Abstract
We investigate the problem of short-term prediction of the free market price for electricity using various types of forecast models. A transition is made from considering traditional regression and autoregressive models to the proposed combined multivariate models, which also include the time trend and dummy variables. The combined forecast models have been constructed using dedicated statistical software packages. Comparison of the level of accuracy of the predicted values of the market electricity price obtained by different models has not revealed any advantages from the use of combined multivariate regression models. However, the use of the latter permits one to assess the influence of exogenous factors on the predicted variable and can be a fundamental advantage when choosing the forecast model type.
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REFERENCES
Karakatsani, N. and Bunn, D., Forecasting electricity prices: the impact of fundamentals and time-varying coefficients, Int. J. Forecast., 2008, vol. 24, pp. 764–785.
Weron, R. and Misiorek, A., Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models, Int. J. Forecast., 2008, vol. 24, pp. 744–763.
Conejo, A., Contreras, J., Espinola, R., and Plazas, M., Forecasting electricity prices for a day-ahead pool-based electric energy market, Int. J. Forecast., 2005, vol. 21, pp. 435–462.
Tan, Z., Zhang, J., Wang, J., and Xu, J., Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models, Appl Energy, 2010, vol. 87, pp. 3606–3610.
Kim, C., Yu, I., and Song, Y., Prediction of system marginal price of electricity using wavelet transform analysis, Energy Convers. Manage., 2002, vol. 43, pp. 1839–1851.
Nogales, F., Contreras, J., Conejo, A., and Espinola, R., Forecasting next-day electricity prices by time series models, IEEE Trans. Power Syst., 2002, vol. 17, no. 2, pp. 342–348.
Garcia-Martos, C., Rodriguez, J., and Sánchez, M., Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities, Appl. Energy, 2013, vol. 101, pp. 363–375.
Higgs, H. and Worthington, A., Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: the Australian wholesale spot electricity market, Energy Econ., 2008, vol. 30, pp. 3172–3185.
Koopman, S., Ooms, M., and Carnero, M., Periodic seasonal Reg-ARFIMA-GARCH models for daily electricity spot prices, J. Am. Stat. Assoc., 2007, vol. 102, no. 477, pp. 16–27.
Cuaresma, J., Hlouskova, J., Kossmeier, S., and Obersteiner, M., Forecasting electricity spot-prices using linear univariate time-series models, Appl. Energy, 2004, vol. 77, pp. 87–106.
Drobysh, I.I., Statistical models for forecasting prices in the wholesale electricity markets: Russian and foreign experience, Vestn. Yuzhno-Ural. Gos. Univ., 2017, vol. 11, no. 3, pp. 41–54.
Baza tarifov na elekroenergiyu (Electricity Tariff Base). http://time2save.ru/calculaters/nereguliruemie-ceni-na-elektroenergiu .
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Translated by V. Potapchouck
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Shikhin, V.A., Shikhina, A.V. & Kouzalis, A. Automated Electricity Price Forecast Using Combined Models. Autom Remote Control 83, 153–163 (2022). https://doi.org/10.1134/S0005117922010118
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DOI: https://doi.org/10.1134/S0005117922010118