Abstract:
This study investigated the performance of machine learning techniques as compared to the
empirical models to forecast the global solar radiation in Limpopo regions. The machine
learning techniques used in this study are Support Vector Machines, Random Forest, and
Artificial Neural Network, and the empirical models used are the Clemence and Hargreaves-
Samani models. To assess the efficiences of the machine learning models against the empirical
models, the researchers calculated and compared the models performance evaluation
using statistical equations such as Coefficient of determination, Mean Square Error, Mean
Absolute Error, and Root Mean Square Error. Calibaration was done to improve performance
of the empirical models. The present study found that machine learning techniques
perform better than the empirical models when estimating the global solar radiation in the
selected Limpopo regions.
