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

Green, Richard, Staffell, Iain and Vasilakos, Nicholas (2014) Divide and conquer? k-Means clustering of demand data allows rapid and accurate simulations of the British electricity system. IEEE Transactions on Engineering Management, 61 (2). pp. 251-260. ISSN 0018-9391

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	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
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Responsible Business Regulation Group Faculty of Social Sciences > Research Centres > Centre for Competition Policy University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research
Depositing User:	Pure Connector
Date Deposited:	25 Jul 2014 13:26
Last Modified:	28 Mar 2025 06:03
URI:	https://ueaeprints.uea.ac.uk/id/eprint/49413
DOI:	10.1109/TEM.2013.2284386

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