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Research on ultra-short-term photovoltaic power forecasting using multimodal data and ensemble learning

Author

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  • Ma, Yifeng
  • Yu, Wenzheng
  • Zhu, Junyu
  • You, Zhiyuan
  • Jia, Aiqing

Abstract

To enhance the ultra-short-term prediction capability of photovoltaic power generation, this study proposes a forecasting method integrating ensemble learning with multimodal data. After systematically comparing the predictive performance of six independent machine learning models (RF, XGBoost, CatBoost, LightGBM, LSTM, and GRU), a fused model was developed using the stacking ensemble strategy. The ensemble model achieved the highest coefficient of determination (R2 = 0.9698) along with the lowest normalized mean square error (NMSE = 0.0020) and normalized root mean square error (NRMSE = 0.0451). Compared to meteorological data models and ground-based cloud image models, the proposed multimodal ensemble learning model improved R2 by 20.8 % and 17.9 %, reduced NMSE by 84.8 % and 83.3 %, and decreased NRMSE by 60.9 % and 58.8 %, respectively. Contribution analysis revealed that power from the previous moment, light of ground-based cloud images, and station irradiance were critical factors influencing model predictions.

Suggested Citation

  • Ma, Yifeng & Yu, Wenzheng & Zhu, Junyu & You, Zhiyuan & Jia, Aiqing, 2025. "Research on ultra-short-term photovoltaic power forecasting using multimodal data and ensemble learning," Energy, Elsevier, vol. 330(C).
    • Handle: RePEc:eee:energy:v:330:y:2025:i:c:s0360544225024739
    DOI: 10.1016/j.energy.2025.136831
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