Identification of customer profiles from electricity consumption data

Abstract

The electric power suppliers are interested in identifying and categorising their consumers’ profiles into different categories according to their energy consumption habits. The profiling of users can help with understanding how the users consume the energy and how the energy usage may affect the electricity distribution grid. However, the privacy of the electricity users is well protected by the current law. This study focuses on data mining methods to extract the relevant knowledge based on anonymous data obtained from smart meters. The K-means clustering algorithm was used in grouping the energy consumption data. To improve the quality of the clusters formed via the K-means clustering and to tackle the common problem of local optimum, the genetic algorithm (GA) was adopted in refining the clusters. The use of two validity indices to compare the methods showed that combining K-means and GA did indeed improve the clustering quality.