基于KPCA和XGBoost算法的非侵入式负荷辨识方法OA北大核心CSTPCD

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.