Supply Function Prediction in Electricity Auctions

Part of the Contributions to Statistics book series (CONTRIB.STAT.)


In the fast growing literature that addresses the problem of the optimal bidding behaviour of power generation companies that sell energy in electricity auctions, it is always assumed that every firm knows the aggregate supply function of its competitors. Since this information is generally not available, real data have to be substituted by predictions. In this paper we propose two alternative approaches to the problem and apply them to the hourly prediction of the aggregate supply function of the competitors of the main Italian generation company.


Mean Absolute Percentage Error Supply Function Bidding Strategy Prediction Mean Square Error Optimal Bidding 
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.


  1. 1.
    Bosco, B., Parisio, L., Pelagatti, M.: Strategic bidding in vertically integrated power markets with an application to the Italian electricity auctions. Energy Econ. 34(6), 2046–2057 (2012). DOI 10.1016/j.eneco. 2011.11.005CrossRefGoogle Scholar
  2. 2.
    Bosq, D.: Linear Processes in Function Spaces: Theory and Applications. Springer, Berlin (2000)MATHCrossRefGoogle Scholar
  3. 3.
    Bunn, D., Farmer, E. (eds.): Comparative Models for Electrical Load Forecasting. Wiley, New York (1985)Google Scholar
  4. 4.
    Harvey, A., Koopman, S.J.: Forecasting hourly electricity demand using time-varying splines. J. Am. Stat. Assoc. 88(424), 1228–1236 (1993)CrossRefGoogle Scholar
  5. 5.
    Hortaçsu, A., Puller, S.L.: Understanding strategic bidding in multi-unit auctions: a case study of the Texas electricity spot market. RAND J. Econ. 39(1), 86–114 (2008)CrossRefGoogle Scholar
  6. 6.
    Hyndman, R., Shang, H.: Forecasting functional time series. J. Korean Stat. Soc. 38, 199–211 (2009)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Hyndman, R., Ullah, M.: Robust forecasting of mortality and fertility rates: a functional data approach. Comput. Stat. Data Anal. 51, 4942–4956 (2007)MATHCrossRefGoogle Scholar
  8. 8.
    Kastl, J.: Discrete bids and empirical inference in divisible good auctions. Rev. Econ. Stud. 78(3), 974–1014 (2011)MATHCrossRefGoogle Scholar
  9. 9.
    Ramanathan, R., Engle, R.F., Granger, C., Vahid-Arahi, F., Brace, C.: Short-run forecasts of electricity loads and peaks. Int. J. Forecast. 13, 161–174 (1997)CrossRefGoogle Scholar
  10. 10.
    Ramsay, J.: When the data are functions. Psychometrika 47, 379–396 (1982)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Ramsay, J., Silverman, B.: Functional Data Analysis, 2nd edn. Springer, Berlin (2005)Google Scholar
  12. 12.
    Reinsel, G., Velu, R.: Multivariate Reduced Rank Regression. Springer, Berlin (1998)MATHGoogle Scholar
  13. 13.
    Wolak, F.: Identification and estimation of cost functions using observed bid data: an application to electricity markets, pp. 133–169. Cambridge University Press, Cambridge (2003)Google Scholar

Copyright information

© Springer-Verlag Italia 2013

Authors and Affiliations

  1. 1.Department of Economics, Quantitative Methods and Business Strategies (DEMS)University of Milano-BicoccaMilanItaly

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