Load Characteristics Analysis Based on Improved K-means Clustering Algorithm

Big data analysis is becoming more and more important in power system and load characteristics analysis becomes great significance for the power stable operation. Based on historical load data, this paper obtains representative load characteristic curves through improved k-means clustering method. In order to make the clustering number K of users closer to the actual optimal clustering number, and more improve the quality of algorithm clustering results. In order to solve the problem that the results of the clustering algorithm are sensitive to the initial clustering center value This paper proposes an improved differential evolution (DE) algorithm is proposed to optimize the initial center value and K value. The clustering of users in Tianjin area by MATLAB, this improved algorithm is verified that better than the original algorithm in terms of feasibility, effectiveness and stability.