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Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

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Author Info
Weron, Rafal
Misiorek, Adam

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Abstract

This empirical paper compares the accuracy of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models and their extensions -- spike preprocessed, threshold and semiparametric autoregressions (i.e., AR models with nonparametric innovations) -- as well as mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California, and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, but that this is not necessarily the case when air temperature is considered as the exogenous variable; and (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, and more importantly, they have the potential to perform well under diverse market conditions.

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Publisher Info
Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 24 (2008)
Issue (Month): 4 ()
Pages: 744-763
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Handle: RePEc:eee:intfor:v:24:y:2008:i:4:p:744-763

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Web page: http://www.elsevier.com/locate/ijforecast

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Related research
Keywords: Electricity market Price forecasts Autoregressive model Nonparametric maximum likelihood Interval forecasts Conditional coverage;

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  1. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics 0503018, EconWPA. [Downloadable!]
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  3. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, vol. 24(4), pages 728-743. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
  5. Bierbrauer, Michael & Menn, Christian & Rachev, Svetlozar T. & Truck, Stefan, 2007. "Spot and derivative pricing in the EEX power market," Journal of Banking & Finance, Elsevier, vol. 31(11), pages 3462-3485, November. [Downloadable!] (restricted)
  6. 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. [Downloadable!] (restricted)
  7. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300. [Downloadable!] (restricted)
  8. 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. [Downloadable!] (restricted)
  9. Ricardo Cao, 1999. "An overview of bootstrap methods for estimating and predicting in time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 8(1), pages 95-116, June. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
  11. Rafal Weron & Adam Misiorek, 2005. "Modeling and forecasting electricity loads: A comparison," Econometrics 0502004, EconWPA. [Downloadable!]
  12. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  13. W. H"Ardle & H. L"Utkepohl & R. Chen, . "A Review of Nonparametric Time Series Analysis," Sonderforschungsbereich 373 1996-48, Humboldt Universitaet Berlin.
  14. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September. [Downloadable!] (restricted)
    Other versions:
    • Huisman, R. & Mahieu, R.J., 2001. "Regime Jumps in Electricity Prices," Research Paper ERS-2001-48-F&A Revision_, 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. [Downloadable!]
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