Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks
David A. Romero,
Saeede Hasanpoor,
Enrico G. A. Antonini
et al.
Abstract:Wind turbine wakes are the most significant factor affecting wind farm performance, decreasing energy production and increasing fatigue loads in downstream turbines. Wind farm turbine layouts are designed to minimize wake interactions using a suite of predictive models, including analytical wake models and computational fluid dynamics simulations (CFD). CFD simulations of wind farms are time-consuming and computationally expensive, which hinder their use in optimization studies that require hundreds of simulat… Show more
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