AI Data Center Energy Optimization

AI-driven optimization of data center cooling, power distribution, and energy efficiency.

The Problem

Analysis in progress...

The Shift

Before AI~85% Manual

Human Does

  • Review every case manually
  • Handle requests one by one
  • Make decisions on each item
  • Document and track progress

Automation

  • Basic routing only
With AI~75% Automated

Human Does

  • Review edge cases
  • Final approvals
  • Strategic oversight

AI Handles

  • Automate routine processing
  • Classify and route instantly
  • Analyze at scale
  • Operate 24/7

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1
Quick Win

Flexible Batch Shifting

QoS-tiered batch scheduling across regions

Stand up an MVP scheduler that identifies low-QoS, massively-parallel Monte Carlo workloads and shifts their execution time/location to better align with grid conditions. This leverages the evidence that not all jobs need strict QoS and that HPC participants can share load across regions to respond to grid signals, enabling initial “spinning demand” behavior via deferrable batch jobs.

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Real-World Use Cases

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