Mulaudzi, T. S.Maluta, N. E.Marandela, Mulalo Veronica2023-11-172023-11-172023-10-05Marandela, M. V. (2023). Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality. University of Venda, Thohoyandou, South Africa.<http://hdl.handle.net/11602/2654>.http://hdl.handle.net/11602/2654MSc (e-Science)Department of Mathematics and Computational Scienceshstimating anct assessing the energy talling in a particular area 1s essential tor installers ot renewable technologies. Different equations have been applied as the most reliable empir­ ical for estimating global solar radiation(GSR) in different climatic conditions. The main objective of this work is to estimate the global solar radiation of two stations namely, Mu­ tale and Messina found in Vhembe District, Limpopo Province, South Africa. Four different methods (Random forest(RF) regression, K-nearest neighour (K-NN), Support Vector Ma­ chines(SVM) and Extreme Gradient Boosting mechanism(XGBoost)) is used to estimate the GRS in this study. The RF model on Mutale station was found to be the best fitting model with R² = 0.9902, MSE = 0.4085 and RMSE = 0.6391, followed by XGB with R² = 0.9898, MSE = 0.4245 and RMSE = 0.6515. RF was also found to be the best for Messina station with R² = 0.9636, MSE = 0.1.4138 and RMSE = 1.1890, followed by XGB model with R² = 0.9595, MSE = 1.5723 and RMSE = 1.2539. From the results, it can be concluded that RF is a better model for estimating GSR for different stations.1 online resource (viii, 65 leaves) : color illustrations, color mapsenUniversity of VendaMachine LearningUCTDEmpirical modelsRandom ForestSupport Vector MechanismArtificial Neural NetworksDecision TreeLinear regression523.20968257Solar radiation -- South Africa -- LimpopoSolar energy -- South Africa -- LimpopoGlobal energy -- South Africa -- LimpopoSolar cells -- South Africa -- LimpopoComparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District MunicipalityDissertationMarandela MV. Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality. []. , 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2654Marandela, M. V. (2023). <i>Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality</i>. (). . Retrieved from http://hdl.handle.net/11602/2654Marandela, Mulalo Veronica. <i>"Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality."</i> ., , 2023. http://hdl.handle.net/11602/2654TY - Dissertation AU - Marandela, Mulalo Veronica AB - hstimating anct assessing the energy talling in a particular area 1s essential tor installers ot renewable technologies. Different equations have been applied as the most reliable empir­ ical for estimating global solar radiation(GSR) in different climatic conditions. The main objective of this work is to estimate the global solar radiation of two stations namely, Mu­ tale and Messina found in Vhembe District, Limpopo Province, South Africa. Four different methods (Random forest(RF) regression, K-nearest neighour (K-NN), Support Vector Ma­ chines(SVM) and Extreme Gradient Boosting mechanism(XGBoost)) is used to estimate the GRS in this study. The RF model on Mutale station was found to be the best fitting model with R² = 0.9902, MSE = 0.4085 and RMSE = 0.6391, followed by XGB with R² = 0.9898, MSE = 0.4245 and RMSE = 0.6515. RF was also found to be the best for Messina station with R² = 0.9636, MSE = 0.1.4138 and RMSE = 1.1890, followed by XGB model with R² = 0.9595, MSE = 1.5723 and RMSE = 1.2539. From the results, it can be concluded that RF is a better model for estimating GSR for different stations. DA - 2023-10-05 DB - ResearchSpace DP - Univen KW - Machine Learning KW - Empirical models KW - Random Forest KW - Support Vector Mechanism KW - Artificial Neural Networks KW - Decision Tree KW - Linear regression LK - https://univendspace.univen.ac.za PY - 2023 T1 - Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality TI - Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality UR - http://hdl.handle.net/11602/2654 ER -