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Efficient integration of solar energy into the electricity mix depends on areliable anticipation of its intermittency. A promising approach to forecastthe temporal variability of solar irradiance resulting from the cloud coverdynamics is based on the analysis of sequences of ground-taken sky images orsatellite observations. Despite encouraging results, a recurrent limitation ofexisting deep learning approaches lies in the ubiquitous tendency of reactingto past observations rather than actively anticipating future events. Thisleads to a frequent temporal lag and limited ability to predict sudden events.To address this challenge, we introduce ECLIPSE, a spatio-temporal neuralnetwork architecture that models cloud motion from sky images to not onlypredict future irradiance levels and associated uncertainties, but alsosegmented images, which provide richer information on the local irradiance map.We show that ECLIPSE anticipates critical events and reduces temporal delaywhile generating visually realistic futures. The model characteristics andproperties are investigated with an ablation study and a comparative study onthe benefits and different ways to integrate auxiliary data into the modelling.The model predictions are also interpreted through an analysis of the principalspatio-temporal components learned during network training.

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy