  The uncertainty of the energy generated by photovoltaic systems incurs an
additional cost for a guaranteed, reliable supply of energy (i.e., energy
storage). This investigation aims to decrease the additional cost by
introducing probabilistic multi-task intra-hour solar forecasting (feasible in
real time applications) to increase the penetration of photovoltaic systems in
power grids. The direction of moving clouds is estimated in consecutive
sequences of sky images by extracting features of cloud dynamics with the
objective of forecasting the global solar irradiance that reaches photovoltaic
systems. The sky images are acquired using a low-cost infrared sky imager
mounted on a solar tracker. The solar forecasting algorithm is based on kernel
learning methods, and uses the clear sky index as predictor and features
extracted from clouds as feature vectors. The proposed solar forecasting
algorithm achieved 16.45\% forecasting skill 8 minutes ahead with a resolution
of 15 seconds. In contrast, previous work reached 15.4\% forecasting skill with
the resolution of 1 minute. Therefore, this solar forecasting algorithm
increases the performances with respect to the state-of-the-art, providing grid
operators with the capability of managing the inherent uncertainties of power
grids with a high penetration of photovoltaic systems.
