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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- IEEE Transactions on Industrial Informaticsdoi:10.1109/tii.2026.3692684
Cite this record
v1Truvace 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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