Researchers at the U.S. Department of Energy’s (DOE’s) National Renewable Energy Laboratory (NREL) have developed a machine learning approach to enhance the resolution of wind velocity data by 50 times and solar irradiance data by 25 times.
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.
Adversarial training is a way of improving the performance of neural networks by having them compete with one another to generate new, more realistic data. The NREL researchers trained two types of neural networks in the model—one to recognize physical characteristics of high-resolution solar irradiance and wind velocity data and another to insert those characteristics into the coarse data. Over time, the networks produce more realistic data and improve at distinguishing between real and fake inputs. The NREL researchers were able to add 2,500 pixels for every original pixel.
This approach can be applied to a wide range of climate scenarios from regional to global scales, changing the paradigm for climate model forecasting.