Prediction of daily peak electricity demand in South Africa using volatility forecasting models
C. Sigauke and
Energy Economics, 2011, vol. 33, issue 5, 882-888
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)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:33:y:2011:i:5:p:882-888
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