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Forecasting Spot Electricity Prices With Time Series Models

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Author Info

  • Rafal Weron

    (Hugo Steinhaus Center)

  • Adam Misiorek

    (Institute of Power Systems Automation)

Abstract

In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and ARMAX (where the exogenous variable is the system load) processes. Models are tested on a time series of California power market system prices and loads from the period proceeding and including the market crash.

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File URL: http://128.118.178.162/eps/em/papers/0504/0504001.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0504001.

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Length: 8 pages
Date of creation: 06 Apr 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0504001

Note: Type of Document - pdf; pages: 8. To appear in ”The European Electricity Market EEM-05”, Proceedings Volume
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Web page: http://128.118.178.162

Related research

Keywords: Electricity; price forecasting; ARMA model; seasonal component;

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References

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  1. Alvaro Escribano & J. Ignacio Peña & Pablo Villaplana, 2011. "Modelling Electricity Prices: International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(5), pages 622-650, October.
  2. Rafal Weron & Michael Bierbrauer & Stefan Trück, 2003. "Modeling electricity prices: jump diffusion and regime switching," HSC Research Reports HSC/03/01, Hugo Steinhaus Center, Wroclaw University of Technology.
  3. Rafal Weron & Adam Misiorek, 2005. "Modeling and forecasting electricity loads: A comparison," Econometrics 0502004, EconWPA.
  4. Huisman, R. & Mahieu, R.J., 2001. "Regime Jumps in Electricity Prices," ERIM Report Series Research in Management ERS-2001-48-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  5. Merton, Robert C., 1975. "Option pricing when underlying stock returns are discontinuous," Working papers 787-75., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  6. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
  7. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
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Citations

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Cited by:
  1. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2007. "The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts," Tinbergen Institute Discussion Papers 07-036/4, Tinbergen Institute.
  2. Liu, Heping & Shi, Jing, 2013. "Applying ARMA–GARCH approaches to forecasting short-term electricity prices," Energy Economics, Elsevier, vol. 37(C), pages 152-166.
  3. Arciniegas, Alvaro I. & Arciniegas Rueda, Ismael E., 2008. "Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system," Utilities Policy, Elsevier, vol. 16(1), pages 39-48, March.
  4. 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.
  5. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2007. "The Power of Weather: Some Empirical Evidence on Predicting Day-ahead Power Prices through Day-ahead Weather Forecasts," Tinbergen Institute Discussion Papers 07-036/4, Tinbergen Institute.
  6. Weron, Rafal, 2008. "Market price of risk implied by Asian-style electricity options and futures," Energy Economics, Elsevier, vol. 30(3), pages 1098-1115, May.

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