Dent , Ian and Craig , Tony and Aickelin , Uwe and Rodden ,

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. 1 Background and Motivation 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. In particular, the change of the frequency of sampling of electricity usage, by using smart meters, alters the level of understanding of households’ behaviour that is possible [1]. One approach to address the pressures on the electricity network is the application of Demand Side Management (DSM) techniques to achieve changes in consumer behaviour. DSM is defined as “systematic utility and government activities designed to change the amount and/or timing of the customer’s use of electricity” for the collective benefit of society, the utility company, and its customers [2]. 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. To allow selection of appropriate DSM interventions, a good understanding of the existing behaviour of households is needed. Firstly, knowledge is needed on an individual household that can be deduced from house-wide electricity metering. Secondly, 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]. There is an extensive body of work on clustering households which includes comparing or combining timed meter readings to create additional attributes that contribute to the quality of the clustering [4]. However, little work has focused on how the daily activity patterns of the household vary from day to day and how this can be used for clustering. For instance, some households will be creatures of habit and will eat their evening meal at almost the same time each evening, whilst others have a much more variable activity pattern and will eat at different times. Ellegȧrd 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. The intervention may be to reward households for NOT changing their current pattern of usage if it is already as desired by the utility company. 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. The judgement of “better” is measured by implementing a number of different clustering techniques and measuring the degree of overlap between the clusters found. A consistent set of clusters across the different clustering algorithms implies a better, and more useful, approach to generating the clusters. The investigation of household electricity load profiles is an important area of research given the centrality of such patterns in directly addressing the needs of the electricity industry, both now and in the future. This work extends existing load profile work by taking electricity meter data streams and developing new ways of representing the household that can be used as the basis for clustering using existing data mining techniques. The identification of repeating motifs and the investigation of how the timing of the motifs varies from day to day, as a key behavioural trait of the household, is a novel area of research. An improvement in creating useful archetypes can have major financial and environmental benefits. 2 Methods and Technical Solutions