Weather Forecasting
1 / 5
AI energy forecasting models are judged on RMSE, CRPS, MAE, and skill score, supported by concepts like NWP, ensembles, lead time, and hindcasts.
2 / 5
Traditional NWP is expensive, slow, and infrequent, while unconstrained AI models can produce outputs that break core conservation laws.
3 / 5
Physics-constrained transformers such as EPT-2 and EPT-2e beat ECMWF HRES and ENS on accuracy, physics compliance, and update frequency across all lead times.
4 / 5
Production deployment depends on hindcast validation, rapid-refresh capability, stable schemas, and physics compliance to support reliable trading decisions.
5 / 5
Book a demo with Jua to benchmark EPT-2 against your current provider and see production-grade forecasts in minutes.
Discover how physics-constrained AI energy forecasting models outperform NWP. Jua's EPT-2 beats ECMWF on accuracy & refresh rate. Book a demo today.