An adapted deep convolutional RNN model for spatio-temporal prediction of wind speed extremes in the short-to-medium range for wind energy applications

Daan Scheepens; Irene Schicker; Petrina Papazek; Katerina Hlavackova-Schindler; Claudia Plant

doi:https://doi.org/10.5194/ems2022-327

[Back] [Session OSA1.9]

EMS Annual Meeting Abstracts

Vol. 19, EMS2022-327, 2022

https://doi.org/10.5194/ems2022-327

EMS Annual Meeting 2022

An adapted deep convolutional RNN model for spatio-temporal prediction of wind speed extremes in the short-to-medium range for wind energy applications

Daan Scheepens¹, Irene Schicker², Petrina Papazek², Katerina Hlavackova-Schindler¹, and Claudia Plant¹

Daan Scheepens et al.

¹Data Mining and Machine Learning Research Group, Faculty of Computer Science, University of Vienna, Vienna, Austria (d.r.scheepens@gmail.com)
²ZAMG, DMM-VHMOD, Vienna, Austria (irene.schicker@zamg.ac.at)

How to cite: Scheepens, D., Schicker, I., Papazek, P., Hlavackova-Schindler, K., and Plant, C.: An adapted deep convolutional RNN model for spatio-temporal prediction of wind speed extremes in the short-to-medium range for wind energy applications, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-327, https://doi.org/10.5194/ems2022-327, 2022.

This abstract has been withdrawn on 31 Aug 2022.

An adapted deep convolutional RNN model for spatio-temporal prediction of wind speed extremes in the short-to-medium range for wind energy applications

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