Neural Networks for Predicting the Output of wind flow Simulations Over Complex Topographies

Abstract

We use deep learning techniques to model computational fluid dynamics (CFD) simulations of wind flow over a complex topography. Our motivation is to "speed up" the optimisation of CFD-based simulations (such as the 3D wind farm layout optimisation problem) by developing surrogate models capable of predicting the output of a simulation at any given point in 3D space, given output from a set of training simulations that have already been run. Our promising results using TensorFlow show that deep neural networks can be learned to model CFD outputs with an error of as low as 2.5 meters per second.

Predicting Topographic Effect Multipliers in Complex Terrain With Shallow Neural Networks

“…Robert et al (2012) produced monthly wind speed maps from a data architecture that include multiscale topographic features extracted from a digital elevation model and weather station data. Burlando and Meissner (2017) and Mayo et al (2018) developed hybrid approaches involving CFD, numerical weather prediction (NWP), and surface wind measurements to inform neural network modeling. More recently, Donadio et al (2021) developed a highly automated "prediction pipeline" that integrated global and regional NWP to predict wind speed and wind energy production.…”