AI for Data Center Capacity & Thermal Optimization

Balance workloads, cooling, and power to maximize capacity, reduce PUE, and protect reliability with AI-driven control.

Thermal limits and power constraints cap capacity. AI can forecast thermal behavior, optimize cooling setpoints/airflow, and place workloads to increase effective capacity, reduce PUE, and avoid hotspots and failures.

Carbon/price-aware placement (with scheduling stack).

Predictive alerts for equipment (pumps/fans/compressors).

Battery/UPS and generator readiness; demand response.

live demo · related simulation● LIVE

OT telemetry; BMS/SCADA integration; time-sync.

Data quality and outlier checks; sensor health.

Digital twin/CFD models for grounding ML.

Frequently asked questions

What is the implementation roadmap for mysimulator.uk?

Implementation Roadmap

How can I integrate telemetry and BMS data, including baseline thermal and PUE models?

Integrate telemetry/BMS; baseline thermal and PUE models.

Can I pilot setpoint and airflow optimization within the simulator in advisory mode, and how do I measure gains?

Pilot setpoint/airflow optimization in advisory mode; measure gains.

How can I add workload placement and DR/carbon-aware coordination to my simulations?

Add workload placement and DR/carbon-aware coordination.

▶ Try it live

Everything above runs in your browser — open Heat Conduction and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Heat Conduction simulation

AI for Data Center Capacity & Thermal Optimization | ML Knowledge Hub