Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-311-2021
https://doi.org/10.5194/wes-6-311-2021
Review article
 | 
05 Mar 2021
Review article |  | 05 Mar 2021

An overview of wind-energy-production prediction bias, losses, and uncertainties

Joseph C. Y. Lee and M. Jason Fields

Related authors

The Power Curve Working Group's assessment of wind turbine power performance prediction methods
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020,https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
OpenOA: An Open-Source Code Base for Operational Analysis of Wind Power Plants
Mike Optis, Jordan Perr-Sauer, Caleb Philips, Anna E. Craig, Joseph C. Y. Lee, Travis Kemper, Shuangwen Sheng, Eric Simley, Lindy Williams, Monte Lunacek, John Meissner, and M. Jason Fields
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-12,https://doi.org/10.5194/wes-2019-12, 2019
Preprint withdrawn
Short summary
Assessing variability of wind speed: comparison and validation of 27 methodologies
Joseph C. Y. Lee, M. Jason Fields, and Julie K. Lundquist
Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018,https://doi.org/10.5194/wes-3-845-2018, 2018
Short summary

Related subject area

Wind and turbulence
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
Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022,https://doi.org/10.5194/wes-7-1153-2022, 2022
Short summary
Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022,https://doi.org/10.5194/wes-7-1069-2022, 2022
Short summary
Large-eddy simulation of airborne wind energy farms
Thomas Haas, Jochem De Schutter, Moritz Diehl, and Johan Meyers
Wind Energ. Sci., 7, 1093–1135, https://doi.org/10.5194/wes-7-1093-2022,https://doi.org/10.5194/wes-7-1093-2022, 2022
Short summary
Investigation into boundary layer transition using wall-resolved large-eddy simulations and modeled inflow turbulence
Brandon Arthur Lobo, Alois Peter Schaffarczyk, and Michael Breuer
Wind Energ. Sci., 7, 967–990, https://doi.org/10.5194/wes-7-967-2022,https://doi.org/10.5194/wes-7-967-2022, 2022
Short summary
Evaluation of the global-blockage effect on power performance through simulations and measurements
Alessandro Sebastiani, Alfredo Peña, Niels Troldborg, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 875–886, https://doi.org/10.5194/wes-7-875-2022,https://doi.org/10.5194/wes-7-875-2022, 2022
Short summary

Cited articles

Abascal, A., Herrero, M., Torrijos, M., Dumont, J., Álvarez, M., and Casso, P.: An approach for estimating energy losses due to ice in pre-construction energy assessments, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019. 
Abiven, C., Brady, O., and Triki, I.: Mesoscale and CFD Coupling for Wind Resource Assessment, in: AWEA Wind Resource and Project Energy Assessment Workshop 2013, AWEA, Las Vegas, NV, 2013. 
Abiven, C., Parisse, A., Watson, G., and Brady, O.: CFD Wake Modeling: Where Do We Stand?, in: AWEA Wind Resource and Project Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014. 
Albers, A., Klug, H., and Westermann, D.: Outdoor Comparison of Cup Anemometers, in: German wind energy conference, DEWEK 2000, Wilhelmshaven, Germany, p. 5, 2000. 
Albers, A., Franke, K., Wagner, R., Courtney, M., and Boquet, M.: Ground-based remote sensor uncertainty – a case study for a wind lidar, available at: https://www.researchgate.net/publication/267780849_Ground-based_remote_sensor_uncertainty_-_a_case_study_for_a_wind_lidar (last access: 31 October 2020), 2013. 
Download
Short summary
This review paper evaluates the energy prediction bias in the wind resource assessment process, and the overprediction bias is decreasing over time. We examine the estimated and observed losses and uncertainties in energy production from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 standard. The considerable uncertainties call for further improvements in the prediction methodologies and more observations for validation.
Altmetrics
Final-revised paper
Preprint