Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Variability in Customer Behaviour

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, limited research is available for external measures. We present a method that distills expert knowledge into competency questions, which we operationalised as external evaluation measures to specify the clustering objective for our application. This approach supported a structured and formal cluster validation process that combined internal and external measures to select a cluster set that is useful for creating residential electricity customer archetypes from electricity meter data in South Africa. We validated the approach in a case study application where we successfully reconstructed customer archetypes previously developed by experts. Our approach enables transparent and repeatable cluster ranking and selection by data scientists, even if they have limited domain knowledge.