Near-real-time energy production forecasts with NVIDIA Earth-2 and AWS Batch

This article discusses how AWS Batch and NVIDIA Earth-2 can be used to perform near-real-time energy production forecasts by combining machine learning weather prediction models with traditional wind farm simulations.

Specifically, the article covers:

• Background on the challenges and limitations of traditional numerical weather prediction (NWP) models
• How machine learning approaches like NVIDIA's FourCastNet model can provide faster and more cost-effective weather forecasting
• Coupling FourCastNet's wind speed predictions with PyWake wind farm simulations to forecast energy production
• The AWS infrastructure used, including AWS Batch and EC2 GPU instances, to run the ML inferences and simulations at scale
• Results showing forecasted energy production for 5 wind farms over 7.5 days using ensemble FourCastNet runs
• Benefits of this approach for rapidly exploring many scenarios and improving prediction accuracy