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Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning

Author

Listed:
  • Wenxiang Luo

    (School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330032, China)

  • Yang Shen

    (School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330032, China)

  • Zewen Li

    (School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330032, China)

  • Fangming Deng

    (School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330032, China)

Abstract

In a distributed photovoltaic system, photovoltaic data are affected by heterogeneity, which leads to the problems of low adaptability and poor accuracy of photovoltaic power prediction models. This paper proposes a distributed photovoltaic power prediction scheme based on Personalized Federated Multi-Task Learning (PFL). The federal learning framework is used to enhance the privacy of photovoltaic data and improve the model’s performance in a distributed environment. A multi-task module is added to PFL to solve the problem that an FL single global model cannot improve the prediction accuracy of all photovoltaic power stations. A cbam-itcn prediction algorithm was designed. By improving the parallel pooling structure of a time series convolution network (TCN), an improved time series convolution network (iTCN) prediction model was established, and the channel attention mechanism CBAMANet was added to highlight the key meteorological characteristics’ information and improve the feature extraction ability of time series data in photovoltaic power prediction. The experimental analysis shows that CBAM-iTCN is 45.06% and 42.16% lower than a traditional LSTM, Mae, and RMSE. Compared with FL, the MAPE of the PFL proposed in this paper is reduced by 9.79%, and for photovoltaic power plants with large data feature deviation, the MAPE experiences an 18.07% reduction.

Suggested Citation

  • Wenxiang Luo & Yang Shen & Zewen Li & Fangming Deng, 2025. "Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning," Energies, MDPI, vol. 18(7), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1796-:d:1627024
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    References listed on IDEAS

    as
    1. Dengchang Ma & Rongyi Xie & Guobing Pan & Zongxu Zuo & Lidong Chu & Jing Ouyang, 2023. "Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group," Energies, MDPI, vol. 16(15), pages 1-22, July.
    2. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    3. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    4. Alen Jakoplić & Dubravko Franković & Juraj Havelka & Hrvoje Bulat, 2023. "Short-Term Photovoltaic Power Plant Output Forecasting Using Sky Images and Deep Learning," Energies, MDPI, vol. 16(14), pages 1-18, July.
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