Article(id=1281608055083221742, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.04.0114, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762790400000, receivedDateStr=2025-11-11, revisedDate=1768838400000, revisedDateStr=2026-01-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488771457, onlineDateStr=2026-07-08, pubDate=1777305600000, pubDateStr=2026-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488771457, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488771457, creator=13701087609, updateTime=1783488771457, updator=13701087609, issue=Issue{id=1281608054340829934, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='4', pageStart='1', pageEnd='286', issueExtLink='null', onlineDate='null', pubDate='1777305600000', pubDateStr='2026-04-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488771281, creator='13701087609', updateTime=1783588648031, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282026967885124001, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282026967885124002, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608054340829934, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=28, endPage=37, ext={EN=ArticleExt(id=1281608055603315440, articleId=1281608055083221742, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Multi-source fusion deep learning for electric vehicle charging station load forecasting and risk early warning, columnId=1277328386947150682, journalTitle=China Safety Science Journal, columnName=Safety Science Theories and Methods, runingTitle=null, highlight=null, articleAbstract=

With the continuous expansion of electric vehicle charging stations, the power grid faces increasing risks such as power overload, load fluctuations, and uneven demand distribution. To address these issues, this paper proposes an Hybrid Deep Fusion(HDF)-Long Short-Term Memory(LSTM)-based method for load forecasting and graded early warning. The method integrates LSTM, Gated Recurrent Unit(GRU), and Transformer architectures with multi-source data, including historical load, meteorological conditions, and traffic flow. Pearson correlation analysis and a dynamic weight allocation mechanism are employed to improve nonlinear feature representation. Based on the transformer capacity and simultaneity factor specified in the Code for Design of Electric Vehicle Charging Stations, a three-level early warning mechanism is developed for rapid alerting near critical thresholds. Results show that the proposed model outperforms eXtreme Gradient Boosting(XGBoost), GRU, LSTM, and Transformer models, with an Mean Squared Error(MSE) of 0.185 2, an Mean Absolute Error(MAE) of 0.2682, and an R² of 0.985 7. The model also shows good computational efficiency and application potential in charging station load forecasting and operational risk warning.

, authors=Fu Li¹, Wei Lyu¹, Wenyan Cheng², authorsList=Fu Li, Wei Lyu, Wenyan Cheng, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281608056207295219, articleId=1281608055083221742, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=多源融合深度学习的电动汽车充电站负荷预测与风险预警, columnId=1277328387270112092, journalTitle=中国安全科学学报, columnName=安全科学理论与方法, runingTitle=null, highlight=null, articleAbstract=

为解决因电动汽车充电站规模持续扩大而导致电力过载、负荷波动及需求分布不均等风险，提出一种基于多源数据融合深度学习模型（混合深度融合（HDF）-长短期记忆（LSTM）网络）的负荷预测与分级预警方法，以实现高精度负荷预测与动态风险分级告警。该模型融合长短期记忆网络LSTM、循环神经网络（GRU）与自注意力机制Transformer结构，综合利用历史负荷、气象和交通流量等多源数据，并通过Pearson相关性分析与动态权重分配机制提升非线性特征的表达能力；在《电动汽车充电站设计标准》中变压器容量和同时率的约束条件下，设计三级风险预警机制，确保在运行接近临界水平时实现快速告警。结果表明：HDF-LSTM在预测精度上优于极端梯度提升（XGBoost）、GRU、LSTM和Transformer等模型，均方误差（MSE）与平均绝对误差（MAE）分别降至0.185 2和0.268 2，决定系数R²达到0.9857，且具备较好的计算效率，适用于能耗预测与风险阈值动态计算等场景。

, authors=李福¹, 吕伟教授¹, 程文燕², authorsList=李福, 吕伟教授, 程文燕, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=pPiJPFZtUyLkTCi1OS8vTQ==, magXml=ZwvamJ1eGZP21zZozw9lzg==, pdfUrl=null, pdf=3uFHp/9nEw9r3jn3Vx/z+g==, pdfFileSize=7056340, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=YgBku2ptDzjIXHdJ8Cb1dA==, mapNumber=null, fund=null)}, authors=[Author(id=1281608056651891451, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608055083221742, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=492566@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608058270892797, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608055083221742, authorId=1281608056651891451, language=EN, stringName=Fu Li, firstName=Fu, middleName=null, lastName=Li, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan Hubei 430070, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608058358973182, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608055083221742, authorId=1281608056651891451, language=CN, stringName=李福, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹武汉理工大学 安全科学与应急管理学院，湖北 武汉 430070, bio={"content":"

李福（1981—），男，湖北恩施人，博士研究生，研究方向为基于人工智能的安全科学与工程、应急管理与风险智能预测控制。E-mail: 492566@qq.com。

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李福（1981—），男，湖北恩施人，博士研究生，研究方向为基于人工智能的安全科学与工程、应急管理与风险智能预测控制。E-mail: 492566@qq.com。

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