ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON MULTI-MODAL MULTI-SCALE FEATURES

Chen Dianhao; Zang Haixiang; Liu Jingxuan; Zhang Yue; Sun Guoqiang; Wei Zhinong

doi:10.19912/j.0254-0096.tynxb.2024-0701

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 472-480. DOI: 10.19912/j.0254-0096.tynxb.2024-0701

ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON MULTI-MODAL MULTI-SCALE FEATURES

Chen Dianhao, Zang Haixiang, Liu Jingxuan, Zhang Yue, Sun Guoqiang, Wei Zhinong

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Abstract

To further improve the accuracy of ultra-short-term photovoltaic power forecasting, a forecasting method based on multi-modal multi-scale features is proposed. Firstly, multi-scale historical cloud images and PV power are obtained based on different sampling intervals as the input data of the forecasting model. Secondly, the spatial and temporal features of multi-scale cloud image data and multi-scale power data are extracted using self-attention convolutional long short-term memory neural network and long short-term memory neural network respectively, to obtain multi-modal multi-scale features. Then, a fusion-attention mechanism based on multi-head self-attention and multi-head cross-attention is proposed to integrate multi-modal multi-scale feature information deeply. Finally, the multi-modal multi-scale fusion features are used as the input of multi-layer perceptron to achieve ultra-short-term photovoltaic power forecasting. Experimental results show that this method can effectively improve the accuracy of ultra-short-term photovoltaic power forecasting.

Key words

photovoltaic power / forecasting / deep learning / multi-modal data / multi-scale features / attention mechanism

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Chen Dianhao, Zang Haixiang, Liu Jingxuan, Zhang Yue, Sun Guoqiang, Wei Zhinong. ULTRA-SHORT-TERM PHOTOVOLTAIC POWER FORECASTING BASED ON MULTI-MODAL MULTI-SCALE FEATURES[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 472-480 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0701

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