  Large-scale deployment of smart meters has made it possible to collect
sufficient and high-resolution data of residential electric demand profiles.
Clustering analysis of these profiles is important to further analyze and
comment on electricity consumption patterns. Although many clustering
techniques have been proposed in the literature over the years, it is often
noticed that different techniques fit best for different datasets. To identify
the most suitable technique, standard clustering validity indices are often
used. These indices focus primarily on the intrinsic characteristics of the
clustering results. Moreover, different indices often give conflicting
recommendations which can only be clarified with heuristics about the dataset
and/or the expected cluster structures -- information that is rarely available
in practical situations. This paper presents a novel scheme to validate and
compare the clustering results objectively. Additionally, the proposed scheme
considers all the steps prior to the clustering algorithm, including the
pre-processing and dimensionality reduction steps, in order to provide
recommendations over the complete framework. Accordingly, the proposed strategy
is shown to provide better, unbiased, and uniform recommendations as compared
to the standard Clustering Validity Indices.
