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A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data
Jiang, H (Jiang, Hou)1,2; Lu, N (Lu, Ning)1,4; Qin, J (Qin, Jun)3; Tang, WJ (Tang, Wenjun)3; Yao, L (Yao, Ling)1,4
Source PublicationRENEWABLE & SUSTAINABLE ENERGY REVIEWS
2019
Volume114Issue:0Pages:109327
DOI10.1016/j.rser.2019.109327
Abstract

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.

Subject AreaEnergy & Fuels
WOS IDWOS:000488871200004
Language英语
Indexed BySCI
KeywordArtificial Neural-network Intelligence Techniques Irradiance Energy Model Prediction
WOS Research AreaScience & Technology - Other Topics ; Energy & Fuels
WOS SubjectGreen & Sustainable Science & Technology ; Energy & Fuels
Cooperation Status国内
ISSN1364-0321
Department环境变化与地表过程重点实验室
URL查看原文
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/9142
Collection图书馆
Corresponding AuthorLu, N (Lu, Ning)
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China;
3.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100085, Peoples R China;
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China.
Recommended Citation
GB/T 7714
Jiang, H ,Lu, N ,Qin, J ,et al. A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data[J]. RENEWABLE & SUSTAINABLE ENERGY REVIEWS,2019,114(0):109327.
APA Jiang, H ,Lu, N ,Qin, J ,Tang, WJ ,&Yao, L .(2019).A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data.RENEWABLE & SUSTAINABLE ENERGY REVIEWS,114(0),109327.
MLA Jiang, H ,et al."A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data".RENEWABLE & SUSTAINABLE ENERGY REVIEWS 114.0(2019):109327.
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