Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 2
Articles | Volume 11, issue 2
https://doi.org/10.5194/wes-11-661-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-661-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
Research article
 | 
24 Feb 2026
Research article |  | 24 Feb 2026

Evaluating the impact of inter-annual variability on long-term wind speed predictions

Johanna Borowski, Sandra Schwegmann, Kerstin Avila, and Martin Dörenkämper

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Cited articles

Baker, R. W., Walker, S. N., and Wade, J. E.: Annual and seasonal variations in mean wind speed and wind turbine energy production, Sol. Energy, 45, 285–289, https://doi.org/10.1016/0038-092X(90)90013-3, 1990. a
Bakhoday-Paskyabi, M.: Predictive Analysis of Machine Learning Schemes in Forecasting Offshore Wind Speed, J. Phys. Conf. Ser., https://doi.org/10.1088/1742-6596/1669/1/012017, 2020. a
Barber, S. and Nordborg, H.: Improving site-dependent power curve prediction accuracy using regression trees, J. Phys. Conf. Ser., 1618, https://doi.org/10.1088/1742-6596/1618/6/062003, 2020. a
Bass, J. H., Rebbeck, M., Landberg, L., Cabré, M. F., and Hunter, A.: An improved measure-correlate-predict algorithm for the prediction of the long term wind climate in regions of complex environment: Final Report JOR3-CT98-0295, in: Renewable Energy Systems Ltd (UK), Risø National Laboratory (Denmark), Ecotecnia (Spain), University of Sunderland (UK), https://hdl.handle.net/10779/lincoln.24373816.v1 (last access: 13 February 2026), 2000. a
Basse, A., Callies, D., Grötzner, A., and Pauscher, L.: Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data, Wind Energ. Sci., 6, 1473–1490, https://doi.org/10.5194/wes-6-1473-2021, 2021. a
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Assessing wind resources and mitigating the associated uncertainties are crucial to wind farm profitability. The study quantifies the uncertainty due to inter-annual variability, averaging 6.5 % and ranging from 1 % to 14 %, using long-term, quality-controlled wind measurements from tall met masts in terrain of varying complexity. Further, the results indicate that machine learning models are beneficial in mitigating the impact of inter-annual variability in heterogeneous and complex terrain.
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