English(EN) Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

强化学习优化风电场数据中心能源使用

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-29 13:59

研究人员探索了使用强化学习（RL）来优化风电场内数据中心的运行。开发了一个仿真框架来测试RL控制器进行工作负载转移，以解决风能利用不足的挑战。研究发现，尽管像近端策略优化（PPO）和软Actor-Critic（SAC）这样的RL方法显示出潜力，但它们仍落后于具有全天预见能力的离线优化方法。 AI

影响这项研究可能有助于提高与可再生能源集成的风电场数据中心的能源管理效率。

排序理由该集群包含一篇详细介绍强化学习新应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-29 13:59

利用强化学习实现风电场数据中心的能源优化运行

This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers. We introduce a reproducible fixed-day simulation framework with synthetic wind and price signals and…