Researchers at the US Department of Energy’s (DOE’s) National Renewable Energy Laboratory (NREL) have developed a novel machine learning approach quickly to enhance the resolution of wind velocity data by 50 times and solar irradiance data by 25 times—an enhancement that had yet to be achieved with climate data. The researchers took an alternative approach by using adversarial training, in which the model produces physically realistic details by observing entire fields at a time, providing high-resolution climate data at a much faster rate. This approach will enable scientists to complete renewable energy studies in future climate scenarios faster and with more accuracy. To be able to enhance the spatial and temporal resolution of climate forecasts hugely impacts not only energy planning, but agriculture, transportation, and so much more. —Ryan King, a senior computational scientist at NREL who specializes in physics-informed deep learning King and NREL colleagues Karen Stengel, Andrew […]