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Homogeneous Climate Regions Using Learning Algorithms

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Renewable Energy: Forecasting and Risk Management (FRM 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 254))

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Abstract

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.

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Notes

  1. 1.

    Réseau de Transport d’Electricité.

  2. 2.

    Laboratoire de Probabilités, Statistique, Modélisation.

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Acknowledgements

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.

Author information

Authors and Affiliations

  1. Université Paris Diderot, LPSM UMR 8001, Sorbonne Paris Cité, 75013, Paris, France

    Mathilde Mougeot & Dominique Picard

  2. RTE-EPT & UPMC-ISUP, 92919, La Défense Cedex, France

    Vincent Lefieux

  3. RTE-R&DI, 92919, La Défense Cedex, France

    Miranda Marchand

Authors
  1. Mathilde Mougeot
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  2. Dominique Picard
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  3. Vincent Lefieux
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  4. Miranda Marchand
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Corresponding author

Correspondence to Mathilde Mougeot .

Editor information

Editors and Affiliations

  1. Laboratoire de Méteorologie Dynamique, CNRS, Palaiseau, France

    Philippe Drobinski

  2. UFR de Mathématiques, Université Paris Diderot, Paris, France

    Mathilde Mougeot

  3. UFR de Mathématiques, Université Paris Diderot, Paris, France

    Dominique Picard

  4. Laboratoire de Météorologie Dynamique, Ecole Polytechnique, Palaiseau, France

    Riwal Plougonven

  5. CREST—ENSAE Paris Tech, Palaiseau, France

    Peter Tankov

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Mougeot, M., Picard, D., Lefieux, V., Marchand, M. (2018). Homogeneous Climate Regions Using Learning Algorithms. In: Drobinski, P., Mougeot, M., Picard, D., Plougonven, R., Tankov, P. (eds) Renewable Energy: Forecasting and Risk Management. FRM 2017. Springer Proceedings in Mathematics & Statistics, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-99052-1_5

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