Renewable energy forecasting in South Africa

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dc.contributor.advisorSigauke, C.
dc.contributor.advisorBere, A.
dc.contributor.authorMamphaga, Ratshilengo
dc.date2021
dc.date.accessioned2021-12-10T07:33:58Z
dc.date.available2021-12-10T07:33:58Z
dc.date.issued2021-06-12
dc.descriptionMSc (Statistics)en_ZA
dc.descriptionDepartment of Statistics
dc.description.abstractRenewable energy forecasts are critical to renewable energy grids and backup plans, operational plans and short-term power purchases. This dissertation focused on forecasting solar irradiance at one radiometric station in South Africa using high-frequency data obtained from the Vuwani radiometric station (USAid Venda). The aim of this dissertation was to compare the predictive performance of the Genetic Algorithm (GA), recurrent neural networks (RNN) and k-nearest neighbour (KNN) models in forecasting short-term solar irradiance where KNN is used as a benchmark model. From the results it is discovered that the RNN is the best forecasting model in terms of the relative mean absolute error (rMAE). The forecasts of the machine learning algorithms combined using convex combination technique and quantile regression averaging (QRA) found that QRA is the best model. Predictive interval widths analysis with 95% level of confidence was performed and the results showed that QRA over RNN is the best model for forecasting solar irradiance when looking at the PICP and PANAW. The Diebold-Mariano test discovered that the tests fall between the -1.96 and 1.96 range, which tells us that it accepts the null hypothesis. The Murphy diagram presented and showed the 95% pointwise confidence intervals. The study will have an impact on the South African power utility decision-makers to align electricity demand and its supply in an efficient way that promotes potential economic growth and environmental sustainability.en_ZA
dc.description.sponsorshipNRFen_ZA
dc.format.extent1 online resource (xi, 91 leaves) : color illustrations
dc.identifier.apacitationMamphaga, R. (2021). <i>Renewable energy forecasting in South Africa</i>. (). . Retrieved from http://hdl.handle.net/11602/1789en_ZA
dc.identifier.chicagocitationMamphaga, Ratshilengo. <i>"Renewable energy forecasting in South Africa."</i> ., , 2021. http://hdl.handle.net/11602/1789en_ZA
dc.identifier.citationMamphaga, R. (2021) Renewable energy forecasting in South Africa. University of Venda, South Africa.<http://hdl.handle.net/11602/1789>.
dc.identifier.ris TY - Dissertation AU - Mamphaga, Ratshilengo AB - Renewable energy forecasts are critical to renewable energy grids and backup plans, operational plans and short-term power purchases. This dissertation focused on forecasting solar irradiance at one radiometric station in South Africa using high-frequency data obtained from the Vuwani radiometric station (USAid Venda). The aim of this dissertation was to compare the predictive performance of the Genetic Algorithm (GA), recurrent neural networks (RNN) and k-nearest neighbour (KNN) models in forecasting short-term solar irradiance where KNN is used as a benchmark model. From the results it is discovered that the RNN is the best forecasting model in terms of the relative mean absolute error (rMAE). The forecasts of the machine learning algorithms combined using convex combination technique and quantile regression averaging (QRA) found that QRA is the best model. Predictive interval widths analysis with 95% level of confidence was performed and the results showed that QRA over RNN is the best model for forecasting solar irradiance when looking at the PICP and PANAW. The Diebold-Mariano test discovered that the tests fall between the -1.96 and 1.96 range, which tells us that it accepts the null hypothesis. The Murphy diagram presented and showed the 95% pointwise confidence intervals. The study will have an impact on the South African power utility decision-makers to align electricity demand and its supply in an efficient way that promotes potential economic growth and environmental sustainability. DA - 2021-06-12 DB - ResearchSpace DP - Univen KW - Genetic algorithms KW - Global horizontal irradiance KW - K-nearest neigh-bour KW - Quantile regression averaging KW - Murphy diagram KW - Recurrent neural networks LK - https://univendspace.univen.ac.za PY - 2021 T1 - Renewable energy forecasting in South Africa TI - Renewable energy forecasting in South Africa UR - http://hdl.handle.net/11602/1789 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11602/1789
dc.identifier.vancouvercitationMamphaga R. Renewable energy forecasting in South Africa. []. , 2021 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/1789en_ZA
dc.language.isoenen_ZA
dc.rightsUniversity of Venda
dc.subjectGenetic algorithmsen_ZA
dc.subjectUCTDen_ZA
dc.subjectK-nearest neigh-bouren_ZA
dc.subjectQuantile regression averagingen_ZA
dc.subjectMurphy diagramen_ZA
dc.subjectRecurrent neural networksen_ZA
dc.subject.ddc333.7940968
dc.subject.lcshRenewable energy resources -- South Africa
dc.subject.lcshRenewable natural resources -- South Africa
dc.subject.lcshSolar energy -- South Africa
dc.subject.lcshSolar radiation -- South Africa
dc.titleRenewable energy forecasting in South Africaen_ZA
dc.typeDissertationen_ZA
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