A shape-based clustering algorithm and its application to load data

Abstract

The popularity of smart metres has brought a huge amount of demand-side data, which provides important information for the demand response of the power sector, to guide practitioners to understand the customers' electricity usage behaviours and patterns. Clustering analysis of customers' daily load data is an important tool for mining users' consumption habits and achieve non-fixed market segmentation. Since the load data is time series, it is inappropriate to perform clustering directly without extracting targeted features. Therefore, according to the shape features of the daily load curve, a shape-based clustering algorithm called BDKM is proposed. The algorithm first uses the B-splines regression to fit the time series data to extract morphological features, and then the objects are segmented based on the dynamic time warping distance by clustering. Finally, the real world daily customers' load data is used to prove the effectiveness of the proposed algorithm based on B-splines regression.