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Title: Clustering disaggregated load profiles using a Dirichlet process mixture model
Authors: Granell, R
Axon, CJ
Wallom, DCH
Keywords: Bayesian statistics;Classification algorithms;Data mining;Energy use;Power demand;Smart grids
Issue Date: 2015
Publisher: Elsevier Ltd
Citation: Energy Conversion and Management, 92 pp. 507 - 516, 2015
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.
Description: This article has been made available through the Brunel Open Access Publishing Fund.
ISSN: 0196-8904
Appears in Collections:Brunel OA Publishing Fund
Dept of Mechanical Aerospace and Civil Engineering Research Papers

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