How Accurate is a Machine Learning-Based Wind Speed Extrapolation Under a Round-Robin Approach?

Nicola Bodini, Mike Optis

Research output: Contribution to journalArticlepeer-review

10 Scopus Citations


As the size of commercial wind turbines keeps increasing, having accurate ways to vertically extrapolate wind speed is essential to obtain a precise characterization of the wind resource for wind energy production. Recently, machine learning has been proposed and applied to extrapolate wind speed to hub heights. However, previous studies trained and tested the machine learning methods at the same site, giving them an unfair advantage over the conventional extrapolation techniques, which are instead more universal. Here, we use data from four sites in Oklahoma to test a round-robin validation approach for machine learning, under which we train a random forest at a site, and test it at a different site, where the model has no prior knowledge of the wind resource. We quantify how the accuracy of this technique varies with distance from the training site, and we find that it outperforms conventional techniques for wind extrapolation at all the considered spatial separations. We then assess how the accuracy of the machine-learning based approach varies when it is used to predict wind speed in a wind farm far wake. Finally, we explore as case study the performance of the random forest in extrapolating winds during a low-level jet event.

Original languageAmerican English
Article numberArticle No. 062037
Number of pages9
JournalJournal of Physics: Conference Series
Issue number6
StatePublished - 22 Sep 2020
EventScience of Making Torque from Wind 2020, TORQUE 2020 - Virtual, Online, Netherlands
Duration: 28 Sep 20202 Oct 2020

Bibliographical note

Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

NREL Publication Number

  • NREL/JA-5000-76234


  • extrapolation
  • machine learning
  • wind energy
  • wind speed


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