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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.

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%.

Suggested Citation

  • Sigauke, C. & Chikobvu, D., 2011. "Prediction of daily peak electricity demand in South Africa using volatility forecasting models," Energy Economics, Elsevier, vol. 33(5), pages 882-888, September.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:5:p:882-888
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    References listed on IDEAS

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    1. repec:eee:eneeco:v:67:y:2017:i:c:p:60-71 is not listed on IDEAS
    2. repec:spr:sistpr:v:20:y:2017:i:2:d:10.1007_s11203-016-9139-z is not listed on IDEAS
    3. Lisi, Francesco & Nan, Fany, 2014. "Component estimation for electricity prices: Procedures and comparisons," Energy Economics, Elsevier, vol. 44(C), pages 143-159.
    4. Aknouche, Abdelhakim, 2013. "Periodic autoregressive stochastic volatility," MPRA Paper 69571, University Library of Munich, Germany, revised 2015.
    5. Aknouche, Abdelhakim & Al-Eid, Eid & Demouche, Nacer, 2016. "Generalized quasi-maximum likelihood inference for periodic conditionally heteroskedastic models," MPRA Paper 75770, University Library of Munich, Germany, revised 19 Dec 2016.
    6. repec:spr:sistpr:v:21:y:2018:i:3:d:10.1007_s11203-017-9160-x is not listed on IDEAS

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