Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa

TL;DR

This paper presents a method combining internal and external validation measures to improve clustering of residential electricity data, enabling creation of meaningful household archetypes in South Africa with minimal domain expertise.

Contribution

It introduces a structured approach to formalize expert knowledge as external validation, enhancing cluster selection for energy consumption archetypes.

Findings

Successfully reconstructed expert-developed archetypes

Enhanced cluster evaluation with combined validation measures

Method shows promise for transparent, repeatable clustering

Abstract

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.