Researchers report that using a deep learning approach leads to up to 50-fold improved resolution of wind velocity and solar irradiance data from global climate models, providing high-resolution modeling of renewable energy prospects under various climate scenarios.
Article #19-18964: "Adversarial super-resolution of climatological wind and solar data," by Karen Stengel, Andrew Glaws, Dylan Hettinger, and Ryan King.
MEDIA CONTACT: Ryan King, National Renewable Energy Laboratory, Golden, CO; e-mail: Ryan.King@nrel.gov
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Proceedings of the National Academy of Sciences