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Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods

Author

Listed:
  • Yue Guo

    (State Grid Economic and Technological Research Institute, Beijing 102209, China)

  • Yu Song

    (State Grid Economic and Technological Research Institute, Beijing 102209, China)

  • Zilong Lai

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310027, China)

  • Xuyang Wang

    (State Grid Economic and Technological Research Institute, Beijing 102209, China)

  • Licheng Wang

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310027, China)

  • Hui Qin

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310027, China)

Abstract

In response to the challenges posed by renewable energy integration, this study introduces a hybrid Attention-TCN-LSTM model for short-term photovoltaic (PV) power forecasting. The LSTM captures the sequence characteristics of PV output, which are then combined with the meteorological sequence features extracted by the Attention-TCN module. The model leverages the strengths of the TCN, the LSTM, and the self-attention mechanism to enhance prediction accuracy and construct reliable prediction intervals. Aiming to optimize both performance and efficiency, the PSO algorithm is used for hyperparameter optimization. Ablation studies and comparisons with other models confirm the effectiveness, accuracy and robustness of the proposed model. This hybrid approach contributes to improved renewable energy integration, offering a more stable and reliable energy supply. Future work will focus on incorporating intelligent systems for autonomous risk management and real-time control of dynamic PV output fluctuations.

Suggested Citation

  • Yue Guo & Yu Song & Zilong Lai & Xuyang Wang & Licheng Wang & Hui Qin, 2025. "Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods," Energies, MDPI, vol. 18(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:308-:d:1565203
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    References listed on IDEAS

    as
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