Hierarchical K-means Method for Clustering Large-Scale Advanced Metering Infrastructure Data

Abstract

Clustering of the load patterns from distribution network customers is of vital importance for several applications. However, the growing number of advanced metering infrastructures (AMI) and a variety of customer behaviors make the clustering task quite challenging due to the increasing amount of load data. K-means is a widely used clustering algorithm in processing a large dataset with acceptable computational efficiency, but it suffers from local optimal solutions. To address this issue, this paper presents a hierarchical K-means (H-K-means) method for better clustering performance for big data problems. The proposed method is applied to a large-scale AMI dataset and its effectiveness is evaluated by benchmarking with several existing clustering methods in terms of five common adequacy indices, outliers detection, and computation time.