Fully Nonparametric Short Term Forecasting Electricity Consumption

  • Pierre-André CornillonEmail author
  • Nick Hengartner
  • Vincent Lefieux
  • Eric Matzner-Løber
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 217)


Electricity Transmission System Operators (TSO) are responsible for operating, maintaining and developing the high and extra high voltage network. They guarantee the reliability and proper operation of the power network. Anticipating electricity demand helps to guarantee the balance between generation and consumption at all times, and directly influences the reliability of the power system. In this paper, we focus on predicting short term electricity consumption in France. Several competitors such as iterative bias reduction, functional nonparametric model or non-linear additive autoregressive approach are compared to the actual SARIMA method. Our results show that iterative bias reduction approach outperforms all competitors both on Mean Absolute Percentage Error and on the percentage of forecast errors higher than 2,000 MW.


Electricity Transmission System Operators Mean Absolute Percentage Error (MAPE) SARIMA Model Multivariate Adaptive Regression Splines (MARS) Consumption Months 
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The authors would like to thank the editors and the two anonymous referees for their valuable comments which helped in improving the paper.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pierre-André Cornillon
    • 1
    Email author
  • Nick Hengartner
    • 2
  • Vincent Lefieux
    • 3
  • Eric Matzner-Løber
    • 4
  1. 1.University Rennes 2RennesFrance
  2. 2.Los Alamos National LaboratoryLos AlamosUSA
  3. 3.RTE-EPT & UPMC-ISUPParisFrance
  4. 4.University Rennes 2 & Agrocampus OuestRennesFrance

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