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Electricity Consumption Clustering Using Smart Meter Data

Author

Listed:
  • Alexander Tureczek

    (Systems Analysis, the Department of Management Engineering, Technical University of Denmark, 2800 Kgs. Lyngby 2800 Kgs, Denmark)

  • Per Sieverts Nielsen

    (Systems Analysis, the Department of Management Engineering, Technical University of Denmark, 2800 Kgs. Lyngby 2800 Kgs, Denmark)

  • Henrik Madsen

    (Dynamical Systems, the Department Compute, Technical University of Denmark, 2800 Kgs. Lyngby 2800 Kgs, Denmark)

Abstract

Electricity smart meter consumption data is enabling utilities to analyze consumption information at unprecedented granularity. Much focus has been directed towards consumption clustering for diversifying tariffs; through modern clustering methods, cluster analyses have been performed. However, the clusters developed exhibit a large variation with resulting shadow clusters, making it impossible to truly identify the individual clusters. Using clearly defined dwelling types, this paper will present methods to improve clustering by harvesting inherent structure from the smart meter data. This paper clusters domestic electricity consumption using smart meter data from the Danish city of Esbjerg. Methods from time series analysis and wavelets are applied to enable the K-Means clustering method to account for autocorrelation in data and thereby improve the clustering performance. The results show the importance of data knowledge and we identify sub-clusters of consumption within the dwelling types and enable K-Means to produce satisfactory clustering by accounting for a temporal component. Furthermore our study shows that careful preprocessing of the data to account for intrinsic structure enables better clustering performance by the K-Means method.

Suggested Citation

  • Alexander Tureczek & Per Sieverts Nielsen & Henrik Madsen, 2018. "Electricity Consumption Clustering Using Smart Meter Data," Energies, MDPI, vol. 11(4), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:859-:d:139878
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    References listed on IDEAS

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    Cited by:

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    5. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
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