Energy optimisation — Hospital
Reinforcement-learning control of chillers, compressors and furnaces. Delivered for a hospital client in Nagpur.
- Location
- Nagpur
- Year
- 2025
- Duration
- 10 weeks
- Team
- 7 engineers
- 18%
- energy saved
- 15.9 mo
- payback
- 2,526
- t CO₂/yr avoided
The challenge
The hospital team was relying on manual effort that could not keep up with volume, and every miss had a direct cost.
What we built
We owned the whole stack — hardware, firmware, models and dashboards — using Ray RLlib, Python, BACnet so there was one accountable team.
Delivery timeline
Phase 1
Discovery sprint and site survey
Phase 2
Prototype built and tested in the Motera lab
Phase 3
Pilot on site with before/after measurement
Phase 4
Scale-out, training and handover
Highlights
- Operator training delivered in Gujarati, Hindi and English
- Documentation, drawings and source handed over at close
- Remote monitoring and over-the-air updates included
- Integrated with existing PLC, ERP or GIS systems — no rip-and-replace
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