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Cluster Analysis of Smart Metering Data

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  • Christoph Flath
  • David Nicolay
  • Tobias Conte
  • Clemens Dinther
  • Lilia Filipova-Neumann

Abstract

The introduction of smart meter technology is a great challenge for the German energy industry. It requires not only large investments in the communication and metering infrastructure, but also a redesign of traditional business processes. The newly incurring costs cannot be fully passed on to the end customers. One option to counterbalance these expenses is to exploit the newly generated smart metering data for the creation of new services and improved processes. For instance, performing a cluster analysis of smart metering data focused on the customers’ time-based consumption behavior allows for a detailed customer segmentation. In the article we present a cluster analysis performed on real-world consumption data from a smart meter project conducted by a German regional utilities company. We show how to integrate a cluster analysis approach into a business intelligence environment and evaluate this artifact as defined by design science. We discuss the results of the cluster analysis and highlight options to apply them to segment-specific tariff design. Copyright Gabler Verlag 2012

Suggested Citation

  • Christoph Flath & David Nicolay & Tobias Conte & Clemens Dinther & Lilia Filipova-Neumann, 2012. "Cluster Analysis of Smart Metering Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(1), pages 31-39, February.
    • Handle: RePEc:spr:binfse:v:4:y:2012:i:1:p:31-39
    DOI: 10.1007/s12599-011-0201-5
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    12. Antonia Albani & Yannic Domigall & Robert Winter, 2017. "Implications of customer value perceptions for the design of electricity efficiency services in times of smart metering," Information Systems and e-Business Management, Springer, vol. 15(4), pages 825-844, November.
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    15. Kang, J. & Reiner, D., 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Cambridge Working Papers in Economics 2143, Faculty of Economics, University of Cambridge.
    16. Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
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    19. Konstantin Hopf & Mariya Sodenkamp & Thorsten Staake, 2018. "Enhancing energy efficiency in the residential sector with smart meter data analytics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 453-473, November.
    20. Delen, Dursun & Zolbanin, Hamed M., 2018. "The analytics paradigm in business research," Journal of Business Research, Elsevier, vol. 90(C), pages 186-195.
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    22. Gärttner, Johannes & Flath, Christoph M. & Weinhardt, Christof, 2018. "Portfolio and contract design for demand response resources," European Journal of Operational Research, Elsevier, vol. 266(1), pages 340-353.

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