A Comparative Analysis of Clustering Algorithms Applied to Load Profiling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. A fair insight on the customers' behavior will permit the definition of specific contract aspects based on the different consumption patterns. In this paper, we propose a KDD project applied to electricity consumption data from a utility client's database. To form the different customers' classes, and find a set of representative consumption patterns, a comparative analysis of the performance of the K-means, Kohonen Self-Organized Maps (SOM) and a Two-Level approach is made. Each customer class will be represented by its load profile obtained with the algorithm with best performance in the data set used.