Density forecasting for long-term electricity demand in South Africa using quantile regression

Abstract

Background: This study involves forecasting electricity demand for long-term planning purposes. Long-term forecasts for hourly electricity demands from 2006 to 2023 are done with in-sample forecasts from 2006 to 2012 and out-of-sample forecasts from 2013 to 2023. Quantile regression (QR) is used to forecast hourly electricity demand at various percentiles. Three contributions of this study are (1) that QR is used to generate long-term forecasts of the full distribution per hour of electricity demand in South Africa; (2) variabilities in the forecasts are evaluated and uncertainties around the forecasts can be assessed as the full demand distribution is forecasted and (3) probabilities of exceedance can be calculated, such as the probability of future peak demand exceeding certain levels of demand. A case study, in which forecasted electricity demands over the long-term horizon were developed using South African electricity demand data, is discussed. Aim: The aim of the study was: (1) to apply a quantile regression (QR) model to forecast hourly distribution of electricity demand in South Africa; (2) to investigate variabilities in the forecasts and evaluate uncertainties around point forecasts and (3) to determine whether the future peak electricity demands are likely to increase or decrease. Setting: The study explored the probabilistic forecasting of electricity demand in South Africa. Methods: The future hourly electricity demands were forecasted at 0.01, 0.02, 0.03, … , 0.99 quantiles of the distribution using QR, hence each hour of the day would have 99 forecasted future hourly demands, instead of forecasting just a single overall hourly demand as in the case of OLS. Results: The findings are that the future distributions of hourly demands and peak daily demands would be more likely to shift towards lower demands over the years until 2023 and that QR gives accurate long-term point forecasts with the peak demands well forecasted. Conclusion: QR gives forecasts at all percentiles of the distribution, allowing the potential variabilities in the forecasts to be evaluated by comparing the 50th percentile forecasts with the forecasts at other percentiles. Additional planning information, such as expected pattern shifts and probable peak values, could also be obtained from the forecasts produced by the QR model, while such information would not easily be obtained from other forecasting approaches. The forecasted electricity demand distribution closely matched the actual demand distribution between 2012 and 2015. Therefore, the forecasted demand distribution is expected to continue representing the actual demand distribution until 2023. Using a QR approach to obtain long-term forecasts of hourly load profile patterns is, therefore, recommended.

Keywords

• probabilistic forecasting
• quantile regression
• density function
• quantiles