电测与仪表2024，Vol.61Issue(5)：71-77,7.DOI:10.19753/j.issn1001-1390.2024.05.011
基于KPCA和XGBoost算法的非侵入式负荷辨识方法
Non-intrusive load identification method based on KPCA and XGBoost algorithm
摘要
Abstract
In order to realize the function of non-intrusive load monitoring and improve the accuracy rate of load iden-tification,a load identification method based on machine learning is proposed in this paper.In the data of current waveform and harmonic characteristics of household appliances,Kernel principal components analysis(KPCA)is used to solve the problem of nonlinear feature extraction and dimension reduction,and extract feature information to the maximum extent.One dimensional convolution kernel is used to extract time series features and compress them into the XGBoost model to obtain load identification results.The algorithm is verified by the data collected in the la-boratory.The proposed algorithm has high accuracy rate in the identification of all kinds of electrical appliances.关键词
非侵入式/负荷辨识/核主成分分析/卷积/XGBoostKey words
non-intrusive/load identification/Kernel principal component analysis/convolution/XGBoost分类
信息技术与安全科学引用本文复制引用
刘岩,王玉君,杨晓坤,李文文,郭磊..基于KPCA和XGBoost算法的非侵入式负荷辨识方法[J].电测与仪表,2024,61(5):71-77,7.基金项目
国家电网有限公司总部科技项目(52010119000R) （52010119000R）
