How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?
Abstract
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.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
- OSTI Identifier:
- 1665798
- Report Number(s):
- NREL/JA-5000-76234
Journal ID: ISSN 1742-6588; MainId:6830;UUID:bd36e9e0-e558-ea11-9c31-ac162d87dfe5;MainAdminID:15180
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Physics. Conference Series
- Additional Journal Information:
- Journal Volume: 1618; Conference: TORQUE 2020, Delft (held virtually) (Netherlands), 28 Sep - 2 Oct 2020; Journal ID: ISSN 1742-6588
- Publisher:
- IOP Publishing
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 17 WIND ENERGY; machine learning; wind energy; wind speed; extrapolation
Citation Formats
Bodini, Nicola, and Optis, Mike. How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?. United States: N. p., 2020.
Web. doi:10.1088/1742-6596/1618/6/062037.
Bodini, Nicola, & Optis, Mike. How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?. United States. https://doi.org/10.1088/1742-6596/1618/6/062037
Bodini, Nicola, and Optis, Mike. Mon .
"How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?". United States. https://doi.org/10.1088/1742-6596/1618/6/062037. https://www.osti.gov/servlets/purl/1665798.
@article{osti_1665798,
title = {How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?},
author = {Bodini, Nicola and Optis, Mike},
abstractNote = {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.},
doi = {10.1088/1742-6596/1618/6/062037},
journal = {Journal of Physics. Conference Series},
number = ,
volume = 1618,
place = {United States},
year = {Mon Sep 21 00:00:00 EDT 2020},
month = {Mon Sep 21 00:00:00 EDT 2020}
}
Search WorldCat to find libraries that may hold this journalFigures / Tables:
Figure 1: Map of the instrument locations at the SGP site used in this study. The clusters of smaller dots represent the wind farms in the area.
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Figures / Tables found in this record: