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Point and interval forecasting of solar irradiance with an active Gaussian process
Huang, Chao1,2,3; Zhao, Zhenyu2; Wang, Long2; Zhang, Zijun3; Luo, Xiong2
Source PublicationIET Renewable Power Generation

A Gaussian process regression (GPR) with active learning is proposed for developing the solar irradiance point andinterval forecasting models, which consider the spatial-temporal information collected from a targeted site and a number ofneighbouring sites. To enhance the performance of the GPR-based model an active learning process is developed forconstructing an ad-hoc input feature set, selecting training data points, and optimising hyper-parameters of GPR models. Tovalidate the advantages of the proposed method, a comprehensive computational study is conducted based on solar irradiancedata collected from the northwest California area. In the point forecasting, the proposed method beats the state-of-the-artbenchmarking methods including classical statistical models and data-driven models according to values of the normalised rootmean squared error, normalised mean absolute error, normalised mean bias error, and coefficient of determination. In theinterval forecasting, the proposed method outperforms the persistence model, autoregressive model with exogenous inputs,generic GPR, as well as two recently reported forecasting methods, the bootstrap-based extreme learning machine and quantileregression, in terms of the forecasting reliability. Computational results show that the proposed method is more effective thanwell-known existing benchmarks in the point and interval forecasting of the solar irradiance.

URLView the original
Indexed BySCIE
WOS Research AreaScience & Technology - Other Topics ; Energy & Fuels ; Engineering
WOS SubjectGreen & Sustainable Science & Technology ; Energy & Fuels ; Engineering, Electrical & Electronic
WOS IDWOS:000528757000008
Scopus ID2-s2.0-85083976483
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Cited Times [WOS]:14   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang, Zijun
Affiliation1.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao
2.School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing, 100083, China
3.School of Data Science, City University of Hong Kong, Hong Kong S.A.R., Hong Kong
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Huang, Chao,Zhao, Zhenyu,Wang, Long,et al. Point and interval forecasting of solar irradiance with an active Gaussian process[J]. IET Renewable Power Generation,2020,14(6):1020-1030.
APA Huang, Chao,Zhao, Zhenyu,Wang, Long,Zhang, Zijun,&Luo, Xiong.(2020).Point and interval forecasting of solar irradiance with an active Gaussian process.IET Renewable Power Generation,14(6),1020-1030.
MLA Huang, Chao,et al."Point and interval forecasting of solar irradiance with an active Gaussian process".IET Renewable Power Generation 14.6(2020):1020-1030.
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