  With the inclusion of smart meters, electricity load consumption data can be
fetched for individual consumer buildings at high temporal resolutions.
Availability of such data has made it possible to study daily load demand
profiles of the households. Clustering households based on their demand
profiles is one of the primary, yet a key component of such analysis. While
many clustering algorithms/frameworks can be deployed to perform clustering,
they usually generate very different clusters. In order to identify the best
clustering results, various cluster validation indices (CVIs) have been
proposed in the literature. However, it has been noticed that different CVIs
often recommend different algorithms. This leads to the problem of identifying
the most suitable CVI for a given dataset. Responding to the problem, this
paper shows how the recommendations of validation indices are influenced by
different data characteristics that might be present in a typical residential
load demand dataset. Furthermore, the paper identifies the features of data
that prefer/prohibit the use of a particular cluster validation index.

Computers and Society