Computer Science - Research and Development

, Volume 31, Issue 3, pp 141–148 | Cite as

Feature extraction and filtering for household classification based on smart electricity meter data

  • Konstantin HopfEmail author
  • Mariya Sodenkamp
  • Ilya Kozlovkiy
  • Thorsten Staake
Special Issue Paper


Knowing household properties, such as number of persons per apartment, age of housing, type of water heating, etc. enables energy consultants and utilities to develop targeted energy conservation services. Load profiles captured by smart power meters, can—besides several other applications—be used to reveal energy efficiency relevant household characteristics. The goal of this work is to develop methods of supervised machine learning that deduce properties of private dwellings using consumption time series recorded in 30-min intervals. The contribution of this paper to the state of the art is threefold: we quadruplicate the number of features that describe power consumption curves to preserve classification relevant structures, indicate dimensionality reduction techniques to reduce the large-scale input data to a set of few significant features and finally, we redefine classes for some properties. As a result, the classification accuracy is elevated up to 82 %, while the runtime complexity is significantly reduced. The classification quality that can be achieved by our eCLASS methodology renders personalized efficiency measures in large-scale practical settings possible.


Smart meter data (SMD) Household classification Supervised machine learning Feature selection (FS) Feature filtering Extended CLASS (eCLASS) 


  1. 1.
    Degen K, Efferson C, Frei F, Goette L, Lalive R (2013) Smart Metering, Beratung oder Sozialer Vergleich: Was beeinflusst den Elektrizitätsverbrauch?, ZürichGoogle Scholar
  2. 2.
    Beckel C, Sadamori L, Santini S (2013) Automatic socio-economic classification of households using electricity consumption data. In: Culler D, Rosenberg C (eds) Proceedings of the fourth international conference on future energy systems. ACM, Berkeley, pp 75–86Google Scholar
  3. 3.
    ISSDA (2014) Data from the Commission for Energy Regulation. Accessed 4 March 2014
  4. 4.
    Verdu SV, Garcia MO, Senabre C, Marin AG, Franco FJG (2006) Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps. IEEE Trans Power Syst 21(4):1672–1682CrossRefGoogle Scholar
  5. 5.
    de Silva D, Xinghuo Y, Alahakoon D, Holmes G (2011) A data mining framework for electricity consumption analysis from meter data. IEEE Trans Ind Inf 7(3):399–407Google Scholar
  6. 6.
    Chicco G, Napoli R, Postolache P, Scutariu M, Toader C (2003) Customer characterization options for improving the tariff offer. IEEE Trans Power Syst 18(1):381–387CrossRefGoogle Scholar
  7. 7.
    Räsänen T, Ruuskanen J, Kolehmainen M (2008) Reducing energy consumption by using self-organizing maps to create more personalized electricity use information. Appl Energy 85(9):830–840Google Scholar
  8. 8.
    Figueiredo V, Rodrigues F, Vale Z, Gouveia JB (2005) An electric energy consumer characterization framework based on data mining techniques. IEEE Trans Power Syst 20(2):596–602CrossRefGoogle Scholar
  9. 9.
    Sánchez IB, Espinos ID, Moreno Sarrion L, Quijano Ló pez A, Burgos IN (2009) Clients segmentation according to their domestic energy consumption by the use of self-organizing maps. In: 6th international conference on the European energy market, 2009, EEM 2009, pp 1–6Google Scholar
  10. 10.
    Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84CrossRefGoogle Scholar
  11. 11.
    Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891CrossRefGoogle Scholar
  12. 12.
    Beckel C, Sadamori L, Santini S (2012) Towards automatic classification of private households using electricity consumption data. In: Pappas GJ (ed) Proceedings of the fourth ACM workshop on embedded sensing systems for energy-efficiency in buildings. ACM, Toronto, pp 169–176Google Scholar
  13. 13.
    Ramsey FL, Schafer DW (2002) The statistical sleuth: a course in methods of data analysis, 2nd edn. Duxbury, Pacific GrovezbMATHGoogle Scholar
  14. 14.
    Guyon I, Elisseeff A (2003) An introduction to variable and feature selection’. J Mach Learn Res 3:1157–1182zbMATHGoogle Scholar
  15. 15.
    Hall MA (1999) Correlation-based feature selection for machine learning. Dissertation, The University of WaikatoGoogle Scholar
  16. 16.
    Biesiada J, Duch W (2005) Feature selection for high-dimensional data: a Kolmogorov–Smirnov correlation-based filter. In: Proceedings of the 4th international conference on computer recognition systems, CORES’05. Springer, pp 95–103.Google Scholar
  17. 17.
    Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517CrossRefGoogle Scholar
  18. 18.
    Richardson JTE (2011) Eta squared and partial eta squared as measures of effect size in educational research. Educ Res Rev 6(2):135–147Google Scholar
  19. 19.
    Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2014) e1071: misc functions of the Department of Statistics (e1071), TU WienGoogle Scholar
  20. 20.
    Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, 3rd edn. Elsevier, AmsterdamzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Konstantin Hopf
    • 1
    Email author
  • Mariya Sodenkamp
    • 1
  • Ilya Kozlovkiy
    • 1
  • Thorsten Staake
    • 1
  1. 1.Energy Efficient Systems GroupUniversity of BambergBambergGermany

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