The K-means cluster algorithm is widely used for the analysis of load characteristics due to its simple calculation process and fast convergence rate.However,selecting the number of clusters in K-means algorithm is challenging,and the random initialization of cluster centers can lead to slow convergence rate and local optima issues.A cluster analysis method for daily load curves based on K-means++algorithm is proposed.It selects the initial cluster centers using heuristic random seeding method,combines the elbow method and clustering evaluation metrics to measure the density and separation of clusters and comprehensively determines the optimal number of clusters,which mitigates the impact of randomness of initial cluster centers on clustering quality.The case studies show that the proposed algorithm can achieve high clustering quality,provide a reasonable user classification and facilitate a comprehensive understanding of load characteristics.
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