Geospatial ML for Building Energy

Scaled from early MVP to production at Fuchs und Eule

Automated building-energy pipeline using public 3D data (LoD2) to predict roof/wall/window components.

Feeds physics-based DIN 18599 simulations via a kernel endpoint, producing estimates of energy state and financial KPIs (renovation cost, amortization, CO₂ savings).

Reduces energy-analyst effort from 3–8 hours to seconds.

• Company: Fuchs und Eule
• Location: Berlin, Germany
• Status: In production (scaled from early MVP)
• Relevant Technologies: Python, FastAPI, Pydantic, PyTest, Docker, AWS, computational geometry, 3D modeling (LoD2), DIN 18599 simulation