Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 3
Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1153-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1153-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 2,283 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,566 644 73 2,283 65 36
  • HTML: 1,566
  • PDF: 644
  • XML: 73
  • Total: 2,283
  • BibTeX: 65
  • EndNote: 36
Views and downloads (calculated since 14 Feb 2022)
Cumulative views and downloads (calculated since 14 Feb 2022)

Viewed (geographical distribution)

Total article views: 2,283 (including HTML, PDF, and XML) Thereof 2,190 with geography defined and 93 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Nov 2024
Download
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.
Read more
Altmetrics
Final-revised paper
Preprint