Electric Energy Customer Characterization by Clustering

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. The knowledge about how and when consumers use the electricity has an important role in a free and competitive electricity market, but this one grows up in a dynamic form. The treatment of this data must be made with the application of Data Mining and Knowledge Discovery techniques to support the development of generic load profiles to each consumer’s class. In this paper, we propose a KDD project applied to electricity consumption data from an utility clients data base. To form the different customers classes a comparative analysis of the performance of the Kohonen Self Organized Maps (SOM) and K-means algorithm for clustering is presented. Each customer class is then represented by its load profile.