Processing of global solar irradiance and ground-based infrared sky images for solar nowcasting and intra-hour forecasting applications

Shadows from moving clouds in the troposphere impact the energy generated by photovoltaic systems. intra-hour solar forecast can be used to regulate solar energy dispatch. This investigation develops a data processing method for cloud dynamic feature extraction from raw sky images and Global Solar Irradiance (GSI) measurements that can be integrated into solar forecasting algorithms to reduce the operational supervision of hardware. Sky images and GSI measurements are acquired from a low-cost long-wave infrared radiometric camera and a pyranometer. This sky imager is mounted on a solar tracker that maintains the Sun in the center of sky images throughout the day. Multiple processing methods are proposed here that take advantage of a hybrid approach to approximate the optimal parameters of physical models using computationally inexpensive machine learning models. A signal processing method removes cyclostationary biases in high-resolution clear sky index values found when detrending GSI measurements using the clear sky GSI. Image processing methods are then used to remove the effects of atmospheric radiation and the Sun's direct radiation from infrared sky images, plus the radiation effect emitted by debris on the sky imager's germanium outdoor lens. The result is an adaptive solar forecasting algorithm that can reduce the operational cost of power grids with the high participation of solar energy in the generation mix.