Articles | Volume 6, issue 6
Wind Energ. Sci., 6, 1363–1377, 2021
https://doi.org/10.5194/wes-6-1363-2021
Wind Energ. Sci., 6, 1363–1377, 2021
https://doi.org/10.5194/wes-6-1363-2021
Research article
03 Nov 2021
Research article | 03 Nov 2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

Nicola Bodini et al.

Related authors

Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022,https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
The Sensitivity of the Fitch Wind Farm Parameterization to a Three-Dimensional Planetary Boundary Layer Scheme
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-127,https://doi.org/10.5194/wes-2021-127, 2021
Preprint under review for WES
Short summary
Extreme wind shear events in US offshore wind energy areas and the role of induced stratification
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021,https://doi.org/10.5194/wes-6-1043-2021, 2021
Short summary
Approaches for predicting wind turbine hub-height turbulence metrics
Hannah Livingston, Nicola Bodini, and Julie K. Lundquist
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2021-68,https://doi.org/10.5194/wes-2021-68, 2021
Preprint withdrawn
Short summary
New methods to improve the vertical extrapolation of near-surface offshore wind speeds
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021,https://doi.org/10.5194/wes-6-935-2021, 2021
Short summary

Related subject area

Design methods, reliability and uncertainty modelling
Effectively using multifidelity optimization for wind turbine design
John Jasa, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter
Wind Energ. Sci., 7, 991–1006, https://doi.org/10.5194/wes-7-991-2022,https://doi.org/10.5194/wes-7-991-2022, 2022
Short summary
Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling
Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, and Gerard Schepers
Wind Energ. Sci., 7, 759–781, https://doi.org/10.5194/wes-7-759-2022,https://doi.org/10.5194/wes-7-759-2022, 2022
Short summary
Fast yaw optimization for wind plant wake steering using Boolean yaw angles
Andrew P. J. Stanley, Christopher Bay, Rafael Mudafort, and Paul Fleming
Wind Energ. Sci., 7, 741–757, https://doi.org/10.5194/wes-7-741-2022,https://doi.org/10.5194/wes-7-741-2022, 2022
Short summary
A simplified, efficient approach to hybrid wind and solar plant site optimization
Charles Tripp, Darice Guittet, Jennifer King, and Aaron Barker
Wind Energ. Sci., 7, 697–713, https://doi.org/10.5194/wes-7-697-2022,https://doi.org/10.5194/wes-7-697-2022, 2022
Short summary
Influence of wind turbine design parameters on linearized physics-based models in OpenFAST
Jason M. Jonkman, Emmanuel S. P. Branlard, and John P. Jasa
Wind Energ. Sci., 7, 559–571, https://doi.org/10.5194/wes-7-559-2022,https://doi.org/10.5194/wes-7-559-2022, 2022
Short summary

Cited articles

Alessandrini, S., Sperati, S., and Pinson, P.: A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data, Appl. Energ., 107, 271–280, https://doi.org/10.1016/j.apenergy.2013.02.041, 2013. a, b
Alessandrini, S., Delle Monache, L., Sperati, S., and Cervone, G.: An analog ensemble for short-term probabilistic solar power forecast, Appl. Energ., 157, 95–110, https://doi.org/10.1016/j.apenergy.2015.08.011, 2015a. a
Alessandrini, S., Delle Monache, L., Sperati, S., and Nissen, J.: A novel application of an analog ensemble for short-term wind power forecasting, Renew. Energ., 76, 768–781, https://doi.org/10.1016/j.renene.2014.11.061, 2015b. a
Alessandrini, S., Sperati, S., and Delle Monache, L.: Improving the analog ensemble wind speed forecasts for rare events, Mon. Weather Rev., 147, 2677–2692, https://doi.org/10.1175/MWR-D-19-0006.1, 2019. a, b, c
Archer, C. L., Colle, B. A., Delle Monache, L., Dvorak, M. J., Lundquist, J., Bailey, B. H., Beaucage, P., Churchfield, M. J., Fitch, A. C., Kosovic, B., Lee, S., Moriarty, P. J., Simao, H., Stevens, R. J. A. M., Veron, D., and Zack, J.: Meteorology for coastal/offshore wind energy in the United States: Recommendations and research needs for the next 10 years, B. Am. Meteorol. Soc., 95, 515–519, https://doi.org/10.1175/BAMS-D-13-00108.1, 2014. a
Download
Short summary
We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.