Product
1 / 5
Automated energy forecasting workflows connect data ingestion, feature engineering, physics-constrained model inference, orchestration, monitoring, and alert delivery into pipelines that remove manual steps.
2 / 5
Jua’s EPT-2 model outperforms ECMWF HRES on every lead time from 0–240 hours across wind, temperature, and solar radiation variables, while EPT-2e beats the 50-member ECMWF ENS mean on both RMSE and CRPS metrics.
3 / 5
The EPT-2 RR rapid-refresh variant enables up to 24 model updates per day at approximately 0.25 kWh per simulation, roughly four orders of magnitude cheaper than traditional NWP runs that consume 8,400 kWh.
4 / 5
Athena, Jua’s AI agent, converts natural-language trader queries into production-grade briefings, benchmarks, and backtests in approximately 90 seconds, replacing manual analyst workflows.
5 / 5
Book a demo with Jua to evaluate automated energy forecasting workflows on your region and variables.
Jua's AI-powered workflows automate energy forecasting end-to-end — from data ingestion to model inference. Outperform ECMWF and cut costs. Start now.