent stations in 2008, yielding an overall coefficient of determination (R-2) of 0.82, and at all stations in 2007 along with an R-2 of 0.88. Comparative experiments confirm that the combination of spatial pattern and point information can lead to more accurate estimation of hourly GSR, achieving a minimum root mean square error (RMSE) of 84.18 W/m(2) (0.30 MJ/m(2)), 1.92 MJ/m(2) and 1.08 MJ/m(2) in hourly, daily total and monthly total scales, respectively. Moreover, the deep network is capable of mapping spatially continuous hourly GSR which reflects the regional differences and reproduce the diurnal cycles of solar radiation properly.
关键词	ARTIFICIAL NEURAL-NETWORK INTELLIGENCE TECHNIQUES IRRADIANCE ENERGY MODEL PREDICTION
英文关键词	Global solar radiation; Convolutional neural network; Deep learning; Geostationary satellite; Temporal and spatial variations
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Energy & Fuels
WOS类目	Green & Sustainable Science & Technology ; Energy & Fuels
WOS记录号	WOS:000488871200004
来源期刊	RENEWABLE & SUSTAINABLE ENERGY REVIEWS
来源机构	中国科学院青藏高原研究所
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/259489
推荐引用方式 GB/T 7714	Jiang, Hou,Lu, Ning,Qin, Jun,et al. A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data[J]. 中国科学院青藏高原研究所,2019,114.
APA	Jiang, Hou,Lu, Ning,Qin, Jun,Tang, Wenjun,&Yao, Ling.(2019).A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data.RENEWABLE & SUSTAINABLE ENERGY REVIEWS,114.
MLA	Jiang, Hou,et al."A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data".RENEWABLE & SUSTAINABLE ENERGY REVIEWS 114(2019).