UnivenIR

Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality

Show simple item record

dc.contributor.advisor Mulaudzi, T. S.
dc.contributor.advisor Maluta, N. E.
dc.contributor.author Marandela, Mulalo Veronica
dc.date 2023
dc.date.accessioned 2023-11-17T02:42:19Z
dc.date.available 2023-11-17T02:42:19Z
dc.date.issued 2023-10-05
dc.identifier.citation 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>.
dc.identifier.uri http://hdl.handle.net/11602/2654
dc.description MSc (e-Science) en_ZA
dc.description Department of Mathematics and Computational Sciences
dc.description.abstract 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. en_ZA
dc.description.sponsorship National e-Science Postgraduate Teaching and Training Platform (NEPTTP) en_ZA
dc.format.extent 1 online resource (viii, 65 leaves) : color illustrations, color maps
dc.language.iso en en_ZA
dc.rights University of Venda
dc.subject Machine Learning en_ZA
dc.subject Empirical models en_ZA
dc.subject Random Forest en_ZA
dc.subject Support Vector Mechanism en_ZA
dc.subject Artificial Neural Networks en_ZA
dc.subject Decision Tree en_ZA
dc.subject Linear regression en_ZA
dc.subject.ddc 523.20968257
dc.subject.lcsh Solar radiation -- South Africa -- Limpopo
dc.subject.lcsh Solar energy -- South Africa -- Limpopo
dc.subject.lcsh Global energy -- South Africa -- Limpopo
dc.subject.lcsh Solar cells -- South Africa -- Limpopo
dc.title Comparative analysis of Machine Learning Algorithms for Estimating Global Solar Radiation at Selected Weather Stations in Vhembe District Municipality en_ZA
dc.type Dissertation en_ZA


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search UnivenIR


Browse

My Account