Ian Dent
Application of a clustering framework to UK domestic electricity data
Dent, Ian; Aickelin, Uwe; Rodden, Tom
Authors
Uwe Aickelin
Professor TOM RODDEN TOM.RODDEN@NOTTINGHAM.AC.UK
Pro-Vice-Chancellor of Research & Knowledge Exchange
Abstract
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.
Citation
Dent, I., Aickelin, U., & Rodden, T. Application of a clustering framework to UK domestic electricity data. Presented at UKCI 2011, the 11th Annual Workshop on Computational Intelligence
Conference Name | UKCI 2011, the 11th Annual Workshop on Computational Intelligence |
---|---|
Deposit Date | Jun 17, 2013 |
Peer Reviewed | Peer Reviewed |
Public URL | https://nottingham-repository.worktribe.com/output/1026057 |
Files
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