ML-based time interpolation of AIFS Ensemble for renewable energy forecasting

Recent advancements in machine learning based weather prediction (MLWP) present novel opportunities for downstream applications like forecasting of renewable energy production from intermittent sources, like wind and solar. MLWP models guarantee shorter simulation run times and lower computational costs, allowing faster updates of downstream models and greater flexibility in the generation of weather scenarios.

Forecasting renewable energy generation critically depends on available weather forecast data at adequate temporal and spatial resolution. Using MLWP weather data in energy system modelling and forecasting has been limited by the coarse temporal resolution of the current generation of models (e.g. ECMWF’s AIFS Ensemble model runs at 6-hour time steps).

In Europe, power market participants are increasingly exposed to weather forecast inaccuracies. This is due to the combined effect of how the power price is calculated for each price area, and the recent increase in intermittent renewable installed capacities. In detail, power prices are set each day for the following day by balancing supply and demand for each Market Time Unit (MTU), which are now 15 minutes long. It is then massively important to benchmark weather forecasts on a time resolution closer to the power market MTU, to properly assess which period will potentially be oversupplied, or undersupplied from intermittent renewable sources. In this context, the 6-hour time resolution of current MLWP models becomes a significant limiting factor for their usefulness.

Using NVIDIA’s Earth2Studio framework, we demonstrate an efficient, integrated MLWP pipeline combining the [open source] AIFS model with the ModAFNO time interpolation model to provide 1-hourly time-resolution MLWP data. This interpolated data is applied to our intermittent renewable energy production models to assess the interpolation quality compared the uninterpolated AIFS data and the best-in-class numerical weather prediction data provided by ECMWF’s IFS Ensemble forecast.