Hierarchical forecasting of electricity demand in South Africa

Abstract

The study focuses on the application of hierarchical time series in forecasting electricity demand using South African data. The methods used are top-down, bottom-up and optimal combination. The top-down method is based on the disaggregation of the forecasts of the total series and distribute these down the hierarchy based on the historical proportions of the data. The bottom-up approach aggregates the individual forecasts at the lower levels, while the optimal combination technique optimally combines the bottom forecasts. Out-of-sample forecast performance evaluation was conducted to get some indication of the forecasting performance of the models. MAPE was used to determine the best model. Bottom–up approach is found to be the best approach compared to optimal combination and top–down approaches. In order to combine forecasts and compute the prediction intervals for the developed models the quantile regression averaging (QRA) and linear regression (LR) is used. The best set of forecasts is selected based on the prediction interval normalised average width (PINAW) and pinball loss. The best model based on pinball loss is QRA and the best model based on PINAW at 95 % is QRA.