  Photovoltaic systems are sensitive to cloud shadow projection, which needs to
be forecasted to reduce the noise impacting the intra-hour forecast of global
solar irradiance. We present a comparison between different kernel
discriminative models for cloud detection. The models are solved in the primal
formulation to make them feasible in real-time applications. The performances
are compared using the j-statistic. The infrared cloud images have been
preprocessed to remove debris, which increases the performance of the analyzed
methods. The use of neighboring features of the pixels also leads to a
performance improvement. Discriminative models solved in the primal yield a
dramatically lower computing time along with high performance in the
segmentation.
