Data Mining Techniques Applied to Electric Energy Consumers Characterization

F.J. Duarte, F. Rodrigues, V. Figueiredo, Z. Vale, and M. Cordeiro (Portugal)


electricity markets, load profiles, clustering, self organized-maps, k-means, C5.0.


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, we have used the Too-Level Approach clustering algorithm that is a combination of Kohonen Self Organized Maps (SOM) and K-means. Each customer class will be represented by its load profile resulting from the clustering. To characterize each customer class we applied the C5.0 classification algorithm.

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