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The power of weather

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Author Info

  • Christian Huurman
  • Francesco Ravazzolo
  • Chen Zhou

Abstract

This paper examines the predictive power of weather for electricity prices in day ahead markets in real time. We find that next-day weather forecasts improve the forecast accuracy of Scandinavian day-ahead electricity prices substantially in terms of point forecasts, suggesting that weather forecasts can price the weather premium. This improvement strengthens the confidence in the forecasting model, which results in high center-mass predictive densities. In density forecast, such a predictive density may not accommodate forecasting uncertainty well. Our density forecast analysis confirms this intuition by showing that incorporating weather forecasts in density forecasting does not deliver better density forecast performances.

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Bibliographic Info

Paper provided by Netherlands Central Bank, Research Department in its series DNB Working Papers with number 236.

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Date of creation: Jan 2010
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Handle: RePEc:dnb:dnbwpp:236

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Related research

Keywords: Electricity prices; weather forecasts; point and density forecasts; GARCH models.;

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References

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  1. James Mitchell & Stephen G. Hall, 2005. "Evaluating, Comparing and Combining Density Forecasts Using the KLIC with an Application to the Bank of England and NIESR 'Fan' Charts of Inflation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 995-1033, December.
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  7. Geweke, John & Amisano, Gianni, 2008. "Comparing and evaluating Bayesian predictive distributions of assets returns," Working Paper Series 0969, European Central Bank.
  8. Alvaro Escribano & Juan Ignacio Peña & Pablo Villaplana, 2002. "Modeling Electricity Prices: International Evidence," Economics Working Papers we022708, Universidad Carlos III, Departamento de Economía.
  9. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
  10. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
  11. Siem Jan Koopman & Marius Ooms & M. Angeles Carnero, 2005. "Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices," Tinbergen Institute Discussion Papers 05-091/4, Tinbergen Institute.
  12. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, vol. 24(4), pages 710-727.
  13. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
  14. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
  15. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
  16. Moral-Carcedo, Julian & Vicens-Otero, Jose, 2005. "Modelling the non-linear response of Spanish electricity demand to temperature variations," Energy Economics, Elsevier, vol. 27(3), pages 477-494, May.
  17. Kosater, Peter, 2006. "On the impact of weather on German hourly power prices," Discussion Papers in Statistics and Econometrics 1/06, University of Cologne, Department for Economic and Social Statistics.
  18. Nelson, Daniel B & Cao, Charles Q, 1992. "Inequality Constraints in the Univariate GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 229-35, April.
  19. Caporin, Massimiliano & Preś, Juliusz, 2012. "Modelling and forecasting wind speed intensity for weather risk management," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3459-3476.
  20. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
  21. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
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Citations

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Cited by:
  1. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
  2. Andrea Monticini & Francesco Ravazzolo, 2011. "Forecasting the intraday market price of money," Working Paper 2011/06, Norges Bank.
  3. Paulo Soares Esteves & António Rua, 2012. "Short-term forecasting for the portuguese economy: a methodological overview," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
  4. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.

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