Homogeneous Climate Regions Using Learning Algorithms

  • Mathilde MougeotEmail author
  • Dominique Picard
  • Vincent Lefieux
  • Miranda Marchand
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 254)


Climate analysis is extremely useful to understand better the differences of electricity consumption within the French territory and to help electricity consumption forecasts. Using a large historical data base of 14 years of meteorological observations, this work aims to study a segmentation of the French territory based on functional time series of temperature and wind. In a first step, 14 clustering instances, one for each year, have been performed using, for each instance, one year of data. Each year, the clustering exhibits several homogeneous and spatially connected regions. Benefits of this approach let to study the stability of the previous regions over the years and to highlight the inter-annual variability of the French climate. A final aggregation of all clustering instances shows a segmentation map in easily interpretable, geographically connected climate zones over the last years. Especially, we observe that the number of clusters remains extremely stable through the years. Exhibiting stable homogeneous regions bring then some valuable knowledge for potentially installing new wind or solar farms on the French territory.


Climate segmentation Graph partitioning Clustering 



The authors thank RTE for the financial support through one industrial contract, LPSM for hosting our researches and Agence Nationale de la Recherche (ANR-14-CE05-0028) through the FOREWER project.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mathilde Mougeot
    • 1
    Email author
  • Dominique Picard
    • 1
  • Vincent Lefieux
    • 2
  • Miranda Marchand
    • 3
  1. 1.Université Paris Diderot, LPSM UMR 8001, Sorbonne Paris CitéParisFrance
  2. 2.RTE-EPT & UPMC-ISUPLa Défense CedexFrance
  3. 3.RTE-R&DILa Défense CedexFrance

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