Deep Reinforcement Learning Optimizes Data Center Energy Use

This paper introduces a new Deep Reinforcement Learning (DRL) framework to manage energy consumption in data centers. The system dynamically coordinates solar, wind, battery storage, and grid power to reduce costs and carbon emissions. It utilizes a Proximal Policy Optimization agent with a hybrid LSTM and temporal attention architecture to model workload and renewable energy fluctuations. AI

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IMPACT This research could lead to more sustainable and cost-effective data center operations by optimizing energy usage.

RANK_REASON This is a research paper published on arXiv detailing a novel DRL framework for energy management. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Deep Reinforcement Learning
• Proximal Policy Optimization
• Long Short-Term Memory

• paper
• infra