Application of a clustering framework to UK domestic electricity data

Abstract—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 workuses data collected in Milton Keynes around 1990 and shows that clusters of households can be identified demonstrating the appropriateness of defining more stereotypical electricity usagepatterns 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 behaviour. 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

Deriving knowledge of household behaviour from domestic electricity usage metering

The electricity market in the UK is undergoing dramatic changes and requires a transformation of existing practices to meet the current and forthcoming challenges. One aspect of the solution is the deployment of demand side management (DSM) programmes to influence domestic behaviour patterns for the benefit of the overall network. Effective deployment of DSM requires segmentation of the population into a small number of groupings. Using a database of electricity meter data collected at a frequency of five minutes over a year from several hundred houses, households are clustered based on the shape of the average daily electricity usage profile. A novel method, incorporating evaluation criteria beyond compactness, of evaluating the resulting groupings is defined and tested. The results indicate the potentially most useful algorithms for use with load profile clustering. Patterns within the electricity meter data are approximated and symbolised to allow motifs (representing repeated behaviours) to be identified. Uninteresting motifs are automatically identified and discarded. The different possible parameters, including size of motif and number of symbols used in representing the data, are explored and the most appropriate values found for use with electricity meter data motif detection. The concept of variability of regular behaviour within a household is introduced and methods of representing the variability are considered. The novel method of using variability in timing of motifs is compared to other techniques and the results tested using the previously defined evaluation criteria. Combining the generated motif data with the meter data to produce a single set of archetypes does not produce more useful results for use with DSM. However, creating complementary sets of archetypes based on each set of data, provides a more complete understanding of the households and allows for better targeting of DSM initiatives

Application of a clustering framework to UK domestic electricity data

Abstract—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 workuses data collected in Milton Keynes around 1990 and shows that clusters of households can be identified demonstrating the appropriateness of defining more stereotypical electricity usagepatterns 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

Finding the creatures of habit: clustering households based on their flexibility in using electricity

Changes in the UK electricity market, particularly with the roll out of smart meters, will provide greatly increased opportunities for initiatives intended to change households' electricity usage patterns for the benefit of the overall system. Users show differences in their regular behaviours and clustering households into similar groupings based on this variability provides for efficient targeting of initiatives. Those people who are stuck into a regular pattern of activity may be the least receptive to an initiative to change behaviour. A sample of 180 households from the UK are clustered into four groups as an initial test of the concept and useful, actionable groupings are found

Finding the creatures of habit: clustering households based on their flexibility in using electricity

Changes in the UK electricity market, particularly with the roll out of smart meters, will provide greatly increased opportunities for initiatives intended to change households' electricity usage patterns for the benefit of the overall system. Users show differences in their regular behaviours and clustering households into similar groupings based on this variability provides for efficient targeting of initiatives. Those people who are stuck into a regular pattern of activity may be the least receptive to an initiative to change behaviour. A sample of 180 households from the UK are clustered into four groups as an initial test of the concept and useful, actionable groupings are found

A method for evaluating options for motif detection in electricity meter data

Investigation of household electricity usage patterns, and matching the patterns to behaviours, is an important area of research given the centrality of such patterns in addressing the needs of the electricity industry. Additional knowledge of household behaviours will allow more effective targeting of demand side management (DSM) techniques. This paper addresses the question as to whether a reasonable number of meaningful motifs, that each represent a regular activity within a domestic household, can be identified solely using the household level electricity meter data. Using UK data collected from several hundred households in Spring 2011 monitored at a frequency of five minutes, a process for finding repeating short patterns (motifs) is defined. Different ways of representing the motifs exist and a qualitative approach is presented that allows for choosing between the options based on the number of regular behaviours detected (neither too few nor too many)

Deriving knowledge of household behaviour from domestic electricity usage metering

The electricity market in the UK is undergoing dramatic changes and requires a transformation of existing practices to meet the current and forthcoming challenges. One aspect of the solution is the deployment of demand side management (DSM) programmes to influence domestic behaviour patterns for the benefit of the overall network. Effective deployment of DSM requires segmentation of the population into a small number of groupings. Using a database of electricity meter data collected at a frequency of five minutes over a year from several hundred houses, households are clustered based on the shape of the average daily electricity usage profile. A novel method, incorporating evaluation criteria beyond compactness, of evaluating the resulting groupings is defined and tested. The results indicate the potentially most useful algorithms for use with load profile clustering. Patterns within the electricity meter data are approximated and symbolised to allow motifs (representing repeated behaviours) to be identified. Uninteresting motifs are automatically identified and discarded. The different possible parameters, including size of motif and number of symbols used in representing the data, are explored and the most appropriate values found for use with electricity meter data motif detection. The concept of variability of regular behaviour within a household is introduced and methods of representing the variability are considered. The novel method of using variability in timing of motifs is compared to other techniques and the results tested using the previously defined evaluation criteria. Combining the generated motif data with the meter data to produce a single set of archetypes does not produce more useful results for use with DSM. However, creating complementary sets of archetypes based on each set of data, provides a more complete understanding of the households and allows for better targeting of DSM initiatives