A comparative analysis of clustering algorithms applied to load profiling

Authors:
Fátima Rodrigues;Jorge Duarte;Vera Figueiredo;Zita Vale;M. Cordeiro
Affiliations:
GECAD-Knowledge Engineering and Decision Support Group, Department of Computer Engineering, Polytechnic Institute of Oporto, Portugal;GECAD-Knowledge Engineering and Decision Support Group, Department of Computer Engineering, Polytechnic Institute of Oporto, Portugal;GECAD-Knowledge Engineering and Decision Support Group, Department of Electrical Engineering, Polytechnic Institute of Oporto, Portugal;GECAD-Knowledge Engineering and Decision Support Group, Department of Electrical Engineering, Polytechnic Institute of Oporto, Portugal;Department of Electrical Engineering, UTAD, Portugal
Venue:
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Year:
2003

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Abstract

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.