The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences Using UK Data

This paper describes a method for defining representative load profiles for domestic electricity users in the UK. It considers bottom up and clustering methods and then details the research plans for implementing and improving existing framework approaches based on the overall usage profile. The work focuses on adapting and applying analysis framework approaches to UK energy data in order to determine the effectiveness of creating a few (single figures) archetypical users with the intention of improving on the current methods of determining usage profiles. The work is currently in progress and the paper details initial results using data collected in Milton Keynes around 1990. Various possible enhancements to the work are considered including a split based on temperature to reflect the varying UK weather conditions.

[1]  Jukka Paatero,et al.  A model for generating household electricity load profiles , 2006 .

[2]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[3]  T. Marijanic,et al.  Load profiling in an opening electricity market , 2007, AFRICON 2007.

[4]  Tadj Oreszczyn,et al.  Milton Keynes Energy Park revisited: Changes in internal temperatures and energy usage , 2007 .

[5]  David Infield,et al.  Domestic electricity use: A high-resolution energy demand model , 2010 .

[6]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[7]  Z. Vale,et al.  An electric energy consumer characterization framework based on data mining techniques , 2005, IEEE Transactions on Power Systems.

[8]  Z. Vale,et al.  Data Mining techniques to support the classification of MV electricity customers , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[9]  Conversion and delivery of electrical energy in the 21st century , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.