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http://bura.brunel.ac.uk/handle/2438/10238
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DC Field | Value | Language |
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dc.contributor.author | Granell, R | - |
dc.contributor.author | Axon, CJ | - |
dc.contributor.author | Wallom, DCH | - |
dc.date.accessioned | 2015-02-13T15:48:19Z | - |
dc.date.available | 2015-03-01 | - |
dc.date.available | 2015-02-13T15:48:19Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Energy Conversion and Management, 92 pp. 507 - 516, 2015 | en_US |
dc.identifier.issn | 0196-8904 | - |
dc.identifier.uri | http://www.sciencedirect.com/science/article/pii/S0196890414011194 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/10238 | - |
dc.description | This article has been made available through the Brunel Open Access Publishing Fund. | - |
dc.description.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 predetermined by the user. We used the so-called 'Chinese restaurant process' method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships. | en_US |
dc.format.extent | 507 - 516 | - |
dc.format.extent | 507 - 516 | - |
dc.language | eng | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.subject | Bayesian statistics | en_US |
dc.subject | Classification algorithms | en_US |
dc.subject | Data mining | en_US |
dc.subject | Energy use | en_US |
dc.subject | Power demand | en_US |
dc.subject | Smart grids | en_US |
dc.title | Clustering disaggregated load profiles using a Dirichlet process mixture model | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.enconman.2014.12.080 | - |
dc.relation.isPartOf | Energy Conversion and Management | - |
dc.relation.isPartOf | Energy Conversion and Management | - |
pubs.volume | 92 | - |
pubs.volume | 92 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Mechanical, Aerospace and Civil Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Mechanical, Aerospace and Civil Engineering/Mechanical and Aerospace Engineering | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Energy Futures | - |
pubs.organisational-data | /Brunel/Brunel Staff by Institute/Theme/Institute of Energy Futures/Resource Efficient Future Cities | - |
Appears in Collections: | Brunel OA Publishing Fund Dept of Mechanical and Aerospace Engineering Research Papers |
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Fulltext.pdf | 950.73 kB | Adobe PDF | View/Open |
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