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

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.

Author dc:creator

• Marandela, Mulalo Veronica

Advisors dc:contributor.advisor

• Mulaudzi, T. S.
• Maluta, N. E.

• Machine Learning
• Empirical models
• Random Forest
• Support Vector Mechanism
• Artificial Neural Networks
• Decision Tree
• Linear regression

Statement dc:rights

• University of Venda

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/11602/2654

OAI identifier oai:identifier

oai:univendspace.univen.ac.za:11602/2654