EPT-2 Leads AI Weather Model Benchmarks on Energy Variables

1 / 5

EPT-2 leads all tested AI and traditional models on the four energy-critical variables (10 m wind, 100 m wind, 2 m temperature, SSRD) across the full 0–240 h range in the 2025 StationBench evaluation against more than 10,000 real…

2 / 5

EPT-2e, the ensemble variant, outperforms the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time, and delivers superior probabilistic skill with only 10 members.

3 / 5

Inference costs for EPT-2 are roughly four orders of magnitude lower than traditional NWP (~0.25 kWh vs. 8,400 kWh), which enables up to 24 updates per day and forecasts that finish 2.5 hours ahead of competing runs.

4 / 5

EPT-2’s physics-constrained architecture avoids the systematic biases seen in other AI models on extreme events and maintains accuracy on wind ramps, temperature extremes, and solar variability that directly affect energy P&L.

5 / 5

Book a demo with Jua to run EPT-2 head-to-head against your current provider on your own region and variables.