Customer Clustering of French Transmission System Operator (RTE) Based on Their Electricity Consumption

  • Gabriel Da Silva
  • Hoai Minh LeEmail author
  • Hoai An Le Thi
  • Vincent Lefieux
  • Bach Tran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


We develop an efficient approach for customer clustering of French transmission system operator (RTE) based on their electricity consumption. The ultimate goal of customer clustering is to automatically detect patterns for understanding the behaviors of customers in their evolution. It will allow RTE to better know its customers and consequently to propose them more adequate services, to optimize the maintenance schedule, to reduce costs, etc. We tackle three crucial issues in high-dimensional time-series data clustering for pattern discovery: appropriate similarity measures, efficient procedures for high-dimensional setting, and fast/scalable clustering algorithms. For that purpose, we use the DTW (Dynamic Time Warping) distance in the original time-series data space, the t-distributed stochastic neighbor embedding (t-SNE) method to transform the high-dimensional time-series data into a lower dimensional space, and DCA (Difference of Convex functions Algorithm) based clustering algorithms. The numerical results on real-data of RTE’s customer have shown that our clutering result is coherent: customers in the same group have similar consumption curves and the dissimilarity between customers of different groups are quite clear. Furthermore, our method is able to detect whether or not a customer changes his way of consuming.


Electricity management Clustering High-dimensional time-series data DTW t-SNE DCA based clustering 



This research is part of the project “Smart Marketing” founded by RTE in collaboration with Computer Science and Applications Department, LGIPM, University of Lorraine, France.

The authors would like to thank Mr Romain Gemignani for his contributions to the starting step of the project. We thank also Dr Duy Nhat Phan for his discussion on the use of t-SNE transformation.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gabriel Da Silva
    • 1
  • Hoai Minh Le
    • 2
    Email author
  • Hoai An Le Thi
    • 2
  • Vincent Lefieux
    • 1
  • Bach Tran
    • 2
  1. 1.French transmission system operator (RTE)ParisFrance
  2. 2.Computer Science and Applications DepartmentLGIPM, University of LorraineMetzFrance

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