Skip to main content

Research Repository

Advanced Search

Variability of behaviour in electricity load profile clustering: who does things at the same time each day?

Dent, Ian; Craig, Tony; Aickelin, Uwe; Rodden, Tom

Variability of behaviour in electricity load profile clustering: who does things at the same time each day? Thumbnail


Authors

Ian Dent

Tony Craig

Uwe Aickelin

TOM RODDEN TOM.RODDEN@NOTTINGHAM.AC.UK
Pro-Vice-Chancellor of Research & Knowledge Exchange



Contributors

Petra Perner
Editor

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. © 2014 Springer International Publishing Switzerland.

Citation

Dent, I., Craig, T., Aickelin, U., & Rodden, T. (2014). Variability of behaviour in electricity load profile clustering: who does things at the same time each day?. In P. Perner (Ed.), Advances in data mining: applications and theoretical aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014: proceedings (70–84). https://doi.org/10.1007/978-3-319-08976-8_6

Presentation Conference Type Edited Proceedings
Conference Name 14th Industrial Conference, ICDM 2014
Start Date Jul 16, 2014
End Date Jul 20, 2014
Publication Date Jan 1, 2014
Deposit Date Sep 30, 2014
Publicly Available Date Sep 30, 2014
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Issue 8557
Pages 70–84
Series Title Lecture notes in computer science
Series ISSN 1611-3349
Book Title Advances in data mining: applications and theoretical aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014: proceedings
ISBN 9783319089751
DOI https://doi.org/10.1007/978-3-319-08976-8_6
Keywords Data Mining, Digital Economy
Public URL https://nottingham-repository.worktribe.com/output/998284
Publisher URL http://link.springer.com/chapter/10.1007/978-3-319-08976-8_6
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-08976-8_6

Files





Downloadable Citations