Home Journals Articles Updates About
CN
image
收藏切换
Short-term Charging Load Forecasting for Electric Vehicles Based on Combined LSTM-GNN Model
收藏切换
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
Less
收藏切换
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
Full
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
收藏切换

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
Less
  • National Natural Science Foundation of China(52277012)
  • Interdisciplinary Program of Wuhan National High Magnetic Field Center(WHMFC202202)
Appendix
Less
Year 2025 volume 23 Issue 8
44
4
Cite this Article
BibTeX
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
References
Share
https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2025.8.145
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House