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Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network

Author

Listed:
  • Haokun Su

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Xiangang Peng

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Hanyu Liu

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Huan Quan

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Kaitong Wu

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Zhiwen Chen

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.

Suggested Citation

  • Haokun Su & Xiangang Peng & Hanyu Liu & Huan Quan & Kaitong Wu & Zhiwen Chen, 2022. "Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2366-:d:856787
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    References listed on IDEAS

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    Cited by:

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    2. Li-Peng Shao & Jia-Jia Chen & Lu-Wen Pan & Zi-Juan Yang, 2022. "A Credibility Theory-Based Robust Optimization Model to Hedge Price Uncertainty of DSO with Multiple Transactions," Mathematics, MDPI, vol. 10(23), pages 1-20, November.

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