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Mulaudzi, T. S. Maluta, N. E. Marandela, Mulalo Veronica 2023 2023-11-17T02:42:19Z 2023-11-17T02:42:19Z 2023-10-05 Marandela, 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/11602/2654 Marandela 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/11602/2654 Marandela, 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/11602/2654 Marandela, 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/11602/2654 TY - 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 - MSc (e-Science) Department of Mathematics and Computational Sciences 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. National e-Science Postgraduate Teaching and Training Platform (NEPTTP) 1 online resource (viii, 65 leaves) : color illustrations, color maps en University of Venda Machine Learning UCTD Empirical models Random Forest Support Vector Mechanism Artificial Neural Networks Decision Tree Linear regression 523.20968257 Solar radiation -- South Africa -- Limpopo Solar energy -- South Africa -- Limpopo Global energy -- South Africa -- Limpopo Solar cells -- South Africa -- Limpopo Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality Dissertation open.access