Electricity consumer archetypes study based on functional data analysis and K-means algorithm

Abstract

In this paper, a functional cluster method to analyze features of sparse and irregular longitudinal electricity data and to cluster all users is proposed. Firstly, kernel method is applied to estimate daily continuous curves of discrete data. Then, inspired by distance in Sobolev space, new distances for functional data usable in k-means algorithm are proposed. Based on experiment, electricity consumer archetypes for all users in different functional distances and cluster numbers are calculated. Result shows that, because users in experiment are mainly city residents, their consumption patterns are similar: big peak period is between June and September, small peak is between January and February, and consumption fluctuations aren’t very intense. However, main difference is in consumption ranges: low-consumption user’s daily consumptions are lower than 13 kW/h overall, while high-consumption users use almost 100 kW/h every day.