| 01 Jun 2026
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.
This seems to be an important study with relevance to wind farm planning. So the reviewer started reading with enthusiasm but found himself stuck after a short while.
The Introduction gives a short abstract of existing research citing a few overview papers. The open caveat is analysed and the desired solution is named: a “U-Net” and a “diffusion-based probabilistic surrogate model”. Unfortunately, no definition of a U-Net or a diffusion model is given. As these terms seem to define the core of the solution, some sentences on U-Nets and diffusion models should be in order.
Due to the missing definition of these two terms the reviewer has not understood the entire second paragraph of Subsection 1.1 (lines 78-90). Section 1 needs reformulation in order to provide access to the content of this manuscript also for non-specialists.
The same applies to Section 2 which is totally written in a formal language. Although this might provide a precise notation of the method it is inaccessible for non-specialists. The link to reality seems to be the term “free-stream atmospheric covariates”, another term which is not explained (lines 214-220 and Table 2 offer something, but quite late).
Nothing is said in the introduction whether the authors refer to onshore or offshore wind farms. It is only from the first sentence in Section 3 that the reader learns that the study refers to onshore conditions. It would have been wise to address this aspect much earlier, because wakes are much more intense and larger in offshore environments than in onshore ones. The authors should amend the introduction respectively.
By the way, the method should be applied to the seas around the British Isles or to the German Bight. Here, losses due to wakes are much more relevant than the onshore example given in this study. This would be the true proof of the concept presented.
Figure 1 should have axes in physical dimensions (kilometres). Otherwise, the given resolution of 1x1 square kilometre of the WRF model cannot be compared to the size of the wind farms depicted in Figure 1. Appendix B can be skipped, as it does not provide any meaningful additional information,
I stop here making any further detailed comments on this manuscript. The manuscript turns out to be a very technical one and is, unfortunately, not digestible for non-specialists (the reviewer is a meteorologist with special interest in wind energy for decades). Only lines 492-520 in the Conclusions give a little taste how the presented work could refer to the real world of a wind engineer or an energy meteorologist.
Although I think that I understand the importance of the presented research, I can only recommend the publication of this manuscript if the Introduction is extended in order to inform the readers in a more detailed way on the purpose of the study and the linkage between this formal research and the reality. This should start, e.g., by using the term “meteorological variables” instead of “atmospheric covariates”. Maybe, a wind engineer or a meteorologist could be added to the team of authors.