Abstract
Electricity markets are composed of different agents that make their offers to sell and/or buy energy. These agents need forecasting tools to have an accurate prediction of the prices that they will face either in the day-ahead or long-term time spans. This work presents the ECOnometrics TOOLbox (ECOTOOL), a new MATLAB forecasting toolbox that embodies several tools for identification, validation and forecasting models based on time series analysis, among them, ARIMA, Exponential Smoothing, Unobserved Components, ARX, ARMAX, Transfer Function, Dynamic Regression and Distributed Lag models. The toolbox is presented in all its potentiality and several real case studies, both on the short and medium term, are shown to illustrate its applicability.
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Notes
- 1.
The toolbox is available upon request to the authors via email.
- 2.
Multiplicative options may be implemented by taking logarithms to the data.
References
Weron R (2006) Modeling and forecasting electricity loads and prices: a statistical approach. Wiley, Chichester
Wang AJ, Ramsay B (1998) A neural network based estimator for electricity spot-pricing with particular reference to weekend and public holidays. Neurocomputing 23:47–57
Amjadi N (2006) Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans Power Syst 21:887–896
Szkuta BR, Sanabria LA, Dillon TS (1999) Electricity price short-term forecasting using artificial neural networks. IEEE Trans Power Syst 14:851–857
Fosso OB, Gjelsvik A, Haugstad A, Birger M, Wangensteen I (1999) Generation scheduling in a deregulated system. The Norwegian case. IEEE Trans Power Syst 14:75–81
Contreras J, Espínola R, Nogales FJ, Conejo AJ (2003) ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst 18:1014–1020
Nogales FJ, Contreras J, Conejo AJ, Espínola R (2002) Forecasting next-day electricity prices by time series models. IEEE Trans Power Syst 17:342–348
Garcia RC, Contreras J, van Akkeren M, Garcia JBC (2005) A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 20:867–874
Byström H (2003) The hedging performance of electricity futures on the Nordic power exchange. Appl Econ 35:1–11
Pedregal DJ, Young PC (2008) Development of improved adaptive approaches to electricity demand forecasting. J Oper Res Soc 59:1066–1076
Trapero JR, Pedregal DJ (2009) Frequency domain methods applied to forecasting electricity markets. Energy Econ 31:727–735
Pedregal DJ, Trapero JR (2010) Mid-term hourly electricity forecasting based on a multi-rate approach. Energy Convers Manage 51:105–111
Conejo AJ, Contreras J, Espínola R, Plazas MA (2005) Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecasting 21:435–462
Weron R, Misiorek A (2008) Forecasting electricity prices: a comparison of parametric and semiparametric time series models. Int J Forecasting 24:744–763
Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis forecasting and control, 3rd edn. Prentice-Hall, Englewood Cliffs
Hyndman RJ, Koehler AB, Ord JK, Snyder RD (2008) Forecasting with exponential smoothing. Springer, Berlin
Harvey AC (1989) Forecasting structural time series models and the Kalman filter. Cambridge University Press, Cambridge
Pedregal DJ, Young PC (2002) Statistical approaches to modelling and forecasting time series. In: Clements M, Hendry D (eds) Companion to economic forecasting. Blackwell, London
Kalman RE (1960) A new approach to linear filtering and prediction problems. ASME Trans J Basic Eng 83-D:95–108
Lütkepohl H (1991) Introduction to multiple time series analysis. Springer, Heidelberg
Tsay RS (1986) Time series model specification in the presence of outliers. J Am Stat Assoc 81:132–141
Brown RG (1959) Statistical forecasting for inventory control. McGraw-Hill, New York
Holt CC (1957) Forecasting seasonals and trends by exponentially weighted moving averages, ONR Research Memorandum 52, Pittsburgh, Carnegie Institute of Technology
Gardner ES Jr, McKenzie E (1985) Forecasting trends in time series. Manag Sci 31:1237–1246
Bryson AE, Ho YC (1969) Applied optimal control, optimization, estimation and control. Blaisdell Publishing, Waltham
EViews. http://www.eviews.com
Iberian market energy operator, OMEL. http://www.omel.es
SAS. http://www.sas.com
Scientific Computing Associates, SCA, http://www.scausa.com
The Mathworks: MATLAB. http://www.mathworks.com
Acknowledgments
This work was supported in part by the Spanish Ministry of Education grant ENE2009-09541 and the Junta de Comunidades de Castilla – La Mancha grants PII2I09-0154-7984 and PII1I09-0209-6050.
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Pedregal, D.J., Contreras, J., de la Nieta, A.A.S. (2012). ECOTOOL: A general MATLAB Forecasting Toolbox with Applications to Electricity Markets. In: Sorokin, A., Rebennack, S., Pardalos, P., Iliadis, N., Pereira, M. (eds) Handbook of Networks in Power Systems I. Energy Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23193-3_6
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