Forecasting Intra Day Load Curves Using Sparse Functional Regression

  • Mathilde MougeotEmail author
  • Dominique Picard
  • Vincent Lefieux
  • Laurence Maillard-Teyssier
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 217)


In this paper we provide a prediction method, the prediction box, based on a sparse learning process elaborated on very high dimensional information, which will be able to include new – potentially high dimensional – influential variables and adapt to different contexts of prediction. We elaborate and test this method in the setting of predicting the national French intra day load curve, over a period of time of 7 years on a large data basis including daily French electrical consumptions as well as many meteorological inputs, calendar statements and functional dictionaries. The prediction box incorporates a huge contextual information coming from the past, organizes it in a manageable way through the construction of a smart encyclopedia of scenarios, provides experts elaborating strategies of prediction by comparing the day at hand to referring scenarios extracted from the encyclopedia, and then harmonizes the different experts. More precisely, the prediction box is built using successive learning procedures: elaboration of a data base of historical scenarios organized on a high dimensional and functional learning of the intra day load curves, construction of expert forecasters using a retrieval information task among the scenarios, final aggregation of the experts. The results on the national French intra day load curves strongly show the benefits of using a sparse functional model to forecast the electricity consumption. They also appear to meet quite well with the business knowledge of consumption forecasters and even shed new lights on the domain.


Ordinary Little Square Cloud Cover Meteorological Variable Load Curve Sparse Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank RTE for the financial support through two industrial contracts, LPMA for hosting our researches, and Karine Tribouley for taking part of an earlier elaboration of this project.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mathilde Mougeot
    • 1
    Email author
  • Dominique Picard
    • 1
  • Vincent Lefieux
    • 2
  • Laurence Maillard-Teyssier
    • 3
  1. 1.Université Paris-Diderot, CNRS-LPMA, UFR de MathématiquesParisFrance
  2. 2.RTE-EPT & UPMC-ISUPLa Défense CedexFrance
  3. 3.RTE-R&D-IVersaillesFrance

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