  In order to improve the efficiency and sustainability of electricity systems,
most countries worldwide are deploying advanced metering infrastructures, and
in particular household smart meters, in the residential sector. This
technology is able to record electricity load time series at a very high
frequency rates, information that can be exploited to develop new clustering
models to group individual households by similar consumptions patterns. To this
end, in this work we propose three hierarchical clustering methodologies that
allow capturing different characteristics of the time series. These are based
on a set of "dissimilarity" measures computed over different features: quantile
auto-covariances, and simple and partial autocorrelations. The main advantage
is that they allow summarizing each time series in a few representative
features so that they are computationally efficient, robust against outliers,
easy to automatize, and scalable to hundreds of thousands of smart meters
series. We evaluate the performance of each clustering model in a real-world
smart meter dataset with thousands of half-hourly time series. The results show
how the obtained clusters identify relevant consumption behaviors of households
and capture part of their geo-demographic segmentation. Moreover, we apply a
supervised classification procedure to explore which features are more relevant
to define each cluster.
