Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Predicting power ramps from joint distributions of future wind speeds
Thomas Muschinski
CORRESPONDING AUTHOR
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Moritz N. Lang
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Georg J. Mayr
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Jakob W. Messner
MeteoServe Wetterdienst GmbH, Innsbruck, Austria
Achim Zeileis
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Thorsten Simon
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Mathematics, Universität Innsbruck, Innsbruck, Austria
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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
ECMWF: Access to forecasts, https://www.ecmwf.int/en/forecasts/accessing-forecasts, last access: 7 December 2022. a
Friedman, J., Hastie, T., and Tibshirani, R.: Sparse inverse covariance
estimation with the graphical lasso, Biostatistics, 9, 432–441,
https://doi.org/10.1093/biostatistics/kxm045, 2008. a
Gallego-Castillo, C., Cuerva-Tejero, A., and Lopez-Garcia, O.: A review on the recent history of wind power ramp forecasting, Renew. Sustain. Energ. Rev., 52, 1148–1157, https://doi.org/10.1016/j.rser.2015.07.154, 2015. a
Gneiting, T. and Raftery, A. E.: Strictly proper scoring rules, prediction, and estimation, J. Am. Stat. Assoc., 102, 359–378,
https://doi.org/10.1198/016214506000001437, 2007. a
Gneiting, T., Raftery, A. E., Westveld III, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev., 133, 1098–1118,
https://doi.org/10.1175/mwr2904.1, 2005. a, b, c
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts,
calibration and sharpness, J. Roy. Stat. Soc. B, 69, 243–268, https://doi.org/10.21236/ada454827, 2007. a, b
Haessig, P., Multon, B., Ahmed, H. B., Lascaud, S., and Bondon, P.: Energy
storage sizing for wind power: impact of the autocorrelation of day-ahead
forecast errors, Wind Energy, 18, 43–57, https://doi.org/10.1002/we.1680, 2015. a
Karagali, I., Badger, M., Hahmann, A. N., Peña, A., Hasager, C. B., and
Sempreviva, A. M.: Spatial and temporal variability of winds in the northern
European seas, Renew. Energy, 57, 200–210, https://doi.org/10.1016/j.renene.2013.01.017, 2013. a
Klein, N., Kneib, T., Klasen, S., and Lang, S.: Bayesian structured additive
distributional regression for multivariate responses, J. Ro. Stat. Soc. C, 64, 569–591, https://doi.org/10.1214/15-AOAS823, 2015. a
Lang, M. N., Mayr, G. J., Stauffer, R., and Zeileis, A.: Bivariate Gaussian
models for wind vectors in a distributional regression framework, Adv. Stat. Climatol. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, 2019. a
Lang, M. N., Lerch, S., Mayr, G. J., Simon, T., Stauffer, R., and Zeileis, A.: Remember the past: a comparison of time-adaptive training schemes for
non-homogeneous regression, Nonlin. Processes Geophys., 27, 23–34,
https://doi.org/10.5194/npg-27-23-2020, 2020. a
Lerch, S. and Thorarinsdottir, T. L.: Comparison of non-homogeneous regression models for probabilistic wind speed forecasting, Tellus A, 65, 21206, https://doi.org/10.3402/tellusa.v65i0.21206, 2013.
a
Leutbecher, M. and Palmer, T. N.: Ensemble forecasting, J. Comput. Phys., 227, 3515–3539, https://doi.org/10.1016/j.jcp.2007.02.014, 2008. a
Li, J., Zhou, J., and Chen, B.: Review of wind power scenario generation
methods for optimal operation of renewable energy systems, Appl. Energy, 280, 115992, https://doi.org/10.1016/j.apenergy.2020.115992, 2020. a
Möller, A., Lenkoski, A., and Thorarinsdottir, T. L.: Multivariate
probabilistic forecasting using ensemble Bayesian model averaging and copulas, Q. J. Roy. Meteorol. Soc., 139, 982–991, https://doi.org/10.1002/qj.2009, 2013. a
Pinson, P.: Wind energy: forecasting challenges for its operational management, Stat. Sci., 28, 564–585, https://doi.org/10.1214/13-STS445, 2013. a
Pinson, P. and Girard, R.: Evaluating the quality of scenarios of short-term
wind power generation, Appl. Energy, 96, 12–20, https://doi.org/10.1016/j.apenergy.2011.11.004, 2012. a, b
Richardson, L. F.: Weather Prediction by Numerical Process, Cambridge University Press, 1922. a
Schefzik, R.: Ensemble copula coupling, Master's Thesis, Faculty of Mathematics and Informatics, University of Heidelberg, Heidelberg, Germany, https://doi.org/10.1002/qj.2984, 2011. a
Schefzik, R., Thorarinsdottir, T. L., and Gneiting, T.: Uncertainty
quantification in complex simulation models using ensemble copula coupling,
Stat. Sci., 28, 616–640, https://doi.org/10.1214/13-STS443, 2013. a
Schölzel, C. and Hense, A.: Probabilistic assessment of regional climate
change in southwest Germany by ensemble dressing, Clim. Dynam., 36, 2003–2014, https://doi.org/10.1007/s00382-010-0815-1, 2011.
a
Schuhen, N., Thorarinsdottir, T. L., and Gneiting, T.: Ensemble model output
statistics for wind vectors, Mon. Weather Rev., 140, 3204–3219,
https://doi.org/10.1175/MWR-D-12-00028.1, 2012. a
Sweeney, C., Bessa, R. J., Browell, J., and Pinson, P.: The future of
forecasting for renewable energy, WIREs Energ. Environ., 9, e365,
https://doi.org/10.1002/wene.365, 2020. a
Tastu, J., Pinson, P., and Madsen, H.: Space-time trajectories of wind power
generation: parametrized precision matrices under a Gaussian copula
approach, in: Modeling and stochastic learning for forecasting in high
dimensions, Springer, 267–296, https://doi.org/10.1007/978-3-319-18732-7_14, 2015. a
Thorarinsdottir, T. L. and Gneiting, T.: Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored
regression, J. Roy. Stat. Soc. A , 173, 371–388, https://doi.org/10.1111/j.1467-985X.2009.00616.x, 2010. a
Wang, J., Botterud, A., Bessa, R., Keko, H., Carvalho, L., Issicaba, D.,
Sumaili, J., and Miranda, V.: Wind power forecasting uncertainty and unit
commitment, Appl. Energy, 88, 4014–4023, https://doi.org/10.1016/j.apenergy.2011.04.011, 2011. a
Wilks, D. S.: Regularized Dawid–Sebastiani score for multivariate ensemble
forecasts, Q. J. Roy. Meteorol. Soc., 146, 2421–2431, https://doi.org/10.1002/qj.3800, 2020. a
Worsnop, R. P., Scheuerer, M., Hamill, T. M., and Lundquist, J. K.: Generating wind power scenarios for probabilistic ramp event prediction using
multivariate statistical post-processing, Wind Energ. Sci., 3, 371–393,
https://doi.org/10.5194/wes-3-371-2018, 2018. a, b
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.
The power generated by offshore wind farms can vary greatly within a couple of hours, and...
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