Data Center Operations

Data Center Cooling: Intelligent Chiller Management

Reduce energy consumption by 18% and eliminate unplanned thermal incidents with ML-driven monitoring and agentic corrective workflows.

18%

Energy Savings

32%

Less Downtime

87%

Anomaly Accuracy

$1.2M

Saved

Challenge & Solution

Why single-method approaches fall short, and what replaces them.

The Challenge

Chillers account for 30 40% of total data center energy

Reactive operations miss early warning signs

Fixed maintenance schedules waste resources on healthy units

Thermal incidents cause costly emergency shutdowns

The Solution

Four specialized ML models provide continuous health assessment while a 6 stage agentic workflow handles alerting, deep dive analysis, field validation, and corrective actions with human oversight.

Workflow Components

10 coordinated components across agents, rules, and models.

Efficiency Model

Anomaly Model

Maintenance Model

Capacity Model

Continuous Monitoring

Smart Alert L1

Contextual Deep Dive

Field Validation L0

Corrective Actions

Oversight L2

Workflow

End to end execution path, including branch points and human gates.

Hourly Monitoring

→

Deviation Detected

→

Alert L1 / Smart Triage

→

Deep Dive / Contextual

→

Action?

→

Ignore

Log & Close

Act

Field Validation L0

→

Corrective Actions

→

L2 Oversight

Results

Measured in production.

18%

Energy reduction

32%

Fewer defaults

45-min

Processing time

$1.2M

Automation rate

Zero

Thermal incidents

Why Multi-Method Matters Here

No single technique covers the whole problem.

ML Models Monitor

Four specialized models track efficiency, anomalies, maintenance, and capacity.

Rules Guard Safety

Thermal thresholds and capacity limits are never overridden.

Workflows Coordinate

Six-stage process ensures proper validation at every step.

LLMs Contextualize

Deep-dive analysis connects sensor data with operational history.