Articles | Volume 6, issue 1
Wind Energ. Sci., 6, 295–309, 2021
https://doi.org/10.5194/wes-6-295-2021
Wind Energ. Sci., 6, 295–309, 2021
https://doi.org/10.5194/wes-6-295-2021

Research article 01 Mar 2021

Research article | 01 Mar 2021

Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

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 Daniel Vassallo on behalf of the Authors (30 Jul 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (13 Aug 2020) by Joachim Peinke
RR by Anonymous Referee #2 (15 Aug 2020)
ED: Publish subject to minor revisions (review by editor) (14 Oct 2020) by Joachim Peinke
AR by Daniel Vassallo on behalf of the Authors (05 Nov 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (17 Nov 2020) by Joachim Peinke
ED: Publish as is (11 Jan 2021) by Joachim Peinke
ED: Publish as is (11 Jan 2021) by Joachim Peinke(Chief Editor)
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Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.