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

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Dent, Ian, Craig, Tony, Aickelin, Uwe and Rodden, Tom (2014) Variability of behaviour in electricity load profile clustering: who does things at the same time each day? In: Advances in data mining: applications and theoretical aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014: proceedings. Lecture notes in computer science (8557). Springer International Publishing, Cham, pp. 70-84. ISBN 9783319089768 (electronic bk.); 9783319089751 (print)

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Abstract

UK electricity market changes provide opportunities to alter

households' electricity usage patterns for the benet of the overall electricity network. Work on clustering similar households has concentrated on daily load proles and the variability in regular household behaviours has not been considered. Those households with most variability in reg-

ular 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 prole clustering. 204 UK households are analysed to nd

repeating patterns (motifs). Variability in the time of the motif is used as the basis for clustering households. Dierent 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 dierent clustering algorithms and allows for more ecient targeting of behaviour change interventions.

Item Type: Book Section
RIS ID: https://nottingham-repository.worktribe.com/output/998284
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-08976-8_6
Keywords: Data Mining, Digital Economy
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1007/978-3-319-08976-8_6
Depositing User: Aickelin, Professor Uwe
Date Deposited: 30 Sep 2014 08:46
Last Modified: 04 May 2020 20:16
URI: https://eprints.nottingham.ac.uk/id/eprint/3347

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