Articles | Volume 11, issue 8
https://doi.org/10.5194/wes-11-3107-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-3107-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Aug 2026
Research article |
| 27 Aug 2026
| 27 Aug 2026
The AWAKEN wind farm benchmark – Part 2: Modeling results
National Laboratory of the Rockies, Golden, CO, USA
Patrick Moriarty
National Laboratory of the Rockies, Golden, CO, USA
Regis Thedin
National Laboratory of the Rockies, Golden, CO, USA
Paula Doubrawa
WSP USA Inc., Boulder, CO, USA
Cristina Archer
Department of Geography and Spatial Sciences, University of Delaware, Newark, DE, USA
Myra Blaylock
Sandia National Laboratories, Livermore, CA, USA
Carlo Bottasso
TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
Bruno Carmo
Departamento de Engenharia Mecânica, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil
Lawrence Cheung
Sandia National Laboratories, Livermore, CA, USA
Camille Dubreuil
Equinor, Sandsli, Norway
Rogier Floors
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Thomas Herges
Sandia National Laboratories, Albuquerque, NM, USA
Daniel Houck
Sandia National Laboratories, Albuquerque, NM, USA
Ali Kanjari
Department of Geography and Spatial Sciences, University of Delaware, Newark, DE, USA
Colleen M. Kaul
Pacific Northwest National Laboratory, Richland, WA, USA
Christopher Kelley
Sandia National Laboratories, Albuquerque, NM, USA
Ru Li
Meteodyn, Saint-Herblain, France
Julie K. Lundquist
National Laboratory of the Rockies, Golden, CO, USA
Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
Department of Earth and Planetary Sciences, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
Desirae Major
TUM School of Engineering and Design, Technical University of Munich, Munich, Germany
Anh Kiet Nguyen
Equinor, Sandsli, Norway
Mike Optis
Veer Renewables, Courtenay, British Columbia, Canada
Luan R. C. Parada
Departamento de Engenharia Mecânica, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil
Alfredo Peña
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Julian Quick
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
David Ricarte
Departamento de Engenharia Mecânica, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil
William C. Radünz
Departamento de Engenharia Mecânica, Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil
Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
Raj K. Rai
Pacific Northwest National Laboratory, Richland, WA, USA
Oscar García-Santiago
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Jonas Schulte
Fraunhofer IWES, Bremerhaven, Germany
Knut S. Seim
Equinor, Sandsli, Norway
M. Paul van der Laan
Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, Denmark
Kisorthman Vimalakanthan
TNO, EMT Unit, Petter, the Netherlands
Adam Wise
Civil and Environmental Engineering, University of California, Berkeley, CA, USA
Lawrence Livermore National Laboratory, Livermore, CA, USA
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Total article views: 729 (including HTML, PDF, and XML)
Cumulative views and downloads
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Short summary
Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more-efficient wind farms.
Predicting wind farm energy production is challenging because wind patterns are complex. We...
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