Ian Dent

Application of a Clustering Framework to UK Domestic Electricity Data

The UK electricity industry will shortly have available a massively increased amount of data from domestic households and this paper is a step towards deriving useful information from non intrusive household level monitoring of electricity. The paper takes an approach to clustering domestic load profiles that has been successfully used in Portugal and applies it to UK data. It is found that the preferred technique in the Portuguese work (a process combining Self Organised Maps and Kmeans) is not appropriate for the UK data. The work uses data collected in Milton Keynes around 1990 and shows that clusters of households can be identified demonstrating the appropriateness of defining more stereotypical electricity usage patterns than the two load profiles currently published by the electricity industry. The work is part of a wider project to successfully apply demand side management techniques to gain benefits across the whole electricity network.

An Approach for Assessing Clustering of Households by Electricity Usage

How a household varies their regular usage of electricity is useful information for organisations to allow accurate targeting of behaviour modification initiatives with the aim of improving the overall efficiency of the electricity network. The variability of regular activities in a household is one possible indication of that household’s willingness to accept incentives to change their behavior. An approach is presented for identifying a way of representing the variability of a household’s behaviour and developing an efficient way of clustering the households, using these measures of variability, into a few, usable groupings.To evaluate the effectiveness of the variability measures, a number of cluster validity indexes are explored with regard to how the indexes vary with the number of clusters, the number of attributes, and the quality of the attributes. The Cluster Dispersion Indicator (CDI) and the Davies-Boulden Indicator (DBI) are selected for future work developing various indicators of household behaviour variability. The approach is tested using data from 180 UK households monitored for over a year at a sampling interval of 5 minutes. Data is taken from the evening peak electricity usage period of 4pm to 8pm.

Multi-scale dynamic modeling to maximize demand side management

This paper presents the work program of DESIMAX, a collaborative research project looking at wide-scale implementation of demand side management (DSM) within electricity networks. To fully understand the implications of extended use of DSM, it is important to develop multi-scale models that will be able to capture, predict and demonstrate the response of the power system at time scales ranging from sub-second intervals to periods of years. A multi-sector modeling framework is proposed that includes the physical (electrical) system, the end-user behavior, the economic and environmental models, as well as the set of digital interventions required in order to assist wider and easier implementation of DSM. The proposed modeling framework allows for a holistic approach to DSM integration and is capable of assessing the implications and effects of devised DSM schemes across the entire electricity system.

The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences Using UK Data

This paper describes a method for defining representative load profiles for domestic electricity users in the UK. It considers bottom up and clustering methods and then details the research plans for implementing and improving existing framework approaches based on the overall usage profile. The work focuses on adapting and applying analysis framework approaches to UK energy data in order to determine the effectiveness of creating a few (single figures) archetypical users with the intention of improving on the current methods of determining usage profiles. The work is currently in progress and the paper details initial results using data collected in Milton Keynes around 1990. Various possible enhancements to the work are considered including a split based on temperature to reflect the varying UK weather conditions.

Variability of Behaviour in Electricity Load Profile Clustering; Who Does Things at the Same Time Each Day?

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.

Creating Personalised Energy Plans: From Groups to Individuals Using Fuzzy C Means Clustering

Changes in the UK electricity market mean that domestic users will be required to modify their usage behaviour in accordance with energy efficiency targets. Clustering allows usage data, collected at the household level, to be clustered into groups and assigned a stereotypical profile which may be used to provide individually tailored energy plans. Fuzzy C Means extends previous work based around crisp K means clustering by allowing a household to be a member of multiple customer profile groups to different degrees, thus providing the opportunity to make personalised offers to the household dependent on their degree of membership of each group. In addition, feedback can be provided on how household’s changing behaviour is moving them towards more ”green” or cost effective stereotypical usage