Abstract
A frequentist and Bayesian regression analysis to a piecewise linear regression model for daily peak electricity load forecasting in South Africa for the period 2000 to 2009 is discussed in this paper. The developed model captures a wide variety of electricity demand drivers such as temperature, seasonal, lagged demand and calendar effects. A Bayesian analysis provides a way of taking into account uncertainty in the estimation of the piecewise linear regression parameters. Uncertainty about the true values of the Bayesian parameter estimates is incorporated into the analysis through the use of a non-informative prior distribution. The results obtained are easy to explain to management. Empirical results showed that an increase in electricity peak demand, if temperature decreases by 1°C, could be any value between 140 and 200 MW during the winter months. Similarly during the summer months the increase in electricity peak demand, if temperature increases by 1°C, ranges from -20 to 80 MW. There is a persistent increase of around 2 MW in hourly electricity peak demand with time in South Africa. Electricity demand in South Africa is more sensitive to the winter period. Demand for electricity during holidays decreases significantly compared to a day before and after a holiday. This information and the quantification of such uncertainty are important for load forecasters in the power Utility Company in South Africa (Eskom) as it helps them in the determination of consistent and reliable supply schedules. Key words: Posterior distribution, temperature, piecewise linear regression, load forecasting.
Highlights
Short term electricity load forecasting is very important for system operators who have to ensure that the amount of electricity drawn from the grid and the amount generated balances (Cottet and Smith, 2003; Taylor, 2006)
The dummy variable is negative showing that if peak temperature decreases by one degree from 18°C, electricity demand will increase by 171.468 MW
We conclude that electricity demand in South Africa is more sensitive to the winter period
Summary
Short term electricity load forecasting is very important for system operators who have to ensure that the amount of electricity drawn from the grid and the amount generated balances (Cottet and Smith, 2003; Taylor, 2006). Most papers in literature concentrate on short-term point forecasts (Munoz et al, 2010). Shortterm load forecasting is important to ensure that there is a balance between demand and supply since electricity cannot be stored (Munoz et al, 2010). Demand drivers of electricity are generally split into, economic factors, weather variables and calendar effects. In short-term forecasting weather variables such as temperature and calendar effects are usually incorporated in models for electricity demand. The influence of temperature on daily electricity load forecasting has been studied extensively in the energy sector using classical (frequentist) statistics time series, regression based methods including artificial neural networks (Fan and Hyndman, 2011; Hekkenberg et al, 2009; Mirasgedis et al, 2006; Saini, 2008)
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