Day-ahead electricity price forecasting with emphasis on its volatility in Iran (GARCH combined with ARIMA models)

Pourghorban, Mojtaba and Mamipour, Siab (2019): Day-ahead electricity price forecasting with emphasis on its volatility in Iran (GARCH combined with ARIMA models). Published in: International Conference on Innovations in Business administration and Economics

Abstract

This paper provides a method to forecast day-ahead electricity prices based on autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedastic (GARCH) models. In the competitive power market environment, electricity price forecasting is an essential task for market participants. However, time series of electricity price has complex behavior such as nonlinearity, nonstationarity, and high volatility. ARIMA is suitable in forecasting, but it is not able to handle nonlinearity and volatility are existent in time series. Therefore, GARCH models are used to handle volatility in the in time series forecasting. The proposed method is computed using the daily electricity price data of Iran market for a five-year period from March 2013 to February 2018. The results reported in this paper illustrate the potential of the proposed ARMA-GARCH model and this combined model has been successfully applied to real prices in the Iranian power market.

• Item Type: MPRA Paper
• Language: Persian
• Keywords: Electricity price forecasting, ARIMA model, GARCH model
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting

References

1- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.

2- -Bourbonnais,R (2007) “The Econometrics of Energy Systems”, Electricity Spot Price Modeling: Univariate Time Series Approach, New York: Palgrave Macmillan, pp. 51-74

3- Catala, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F., & Ferreira, L. A. F. M. (2007). Short-term electricity prices forecasting in a competitive market: a neural network approach. Elect Power Syst Res, 77, 1297 1304.

4- Conejo, A., Plazas, M., Espinola, R., & Molina, A. (2005). Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans Power Syst, 20(2), 1035–1042.

5- Contreras, J., Espinola, R., Nogales, F., & Conejo, A. (2003). ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst, 18(3), 1014–1020.

6- Garcia, R., Contreras, J., Van Akkeren, M., & Garcia, J. (2005). A GARCH forecasting model to predict day ahead electricity prices. IEEE Trans Power Syst, 20(2), 867–874.

7- Gonzalez, A. M., San Roque, A. M., & Garcia-Gonzalez, J. (2005). Modeling and forecasting electricity prices with input/output hidden Markov models. IEEE Trans Power Syst, 20(1), 13–24.

8- Nicholas, B., & James, E. P. (2008). Short term forecasting of electricity prices for MISO hubs: evidence from ARIMA–EGARCH models. Energy Econ, 30(6), 3186–3197.

9- Razak, A.W.A., Abidin, I. Z., Yap, K. S., Abidin, A. A. Z., Rahman, T. K. A., & Nasir, M. N. M. (2016). A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting. IEEE International Conference on Power and Energy (PECon), Melaka (2016), 390–395.

10- Tan, Z., Zhang, J., Wang, J., & Xu, J. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl. Energy 2010, 87, 3606–3610.

11- Yang, Z., Ce, L., Lian, L., 2017. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy 190, 291–305.

12- سوری ع، 1391. اقتصاد سنجی جلد 2، انتشارات فرهنگ شناسی و نور علم، شماره کتاب شناسی ملی 3390203.

13- مشهور ا و ﻣﻘﺪﺳﻲ م، 1385. ﺗﺎﺛﻴﺮ ﺗﺠﺪﻳﺪ ﺳﺎﺧﺘﺎر ﺻﻨﻌﺖ ﺑﺮق ﺑﺮ ﺗﻮﺳﻌﻪ ﻣﻨﺎﺑﻊ اﻧﺮژﻳﻬﺎي ﺗﺠﺪﻳﺪ ﭘﺬﻳﺮ.

14-منظور د و یادي پور م، 1395. ارزیابی و پیش بینی نوسانات قیمت در بازار برق ایران به کمک مدل ARMAX-GARCH، فصلنامه اقتصاد مقداري (بررسیهاي اقتصادي سابق)، دوره 13، شماره 1، صفحات 97-117.

15- ﻣﻨﻈﻮر د و ﺻﻔﺎﻛﻴﺶ ا 1388. ﭘﻴﺶ ﺑﻴﻨﻲ ﻗﻴﻤﺖ ﺑﺮق در ﺑﺎزار ﺑﺮق رﻗﺎﺑﺘﻲ اﻳﺮان ﺑﺎ روﻳﻜﺮد ﻣﺪل‌هاي ﺳﺮي زﻣﺎﻧﻲ، هفتمین همایش ملی انرژی.

16- نشریه آمار تفصیلی صنعت برق ایران ویژه تولید نیروی برق در سال 1395، ناشر شرکت مادر تخصصی توانیر، مرداد 1396 صفحات 4-5.

17- نمازی ح، 1391. نظام‌های اقتصادی، شرکت سهامی انتشار، شابک 1-260-325-964-978.

18- وزارت ﻧﻴﺮو، ﻫﻴﺎت ﺗﻨﻈﻴﻢ ﺑﺎزار ﺑﺮق، 1384. آﻳﻴﻦ ﻧﺎﻣﻪ ﺗﻨﻈﻴﻢ ﺗﻌﻴﻴﻦ ﺷﺮاﻳﻂ و روش ﺧﺮﻳﺪ و ﻓﺮوش ﺑﺮق در ﺷﺒﻜﻪ ﻛﺸﻮر.