Automating Cluster Analysis to Generate Customer Archetypes for Residential Energy Consumers in South Africa

by   Wiebke Toussaint, et al.
University of Cape Town

Time series clustering is frequently used in the energy domain to generate representative energy consumption patterns of households, which can be used to construct customer archetypes for long term energy planning. Selecting the optimal set of clusters however requires extensive experimentation and domain knowledge, and typically relies on a combination of metrics together with additional expert guidance through visual inspection of the clustering results. This can be time consuming, subjective and difficult to reproduce. In this work we present an approach that uses competency questions to elicit expert knowledge and to specify the requirements for creating residential energy customer archetypes from energy meter data. The approach enabled a structured and formal cluster analysis process, while easing cluster evaluation and reducing the time to select an optimal cluster set that satisfies the application requirements. The usefulness of the selected cluster set is demonstrated in a use case application that reconstructs a customer archetype developed manually by experts.


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