Articles | Volume 6, issue 6
Wind Energ. Sci., 6, 1363–1377, 2021
Wind Energ. Sci., 6, 1363–1377, 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.

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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,, 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,, 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,, 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,, 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,, 2014. a
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.