  The increasing demand for electricity and the need for clean energy sources
have increased solar energy use. Accurate forecasts of solar energy are
required for easy management of the grid. This paper compares the accuracy of
two Gaussian Process Regression (GPR) models combined with Additive Quantile
Regression (AQR) and Bayesian Structural Time Series (BSTS) models in the 2-day
ahead forecasting of global horizontal irradiance using data from the
University of Pretoria from July 2020 to August 2021. Four methods were adopted
for variable selection, Lasso, ElasticNet, Boruta, and GBR (Gradient Boosting
Regression). The variables selected using GBR were used because they produced
the lowest MAE (Minimum Absolute Errors) value. A comparison of seven models
GPR (Gaussian Process Regression), Two-layer DGPR (Two-layer Deep Gaussian
Process Regression), bstslong (Bayesian Structural Time Series long), AQRA
(Additive Quantile Regression Averaging), QRNN(Quantile Regression Neural
Network), PLAQR(Partial Linear additive Quantile Regression), and Opera(Online
Prediction by ExpRt Aggregation) was made. The evaluation metrics used to
select the best model were the MAE (Mean Absolute Error) and RMSE (Root Mean
Square Error). Further evaluations were done using proper scoring rules and
Murphy diagrams. The best individual model was found to be the GPR. The best
forecast combination was AQRA ((AQR Averaging) based on MAE. However, based on
RMSE, GPNN was the best forecast combination method. Companies such as Eskom
could use the methods adopted in this study to control and manage the power
grid. The results will promote economic development and sustainability of
energy resources.
