A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction

Ping Tang, Ying Su, Weisheng Zhao, Qian Wang, Lianglin Zou and Jifeng Song ()
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Ping Tang: School of New Energy, North China Electric Power University, Beijing 102206, China
Ying Su: Institute of Science and Technology, China Three Gorges Corporation, Beijing 100038, China
Weisheng Zhao: School of New Energy, North China Electric Power University, Beijing 102206, China
Qian Wang: Institute of Science and Technology, China Three Gorges Corporation, Beijing 100038, China
Lianglin Zou: School of New Energy, North China Electric Power University, Beijing 102206, China
Jifeng Song: Institute of Energy Power Innovation, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 12, 1-20

Abstract: Accurate photovoltaic (PV) power forecasting is essential to mitigating the security and stability challenges associated with PV integration into power grids. Ground-based sky images can quickly reveal cloud changes, and the spatiotemporal feature information extracted from these images can improve PV power forecasting. Therefore, this paper proposes a hybrid framework based on shifted windows Transformer (Swin Transformer), convolutional neural network, and long short-term memory network to comprehensively extract spatiotemporal feature information, including global spatial, local spatial, and temporal features, from ground-based sky images and PV power data. The mean absolute error and root mean squared error are reduced by 13.06% and 4.49% compared with ResNet-18. The experimental results indicate that the proposed framework demonstrates competitive predictive performance and generalization capability across different time horizons and weather conditions compared with benchmark frameworks.

Keywords: photovoltaic power forecasting; ground-based sky images; spatiotemporal feature information; shifted windows transformer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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