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

 

 

Delson Chikobvu^I; Caston Sigauke^II

^IDepartment of Mathematical Statistics and Actuarial Science, University of the Free State, South Africa
^IIDepartment of Statistics and Operations Research, School of Mathematical and Computer Sciences, University of Limpopo, South Africa

 

 

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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.

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

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Received 7 June 2011
Revised 14 May 2012