Leveraging high resolution wind data for multiple applications

We have developed a methodology to enhance the resolution of wind forecasts in complex environments, such as mountainous or urban areas, where wind variability occurs on a local scale. To achieve this, we combine weather forecasting with computational fluid dynamics (CFD) simulations and artificial intelligence. This approach allows us to downscale wind speeds from kilometers to meters while keeping computational costs manageable.

We aim to continue refining this methodology and applying it to projects where it can provide significant value. Potential applications include creating digital twins for wind turbines, optimizing operations and maintenance for wind energy, implementing adaptive controls for wind energy systems, hybridizing wind and solar energy, and improving wind generation forecasting, among other initiatives.

We have attached several articles detailing the developed methodology.