  Clustering is frequently used in the energy domain to identify dominant
electricity consumption patterns of households, which can be used to construct
customer archetypes for long term energy planning. Selecting a useful set of
clusters however requires extensive experimentation and domain knowledge. While
internal clustering validation measures are well established in the electricity
domain, they are limited for selecting useful clusters. Based on an application
case study in South Africa, we present an approach for formalising implicit
expert knowledge as external evaluation measures to create customer archetypes
that capture variability in residential electricity consumption behaviour. By
combining internal and external validation measures in a structured manner, we
were able to evaluate clustering structures based on the utility they present
for our application. We validate the selected clusters in a use case where we
successfully reconstruct customer archetypes previously developed by experts.
Our approach shows promise for transparent and repeatable cluster ranking and
selection by data scientists, even if they have limited domain knowledge.
