Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2393-2022
https://doi.org/10.5194/wes-7-2393-2022
Research article
 | 
08 Dec 2022
Research article |  | 08 Dec 2022

Predicting power ramps from joint distributions of future wind speeds

Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon

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Cited articles

Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Ben Bouallègue, Z., Heppelmann, T., Theis, S. E., and Pinson, P.: Generation of scenarios from calibrated ensemble forecasts with a dual-ensemble copula-coupling approach, Mon. Weather Rev., 144, 4737–4750, https://doi.org/10.1175/MWR-D-15-0403.1, 2016.  a
Browell, J., Gilbert, C., and Fasiolo, M.: Covariance structures for high-dimensional energy forecasting, Elect. Power Syst. Res., 211, 108446, https://doi.org/10.1016/j.epsr.2022.108446, 2022. a, b
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R.: The Schaake shuffle: a method for reconstructing space–time variability in forecasted precipitation and temperature fields, J. Hydrometeorol., 5, 243–262, https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2, 2004. a
Dawid, A. P. and Sebastiani, P.: Coherent dispersion criteria for optimal experimental design, Ann. Stat., 27, 65–81, https://doi.org/10.1214/aos/1018031101, 1999. a
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Short summary
The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
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