Forecasting of Meteorologically Driven “Extremes” in Wind and Solar Power: Can We Tackle and Improve Selected Cases of Non-forecasted Extreme Events using Deep Learning?

With the rapid transition towards an increased usage of renewable energy accurate predictions of the expected power production are needed to ensure grid stability, energy trading, and (re)scheduling of maintenance and energy transfer. In the last decades, both numerical weather prediction models as well as post-processing methods have significantly improved their prediction skills when applied to renewable energy. There are, however, events in renewable energy production which can be considered as extreme events but are not necessarily extremes in terms of meteorological conditions. The MEDEA project, funded by the Austrian Climate Research Program, aims at improving the definition and detection of extreme events relevant for renewable energies and to use these findings to improve both weather and climate predictions of such extreme events. In this MEDEA case study, we investigate selected (extremes) cases in renewable energy which were not properly reproduced by the models. We will have a deeper dive into two Saharan dust events in 2021 where none of the models was able to properly reproduce the amount of aerosols in Central Europe and solar power production was off by more than 5 GW in contrast to the predictions.  Here, several NWP models have failed to properly recognize its impact and, thus, impaired results based on raw model output. To tackle such events and improve the predictability, a deep learning framework consisting of an auto-encoder LSTM (long short-term memory; type of an artificial neural network) and random forest will be used and adopted for day-ahead predictions of these events. Relevant features for the learning algorithms are extracted from different NWP models, satellite data, and observations. Similarly, for wind energy production we demonstrate the methods in two selected case studies of extreme events. Results obtained by the deep learning framework yield, in general, high forecast-skills where we elaborate on interesting cases studies from a meteorological point of view. Different combinations of inputs and processing-steps are part of the analysis. We compare results to traditional forecast methods in order to validate the performance of our methods.