52/100

Moderate evidence

GainPublished recordClimateGood Space

PoCo: Joint Optimization of Power and Cooling for Low-Carbon CCHP-Integrated Data Centers

Truvace record
TRV-2026-2627raw data (JSON)
Version
v1Published
First published
Evidence type
Benchmark or lab evaluation
Sources
1 sourcePeer-reviewed
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The recent explosive growth of artificial intelligence (AI) applications has sharply increased the energy consumption and carbon emissions of data centers. Conventional energy-saving strategies often regulate power and cooling subsystems separately, resulting in inefficiencies and overlooking lifecycle carbon impacts. To address this issue, this article proposes PoCo, a deep reinforcement learning-based framework for the joint optimization of power generation and cooling control in combined cooling, heating, and…

The gain

In experiments replaying real Alibaba cluster traces, a reinforcement-learning controller for combined cooling, heating and power data centers cut energy costs by 25.8% and carbon emissions by 15.3% versus baseline algorithms.

Sources

1 on file
  1. Peer-reviewed2026-05-27

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v1

Truvace Impact Record TRV-2026-2627, v1: “PoCo: Joint Optimization of Power and Cooling for Low-Carbon CCHP-Integrated Data Centers.” Truvace, 2026-11-12. /record/TRV-2026-2627 (accessed on the day you copy it). sha256 e55fcf0488f7a0c1…

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