Ian Dent
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
Authors
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 |
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