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Deep Learning Models for PV Power Forecasting: Review

Author

Listed:
  • Junfeng Yu

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    These authors contributed equally to this work.)

  • Xiaodong Li

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    These authors contributed equally to this work.)

  • Lei Yang

    (CTG Wuhan Science and Technology Innovation Park, China Three Gorges Corporation, Wuhan 430074, China)

  • Linze Li

    (CTG Wuhan Science and Technology Innovation Park, China Three Gorges Corporation, Wuhan 430074, China)

  • Zhichao Huang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Keyan Shen

    (Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China)

  • Xu Yang

    (Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China)

  • Xu Yang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhikang Xu

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Dongying Zhang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Watershed Science and Technology, Wuhan 430074, China)

  • Shuai Du

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning models for PV power forecasting, concentrating on comparisons of the features, advantages, and limitations of different model architectures. First, we analyze the commonly used datasets for PV power forecasting. Additionally, we provide an overview of mainstream deep learning model architectures, including multilayer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN), and graph neural networks (GNN), and explain their fundamental principles and technical features. Moreover, we systematically organize the research progress of deep learning models based on different architectures for PV power forecasting. This study indicates that different deep learning model architectures have their own advantages in PV power forecasting. MLP models have strong nonlinear fitting capabilities, RNN models can capture long-term dependencies, CNN models can automatically extract local features, and GNN models have unique advantages for modeling spatiotemporal characteristics. This manuscript provides a comprehensive research survey for PV power forecasting using deep learning models, helping researchers and practitioners to gain a deeper understanding of the current applications, challenges, and opportunities of deep learning technology in this area.

Suggested Citation

  • Junfeng Yu & Xiaodong Li & Lei Yang & Linze Li & Zhichao Huang & Keyan Shen & Xu Yang & Xu Yang & Zhikang Xu & Dongying Zhang & Shuai Du, 2024. "Deep Learning Models for PV Power Forecasting: Review," Energies, MDPI, vol. 17(16), pages 1-35, August.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3973-:d:1453986
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    References listed on IDEAS

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    1. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    2. Francis Eng-Hock Tay & Lixiang Shen & Lijuan Cao, 2003. "Application of Support Vector Machines in Financial Time Series Forecasting," World Scientific Book Chapters, in: Ordinary Shares, Exotic Methods Financial Forecasting Using Data Mining Techniques, chapter 7, pages 111-129, World Scientific Publishing Co. Pte. Ltd..
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