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

Yue Guo, Yu Song, Zilong Lai, Xuyang Wang, Licheng Wang and Hui Qin ()
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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

Energies, 2025, vol. 18, issue 2, 1-17

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.

Keywords: PV forecast; LSTM; temporal convolutional network (TCN) (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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