Full paper at EvoStar’16, Porto, Portugal, 30 March – 1 April 2016
Electrical Load Pattern Shape (LPS) clustering of customers is an important part of the tariff formulation process. Nevertheless, the patterns describing the energy consumption of a customer have some characteristics (e.g., a high number of features corresponding to time se- ries reflecting the measurements of a typical day) that make their analysis different from other pattern recognition applications. In this paper, we propose a clustering algorithm based on ant colony optimization (ACO) to solve the LPS clustering problem. We use four well-known clustering metrics (i.e., CDI, SI, DEV and CONN), showing that the selection of a clustering quality metric plays an important role in the LPS cluster- ing problem. Also, we compare our LPS-ACO algorithm with traditional algorithms, such as k-means and single-linkage, and a state-of-the-art Electrical Pattern Ant Colony Clustering (EPACC) algorithm designed for this task. Our results show that LPS-ACO performs remarkably well using any of the metrics presented here.