  Increased deployment of residential smart meters has made it possible to
record energy consumption data on short intervals. These data, if used
efficiently, carry valuable information for managing power demand and
increasing energy consumption efficiency. However, analyzing smart meter data
of millions of customers in a timely manner is quite challenging. An efficient
way to analyze these data is to first identify clusters of customers, and then
focus on analyzing these clusters. Deciding on the optimal number of clusters
is a challenging task. In this manuscript, we propose a metric to efficiently
find the optimal number of clusters. A genetic algorithm based feature
selection is used to reduce the number of features, which are then fed into
self-organizing maps for clustering. We apply the proposed clustering technique
on two electricity consumption datasets from Victoria, Australia and Ireland.
The numerical simulations reveal effectiveness of the proposed method in
finding the optimal clusters.
