Articles | Volume 7, issue 3
Wind Energ. Sci., 7, 1153–1169, 2022
https://doi.org/10.5194/wes-7-1153-2022
Wind Energ. Sci., 7, 1153–1169, 2022
https://doi.org/10.5194/wes-7-1153-2022
Research article
02 Jun 2022
Research article | 02 Jun 2022

Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands

Caleb Phillips et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-164', Anonymous Referee #1, 14 Mar 2022
  • RC2: 'Comment on wes-2021-164', Anonymous Referee #2, 16 Mar 2022
  • RC3: 'Comment on wes-2021-164', Anonymous Referee #3, 24 Mar 2022
  • EC1: 'Comment on wes-2021-164', Alessandro Bianchini, 29 Mar 2022
  • AC1: 'Comment on wes-2021-164', Caleb Phillips, 05 Apr 2022
  • AC2: 'Comment on wes-2021-164', Caleb Phillips, 12 Apr 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Caleb Phillips on behalf of the Authors (12 Apr 2022)  Author's response
ED: Referee Nomination & Report Request started (19 Apr 2022) by Alessandro Bianchini
RR by Anonymous Referee #3 (25 Apr 2022)
RR by Anonymous Referee #1 (26 Apr 2022)
ED: Publish as is (26 Apr 2022) by Alessandro Bianchini
ED: Publish as is (02 May 2022) by Paul Veers(Chief Editor)
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Short summary
Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.