Post-processing for wind and PV power production of hectometric NWP forecasts - which Machine Learning methods are beneficial for sparse data and extreme events?

Irene Schicker; Petrina Papazek; Pascal Gfäller; Iris Odak Plenkovic; Ivan Vujec; Alexander Kann; Kristian Horvath

doi:https://doi.org/10.5194/ems2024-711

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EMS Annual Meeting Abstracts

Vol. 21, EMS2024-711, 2024, updated on 05 Jul 2024

https://doi.org/10.5194/ems2024-711

EMS Annual Meeting 2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Post-processing for wind and PV power production of hectometric NWP forecasts - which Machine Learning methods are beneficial for sparse data and extreme events?

Irene Schicker

¹, Petrina Papazek¹, Pascal Gfäller¹, Iris Odak Plenkovic², Ivan Vujec², Alexander Kann¹, and Kristian Horvath²

Irene Schicker et al.

¹GeoSphere Austria, Postprocessing, Vienna, Austria (irene.schicker@geosphere.at)
²Croatian Hydrological and Meteorological Service DHMZ, Zagreb, Croatia

How to cite: Schicker, I., Papazek, P., Gfäller, P., Odak Plenkovic, I., Vujec, I., Kann, A., and Horvath, K.: Post-processing for wind and PV power production of hectometric NWP forecasts - which Machine Learning methods are beneficial for sparse data and extreme events?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-711, https://doi.org/10.5194/ems2024-711, 2024.

This abstract has been withdrawn on 30 Aug 2024.

Abstract EMS2024-711

Post-processing for wind and PV power production of hectometric NWP forecasts - which Machine Learning methods are beneficial for sparse data and extreme events?

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