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ECLIPSE: Envisioning Cloud Induced Perturbations in Solar Energy

Quentin Paletta  Anthony Hu   Guillaume Arbod   Joan Lasenby

University of Cambridge, UK. ENGIE Lab CRIGEN, France.


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 spatiotemporal 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.