Unsupervised classification model of residential power users based on typical load patterns

Abstract

It is critical for the smart grid to mining the power users’ consumption behaviors. The accumulation of power data provides the possibility of conducting experiments for research focus on behavior analysis. This paper proposes a residential power users’ classification method based on typical load patterns called UCM-LP, to classify users’ power consumption behavior. The method firstly performs fuzzy grouping on the typical power consumption characteristics of multiple users through two-stage clustering, then divides cluster label to obtain the final power consumption categories of the user. Finally, this paper verified the algorithm using the real household power consumption data, obtaining the specific power consumption category patterns and discussed the differences among power consumption categories.