  The increasing global demand for clean and environmentally friendly energy
resources has caused increased interest in harnessing solar power through
photovoltaic (PV) systems for smart grids and homes. However, the inherent
unpredictability of PV generation poses problems associated with smart grid
planning and management, energy trading and market participation, demand
response, reliability, etc. Therefore, solar irradiance forecasting is
essential for optimizing PV system utilization. This study proposes the
next-generation machine learning algorithms such as random forests, Extreme
Gradient Boosting (XGBoost), Light Gradient Boosted Machine (lightGBM)
ensemble, CatBoost, and Multilayer Perceptron Artificial Neural Networks
(MLP-ANNs) to forecast solar irradiance. Besides, Bayesian optimization is
applied to hyperparameter tuning. Unlike tree-based ensemble algorithms that
select the features intrinsically, MLP-ANN needs feature selection as a
separate step. The simulation results indicate that the performance of the
MLP-ANNs improves when feature selection is applied. Besides, the random forest
outperforms the other learning algorithms.

Machine Learning;Systems and Control;Systems and Control