Energy optimisation — Data centre
Reinforcement-learning control of chillers, compressors and furnaces. Delivered for a data centre client in Nagpur.
- Location
- Nagpur
- Year
- 2023
- Duration
- 24 weeks
- Team
- 3 engineers
- 11%
- energy saved
- 10.8 mo
- payback
- 3,311
- t CO₂/yr avoided
The challenge
Leadership wanted measurable results inside one quarter, with no disruption to running operations.
What we built
We ran a two-week discovery sprint, then built and fielded the system end to end on Ray RLlib, Python and BACnet, with operator training and an SLA.
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
- Before-and-after measurements agreed with the client up front
- Integrated with existing PLC, ERP or GIS systems — no rip-and-replace
- Safety case reviewed with the client's EHS team
- Documentation, drawings and source handed over at close
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