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Application of a clustering framework to UK domestic electricity data

Dent, Ian; Aickelin, Uwe; Rodden, Tom

Authors

Ian Dent

Uwe Aickelin uwe.aickelin@nottingham.ac.uk

TOM RODDEN TOM.RODDEN@NOTTINGHAM.AC.UK
Professor of Computer Science



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

Conference Name UKCI 2011, the 11th Annual Workshop on Computational Intelligence
Deposit Date Jun 17, 2013
Peer Reviewed Peer Reviewed
Public URL http://eprints.nottingham.ac.uk/id/eprint/2021
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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