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Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models

Author

Listed:
  • Honglin Xue

    (Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China)

  • Junwei Ma

    (Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China)

  • Jianliang Zhang

    (Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China)

  • Penghui Jin

    (State Grid Block Chain Technology (Beijing) Co., Ltd., Beijing 100053, China)

  • Jian Wu

    (Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China)

  • Feng Du

    (Information and Communication Branch, State Grid Shanxi Electric Power Company, Taiyuan 030001, China)

Abstract

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset.

Suggested Citation

  • Honglin Xue & Junwei Ma & Jianliang Zhang & Penghui Jin & Jian Wu & Feng Du, 2024. "Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models," Energies, MDPI, vol. 17(16), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:16:p:3877-:d:1450990
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    References listed on IDEAS

    as
    1. Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. VanDeventer, William & Jamei, Elmira & Thirunavukkarasu, Gokul Sidarth & Seyedmahmoudian, Mehdi & Soon, Tey Kok & Horan, Ben & Mekhilef, Saad & Stojcevski, Alex, 2019. "Short-term PV power forecasting using hybrid GASVM technique," Renewable Energy, Elsevier, vol. 140(C), pages 367-379.
    3. Qu, Yinpeng & Xu, Jian & Sun, Yuanzhang & Liu, Dan, 2021. "A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting," Applied Energy, Elsevier, vol. 304(C).
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