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Clustering disaggregated load profiles using a Dirichlet process mixture model

Abstract:

The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predet...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.enconman.2014.12.080

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
Publisher:
Elsevier Publisher's website
Journal:
Energy Conversion and Management Journal website
Volume:
92
Pages:
507-516
Publication date:
2015-01-16
Acceptance date:
2014-12-24
DOI:
ISSN:
0196-8904
Source identifiers:
505984
Keywords:
Pubs id:
pubs:505984
UUID:
uuid:38fea267-d554-433b-9f36-2cee47870a04
Local pid:
pubs:505984
Deposit date:
2016-06-06

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