基于数据挖掘的高频用电大数据信息识别分类方法研究OA

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.