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TRUVACE RECORD VERSION
record: TRV-2026-2627
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T08:05:44.165800Z
status: published
lens: g_space
sector: climate
headline: PoCo: Joint Optimization of Power and Cooling for Low-Carbon CCHP-Integrated Data Centers
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: PoCo reduces energy costs by 25.8%, lowers carbon emissions by 15.3%, and improves power usage effectiveness by 4.9% compared with the baseline algorithms
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers propose PoCo, a deep reinforcement learning framework that controls power generation and cooling together in data centers fed by combined cooling, heating and power (CCHP) systems. Tested on real-world Alibaba cluster traces, it cut energy costs by 25.8% and carbon emissions by 15.3% against baseline algorithms, and improved power usage effectiveness by 4.9%.

The work responds to rising data-center energy use and emissions driven by the rapid growth of AI applications. Conventional strategies tend to regulate power and cooling subsystems separately, which the authors say causes inefficiencies and ignores lifecycle carbon impacts.
limitation: 
tag: Evidence-backed gain
key_points: PoCo reduced energy costs by 25.8% compared with baseline algorithms in experiments on Alibaba cluster traces. | Carbon emissions fell by 15.3% and power usage effectiveness improved by 4.9% against the same baselines. | The framework jointly controls power generation and cooling in CCHP-integrated data centers instead of treating them separately. | A parameterized action space handles hybrid discrete-continuous decisions that arise in joint optimization. | A long- and short-term time-series network with attention enables predictive cooling control and mitigates thermal inertia effects. | A parameterized deep Q-network regulates power generation and cooling together, resolving thermoelectric coupling conflicts.
rundown: PoCo is built on three components. A parameterized action space handles the mix of discrete and continuous choices in joint power and cooling decisions, and a long- and short-term time-series network with attention predicts cooling needs to counter thermal inertia.

A parameterized deep Q-network then regulates power generation and cooling provision together. According to the authors, this addresses the high-dimensional, dynamic nature of CCHP systems and the thermoelectric coupling conflicts between heat and electricity production.

The evaluation used real-world Alibaba cluster traces and compared PoCo with baseline algorithms. Results were a 25.8% cut in energy costs, a 15.3% cut in carbon emissions and a 4.9% gain in power usage effectiveness.
sources:
- peer_reviewed | IEEE Transactions on Industrial Informatics | https://doi.org/10.1109/tii.2026.3692684 | 2026-05-27
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