ULTRA SHORT-TERM SOLAR IRRADIANCE PREDICTION BASED ON ADAPTIVE TIME SERIES DECOUPLING AND DYNAMIC IMPACT EVALUATION OF METEOROLOGICAL FACTORS

Zang Haixiang; Huang Haiyang; Cheng Lilin; Zhang Yue; Sun Guoqiang; Wei Zhinong

doi:10.19912/j.0254-0096.tynxb.2023-1208

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 411-417. DOI: 10.19912/j.0254-0096.tynxb.2023-1208

ULTRA SHORT-TERM SOLAR IRRADIANCE PREDICTION BASED ON ADAPTIVE TIME SERIES DECOUPLING AND DYNAMIC IMPACT EVALUATION OF METEOROLOGICAL FACTORS

Zang Haixiang, Huang Haiyang, Cheng Lilin, Zhang Yue, Sun Guoqiang, Wei Zhinong

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Abstract

Because of the fluctuation of solar irradiation sequences and the influence of meteorological factors, the accuracy of solar irradiance prediction is reduced. An ultra-short-term solar irradiance prediction model based on sliding variational modal decomposition, adaptive graph convolution network and quad-kernel temporal convolutional network is proposed. Firstly, the historical irradiation series are decoupled by SWVMD to generate modal components with different feature scales in real time. Secondly, the original data set is reconstructed into graph data to dynamically evaluate the impact of meteorological factors through AGCN. Finally, the quad-kernel TCN model is constructed to extract the temporal features of the fused feature series, and predict the solar irradiance in the next 30 minutes. The experimental results show that compared with LSTM, TCN model and CNN-Bi-LSTM model, the proposed model can effectively improve the prediction accuracy.

Key words

solar radiation / deep learning / variational mode decomposition / graph convolutional networks / temporal convolutional network

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Zang Haixiang, Huang Haiyang, Cheng Lilin, Zhang Yue, Sun Guoqiang, Wei Zhinong. ULTRA SHORT-TERM SOLAR IRRADIANCE PREDICTION BASED ON ADAPTIVE TIME SERIES DECOUPLING AND DYNAMIC IMPACT EVALUATION OF METEOROLOGICAL FACTORS[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 411-417 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1208

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