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Forecasting Spot Electricity Prices With Time Series Models

Author

Listed:
  • 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.

Suggested Citation

  • Rafal Weron & Adam Misiorek, 2005. "Forecasting Spot Electricity Prices With Time Series Models," Econometrics 0504001, University Library of Munich, Germany.
  • 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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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0504/0504001.pdf
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    References listed on IDEAS

    as
    1. Weron, R & Bierbrauer, M & Trück, S, 2004. "Modeling electricity prices: jump diffusion and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 39-48.
    2. Rafal Weron & Adam Misiorek, 2005. "Modeling and forecasting electricity loads: A comparison," Econometrics 0502004, University Library of Munich, Germany.
    3. Vasicek, Oldrich, 1977. "An equilibrium characterization of the term structure," Journal of Financial Economics, Elsevier, vol. 5(2), pages 177-188, November.
    4. 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.
    5. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    6. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Lars Ivar Hagfors & Hilde Hørthe Kamperud & Florentina Paraschiv & Marcel Prokopczuk & Alma Sator & Sjur Westgaard, 2016. "Prediction of extreme price occurrences in the German day-ahead electricity market," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1929-1948, December.
    2. 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.
    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. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    5. repec:eco:journ2:2017-03-31 is not listed on IDEAS
    6. Christian Huurman & Francesco Ravazzolo & Chen Zhou, 2008. "The power of weather. Some empirical evidence on predicting day-ahead power prices through weather forecasts," Working Paper 2008/08, Norges Bank.
    7. 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.
    8. Liu, Heping & Shi, Jing, 2013. "Applying ARMA–GARCH approaches to forecasting short-term electricity prices," Energy Economics, Elsevier, vol. 37(C), pages 152-166.
    9. 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.
    10. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    11. Paraschiv, Florentina & Fleten, Stein-Erik & Schürle, Michael, 2015. "A spot-forward model for electricity prices with regime shifts," Energy Economics, Elsevier, vol. 47(C), pages 142-153.
    12. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    13. S. Vijayalakshmi & G. P. Girish, 2015. "Artificial Neural Networks for Spot Electricity Price Forecasting: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 1092-1097.
    14. Kosater, Peter, 2006. "On the impact of weather on German hourly power prices," Discussion Papers in Econometrics and Statistics 1/06, University of Cologne, Institute of Econometrics and Statistics.
    15. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.
    16. Lo Prete, Chiara & Norman, Catherine S., 2013. "Rockets and feathers in power futures markets? Evidence from the second phase of the EU ETS," Energy Economics, Elsevier, vol. 36(C), pages 312-321.
    17. Francisco Martínez-Álvarez & Alicia Troncoso & Gualberto Asencio-Cortés & José C. Riquelme, 2015. "A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting," Energies, MDPI, Open Access Journal, vol. 8(11), pages 1-32, November.
    18. Karakatsani Nektaria V & Bunn Derek W., 2010. "Fundamental and Behavioural Drivers of Electricity Price Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-42, September.
    19. repec:eee:appene:v:221:y:2018:i:c:p:386-405 is not listed on IDEAS

    More about this item

    Keywords

    Electricity; price forecasting; ARMA model; seasonal component;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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