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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ping Tang & Ying Su & Weisheng Zhao & Qian Wang & Lianglin Zou & Jifeng Song, 2025. "A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction," Energies, MDPI, vol. 18(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:12:p:3193-:d:1681594
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