关键词: 负荷聚类, 并行计算, Canopy, K-Means

Abstract: The electrical power enterprise usually based on power load data, uses the traditional K-Means algorithm to classify the customers, but the biggest drawback of this method must be specified by the user manual clustering number of clusters. It proposes a method combining Canopy algorithm and K-Means algorithm based on load clustering, without the need to manually specify the number of clusters, the automatic division of the customer. First of all, it collects users’ electricity data, uses the parallel computing framework MapReduce to preprocess the original data. Then, it uses Canopy and K-Means algorithm to establish the clustering model of automatic load. Finally, in the real consumption data on the empirical analysis, by using the Silhouette index to evaluate, it shows that the proposed method is more stable and convenient, and has wider applicability.

Key words: load clustering, parallel computing, Canopy, K-Means