The recent evolution of the electricity business regulation has given new possibilities to the electricity providers for formulating dedicated tariff offers. A key aspect for building specific tariff structures is the identification of the consumption patterns of the customers, in order to form specific customer classes containing customers exhibiting similar patterns. This paper illustrates and compares the results obtained by using various unsupervised clustering algorithms (modified follow-the-leader, hierarchical clustering, K-means, fuzzy K-means) and the self-organizing maps to group together customers with similar electrical behavior. Furthermore, this paper discusses and compares various techniques-Sammon map, principal component analysis (PCA), and curvilinear component analysis (CCA)-able to reduce the size of the clustering input data set, in order to allow for storing a relatively small amount of data in the database of the distribution service provider for customer classification purposes. The effectiveness of the classifications obtained with the algorithms tested is compared in terms of a set of clustering validity indicators. Results obtained on a set of nonresidential customers are presented.

This paper deals with the classification of electricity customers on the basis of their electrical behavior. Starting from an extensive field measurement-based database of customer daily load diagrams, the authors searched for the most appropriate indices or sets of indices to be used for customer classification. They propose two original measures to quantify the degree of adequacy of each index. Using the indices as distinguishing features, they adopt an automatic clustering algorithm to form customer classes. Each customer class is then represented by its load profile. They use the load profiles to study the margins left to a distribution company for fixing dedicated tariffs to each customer class. They take into account new degrees of freedom available in the competitive electricity markets, which increase flexibility in the tariff definition under imposed revenue caps. Results of a case study performed on a set of customers of a large distribution company are presented.