 Prediction of daily peak electricity demand in South Africa using volatility forecasting modelsCaston Sigauke () and D. Chikobvu Energy Economics, 2011, vol. 33, issue 5, 882-888 Abstract: Daily peak electricity demand forecasting in South Africa using a seasonal autoregressive integrated moving average (SARIMA) model, a SARIMA model with generalized autoregressive conditional heteroskedastic (SARIMA-GARCH) errors and a regression-SARIMA-GARCH (Reg-SARIMA-GARCH) model is presented in this paper. The GARCH modeling methodology is introduced to accommodate the possibility of serial correlation in volatility since the daily peak demand data exhibits non-constant mean and variance, and multiple seasonality corresponding to weekly and monthly periodicity. The proposed Reg-SARIMA-GARCH model is designed in such a way that the predictor variables are initially selected using a multivariate adaptive regression splines algorithm. The developed models are used for out of sample prediction of daily peak demand. A comparative analysis is done with a piecewise linear regression model. Results from the study show that the Reg-SARIMA-GARCH model produces better forecast accuracy with a mean absolute percent error (MAPE) of 1.42%. Keywords: Volatility; Daily; peak; demand; SARIMA; GARCH; Piecewise; linear; regression (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:33:y:2011:i:5:p:882-888 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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