Hyper-Local Weather Intelligence

Merging physical climate models with Generative AI for the Nordic Energy Market.

model_prediction.py
import atmolabs as atmo

# Initialize hybrid model
model = atmo.HybridPredictor(
    resolution="1km",
    horizon="72h",
    region="nordic"
)

# Generate forecast
forecast = model.predict(
    lat=55.6761, lon=12.5683
)
print(f"Precision: {forecast.accuracy}%")

Powering the next generation of energy trading

Ørsted
Vattenfall
Equinor
Fortum
DONG Energy

Physics + AI Hybrid Core

Kilometer-scale precision combining numerical weather prediction with generative AI. Our models deliver unprecedented accuracy for wind and solar forecasting.

<1km
Resolution
96.7%
Accuracy
72h
Horizon

Grid Stability

Reducing imbalance costs by up to 40% through predictive analytics and real-time grid optimization.

Cost Reduction40%
Uptime Improvement99.8%

Made in Denmark

Built in Copenhagen for the Nordic region

Enterprise API

RESTful and WebSocket APIs with 99.99% uptime SLA. Real-time data streaming for critical operations.

Live