Modeling and Forecasting the Electricity Price in Iran Using Wavelet-Based GARCH Model

Pourghorban, Mojtaba; Mamipour, Siab

doi:10.22099/ijes.2021.38026.1691

	Modeling and Forecasting the Electricity Price in Iran Using Wavelet-Based GARCH Model
Iranian Journal of Economic Studies
مقاله 10، دوره 9، شماره 1 - شماره پیاپی 17، خرداد 2020، صفحه 233-260 اصل مقاله (1.74 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22099/ijes.2021.38026.1691
نویسندگان
Mojtaba Pourghorban¹؛ Siab Mamipour^* ²
¹Faculty of Law, Management and Economics, Johannes Gutenberg University Mainz (JGU), Mainz, Germany
²Faculty of Economics, Kharazmi University, Tehran, Iran
چکیده
The restructuring of Iranian electricity industry allowed electricity price to be determined through market forces in 2005. The main purpose of this paper is to present a method for modeling and forecasting the electricity prices based on complex features such as instability, nonlinear conditions, and high fluctuations in Iran during the spring 2013 and winter 2018. For this purpose, time-series data of the daily average electricity price was decomposed into one approximation series (low frequency) and four details series (high frequency) utilizing the wavelet transform technique. The approximation and details series are estimated and predicted by ARIMA and GARCH models, respectively. Then, the electricity price is predicted by reconstructing and composing the forecasted values of different frequencies as a proposed method (Wavelet-ARMA-GARCH). The results demonstrated that the proposed method has higher predictive power and can forecast volatility of electricity prices more accurately by taking into consideration different domains of the time-frequency; although, more errors are produced if the wavelet transform process is not used. The mean absolute percentage error values of the proposed method during spring 2017 to winter 2018 are significantly less than that of the alternative method, and the proposed method can better and more accurately capture the complex features of electricity prices.
کلیدواژه‌ها
Electricity price؛ Forecasting؛ Volatility؛ Wavelet transform؛ ARMA-GARCH Model

مراجع
Awiagah, R., & Choi, S. S. B. (2018). Predictable or random? A test of the weakform efficient market hypothesis on the Ghana stock exchange. Journal of Finance and Economics, 6(6), 213-222. Bento, P., Pombo, J., Calado, M., & Mariano, S. J. A. E. (2018). A bat optimized neural network and wavelet transform approach for short-term price forecasting. Applied Energy, 210, 88-97. Bollerslev, T. J. J. o. e. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. Bourbonnais, R., & Meritet, S. (2007). Electricity spot price modelling: Univariate time series approach. In The Econometrics of Energy Systems, 51- 74. Springer. Bowden, N., & Payne, J. E. J. E. E. (2008). Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models. Energy Economics, 30(6), 3186-3197. Catalão, J. P. d. S., Mariano, S. J. P. S., Mendes, V., & Ferreira, L. J. E. P. S. R. (2007). Short-term electricity prices forecasting in a competitive market: A neural network approach. Electric Power Systems Research, 77(10), 1297- 1304. Chang, Z., Zhang, Y., & Chen, W. (2019). Electricity price prediction based on hybrid model of adam optimized LSTM neural network and wavelet transform. Energy, 187, 115804. Chen, H., Wan, Q., & Wang, Y. (2014). Refined Diebold-Mariano test methods for the evaluation of wind power forecasting models. Energies, 7(7), 4185- 4198. Conejo, A. J., Plazas, M. A., Espinola, R., & Molina, A. B. J. I. t. o. p. s. (2005). Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Transactions on Power Systems, 20(2), 1035-1042. Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. J. I. t. o. p. s. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. Crowley, P. M. J. J. o. E. S. (2007). A guide to wavelets for economists. Journal of Economic Surveys, 21(2), 207-267. De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. J. N. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38- 48. Dickey, D. A., & Fuller, W. A. J. J. o. t. A. s. a. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. Engle, R. F. J. E. J. o. t. E. S. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
آمار تعداد مشاهده مقاله: 600 تعداد دریافت فایل اصل مقاله: 573

سامانه مدیریت نشریات علمی دانشگاه شیراز

Modeling and Forecasting the Electricity Price in Iran Using Wavelet-Based GARCH Model