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Short-Term Photovoltaic Power Forecasting Using a Bi-LSTM Neural Network Optimized by Hybrid Algorithms

Author

Listed:
  • Jibo Wang

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China)

  • Zihao Zhang

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China)

  • Wenhao Xu

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China)

  • Yijin Li

    (School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Haidian District, Beijing 100083, China)

  • Geng Niu

    (State Grid Shanghai Energy Interconnection Research Institute, China Electric Power Research Institute, Haidian District, Beijing 100192, China)

Abstract

Photovoltaic (PV) power generation is characterized by high fluctuation and intermittency. The accurate forecasting of PV power is crucial for optimizing grid operation and scheduling. Thus, a novel short-term PV power-forecasting method based on genetic algorithm-adaptive multi-objective differential evolution (GA-AMODE)-optimized bidirectional long short-term memory (BiLSTM) is proposed. Firstly, a data preprocessing method, including principal component analysis, a sliding window mechanism, and Gaussian noise injection, is designed to achieve dimension reduction and data robustness. Then, a GA-AMODE-BiLSTM model for PV power forecasting is proposed. GA and AMODE algorithms are integrated to balance global and local searching processes during the optimization of the BiLSTM network’s hyperparameters. Bi-LSTM is more suitable for complex time series tasks involving long-term dependencies and asymmetric relationships. The forecasting method is evaluated by typical indexes and is statistically tested. Comparative experiments using the same dataset across various models have been performed. The results show that the proposed GA-AMODE-BiLSTM model significantly outperforms other models in forecasting accuracy. Additionally, its superior stability and generalization is demonstrated, making the proposed method an effective tool for optimizing the management of renewable energy generation and enhancing the sustainability of energy systems.

Suggested Citation

  • Jibo Wang & Zihao Zhang & Wenhao Xu & Yijin Li & Geng Niu, 2025. "Short-Term Photovoltaic Power Forecasting Using a Bi-LSTM Neural Network Optimized by Hybrid Algorithms," Sustainability, MDPI, vol. 17(12), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5277-:d:1674030
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    References listed on IDEAS

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