Attention-based postprocessing of ensemble weather forecasts for renewable energy applications by leveraging inter-ensemble relationships of multiple predictors

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 information from other ensemble members. Variables required for renewable energy forecasting are postprocessed at the station level by regressing ensemble forecasts of multiple predictors, including the forecasted variable itself, against observational data. The training data utilized is sourced from the EUPPBench dataset, which contains ensemble predictions from the integrated forecasting system of the ECMWF and corresponding observations. Transformer modules built around Self-Attention are employed to capture dependencies between different predictors, such as temperature and total cloud cover, next to significant relationships between the ensemble members themselves. Additionally, our model postprocesses the forecasts for all lead times simultaneously, taking into account the correlation between the postprocessed variable and  forecasts generated at earlier and later lead times. This results in postprocessing techniques that can be employed in downstream applications for conversion to renewable energy forecasts.