电测与仪表2025，Vol.62Issue(11)：61-67,7.DOI:10.19753/j.issn1001-1390.2025.11.007
基于改进随机森林算法的电能计量检测异常研究
Research on anomaly detection of power metering based on improved random forest algorithm
摘要
Abstract
The abnormal power consumption of power users is the main factor that leads to the high line loss rate and the increase of power supply cost of power supply enterprises.Therefore,under the current situation of smart grid construction,the development of advanced metering infrastructure(AMI)and the popularization of smart elec-tricity meters,how to select advanced detection algorithm to realize the effective detection of abnormal power con-sumption of power users has become the focus of scholars at home and abroad.On this basis,an abnormal electrici-ty fusion detection model based on RF-GBDT is constructed.In this scheme,data cleaning and missing value filling are used to preprocess the original data set,principal component analysis(PCA)and Pearson correlation coeffi-cient method are used to reduce the dimension of the original data,and feature correlation analysis.The random forest(RF)algorithm is used to randomly select the modeling data set and data feature quantity to obtain a mature classifier.Finally,the feature quantity is input into the gradient to improve upgrade the decision tree(GBDT)to complete the detection and classification of the test data set.The experimental results show that the fusion detection model based on RF-GBDT has better recognition speed and higher detection accuracy.关键词
主元成分分析/随机森林/梯度提升决策树/异常用电Key words
principal component analysis/random forest/gradient to improve upgrade the decision tree/abnormal power consumption分类
动力与电气工程引用本文复制引用
朱铮,许堉坤,蒋超,刘畅..基于改进随机森林算法的电能计量检测异常研究[J].电测与仪表,2025,62(11):61-67,7.基金项目
国家电网公司总部科技项目(52094022001Q) （52094022001Q）
