Hierarchical forecasting of monthly electricity demand
| dc.contributor.advisor | Sigauke, C. | |
| dc.contributor.advisor | Bere, A. | |
| dc.contributor.author | Chauke, Ignitious | |
| dc.date | 2022 | |
| dc.date.accessioned | 2022-09-20T18:34:39Z | |
| dc.date.available | 2022-09-20T18:34:39Z | |
| dc.date.issued | 2022-07-15 | |
| dc.description | MSc (e-Science) | en_ZA |
| dc.description | Department of Mathematical and Computational Sciences | |
| dc.description.abstract | Energy demand forecasting is a vital tool for energy management, maintenance planning, environmental security, and investment decision-making in liberalised energy markets. The mini-dissertation investigates ways to anticipate power usage using hierarchical time series and South African data. Approaches such as topdown, bottom-up, and optimal combination are applied. Top-down forecasting is based on disaggregating total series projections and spreading them down the hierarchy based on historical data proportions. The bottom-up strategy aggregates individual projections at lower levels, whereas the optimal combination methodology optimally combines bottom forecasts. An out-of-sample prediction performance evaluation was performed to assess the models’ predicting ability. The best model was chosen using mean absolute percentage error. The top-down technique based on predicted proportions (Top-down forecasted proportions) was superior to the optimal combination and bottom-up approach. To integrate forecasts and build prediction ranges for the proposed models, linear quantile regression, linear regression, simple average, and median were used. The best set of forecasts was picked based on the prediction interval normalised average width. At 95%, the best model based on the prediction interval normalised average width was a simple average. | en_ZA |
| dc.description.sponsorship | NRF | en_ZA |
| dc.format.extent | 1 online resource (xi, 64 leaves) | |
| dc.identifier.apacitation | Chauke, I. (2022). <i>Hierarchical forecasting of monthly electricity demand</i>. (). . Retrieved from http://hdl.handle.net/11602/2298 | en_ZA |
| dc.identifier.chicagocitation | Chauke, Ignitious. <i>"Hierarchical forecasting of monthly electricity demand."</i> ., , 2022. http://hdl.handle.net/11602/2298 | en_ZA |
| dc.identifier.citation | Chauke, I. (2022) Hierarchical forecasting of monthly electricity demand. University of Venda. South Africa.<http://hdl.handle.net/11602/2298>. | |
| dc.identifier.ris | TY - Dissertation AU - Chauke, Ignitious AB - Energy demand forecasting is a vital tool for energy management, maintenance planning, environmental security, and investment decision-making in liberalised energy markets. The mini-dissertation investigates ways to anticipate power usage using hierarchical time series and South African data. Approaches such as topdown, bottom-up, and optimal combination are applied. Top-down forecasting is based on disaggregating total series projections and spreading them down the hierarchy based on historical data proportions. The bottom-up strategy aggregates individual projections at lower levels, whereas the optimal combination methodology optimally combines bottom forecasts. An out-of-sample prediction performance evaluation was performed to assess the models’ predicting ability. The best model was chosen using mean absolute percentage error. The top-down technique based on predicted proportions (Top-down forecasted proportions) was superior to the optimal combination and bottom-up approach. To integrate forecasts and build prediction ranges for the proposed models, linear quantile regression, linear regression, simple average, and median were used. The best set of forecasts was picked based on the prediction interval normalised average width. At 95%, the best model based on the prediction interval normalised average width was a simple average. DA - 2022-07-15 DB - ResearchSpace DP - Univen KW - Bottom up approach KW - Energy demand KW - Hierarchical forecasting KW - Optimal approach KW - Top-down approach LK - https://univendspace.univen.ac.za PY - 2022 T1 - Hierarchical forecasting of monthly electricity demand TI - Hierarchical forecasting of monthly electricity demand UR - http://hdl.handle.net/11602/2298 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11602/2298 | |
| dc.identifier.vancouvercitation | Chauke I. Hierarchical forecasting of monthly electricity demand. []. , 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2298 | en_ZA |
| dc.language.iso | en | en_ZA |
| dc.rights | University of Venda | |
| dc.subject | Bottom up approach | en_ZA |
| dc.subject | Energy demand | en_ZA |
| dc.subject | Hierarchical forecasting | en_ZA |
| dc.subject | Optimal approach | en_ZA |
| dc.subject | Top-down approach | en_ZA |
| dc.subject.ddc | 333.791320968 | |
| dc.subject.lcsh | Electricity power consumption -- South Africa | |
| dc.subject.lcsh | Energy policy -- South Africa | |
| dc.subject.lcsh | South Africa -- Forecastiong | |
| dc.title | Hierarchical forecasting of monthly electricity demand | en_ZA |
| dc.type | Dissertation | en_ZA |