How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
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.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
- Grant/Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1665798
- Report Number(s):
- NREL/JA-5000-76234; MainId:6830; UUID:bd36e9e0-e558-ea11-9c31-ac162d87dfe5; MainAdminID:15180
- Journal Information:
- Journal of Physics. Conference Series, Vol. 1618; Conference: TORQUE 2020, Delft (held virtually) (Netherlands), 28 Sep - 2 Oct 2020; ISSN 1742-6588
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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The Importance of Round-Robin Validation When Assessing Machine-Learning-Based Vertical Extrapolation of Wind Speeds.
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