基于粒计算和双尺度相似性的负荷曲线集成聚类算法

电力负荷曲线聚类通常依靠负荷形态差异和负荷数值差异对负荷曲线进行分类.提出了一种基于粒计算和双尺度相似性的集成聚类算法,采用以欧氏距离和皮尔森相关系数作为相似性度量的K-means算法生成基聚类,再通过粒度距离度量基聚类间的相似性,从而选择部分基聚类参与集成,最后生成相似度矩阵并采用层次聚类获得最终聚类结果.算例结果表...

Clustering Ensemble Model Based on Granular Computing and Dual-Scale Similarity

Power load profile clustering usually classifies load profiles according to shape difference and numerical difference of the curves. In this paper, an ensemble clustering algorithm based on granular computing and dual-scale similarity is proposed. The K-means algorithm, which takes Euclidean distance and Pearson correlation coefficient as similarity measures, is used to generate base clustering. Then the part of base clustering is selected to participate in the ensemble algorithm through granular computing. Finally, the similarity matrix is generated and the hierarchical clustering is used to obtain the final clustering. The result of the experiment shows that, the proposed algorithm can overcome the limitation that the traditional load profile clustering can only measure the load similarity from the value or shape, and significantly improves the quality of power load profile clustering.