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Sigauke, Caston Bere, Alphonce Netshiomvani, Rofhiwa 2020 2021-02-02T12:53:39Z 2021-02-02T12:53:39Z 2020-08-11 Netshiomvani, Rofhiwa (2020) Hierarchical forecasting of electricity demand in South Africa. University of Venda, South Africa.<http://hdl.handle.net/11602/1660>. http://hdl.handle.net/11602/1660 Netshiomvani R. Hierarchical forecasting of electricity demand in South Africa. []. , 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1660 Netshiomvani, R. (2020). <i>Hierarchical forecasting of electricity demand in South Africa</i>. (). . Retrieved from http://hdl.handle.net/11602/1660 Netshiomvani, Rofhiwa. <i>"Hierarchical forecasting of electricity demand in South Africa."</i> ., , 2020. http://hdl.handle.net/11602/1660 TY - Dissertation AU - Netshiomvani, Rofhiwa AB - 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. DA - 2020-08-11 DB - ResearchSpace DP - Univen KW - Modelling framework KW - Disaggregation KW - Hierarchical time series KW - Top-down method KW - Bottom-up method KW - Optimal combination method KW - Upper levels KW - Lower level forecast LK - https://univendspace.univen.ac.za PY - 2020 T1 - Hierarchical forecasting of electricity demand in South Africa TI - Hierarchical forecasting of electricity demand in South Africa UR - http://hdl.handle.net/11602/1660 ER - MSc (Statistics) Department of Statistics 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. NRF 1 online resource (ix, 80 leaves : color illustrations, color maps) en University of Venda Modelling framework UCTD Disaggregation Hierarchical time series Top-down method Bottom-up method Optimal combination method Upper levels Lower level forecast Hierarchical forecasting of electricity demand in South Africa Dissertation open.access