Functional Prediction for the Residual Demand in Electricity Spot Markets

  • Germán Aneiros
  • Ricardo Cao
  • Juan M. Vilar-Fernández
  • Antonio Muñoz-San-Roque
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


The problem of residual demand prediction in electricity spot markets is considered in this paper. Hourly residual demand curves are predicted using nonparametric regressionwith functional explanatory and functional response variables. Semi-functional partial linear models are also used in this context. Forecasted values of wind energy as well as hourly price and demand are considered as linear predictors. Results from the electricity market of mainland Spain are reported. The new forecasting functional methods are compared with a naive approach.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aneiros-P´erez, G., Cao, R., Vilar-Fern´andez, J.M.: Functional methods for time series prediction: a nonparametric approach. To appear in Journal of Forecasting (2010)Google Scholar
  2. 2.
    Aneiros-P´erez, G., Vieu, P.: Semi-functional partial linear regression. Statist. Probab. Lett. 76, 1102–1110 (2006)Google Scholar
  3. 3.
    Aneiros-P´erez, G., Vieu, P.: Nonparametric time series prediction: A semi-functional partial linear modeling. J. Multivariate Anal. 99, 834–857 (2008)Google Scholar
  4. 4.
    Antoniadis, A., Paparoditis, E., Sapatinas, T.: A functional waveletkernel approach for time series prediction. J. Roy. Statist. Soc. Ser. B 68, 837–857 (2006)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Antoniadis, A., Paparoditis, E., Sapatinas, T.: Bandwidth selection for functional time series prediction. Statist. Probab. Lett. 79, 733–740 (2009)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Antoch, J., Prchal, L., De Rosa, M.R., Sarda, P. (2008). Functional linear regression with functional response: application to prediction of elecricity cosumption. In: Dabo-Niang, S., Ferraty, F. (eds.) Functional and Operatorial Statistics, pp. 23-29. Physica-Verlag, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Baillo, A., Ventosa, M., Rivier, M., Ramos, A.: Optimal Offering Strategies for Generation Companies Operating in Electricity Spot Markets. IEEE Transactions on Power Systems 19, 745–753 (2004)CrossRefGoogle Scholar
  8. 8.
    Bosq, D.: Linear Processes in Function Spaces: Theory and Applications. Lecture Notes in Statistics, 149, Springer (2000)Google Scholar
  9. 9.
    Carbon, M., Delecroix, M.: Nonparametric vs parametric forecasting in time series: a computational point of view. Applied Stochastic Models and Data Analysis 9, 215–229 (1993)MathSciNetMATHCrossRefGoogle Scholar
  10. 10.
    Cardot, H., Dessertaine, A., Josserand E.: Semiparametric models with functional responses in a model assisted survey sampling setting. Presented at COMPSTAT 2010 (2010)Google Scholar
  11. 11.
    Faraway, J.: Regression analysis for a functional reponse. Technometrics 39, 254–261 (1997) 12. Ferraty, F. and Vieu, P.: Nonparametric Functional Data Analysis. Series in Statistics, Springer, New York (2006)Google Scholar
  12. 12.
    Gross, G., Galiana, F.D.: Short-term load forecasting. Proc. IEEE 75, 1558–1573 (1987) 14. H¨ardle, W., L¨utkepohl, H., Chen, R.: A review of nonparametric time series analysis. International Statistical Review 65, 49–72 (1997)Google Scholar
  13. 13.
    H¨ardle, W., Vieu, P.: Kernel regression smoothing of time series. J. Time Ser. Anal.13, 209– 232 (1992)Google Scholar
  14. 14.
    Hart, J. D.: Some automated methods of smoothing time-dependent data. J. Nonparametr. Stat. 6, 115–142 (1996)MATHCrossRefGoogle Scholar
  15. 15.
    Matzner-Lober, E., Gannoun, A., De Gooijer, J. G.: Nonparametric forecasting: a comparison of three kernel based methods. Commun. Stat.-Theor. M.27, 1593–1617 (1998)Google Scholar
  16. 16.
    Nadaraya, E. A.: On Estimating Regression. Theor. Probab. Appl. 9, 141–142 (1964)Google Scholar
  17. 17.
    Vilar-Fern´andez, J.M., Cao, R.: Nonparametric forecasting in time series – A comparative study. Commun. Stat. Simulat. C. 36, 311–334 (2007)Google Scholar
  18. 18.
    Vilar-Fern´andez, J.M., Cao, R., Aneiros-P´erez, G.: Forecasting next-day electricity demand and price using nonparametric functional methods. Preprint (2010)Google Scholar
  19. 19.
    Watson, G.S.: Smooth regression analysis. Sankhy¯a Ser. A26, 359–372 (1964)Google Scholar
  20. 20.
    Xu, L., and Baldick, R.: Transmission-constrained residual demand derivative in electricity markets. IEEE Transactions on Power Systems 22, 1563–1573 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Germán Aneiros
    • 1
  • Ricardo Cao
    • 1
  • Juan M. Vilar-Fernández
    • 1
  • Antonio Muñoz-San-Roque
    • 2
  1. 1.Universidade da CoruñaCoruñaSpain
  2. 2.Universidad Pontificia de ComillasMadridSpain

Personalised recommendations