PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON SIMILAR DAY CLUSTERING AND TEMPORAL IMAGE FEATURE EXTRACTION

Guo Wei; Xu Li; Tang Xujing; Zhao Danyang; Chen Fanglin; Wang Tian

doi:10.19912/j.0254-0096.tynxb.2024-2355

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (5) : 650-659. DOI: 10.19912/j.0254-0096.tynxb.2024-2355

PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON SIMILAR DAY CLUSTERING AND TEMPORAL IMAGE FEATURE EXTRACTION

Guo Wei¹, Xu Li¹, Tang Xujing^1~3, Zhao Danyang¹, Chen Fanglin¹, Wang Tian¹

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Abstract

This paper introduces a hybrid deep learning PV power prediction model that leverages feature extraction from time-series images. Firstly, a multi-dimensional meteorological feature screening mechanism was developed using the comprehensive correlation coefficient method to optimize the selection of key influencing factors. Secondly, an enhanced K-Medoids clustering algorithm was proposed by incorporating a dynamic time warping （DTW） distance measure based on LB_Keogh distance, which improves the robustness and clustering efficiency of the algorithm and enables precise classification of sunny, cloudy, and rainy weather patterns. Finally, the selected high-correlation meteorological variables and historical PV power data were transformed into two-dimensional Gramian angular field images and fed into the CSWin-Transformer model for power prediction. Compared with other models, the proposed approach demonstrates superior prediction accuracy under three typical weather conditions, offering a novel methodology for PV power generation prediction.

Key words

photovoltaic power generation / power forecasting / deep learning / clustering algorithms / Gramian angular field / CSWin-Transformer

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Guo Wei, Xu Li, Tang Xujing, Zhao Danyang, Chen Fanglin, Wang Tian. PHOTOVOLTAIC POWER PREDICTION METHOD BASED ON SIMILAR DAY CLUSTERING AND TEMPORAL IMAGE FEATURE EXTRACTION[J]. Acta Energiae Solaris Sinica. 2026, 47(5): 650-659 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2355

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