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AI-driven HVAC optimisation at Exchange Quay

Continuous fault detection across a multi-building office estate — measurable improvement without capital replacement.

Zeroadditional hardware required
Multi-buildingestate coverage
Reactive → predictivemaintenance model
Background

Exchange Quay is a premier multi-building commercial office estate in Salford, Greater Manchester. With complex HVAC infrastructure and a diverse tenant mix, the estate faced the universal challenge of maintaining peak operational efficiency in a live, high-demand environment. A strategic partnership was formed between the asset owner TILL, managing agent CBRE, and CSR Sustain.

Client: TILL (asset owner) · CBRE (managing agent)  ·  Location: Salford

What we did
  • Control conflicts identified where heating and cooling were fighting each other
  • “Ghost running” found — AHUs, pumps and fans operating during unoccupied hours
  • Sub-optimal setpoints, control deadbands and plant sequencing corrected
  • Invisible degradation spotted before it was severe enough to trigger BMS alarms
Scope & approach

How the programme ran

Non-intrusive integration

The CIM PEAK AI-powered Fault Detection & Diagnostics platform was connected to the existing BMS with zero additional hardware and no tenant disruption.

Real-time AI analysis

Automated monitoring of thousands of data points to detect faults and abnormal operation as it occurred.

Expert oversight

CIBSE-certified Building Performance Engineers reviewed and validated every AI finding, ensuring recommendations aligned with best-practice engineering standards.

Challenges

What made this difficult

01

Operational drift

Plant running outside occupancy schedules, with control settings drifting through seasonal change and manual overrides.

02

Conflict and inefficiency

Plant running outside occupancy schedules, with control settings drifting through seasonal change and manual overrides.

03

Reactive maintenance

Plant running outside occupancy schedules, with control settings drifting through seasonal change and manual overrides.

04

The data gap

Plant running outside occupancy schedules, with control settings drifting through seasonal change and manual overrides.

Results

The outcome

Reduced HVAC energy consumption, enhanced plant longevity through lower runtime and mechanical stress, improved occupant comfort with fewer complaints, and a transition to proactive, data-led maintenance where performance drift is corrected in real time — all achieved without capital replacement.

Zeroadditional hardware required
Multi-buildingestate coverage
Reactive → predictivemaintenance mode
What we learned

Lessons

Takeaways
  • The most sustainable building is the one already built — when it is optimised correctly
  • Pairing AI diagnostics with certified engineering returns an estate to its original design intent
  • Independence between the controls layer and the performance verification layer builds owner confidence

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