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Increased deployment of residential smart meters has made it possible torecord energy consumption data on short intervals. These data, if usedefficiently, carry valuable information for managing power demand andincreasing energy consumption efficiency. However, analyzing smart meter dataof millions of customers in a timely manner is quite challenging. An efficientway to analyze these data is to first identify clusters of customers, and thenfocus on analyzing these clusters. Deciding on the optimal number of clustersis a challenging task. In this manuscript, we propose a metric to efficientlyfind the optimal number of clusters. A genetic algorithm based featureselection is used to reduce the number of features, which are then fed intoself-organizing maps for clustering. We apply the proposed clustering techniqueon two electricity consumption datasets from Victoria, Australia and Ireland.The numerical simulations reveal effectiveness of the proposed method infinding the optimal clusters.

Optimal Clustering of Energy Consumers based on Entropy of the Correlation Matrix between Clusters