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Electricity price forecasting through transfer function models

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Journal of the Operational Research Society

Abstract

Forecasting electricity prices in presentday competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a transfer function model to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build it. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The proposed model is compared with naïve and other techniques.

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References

  • Barlow M (2002). A diffusion model for electricity prices. Math Finance 12 (4): 287–298.

    Article  Google Scholar 

  • Conejo AJ, Contreras J, Espínola R and Plazas MA (2005). Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecasting (In press).

  • Conejo AJ and Prieto FJ (2001). Mathematical programming and electricity markets. TOP 9 (1): 1–54.

    Article  Google Scholar 

  • Contreras J, Espínola R, Nogales FJ and Conejo AJ (2003). ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst 18: 1014–1020.

    Article  Google Scholar 

  • Fosso OB et al (1999). Generation scheduling in a deregulated system. IEEE Trans Power Syst 14: 75–81.

    Article  Google Scholar 

  • Guerrero VM (1993). Time-series analysis supported by power transformations. J Forecasting 12: 37–48.

    Article  Google Scholar 

  • Hillmer SC and Tiao GC (1979). Likelihood function of stationary multiple autoregressive moving average models. J Am Stat Assoc 74: 652–660.

    Article  Google Scholar 

  • Hogan W, Read E and Ring B (1996). Using mathematical programming for electricity spot pricing. Int Trans Opl Res 3: 209–221.

    Article  Google Scholar 

  • Hong Y-Y and Hsiao C-Y (2002). Locational marginal price forecasting in deregulated electricity markets using artificial intelligence. IEE Proceedings—Generation, Transm Distrib 149: 621–626.

    Article  Google Scholar 

  • Ilic M, Galiana FD and Fink L (1998). Power Systems Restructuring. Engineering and Economics. Kluwer Academic Publishers: Norwell, MA.

    Book  Google Scholar 

  • Kim C-I, Yu I-K and Song YH (2002). Prediction of system marginal price of electricity using wavelet transform analysis. Energy Conversion Mngt 43: 1839–1851.

    Article  Google Scholar 

  • Liu L and Hudak GP (1994). Forecasting and Time Series Analysis using the SCA Statistical System. Scientific Computing Associated: Chicago.

    Google Scholar 

  • Nogales FJ, Contreras J, Conejo AJ and Espínola R (2002). Forecasting next-day electricity prices by time series models. IEEE Trans Power Syst 17 (2): 342–348.

    Article  Google Scholar 

  • PJM (2004). PJM interconnection [online], http://www.pjm.com/.

  • Ramsay B and Wang AJ (1998). A neural network based estimator for electricity spot-pricing with particular reference to weekend and public holidays. Neurocomputing 23: 47–57.

    Article  Google Scholar 

  • Rodríguez CP and Anders GJ (2004). Energy price forecasting in the Ontario competitive power system market. IEEE Trans Power Syst 19: 366–374.

    Article  Google Scholar 

  • Shahidehpour M, Yamin H and Li Z (2002). Market Operations in Electric Power Systems: Forecasting, Scheduling and Risk Management. John Wiley and Sons: Hoboken, NJ.

    Book  Google Scholar 

  • Sheblé GB (1999). Computational Auction Mechanisms for Restructured Power Industry Operation. Kluwer Academic Publishers: Norwell, MA.

    Book  Google Scholar 

  • Szkuta BR, Sanabria LA and Dillon TS (1999). Electricity price short-term forecasting using artificial neural networks. IEEE Trans Power Syst 14: 851–857.

    Article  Google Scholar 

  • Tashman L (2000). Out-of-sample tests of forecasting accuracy: an analysis and review. Int J Forecasting 16: 437–450.

    Article  Google Scholar 

  • Zhang L, Luh PB and Kasiviswanathan K (2003). Energy clearing price prediction and confidence interval estimation with cascaded neural networks. IEEE Trans Power Syst 18: 99–105.

    Article  Google Scholar 

Download references

Acknowledgements

This research was partly supported by the Ministerio de Educación y Ciencia of Spain, through Projects MTM2004-02334 and DPI2003-01362.

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Authors and Affiliations

  1. Universidad Carlos III de Madrid, Madrid, Spain

    F J Nogales

  2. Universidad de Castilla-La Mancha, Ciudad Real, Spain

    A J Conejo

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  1. F J Nogales
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  2. A J Conejo
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Correspondence to F J Nogales.

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Nogales, F., Conejo, A. Electricity price forecasting through transfer function models. J Oper Res Soc 57, 350–356 (2006). https://doi.org/10.1057/palgrave.jors.2601995

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  • DOI: https://doi.org/10.1057/palgrave.jors.2601995

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