Application of a clustering framework to UK domestic electricity data
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 dome...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Online Access: | https://eprints.nottingham.ac.uk/2021/ |
| _version_ | 1848790706313756672 |
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| author | Dent, Ian Aickelin, Uwe Rodden, Tom |
| author_facet | Dent, Ian Aickelin, Uwe Rodden, Tom |
| author_sort | Dent, Ian |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:16:52Z |
| format | Conference or Workshop Item |
| id | nottingham-2021 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:16:52Z |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-20212020-05-04T20:34:31Z https://eprints.nottingham.ac.uk/2021/ Application of a clustering framework to UK domestic electricity data Dent, Ian Aickelin, Uwe Rodden, Tom 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. Conference or Workshop Item PeerReviewed Dent, Ian, Aickelin, Uwe and Rodden, Tom Application of a clustering framework to UK domestic electricity data. In: UKCI 2011, the 11th Annual Workshop on Computational Intelligence, 2011, Manchester. (Unpublished) |
| spellingShingle | Dent, Ian Aickelin, Uwe Rodden, Tom Application of a clustering framework to UK domestic electricity data |
| title | Application of a clustering framework to UK domestic electricity data |
| title_full | Application of a clustering framework to UK domestic electricity data |
| title_fullStr | Application of a clustering framework to UK domestic electricity data |
| title_full_unstemmed | Application of a clustering framework to UK domestic electricity data |
| title_short | Application of a clustering framework to UK domestic electricity data |
| title_sort | application of a clustering framework to uk domestic electricity data |
| url | https://eprints.nottingham.ac.uk/2021/ |