Application of clustering algorithms and self organising maps to classify electricity customers

In competitive electricity markets, the distribution service providers have been given new degrees of freedom in formulating dedicated tariff offers to be applied to properly defined customer classes. For this purpose, they may take advantages from identifying the consumption patterns of their customers and grouping together customers exhibiting similar load diagrams. In this paper, we report on results obtained by using various unsupervised clustering algorithms (modified follow-the-leader, k-means, fuzzy k-means and two types of hierarchical clustering) and the self organising map to group together customers having a similar electrical behaviour. The customer classification is investigated in the paper by considering a set of 234 non-residential customers. Each customer is characterised by a set of values extracted from the load diagrams in a given loading condition. The effectiveness of the classifications obtained with the various algorithms has been compared in terms of a set of adequacy indicators based on properly defined metrics, some of which have been originally developed by the authors. The results show that the modified follow-the-leader and one type of hierarchical clustering exhibit better characteristics than the other algorithms in terms of adequacy.

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