Abstract: A clustering method of power load profiles based on kernel principal component analysis (KPCA) and improved K-means algorithm was proposed to improve clustering accuracy of power load profiles． Firstly，a K-means algorithm based on density idea，namely，density K-means (DK-means) was proposed by combining the density clustering method with partitioning clustering algorithm K-means． And the clustering effect was comparatively ana-lyzed on the experimental set of power load profiles． Then the dimensionality reduction accuracy and speed of vari-ous dimensionality reduction algorithms were compared on the experimental set． Finally，the dimensionality reduc-tion and clustering ability of the KPCA + DK-means combination algorithm was analyzed． The results show that，firstly，Davies-Bouldin Index (DBI) is more suitable for the evaluation index of power load profiles clustering; se-condly，taking DBI as the evaluation index，the clustering accuracy of DK-means is higher than that of K-means，BIRCH，DBSCAN and EnsClust algorithms; thirdly，compared with LLE，MDS and ISOMAP，the dimensionality reduction speed of KPCA is faster; finally，the KPCA + DK-means combination algorithm has good dimensionality reduction and clustering ability，and its clustering accuracy and clustering efficiency are better than those of DK-means． In short，the KPCA + DK-means combination algorithm can achieve efficient dimensionality reduction and accurate clustering of power load profiles，thus plays a key technical role in accurately extracting information of electricity consumption behavior．

Key words: power load profile, DK-means algorithm, kernel principal component analysis (KPCA), dimensio-nality reduction, clustering