  Renewable energy forecasting is attaining greater importance due to its
constant increase in contribution to the electrical power grids. Solar energy
is one of the most significant contributors to renewable energy and is
dependent on solar irradiation. For the effective management of electrical
power grids, forecasting models that predict solar irradiation, with high
accuracy, are needed. In the current study, Machine Learning techniques such as
Linear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization
are used to forecast solar irradiation. The data used for training and
validation is recorded from across three different geographical stations in the
United States that are part of the SURFRAD network. A Global Horizontal Index
(GHI) is predicted for the models built and compared. Genetic Algorithm
Optimization is applied to XGB to further improve the accuracy of solar
irradiation prediction.
