Attention-based postprocessing of ensemble weather forecasts for renewable energy applications by leveraging inter-ensemble relationships of multiple predictors
Van Poecke, Aaron, Meng, Ruoke, Demaeyer, Jonathan, Van den Bergh, Joris, Smet, Geert, Termonia, Piet, Hellinckx, Peter and Tabari, Hossein ed. Attention-based postprocessing of ensemble weather forecasts for renewable energy applications by leveraging inter-ensemble relationships of multiple predictors 14-19 April Vienna Austria. Göttingen: Copernicus Meetings, 2024.
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http://dx.doi.org/https://doi.org/10.5194/egusphere-egu24-11394 Full text from publisher -
Sub-type Conference proceedings Author Van Poecke, Aaron
Meng, Ruoke
Demaeyer, Jonathan
Van den Bergh, Joris
Smet, Geert
Termonia, Piet
Hellinckx, Peter
Tabari, HosseinEvent series EGU General Assembly Title of Event Attention-based postprocessing of ensemble weather forecasts for renewable energy applications by leveraging inter-ensemble relationships of multiple predictors Date of Event 14-19 April Place of Event Vienna Austria Organizer The European Geosciences Union Publication Date 2024-05-07 Place of Publication Göttingen Publisher Copernicus Meetings Pages egu24-11394 Language eng Abstract Indirect models for renewable energy forecasting rely heavily on accurate weather predictions. Operational weather forecasting today is mainly based on numerical weather prediction models, often employing ensembles to estimate the day-to-day forecast uncertainty. To correct for errors due to simplifications in these models, inaccurate initial conditions, and representativeness problems, statistical postprocessing becomes necessary for these ensemble forecasts. Current postprocessing techniques often disregard possible inter-ensemble relationships by correcting each member separately, or employ a distributional approach that requires extra multivariate methods to restore spatio-temporal and inter-variable correlations. In this work, we tackle these shortcomings with an innovative, attention-based member-by-member approach which postprocesses each member individually while simultaneously integrating Copyright Holder author(s) Copyright Year 2024 Copyright type Creative commons DOI https://doi.org/10.5194/egusphere-egu24-11394 -
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