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Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Author

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  • Hong Wu

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China
    These authors contributed equally to this work.)

  • Haipeng Liu

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China
    These authors contributed equally to this work.)

  • Huaiping Jin

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China)

  • Yanping He

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China)

Abstract

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.

Suggested Citation

  • Hong Wu & Haipeng Liu & Huaiping Jin & Yanping He, 2024. "Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data," Energies, MDPI, vol. 17(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4739-:d:1483579
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    References listed on IDEAS

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    1. Shengli Wang & Xiaolong Guo & Tianle Sun & Lihui Xu & Jinfeng Zhu & Zhicai Li & Jinjiang Zhang, 2025. "Short-Term Photovoltaic Power Forecasting Based on the VMD-IDBO-DHKELM Model," Energies, MDPI, vol. 18(2), pages 1-17, January.

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