Regression-SARIMA modelling of daily peak electricity demand in South Africa

Authors

  • Delson Chikobvu University of Cape Town
  • Caston Sigauke

DOI:

https://doi.org/10.17159/2413-3051/2012/v23i3a3169

Keywords:

daily peak demand, SARIMA, regression-SARIMA, short term load forecasting

Abstract

In this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them with Winter’s triple exponential smoothing model. Empirical results from the study show that the SARIMA model produces more accurate short-term forecasts. The regression-SARIMA modelling framework captures important drivers of electricity demand. These results are important to decision makers, load forecasters and systems operators in load flow analysis and scheduling of electricity.

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Author Biography

  • Delson Chikobvu, University of Cape Town
    Energy Research Centre Snr Research Officer

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Published

2012-08-01

How to Cite

Regression-SARIMA modelling of daily peak electricity demand in South Africa. (2012). Journal of Energy in Southern Africa, 23(3), 23-30. https://doi.org/10.17159/2413-3051/2012/v23i3a3169