Divide and conquer? k-Means clustering of demand data allows rapid and accurate simulations of the British electricity system

Richard Green, Iain Staffell, Nicholas Vasilakos

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)


We use a k-means clustering algorithm to partition national electricity demand data for Great Britain and apply a novel profiling method to obtain a set of representative demand profiles for each year over the period 1994-2005. We then use a simulated dispatch model to assess the accuracy of these daily profiles against the complete dataset on a year-to-year basis. We find that the use of data partitioning does not compromise the accuracy of the simulations for most of the main variables considered, even when simulating significant intermittent wind generation. This technique yields 50-fold gains in terms of computational speed, allowing complex Monte Carlo simulations and sensitivity analyses to be performed with modest computing resource.
Original languageEnglish
Pages (from-to)251-260
Number of pages10
JournalIEEE Transactions on Engineering Management
Issue number2
Early online date30 Jan 2014
Publication statusPublished - 1 May 2014


  • pattern clustering
  • power generation dispatch
  • power system simulation
  • British electricity system
  • complex Monte Carlo simulations
  • data partitioning
  • dispatch model
  • intermittent wind generation
  • k-means clustering algorithm
  • national electricity demand data
  • profiling method
  • sensitivity analysis

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