Hierarchical forecasting of electricity demand in South Africa

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dc.contributor.advisor Sigauke, Caston
dc.contributor.advisor Bere, Alphonce
dc.contributor.author Netshiomvani, Rofhiwa
dc.date 2020
dc.date.accessioned 2021-02-02T12:53:39Z
dc.date.available 2021-02-02T12:53:39Z
dc.date.issued 2020-08-11
dc.identifier.citation Netshiomvani, Rofhiwa (2020) Hierarchical forecasting of electricity demand in South Africa. University of Venda, South Africa.<http://hdl.handle.net/11602/1660>.
dc.identifier.uri http://hdl.handle.net/11602/1660
dc.description MSc (Statistics) en_ZA
dc.description Department of Statistics
dc.description.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. en_ZA
dc.description.sponsorship NRF en_ZA
dc.format.extent 1 online resource (ix, 80 leaves : color illustrations, color maps)
dc.language.iso en en_ZA
dc.rights University of Venda
dc.subject Modelling framework en_ZA
dc.subject Disaggregation en_ZA
dc.subject Hierarchical time series en_ZA
dc.subject Top-down method en_ZA
dc.subject Bottom-up method en_ZA
dc.subject Optimal combination method en_ZA
dc.subject Upper levels en_ZA
dc.subject Lower level forecast en_ZA
dc.title Hierarchical forecasting of electricity demand in South Africa en_ZA
dc.type Dissertation en_ZA

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