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Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm

Author

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  • Keyong Hu

    (School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
    Mobile Health Management System Engineering Research Center of the Ministry of Education, Hangzhou 311121, China)

  • Chunyuan Lang

    (School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China)

  • Zheyi Fu

    (School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China)

  • Yang Feng

    (School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
    Mobile Health Management System Engineering Research Center of the Ministry of Education, Hangzhou 311121, China)

  • Shuifa Sun

    (School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
    Mobile Health Management System Engineering Research Center of the Ministry of Education, Hangzhou 311121, China)

  • Ben Wang

    (School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
    Mobile Health Management System Engineering Research Center of the Ministry of Education, Hangzhou 311121, China)

Abstract

With the continuous increase in the proportion of distributed photovoltaic power stations, the demand for photovoltaic power grid connection is becoming more and more urgent, and the requirements for the accuracy of regional distributed photovoltaic power forecasting are also increasing. A distributed regional photovoltaic power prediction model based on a stacked ensemble algorithm is proposed here. This model first uses a graph attention network (GAT) to learn the structural features and relationships between sub-area photovoltaic power stations, dynamically calculating the attention weights of the photovoltaic power stations to capture the global relationships and importance between stations, and selects representative stations for each sub-area. Subsequently, the CNN-LSTM-multi-head attention parallel multi-channel (CNN-LSTM-MHA (PC)) model is used as the basic model to predict representative stations for sub-areas by integrating the advantages of both the CNN and LSTM models. The predicted results are then used as new features for the input data of the meta-model, which finally predicts the photovoltaic power of the large area. Through comparative experiments at different seasons and time scales, this distributed regional approach reduced the MAE metric by a total of 22.85 kW in spring, 17 kW in summer, 30.26 kW in autumn, and 50.62 kW in winter compared with other models.

Suggested Citation

  • Keyong Hu & Chunyuan Lang & Zheyi Fu & Yang Feng & Shuifa Sun & Ben Wang, 2024. "Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm," Mathematics, MDPI, vol. 12(16), pages 1-25, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2561-:d:1459404
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    References listed on IDEAS

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    1. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
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    Cited by:

    1. Wei Wang & Yaolin Dong & Yue Liu & Ripeng Li & Chunsheng Wang, 2024. "Inductor Current-Based Control Strategy for Efficient Power Tracking in Distributed PV Systems," Mathematics, MDPI, vol. 12(24), pages 1-17, December.

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