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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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h5-index value: 13
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Short summary
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 meters. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Preprints
https://doi.org/10.5194/wes-2021-5
https://doi.org/10.5194/wes-2021-5

  27 Jan 2021

27 Jan 2021

Review status: a revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

New methods to improve the vertical extrapolation of near-surface offshore wind speeds

Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa Mike Optis et al.
  • National Renewable Energy Laboratory, Golden, Colorado, USA

Abstract. Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in U.S. Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air-sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10-m wind speeds from spatially resolved satellite-based wind atlases.

Mike Optis et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-5', Anonymous Referee #1, 05 Mar 2021
    • AC2: 'Reply on RC1', Mike Optis, 23 Mar 2021
  • RC2: 'RC2', Anonymous Referee #2, 15 Mar 2021
    • AC1: 'Reply on RC2', Mike Optis, 23 Mar 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-5', Anonymous Referee #1, 05 Mar 2021
    • AC2: 'Reply on RC1', Mike Optis, 23 Mar 2021
  • RC2: 'RC2', Anonymous Referee #2, 15 Mar 2021
    • AC1: 'Reply on RC2', Mike Optis, 23 Mar 2021

Mike Optis et al.

Mike Optis et al.

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Short summary
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 meters. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Citation