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

Mojtaba Pourghorban () and Siab Mamipour

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Electricity price forecasting; ARIMA model; GARCH model (search for similar items in EconPapers)
JEL-codes: C3 C32 C5 C53 Q4 Q47 (search for similar items in EconPapers)
Date: 2019-02-14
New Economics Papers: this item is included in nep-ara, nep-ene, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/94826/1/MPRA_paper_94818.pdf original version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:94826

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().