Solar Irradiance

Solar irradiance research focuses on accurately predicting solar energy availability, crucial for integrating solar power into energy grids and optimizing solar energy systems. Current research heavily utilizes machine learning, employing diverse architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and gradient boosting models, often incorporating satellite imagery and ground-based sensor data to improve forecasting accuracy and spatial resolution. These advancements are significant for enhancing the reliability and efficiency of solar energy utilization, enabling better grid management and more effective deployment of solar technologies. Improved forecasting reduces reliance on fossil fuel-based backup power, contributing to cleaner energy production.