Operating challenge
Fluctuating campus demand and ice-storage scheduling made fixed setpoints inefficient across comparable-load days.
Deployment registry
A small set of verified deployments shows how supervisory AI adapts to existing cooling plants, operating constraints, and contractual baselines without turning this page into a broad market taxonomy.
Primary case // 001
Fluctuating campus demand and ice-storage scheduling made fixed setpoints inefficient across comparable-load days.
Weather, load, and tariff signals informed supervisory setpoint updates for chillers, pumps, towers, and storage dispatch.
Comparable-day review showed a 10.12% energy reduction while preserving operator oversight and monthly reporting.
Verified fleet performance logs
High-comfort venue hotel
A comfort-critical hotel plant used hard operating guardrails and local deployment to reduce tuning risk.
Data center cold plant
A three-chiller data center plant used coordinated AI control across chillers, pumps, and towers.
Mode-split data center
A data center plant separated free-cooling and mechanical-cooling modes so each operating state could be measured cleanly.
Phased data center campus
A staged data center deployment used transfer reinforcement learning and advisory value push mode to reduce adoption risk.
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Compare your facility performance against this verified deployment set. Our engineering team can provide a tailored feasibility review.