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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)

Abstract

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.

Keywords

Electricity Transmission System Operators Mean Absolute Percentage Error (MAPE) SARIMA Model Multivariate Adaptive Regression Splines (MARS) Consumption Months 
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.

Notes

Acknowledgements

The authors would like to thank the editors and the two anonymous referees for their valuable comments which helped in improving the paper.

References

  1. 1.
    Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Second international symposium on information theory (pp. 267–281). Budapest: Akademiai Kiado.Google Scholar
  2. 2.
    Aneiros, G., Vilar, J. M., Cao, R., & Muñoz San Roque, A. (2013) Functional prediction for the residual demand in electricity spot markets. IEEE Transactions on Power Systems, 28(4), 4201–4208.CrossRefGoogle Scholar
  3. 3.
    Antoniadis, A., Paparoditis, E., & Sapatinas, T. (2008). Bandwidth selection for functional time series prediction. Statistics and Probability Letters, 79, 733–740.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chakhchoukh, Y. (2010). A new robust estimation method for ARMA models. IEEE Transactions on Signal Processing, 58(7), 3512–3522.MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cornillon, P. A., Hengartner, N., & Matzner-Løber, E. (2014, to appear). Recursive bias estimation for multivariate regression. ESAIM/probability and statistics, 18, 483–502.Google Scholar
  6. 6.
    Cornillon, P. A., Hengartner, N., Jégou, N., & Matzner-Løber, E. (2013). Iterative bias reduction: A comparative study. Statistics and Computing, 23, 777–791.MathSciNetCrossRefGoogle Scholar
  7. 7.
    Craven, P., & Wahba, G. (1979). Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik, 31, 377–403.zbMATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Cugliari, J. (2011). Prévision non-paramétrique de processus à valeurs fonctionnelles. Application à la prévision de la consommation d’électricité. Phd thesis, University Paris Sud 11.Google Scholar
  9. 9.
    Fan, S., & Hyndamn, R. J. (2012). Short-term load forecasting based on a semi-parametric additive model. IEEE transactions on power systems, 27(1), 134–141.CrossRefGoogle Scholar
  10. 10.
    Goude, Y. (2008). Mélange de prédicteurs et application à la prévision de la consommation électrique. Phd thesis, University Paris Sud 11.Google Scholar
  11. 11.
    Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning (2nd ed.). New York: Springer.zbMATHCrossRefGoogle Scholar
  12. 12.
    Hengartner, N., Wegkamp, M., & Matzner-Løber, E. (2002). Bandwidth selection for local linear regression smoothers. Journal of the Royal Statistical Society: Series B, 64, 1–14.CrossRefGoogle Scholar
  13. 13.
    Hurvich, C., Simonoff, G., & Tsai, C. L. (1998). Smoothing parameter selection in nonparametric regression using and improved Akaike information criterion. Journal of the Royal Statistical Society: Series B, 60, 271–294.zbMATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Lefieux, V. (2007). Modèles semi-paramétriques appliqués à la prévision des séries temporelles: cas de la consommation d’électricité. Phd thesis, Rennes.Google Scholar
  15. 15.
    Li, K. C. (1987). Asymptotic optimality for C p, C L, cross-validation and generalized cross-validation: Discrete index set. The Annals of Statistics, 15, 958–975.zbMATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Martin, M. M. (1999). Filtrage de Kalman d’une série temporelle saisonnière. Application à la prévision de consommation d’électricité. Revue de Statistique Appliquée, 47(4), 69–86.Google Scholar
  17. 17.
    Meslier, F. (1976). Contribution à l’analyse des séries chronologiques et application à la mise au point de modèles de prévision à court terme relatifs à la demande journalière relevée à Paris Monsouris. Phd thesis, University Paris Sud 9.Google Scholar
  18. 18.
    Pierrot, A., & Goude, Y. (2011). Short-term electricity load forecasting with generalized additive models. In Proceedings of ISAP power, Hersonissos, Greece.Google Scholar
  19. 19.
    Poggi, J. M. (1994). Prévision non-paramétrique de la consommation électrique. Revue de Statistique Appliquée, 42(4), 83–98.Google Scholar
  20. 20.
    Reiss, P., & Ogden, R. (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society: Series B, 71, 505–523.zbMATHMathSciNetCrossRefGoogle Scholar
  21. 21.
    Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.zbMATHMathSciNetCrossRefGoogle Scholar
  22. 22.
    Taylor, J. W., & McSharry, P. E. (2007). A new robust estimation method for ARMA models. IEEE Transactions on Power Systems, 22(4), 2213–2219.CrossRefGoogle Scholar
  23. 23.
    Vilar, J. M., Cao, R., & Aneiros, G. (2012). Forecasting next-day electricity demand and price using nonparametric functional methods. Electrical Power and Energy Systems, 39, 48–55.CrossRefGoogle Scholar
  24. 24.
    Wood, S. N. (2006). Generalized additive models: An introduction with R. Boca Raton: Chapman & Hall/CRC.Google Scholar

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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