Accelerating regional wind energy assessment with deep-learning surrogates of WRF wind farm simulations

Abstract. Downwind wake effects are becoming increasingly important as wind farms grow larger and turbine capacities increase. Mesoscale weather models with wind farm parameterizations have emerged as a key tool in modelling long-distance wakes between wind farms. However, they can be too computationally expensive for evaluating dozens or more layouts considered in regional planning and optimization. In this study, we develop deep-learning surrogate models that reproduce the power losses and wind speed deficits caused by turbine layouts in mesoscale simulations at a fraction of the computational cost. The models combine atmospheric inputs from free-stream Weather Research and Forecasting (WRF) model simulations with turbine layouts to predict spatial power fields produced by WRF when the wind farm parameterization is activated. First, convolutional neural networks (U-Net) are developed as deterministic surrogates and achieve strong accuracy on two unseen scenarios. Second, diffusion-based models are developed to generate predictive ensembles and quantify uncertainty, including a residual diffusion model that learns the error of a deterministic U-Net prediction. Overall, the all models show a strong ability to predict wind power, both on a per-grid cell basis and aggregated across wind farms. The U-Net model strength shows sensitivity to the predictand (capacity factor vs. normalized power output), the combination of predictors (wind speed, wind direction, turbulence, and temperature), the number of training scenarios, and the type of loss function. Among the probabilistic models, DDPM provides the best calibrated ensembles, whereas residual diffusion yields more accurate point predictions and better farm-level bias control. These results demonstrate that deep-learning surrogates can enable rapid and cost-effective evaluation of candidate wind farm layouts, while also supporting uncertainty-aware planning-stage assessment.