电测与仪表2026，Vol.63Issue(8)：68-74,7.DOI:10.19753/j.issn1001-1390.2026.08.007
基于数据挖掘的高频用电大数据信息识别分类方法研究
Research on information recognition and classification method via high-frequency power consumption big data based on data mining
摘要
Abstract
Smart electricity meters and other advanced metering infrastructures have the capability to collect data at ultra-high frequencies,which helps to more accurately depict the characteristics of electricity usage.However,this process also poses a risk of user privacy leakage.To address this issue,a decomposition-reconstruction technique is proposed,which generates privacy-preserving load curves from the original data while maintaining the basic trend and peak features.Through discrete wavelet transformation,the high-frequency load curve is decomposed into low-frequency basic load components and high-frequency variation components.Subsequently,load curves from differ-ent users are adjusted,shifted,and recombined in a random load curve generator according to certain rules to ob-tain a high-fidelity new load curve.Experiments on real data show that the new load curves generated by the pro-posed method can fully retain the statistical characteristics of the original data,ensuring the reliability of down-stream data mining tasks,while avoiding the privacy leakage risks from high-frequency metering data.关键词
智能电能表/负荷特性分析/离散小波变换/数据隐私Key words
smart electricity meter/load profile analysis/discrete wavelet transformation/data privacy分类
信息技术与安全科学引用本文复制引用
王婧骅,朱铮,许堉坤,刘畅,蒋超..基于数据挖掘的高频用电大数据信息识别分类方法研究[J].电测与仪表,2026,63(8):68-74,7.基金项目
国网上海市电力公司科技项目(B3090D220000) （B3090D220000）
