Výsledky vyhledávání - "Daan R. Scheepens"

Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range

Autor: Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, Claudia Plant

The number of wind farms and amount of wind power production in Europe, both on- and offshore, have increased rapidly in the past years. To ensure grid stability and on-time (re)scheduling of maintenance tasks and to mitigate fees in energy trading,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a54e279ada319032655d212a4a7e0215
https://doi.org/10.5194/gmd-16-251-2023

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

Autor: Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, Claudia Plant

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::171ffd853c92a24273324567c03899cb
https://doi.org/10.5194/egusphere-2022-599-supplement

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

Autor: Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, Claudia Plant

The amount of wind farms and wind power production in Europe, both on- and off-shore, has increased rapidly in the past years. To ensure grid stability, on-time (re)scheduling of maintenance tasks and mitigate fees in energy trading, accurate predict

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f33448246f18a3ee36308975f95ff39b
https://doi.org/10.5194/egusphere-2022-599

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