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Article(id=1262079399126548760, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, articleNumber=null, orderNo=null, doi=10.19651/j.cnki.emt.2519599, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755100800000, receivedDateStr=2025-08-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1778832777053, onlineDateStr=2026-05-15, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1778832777053, onlineIssueDateStr=2026-05-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1778832777053, creator=13701087609, updateTime=1778832777053, updator=13701087609, issue=Issue{id=1262079396291141802, tenantId=1146029695717560320, journalId=1260987750510510108, year='2026', volume='49', issue='6', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1778832776377, creator=13701087609, updateTime=1778832914473, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1262079975688122904, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1262079975688122905, tenantId=1146029695717560320, journalId=1260987750510510108, issueId=1262079396291141802, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=67, endPage=75, ext={EN=ArticleExt(id=1262079401739600174, articleId=1262079399126548760, tenantId=1146029695717560320, journalId=1260987750510510108, language=EN, title=Short-term load forecasting of electric vehicles based on improved VMD-TCN-LSTM, columnId=1262079399856300214, journalTitle=Electronic Measurement Technology, columnName=Theory and Algorithms, runingTitle=null, highlight=null, articleAbstract=

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.

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针对大规模电动汽车接入电网引发负荷波动的问题，电动汽车充电负荷预测可为电力调度决策提供关键支撑。为进一步保障电网运行的稳定性与可靠性，提出一种电动汽车短期充电负荷预测新方法，旨在提升负荷预测的精准度。首先，运用变分模态分解（VMD）将历史负荷数据拆解为多个子分量，将各子分量连同温度信息输入至多个（TCN-LSTM）分支中进行特征提取与学习，降低电动汽车负荷序列的复杂度。其次，构建双阶段注意力机制改进长短期记忆网络（LSTM）结构，实现负荷特征维度与时序维度的协同融合，从而提升对复杂负荷模式的捕捉能力。最后，提出时间转换预测模块经全连接层对预测结果进行加权整合，进一步提升模型预测精度，降低预测误差。在绍兴某社区实际电动汽车充电桩充电负荷数据集上进行算例分析。实验结果显示，所提方法在均方误差（MSE）、平均绝对误差（MAE）、和R2性能指标上分别提升68%、60%、4%，表明该模型具有较好的预测效果。

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李鹏（通信作者），教授，博士生导师，主要研究方向为超声成像、通信信号处理、北斗定位、图像处理等。E-mail:
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花妍，硕士研究生，主要研究方向为有序充电、机器视觉。E-mail:

闫冬，硕士研究生，主要研究方向为图像处理、视觉控制。E-mail:

张翔凯，硕士研究生，主要研究方向为机器视觉。E-mail:

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基于改进VMD-TCN-LSTM的电动汽车短期负荷预测
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花妍 1, 2 , 李鹏 1, 2 , 闫冬 1, 2 , 张翔凯 1, 2
电子测量技术 | 理论与算法 2026,49(6): 67-75
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电子测量技术 | 理论与算法 2026, 49(6): 67-75
基于改进VMD-TCN-LSTM的电动汽车短期负荷预测
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花妍1, 2 , 李鹏1, 2 , 闫冬1, 2 , 张翔凯1, 2
作者信息
  • 1．南京信息工程大学电子与信息工程学院 南京 210044
  • 2．南京信息工程大学江苏省气象探测与信息处理重点实验室 南京 210044

    • 花妍，硕士研究生，主要研究方向为有序充电、机器视觉。E-mail:

    闫冬，硕士研究生，主要研究方向为图像处理、视觉控制。E-mail:

    张翔凯，硕士研究生，主要研究方向为机器视觉。E-mail:

通讯作者:

李鹏（通信作者），教授，博士生导师，主要研究方向为超声成像、通信信号处理、北斗定位、图像处理等。E-mail:
Short-term load forecasting of electric vehicles based on improved VMD-TCN-LSTM
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
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摘要
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针对大规模电动汽车接入电网引发负荷波动的问题，电动汽车充电负荷预测可为电力调度决策提供关键支撑。为进一步保障电网运行的稳定性与可靠性，提出一种电动汽车短期充电负荷预测新方法，旨在提升负荷预测的精准度。首先，运用变分模态分解（VMD）将历史负荷数据拆解为多个子分量，将各子分量连同温度信息输入至多个（TCN-LSTM）分支中进行特征提取与学习，降低电动汽车负荷序列的复杂度。其次，构建双阶段注意力机制改进长短期记忆网络（LSTM）结构，实现负荷特征维度与时序维度的协同融合，从而提升对复杂负荷模式的捕捉能力。最后，提出时间转换预测模块经全连接层对预测结果进行加权整合，进一步提升模型预测精度，降低预测误差。在绍兴某社区实际电动汽车充电桩充电负荷数据集上进行算例分析。实验结果显示，所提方法在均方误差（MSE）、平均绝对误差（MAE）、和R2性能指标上分别提升68%、60%、4%，表明该模型具有较好的预测效果。

负荷预测  /  电动汽车  /  时间卷积网络  /  长短期记忆网络  /  注意力机制

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
花妍, 李鹏, 闫冬, 张翔凯. 基于改进VMD-TCN-LSTM的电动汽车短期负荷预测. 电子测量技术, 2026 , 49 (6) : 67 -75 . DOI: 10.19651/j.cnki.emt.2519599
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
基金
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  • 国家自然科学基金项目(41075115)
  • 江苏省重点研发计划社会发展项目(BE2015692)
  • 无锡市社会发展科技示范工程项目(N20191008)
2026年第49卷第6期
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文章信息
doi: 10.19651/j.cnki.emt.2519599
  • 接收时间：2025-08-14
  • 首发时间：2026-05-15
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出版历史
  • 收稿日期：2025-08-14
基金
国家自然科学基金项目(41075115)
江苏省重点研发计划社会发展项目(BE2015692)
无锡市社会发展科技示范工程项目(N20191008)
作者信息
    1．南京信息工程大学电子与信息工程学院 南京 210044
    2．南京信息工程大学江苏省气象探测与信息处理重点实验室 南京 210044

通讯作者:

李鹏（通信作者），教授，博士生导师，主要研究方向为超声成像、通信信号处理、北斗定位、图像处理等。E-mail:
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2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
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