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Clustering electricity consumers using high‐dimensional regression mixture models

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  • Emilie Devijver
  • Yannig Goude
  • Jean‐Michel Poggi

Abstract

A massive amount of data about individual electrical consumptions are now provided with new metering technologies and smart grids. These new data are especially useful for load profiling and load modeling at different scales of the electrical network. A new methodology based on mixture of high‐dimensional regression models is used to perform clustering of individual customers. It leads to uncovering clusters corresponding to different regression models. Temporal information is incorporated in order to prepare the next step, the fit of a forecasting model in each cluster. Only the electrical signal is involved, slicing the electrical signal into consecutive curves to consider it as a discrete time series of curves. Interpretation of the models is given on a real smart meter dataset of Irish customers.

Suggested Citation

  • Emilie Devijver & Yannig Goude & Jean‐Michel Poggi, 2020. "Clustering electricity consumers using high‐dimensional regression mixture models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 159-177, January.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:1:p:159-177
    DOI: 10.1002/asmb.2453
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    Cited by:

    1. Michalakopoulos, Vasilis & Sarmas, Elissaios & Papias, Ioannis & Skaloumpakas, Panagiotis & Marinakis, Vangelis & Doukas, Haris, 2024. "A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs," Applied Energy, Elsevier, vol. 361(C).

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