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Short-term Charging Load Forecasting for Electric Vehicles Based on Combined LSTM-GNN Model
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Yizhou LIU  Student Member, CPSS1, 2, Zhenhua LI1, 2, Wei WEI3, Zhenxing LI1, 2, Yanchun XU1, 2, Lei ZHANG1, 2
Journal of Power Supply | 2025, 23(8) : 145 - 156
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Journal of Power Supply | 2025, 23(8): 145-156
• Power System •
Short-term Charging Load Forecasting for Electric Vehicles Based on Combined LSTM-GNN Model
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Yizhou LIU  Student Member, CPSS1, 2, Zhenhua LI1, 2, Wei WEI3, Zhenxing LI1, 2, Yanchun XU1, 2, Lei ZHANG1, 2
Affiliations
  • 1.College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
  • 2.Hubei Provincial Engineering Research Center of Intelligent Energy Technology, China Three Gorges University, Yichang 443002, China
  • 3.Marketing Service Center (Measurement Center), State Grid Hubei Electric Power Co., Ltd., Wuhan 443080, China
Published: 2025-12-30 doi: 10.13234/j.issn.2095-2805.2025.8.145
Outline
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In the context of new energy era, aimed at the hot issue of the impact of electric vehicle charging loads on power grid, a short-term charging load forecasting method for electric vehicles based on a combined long short-term memory network and graph neural network (LSTM-GNN) model is proposed to address the shortcomings of existing models. First, three typical daily loads are correlated using the Copula theory, a feature input matrix containing the main factors and historical loads of charging stations is constructed, and a sliding window data set is established. Second, LSTM and GNN are used for feature extraction and graph data construction of the feature input matrix, and the GNN layer completes the feature aggregation and outputs the predicted load value and error evaluation indexes. The results of an example show that the proposed model can effectively extract the key features of charging load at charging stations in different regions, and it has better forecasting performance and generalization capability than traditional models.

Load forecasting  /  deep learning  /  graph neural networks (GNN)  /  long and short-term memory (LSTM)  /  electric vehicle  /  charging load
刘奕舟中国电源学会学生会员, 李振华, 魏伟, 李振兴, 徐艳春, 张磊. 基于LSTM-GNN组合模型的电动汽车短期充电负荷预测. 电源学报, 2025 , 23 (8) : 145 -156 . DOI: 10.13234/j.issn.2095-2805.2025.8.145
Yizhou LIU, Zhenhua LI, Wei WEI, Zhenxing LI, Yanchun XU, Lei ZHANG. Short-term Charging Load Forecasting for Electric Vehicles Based on Combined LSTM-GNN Model[J]. Journal of Power Supply, 2025 , 23 (8) : 145 -156 . DOI: 10.13234/j.issn.2095-2805.2025.8.145
Funding
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  • National Natural Science Foundation of China(52277012)
  • Interdisciplinary Program of Wuhan National High Magnetic Field Center(WHMFC202202)
Appendix
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Year 2025 volume 23 Issue 8
46
5
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Article Info
doi: 10.13234/j.issn.2095-2805.2025.8.145
  • Receive Date：2024-06-19
  • Online Date：2026-10-26
  • Published：2025-12-30
Article Data
Affiliations
History
  • Received：2024-06-19
  • Revised：2024-07-11
  • Accepted：2024-07-18
Funding
National Natural Science Foundation of China(52277012)
Interdisciplinary Program of Wuhan National High Magnetic Field Center(WHMFC202202)
Affiliations
    1.College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
    2.Hubei Provincial Engineering Research Center of Intelligent Energy Technology, China Three Gorges University, Yichang 443002, China
    3.Marketing Service Center (Measurement Center), State Grid Hubei Electric Power Co., Ltd., Wuhan 443080, China
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