Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields

TL;DR

This paper introduces FuXi-CFD, an AI-based framework that generates high-resolution 3D wind fields from coarse inputs, significantly improving wind energy forecasting in complex terrains with fast inference times.

Contribution

The novel FuXi-CFD model enables detailed 3D wind field prediction at 30-meter resolution using only coarse atmospheric data, trained on CFD-generated datasets, bridging the resolution gap in wind forecasting.

Findings

Achieves CFD-level accuracy in wind field prediction

Reduces inference time from hours to seconds

Effectively models vertical wind and turbulence

Abstract

High-resolution wind information is essential for wind energy planning and power forecasting, particularly in regions with complex terrain. However, most AI-based weather forecasting models operate at kilometer-scale resolution, constrained by the reanalysis datasets they are trained on. Here we introduce FuXi-CFD, an AI-based downscaling framework designed to generate detailed three-dimensional wind fields at 30-meter horizontal resolution, using only coarse-resolution atmospheric inputs. The model is trained on a large-scale dataset generated via computational fluid dynamics (CFD), encompassing a wide range of terrain types, surface roughness, and inflow conditions. Remarkably, FuXi-CFD predicts full 3D wind structures -- including vertical wind and turbulent kinetic energy -- based solely on horizontal wind input at 10 meters above ground, the typical output of AI-based forecast systems. It achieves CFD-comparable accuracy while reducing inference time from hours to seconds. By bridging the resolution gap between regional forecasts and site-specific wind dynamics, FuXi-CFD offers a scalable and operationally efficient solution to support the growing demands of renewable energy deployment.