Research on cluster analysis of power load characteristics based on combination algorithm

In recent years, with the increasing number of power users and the improvement of power consumption flexibility, how to cluster load curves effectively becomes more and more important. Therefore, for the problem that a single clustering analysis method cannot achieve accurate clustering of power load curves, an algorithm combining K-means and Agglomerative Hierarchical Clustering is applied, and the clustering effect is optimized by the Gap Statistic method. In this algorithm, the centroid is updated iteratively by using a mixture of the two methods to get the best clustering effect. Finally, the actual load data of a power grid in 2020 was selected for cluster analysis and compared with K-means clustering algorithm and agglomerative hierarchical clustering algorithm respectively, the advantages and effectiveness of the combined algorithm are demonstrated.