Machine Learning Enables National Assessment of Wind Plant Controls with Implications for Land Use

Dylan Harrison-Atlas, Ryan King, Andrew Glaws

Research output: Contribution to journalArticlepeer-review

9 Scopus Citations


As deployment of wind energy continues to expand, computationally efficient tools for predicting wind plant performance over a wide range of layout designs, technology innovations, and spatial locations are increasingly important for policy and investment decisions. We demonstrate two approaches to training a surrogate model to predict annual energy production (AEP) of parameterized wind plant layouts: one using a Gaussian process (GP) and the other using a fully convolutional neural network (FCNN). We leverage the powerful FCNN architecture by encoding wind plant design parameters and output response surface as an image. The FCNN produces more accurate results than the GP with mean absolute errors equivalent to 1% and 1.9% of plant rated power, respectively, although the GP performs well under limited training data and provides useful uncertainty information. We also evaluate a surrogate model for wake steering, enabling a nationwide assessment of the impact of plant control strategies and plant layout decisions. Across two million locations, we find that wake steering strategies boost AEP with relative gains upwards of 3%. Gains are most pronounced at sites without a dominant wind direction and where layout optimization is less fruitful. Additionally, we perform a nationwide sensitivity analysis showing that wake steering can mitigate wake losses from higher density plant layouts. Our results suggest that regions which have not been previously viable for wind deployment due to moderate wind resources are especially well enhanced by wake steering strategies that could help overcome land constraints and inflexible layout options, potentially identifying new deployment opportunities.

Original languageAmerican English
Pages (from-to)618-638
Number of pages21
JournalWind Energy
Issue number4
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Alliance for Sustainable Energy, LLC. Wind Energy published by John Wiley & Sons Ltd.

NREL Publication Number

  • NREL/JA-6A20-79267


  • convolutional neural network
  • Gaussian process
  • machine learning
  • surrogate modeling
  • wake steering


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