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Short-term photovoltaic power forecasting method considering historical information reuse and numerical weather forecasting

Author

Listed:
  • Yang, Mao
  • Guo, Zhenpeng
  • Wang, Da
  • Wang, Bo
  • Wang, Zhao
  • Huang, Tao

Abstract

We proposed a novel historical feature reuse scheme to improve the accuracy of photovoltaic power forecasting model. Firstly, a weather type classification method based on the Elkan K-means algorithm was proposed, and a feature matching mechanism based on Markov distance was constructed to fuse information; Then, a bidirectional recurrent residual network was constructed, which improved the feature extraction performance of the forecasting model for different photovoltaic output scenarios; Finally, an error decoupling mechanism was proposed to evaluate the upper limit of the forecasting accuracy of the model. Taking the data provided by a photovoltaic power station in Jilin Province, China as the research object, the day-ahead power forecasting accuracy is 91.12 %, verifying the validity of the proposed model.

Suggested Citation

  • Yang, Mao & Guo, Zhenpeng & Wang, Da & Wang, Bo & Wang, Zhao & Huang, Tao, 2026. "Short-term photovoltaic power forecasting method considering historical information reuse and numerical weather forecasting," Renewable Energy, Elsevier, vol. 256(PB).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pb:s0960148125015976
    DOI: 10.1016/j.renene.2025.123933
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    References listed on IDEAS

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    1. Chuan Xiang & Xiang Liu & Wei Liu & Tiankai Yang, 2025. "A Cascaded Data-Driven Approach for Photovoltaic Power Output Forecasting," Mathematics, MDPI, vol. 13(17), pages 1-23, August.

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