  The increasing number of Photovoltaic (PV) systems connected to the power
grid are vulnerable to the projection of shadows from moving clouds. Global
Solar Irradiance (GSI) forecasting allows smart grids to optimize the energy
dispatch, preventing energy shortages caused by occlusion of the sun. This
investigation compares the performances of machine learning algorithms (not
requiring labelled images for training) for real-time segmentation of clouds in
images acquired using a ground-based infrared sky imager. Real-time
segmentation is utilized to extract cloud features using only the pixels in
which clouds are detected.
