Articles | Volume 7, issue 3
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, Lindsay M. Sheridan, Patrick Conry, Dimitrios K. Fytanidis, Dmitry Duplyakin, Sagi Zisman, Nicolas Duboc, Matt Nelson, Rao Kotamarthi, Rod Linn, Marc Broersma, Timo Spijkerboer, and Heidi Tinnesand


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 | EF: Editorial file upload
AR by Caleb Phillips on behalf of the Authors (12 Apr 2022)  Author's response 
EF by Sarah Buchmann (19 Apr 2022)  Author's tracked changes 
EF by Sarah Buchmann (19 Apr 2022)  Manuscript 
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)
AR by Caleb Phillips on behalf of the Authors (08 May 2022)
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.
Final-revised paper