ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION

Guo Jiqiang; Xu Xudong; Zhang Shuai; Ren Mifeng; Wang Fang; Yan Gaowei

doi:10.19912/j.0254-0096.tynxb.2023-1799

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (3) : 494-503. DOI: 10.19912/j.0254-0096.tynxb.2023-1799

ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION

Guo Jiqiang¹, Xu Xudong², Zhang Shuai², Ren Mifeng¹, Wang Fang¹, Yan Gaowei¹

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Abstract

Aiming at the direct impact of dynamic changes of the clouds on irradiance, this paper obtains the cloud dynamic information matrix （DIM） from continuous ground-based cloud images based on the Fourier phase correlation （PC） theory. To minimize the effects on the model led by noise and redundant information, a low-rank matrix feature extraction （LMFE） model is constructed as an extraction module （EM） to extract valid information from the DIM productively. EM obtains the optimal feature extraction matrix W and bias terms b through iteration. Simultaneously, the regression module （RM） is constructed based on the bidirectional gated recurrent unit （Bi-GRU）, where RM shares EM outputs W and b, and the valid information extracted from the cloud images is combined with the historical irradiance sequence as input to achieve multi-step regression prediction of irradiance. The experimental simulation of the actual collected data demonstrates that the prediction method can effectively improve the irradiance prediction accuracy.

Key words

solar radiation / forecasting / feature extraction / image correlation / neural networks

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Guo Jiqiang, Xu Xudong, Zhang Shuai, Ren Mifeng, Wang Fang, Yan Gaowei. ULTRA-SHORT-TERM IRRADIANCE PREDICTION BASED ON CLOUD IMAGES PHASE CORRELATION AND LOW-RANK FEATURE EXTRACTION[J]. Acta Energiae Solaris Sinica. 2025, 46(3): 494-503 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1799

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