大数据驱动的电动汽车充电负荷分析系统构建

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中图分类号：TM73 文献标志码：A 文章编号：2095-2945（2026）08-0055-04

Abstract：Loadmanagementof electricvehiclecharginginfrastructureisfacedwithhugechallenges，butbigdatatechnology providesinnovativesolutionsforthis.Taking ShenzhenNanshanScienceandTechnologyParkasapracticebase，anelectric vehiclehargingloadanalysissystemwasbuilt，integratingIoTdatacolectiondatacleaningandLSTMpredictionalgoriths. Thesystemadoptsmulti-sourcedatafusiontechnologytoimprovedataqualitythroughtensordecompositionandimproved Zscore standardization. Actual operation results show that the system prediction accuracy is increased by 27.3% ，thepeak-to-valley load difference is reduced by 31.5% ，the utilization rate of charging stations is increased by 36.6% ，and user waiting time is reduced by 69.6 % .The system efctively solves theproblem of charging load fluctuations，provides scientific basis for power grid scheduling and charging facility planning，and has broad application prospects.

Keywords：electric vehicle;charging load; big data; LSTM; intelligent scheduling

电动汽车规模化发展使充电负荷呈现高度随机性和波动性特征，传统分析方法难以应对当前管理需求。（剩余4954字）