Abstract
UK electricity market changes provide opportunities to alter households’ electricity usage patterns for the benefit of the overall electricity network. Work on clustering similar households has concentrated on daily load profiles and the variability in regular household behaviours has not been considered. Those households with most variability in regular activities may be the most receptive to incentives to change timing. Whether using the variability of regular behaviour allows the creation of more consistent groupings of households is investigated and compared with daily load profile clustering. 204 UK households are analysed to find repeating patterns (motifs). Variability in the time of the motif is used as the basis for clustering households. Different clustering algorithms are assessed by the consistency of the results. Findings show that variability of behaviour, using motifs, provides more consistent groupings of households across different clustering algorithms and allows for more efficient targeting of behaviour change interventions.
Highlights
Background and MotivationThe electricity market in the UK is undergoing dramatic changes
The peak time for electricity usage in the UK is during the early evening and the successful application of techniques to reduce, or move, the peak usage would improve the overall efficiency of the electricity network
This paper addresses the question of whether making use of the variability of behaviour provides “better” groupings of households for the purpose of Demand Side Management (DSM) than those provided by using daily load profiles
Summary
The electricity market in the UK is undergoing dramatic changes. Legal, social and political drivers for a more carbon efficient electricity network, along with the dramatically increased flow of data from households through the deployment of smart meters, requires a transformation of existing practices. A method is required to group large numbers of households into a manageable number of archetypal groups where the members display similar characteristics This approach allows for cost effective targeting of the most appropriate subset of customers whilst allowing the company management to deal with a manageable number of archetypes [3]. Ellegard and Palm [5] have investigated the variability of behaviour using diaries and interviews but have not used analysis of meter data Clustering households using their degree of variability in behaviour, as shown by electricity consumption, provides a way of identifying the subset of electricity users who may be most receptive to an intervention to influence their activity patterns. This paper addresses the question of whether making use of the variability of behaviour (as shown by the electricity meter data) provides “better” groupings of households for the purpose of DSM than those provided by using daily load profiles. An improvement in creating useful archetypes can have major financial and environmental benefits
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