Home Journals Articles Updates About
CN
image
收藏切换
Short-term load forecasting of electric vehicles based on improved VMD-TCN-LSTM
收藏切换
PDF
Yan Hua1, 2, Peng Li1, 2, Dong Yan1, 2, Xiangkai Zhang1, 2
Electronic Measurement Technology | 2026, 49(6) : 67 - 75
Less
收藏切换
Electronic Measurement Technology | 2026, 49(6): 67-75
• Theory and Algorithms •
Short-term load forecasting of electric vehicles based on improved VMD-TCN-LSTM
Full
Yan Hua1, 2, Peng Li1, 2, Dong Yan1, 2, Xiangkai Zhang1, 2
Affiliations
  • 1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
doi: 10.19651/j.cnki.emt.2519599
Outline
收藏切换

Electric vehicle charging load forecasting supports power dispatch decisions by addressing load fluctuations from widespread EV grid integration. A new method for predicting short-term EV charging loads is proposed to enhance power grid stability and reliability by improving load forecasting accuracy. First, historical load data is decomposed into subcomponents using VMD, then combined with temperature data and input into multiple TCN-LSTM branches for feature extraction, simplifying EV load sequence complexity. Secondly, a two-stage attention mechanism enhances the LSTM structure, improving load characteristic capture at specific times and feature dimension fusion, boosting complex load pattern recognition. Finally, a time conversion prediction module integrates results via a fully connected layer to enhance prediction accuracy and reduce errors. Case study analyzes real EV charging station load data from a Shaoxing community. Experimental results show the proposed method reduces MSE by 68%, MAE by 60%, and improves the performance index by 4%, demonstrating strong predictive performance.

load forecasting  /  electric vehicles  /  temporal convolutional network  /  long short-term memory network  /  attention mechanism
Yan Hua, Peng Li, Dong Yan, Xiangkai Zhang. Short-term load forecasting of electric vehicles based on improved VMD-TCN-LSTM[J]. Electronic Measurement Technology, 2026 , 49 (6) : 67 -75 . DOI: 10.19651/j.cnki.emt.2519599
Appendix
Less
Year 2026 volume 49 Issue 6
PDF
34
6
Cite this Article
BibTeX
Article Info
doi: 10.19651/j.cnki.emt.2519599
  • Receive Date：2025-08-14
  • Online Date：2026-05-15
Article Data
Affiliations
History
  • Received：2025-08-14
Funding
Affiliations
    1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
    2.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
References
Share
https://castjournals.cast.org.cn/joweb/dzcljs/EN/10.19651/j.cnki.emt.2519599
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
010-62199257
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