  Efficient integration of solar energy into the electricity mix depends on a
reliable anticipation of its intermittency. A promising approach to forecast
the temporal variability of solar irradiance resulting from the cloud cover
dynamics, is based on the analysis of sequences of ground-taken sky images.
Despite encouraging results, a recurrent limitation of current Deep Learning
approaches lies in the ubiquitous tendency of reacting to past observations
rather than actively anticipating future events. This leads to a systematic
temporal lag and little ability to predict sudden events. To address this
challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture
that models cloud motion from sky images to predict both future segmented
images and corresponding irradiance levels. We show that ECLIPSE anticipates
critical events and considerably reduces temporal delay while generating
visually realistic futures.
