Solar Irradiation Forecasting using Genetic Algorithms
This work addresses the need for accurate solar energy forecasting to manage electrical power grids, but it is incremental as it applies existing methods to new data.
The study tackled solar irradiation forecasting by applying machine learning techniques, including Linear Regression, Extreme Gradient Boosting, and Genetic Algorithm Optimization, to data from three SURFRAD stations in the U.S., resulting in improved accuracy for predicting the Global Horizontal Index.
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.