Research
1 / 5
AI weather intelligence replaces slow, expensive NWP workflows with physics-constrained foundation models that deliver faster, cheaper, more accurate forecasts for energy trading.
2 / 5
Physics-constrained models like EPT-2 outperform traditional NWP and unconstrained AI systems by learning conservation laws at the representation level and avoiding nonphysical predictions.
3 / 5
EPT-2 beats ECMWF HRES on every lead time for wind, temperature, and solar radiation, while EPT-2e surpasses the 50-member ECMWF ENS on RMSE and CRPS across virtually all horizons.
4 / 5
Jua for Energy combines the EPT foundation model, Athena agent layer, unified data pipeline, and decision-support tools to deliver production-grade briefings, alerts, and benchmarks that plug directly into trading systems.
5 / 5
Run live benchmarks against 25+ models on your own region and variables in under five minutes and see the comparison on the Jua platform.
Jua's physics-constrained AI weather intelligence outperforms ECMWF for energy trading. Faster, more accurate forecasts for your trading systems.