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Random effects mixture models for clustering electrical load series

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  • Geoffrey Coke
  • Min Tsao

Abstract

For purposes such as rate setting and long-term capacity planning, electrical utility companies are interested in dividing their customers into homogeneous groups or clusters in terms of the customers' electricity demand profiles. Such demand profiles are typically represented by load series, long time series of daily or even hourly rates of energy consumption of individual customers. The high dimension and time series nature inherent in the load series render existing methods of clustering analysis ineffective. To handle the high dimension and to take advantage of the time-series nature of load series, we introduce a class of mixture models for time series, the random effects mixture models, which are particularly useful for clustering the load series. The random effects mixture models are based on a hierarchical model for individual components. They employ highly flexible antedependence models to effectively capture the time-series characteristics of the covariance of the load series. We present details on the construction of such mixture models and discuss a special Expectation-maximization (EM) algorithm for their computation. We also apply these models to cluster the data set which had motivated this research, a set of 923 load series from BC Hydro, a crown utility company in British Columbia, Canada. Copyright 2010 Blackwell Publishing Ltd

Suggested Citation

  • Geoffrey Coke & Min Tsao, 2010. "Random effects mixture models for clustering electrical load series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 451-464, November.
  • Handle: RePEc:bla:jtsera:v:31:y:2010:i:6:p:451-464
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    References listed on IDEAS

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    1. Li-Xuan Qin & Steven G. Self, 2006. "The Clustering of Regression Models Method with Applications in Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 526-533, June.
    2. Tarpey, Thaddeus, 2007. "Linear Transformations and the k-Means Clustering Algorithm: Applications to Clustering Curves," The American Statistician, American Statistical Association, vol. 61, pages 34-40, February.
    3. Franses, Philip Hans & Paap, Richard, 2004. "Periodic Time Series Models," OUP Catalogue, Oxford University Press, number 9780199242030.
    4. Chris Fraley & Adrian E. Raftery, 1999. "MCLUST: Software for Model-Based Cluster Analysis," Journal of Classification, Springer;The Classification Society, vol. 16(2), pages 297-306, July.
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    1. repec:gam:jeners:v:11:y:2018:i:4:p:859-:d:139878 is not listed on IDEAS
    2. repec:gam:jeners:v:10:y:2017:i:5:p:584-:d:96702 is not listed on IDEAS
    3. Allou Samé & Faicel Chamroukhi & Gérard Govaert & Patrice Aknin, 2011. "Model-based clustering and segmentation of time series with changes in regime," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(4), pages 301-321, December.

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