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Artificial IntelligenceDeliveredEnergy optimisation

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
Interactive 3D · drag to rotate
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

  1. Phase 1

    Discovery sprint and site survey

  2. Phase 2

    Prototype built and tested in the Motera lab

  3. Phase 3

    Pilot on site with before/after measurement

  4. 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

Have a problem that needs a machine built for it?

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