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Prediction of daily peak electricity demand in South Africa using volatility forecasting models

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  • Sigauke, C.
  • Chikobvu, D.
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    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%.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 33 (2011)
    Issue (Month): 5 (September)
    Pages: 882-888

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    Handle: RePEc:eee:eneeco:v:33:y:2011:i:5:p:882-888

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Volatility Daily peak demand SARIMA GARCH Piecewise linear regression;

    References

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