AI Data Center Cooling Optimization
The Problem
“Cut data center cooling costs without risk”
Organizations face these key challenges:
1
Overcooling driven by risk aversion, leading to persistently low supply air temperatures and excessive fan speeds
2
Fragmented data across BMS, DCIM, meters, and IT load signals, limiting actionable visibility and root-cause analysis
3
Difficulty balancing thermal safety, humidity control, redundancy constraints, and peak-load events without increasing operational risk
Impact When Solved
10–20% reduction in cooling kWh while maintaining ASHRAE thermal envelopes and SLA uptime0.05–0.15 PUE improvement through dynamic setpoint and airflow optimization5–15% lower HVAC maintenance spend via early detection of degrading components and inefficient operating states