Clustering methods for electrical load pattern classification

In the current structure of the electricity business, distribution and supply services have been unbundled in many jurisdictions. As a consequence of unbundling, electricity supply to customers is now provided on a competitive basis. In this context, the electricity suppliers need to get accurate information on the actual behaviour of the electricity customers, for the purpose of setting up effective commercial offers. Grouping the electrical load patterns on the basis of information on their activity or commercial codes has proven to be ineffective, since very different load patterns would result in the same group. Customer classification on the basis of consumption pattern similarity is likely to provide more effective results. In order to establish customer grouping based on similarity aspects, various clustering techniques have been tested on electrical load pattern data. This paper provides an overview of these techniques, included in a more general scheme for analyzing electrical demand data. The various stages of the customer classification procedure include the definition of the information to be gathered on the field, the selection of the features to be used to run the clustering methods, the use of clustering methods with assessment of their effectiveness through the calculation of appropriate clustering validity indicators, and the formation of the final load profiles representing a relatively limited number of final customer classes. The characteristics of these stages are illustrated and discussed, providing links to relevant literature references.