Articles | Volume 5, issue 3
Wind Energ. Sci., 5, 959–975, 2020
https://doi.org/10.5194/wes-5-959-2020

Special issue: Flow in complex terrain: the Perdigão campaigns (WES/ACP/AMT...

Wind Energ. Sci., 5, 959–975, 2020
https://doi.org/10.5194/wes-5-959-2020

Research article 26 Jul 2020

Research article | 26 Jul 2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Daniel Vassallo et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Lorena Grabowski on behalf of the Authors (10 Jan 2020)  Author's response
ED: Referee Nomination & Report Request started (16 Feb 2020) by Jakob Mann
RR by Leonardo Alcayaga (04 Mar 2020)
RR by Tuhfe Gocmen (09 Mar 2020)
ED: Publish subject to minor revisions (review by editor) (09 Mar 2020) by Jakob Mann
AR by Daniel Vassallo on behalf of the Authors (16 Mar 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (09 Apr 2020) by Jakob Mann
AR by Daniel Vassallo on behalf of the Authors (16 Apr 2020)  Author's response    Manuscript
ED: Publish as is (20 May 2020) by Jakob Mann
ED: Publish as is (20 May 2020) by Jakob Mann(Chief Editor)
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Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.