Application of a clustering framework to UK domestic electricity data
This work addresses the problem of improving demand-side management for electricity providers by offering more detailed customer segmentation than the current two-profile system, though it is incremental as it adapts an existing method to new data.
The paper applied a clustering framework previously used on Portuguese electricity data to UK domestic electricity data, finding that the previously preferred two-stage method was unsuitable and identifying up to nine distinct household usage clusters with visually striking differences.
This paper takes an approach to clustering domestic electricity load profiles that has been successfully used with data from Portugal and applies it to UK data. Clustering techniques are applied and it is found that the preferred technique in the Portuguese work (a two stage process combining Self Organised Maps and Kmeans) is not appropriate for the UK data. The work shows that up to nine clusters of households can be identified with the differences in usage profiles being visually striking. This demonstrates the appropriateness of breaking the electricity usage patterns down to more detail than the two load profiles currently published by the electricity industry. The paper details initial results using data collected in Milton Keynes around 1990. Further work is described and will concentrate on building accurate and meaningful clusters of similar electricity users in order to better direct demand side management initiatives to the most relevant target customers.