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Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

Author

Listed:
  • Jizhong Xue

    (Department of Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Zaohui Kang

    (Department of Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Chun Sing Lai

    (Department of Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China
    Brunel Interdisciplinary Power Systems Research Centre, Department of Electronic and Electrical Engineering, Brunel University London, London UB8 3PH, UK)

  • Yu Wang

    (Department of Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Fangyuan Xu

    (Department of Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Haoliang Yuan

    (Department of Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy.

Suggested Citation

  • Jizhong Xue & Zaohui Kang & Chun Sing Lai & Yu Wang & Fangyuan Xu & Haoliang Yuan, 2023. "Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)," Energies, MDPI, vol. 16(11), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4436-:d:1160335
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    References listed on IDEAS

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