Research on Interpolation Method for Missing Electricity Consumption Data

作     者：Junde Chen Jiajia Yuan Weirong Chen Adnan Zeb Md Suzauddola Yaser A.Nanehkaran 

作者机构：Department of Electronic CommerceXiangtan UniversityXiangtan411105China School of Information EngineeringYancheng Teachers UniversityYancheng224000China Department of Information and Electrical EngineeringNingde Normal UniversityNingde352100China College of EngineeringSouthern University of Science and TechnologyShenzhen518005China School of InformaticsXiamen UniversityXiamen361005China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第2期

页      面：2575-2591页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：This research was funded by the National Nature Sciences Foundation of China(Grant No.42250410321) 

主　　题：Data interpolation GMDH electricity consumption data distribution system 

摘      要：Missing value is one of the main factors that cause dirty *** high-quality data,there will be no reliable analysis results and precise ***,the data warehouse needs to integrate high-quality data *** the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss *** the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity ***,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data *** missing data are randomly interpolated within the upper and lower ***,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption *** last,this process is implemented iteratively until the missing values do not *** a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete ***,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.