mlsanityData to 14 Oct 2026

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

arXiv 2606.30316 ↗First posted 29 Jun 2026cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Decision transformers and sequence modeling

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Abstract (arXiv)

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 delayed completion feedback, designed to be extensible toward more complex scenarios. As a controlled benchmarking basis, we then focus on the minimal case with one wind turbine and one co-located data center. In this setting, pure Reinforcement Learning exhibits a pronounced credit-assignment problem and tends to underuse free wind energy early in the day. We therefore evaluate two complementary countermeasures: optimization-based Imitation Learning and potential-based Reward Shaping. Across multi-seed training and a 200-day test set, Proximal Policy Optimization (PPO) and a Soft Actor-Critic (SAC) variant with an additional on-policy update routine achieve strong empirical performance among learned policies, and both Imitation Learning and Reward Shaping provide improvements in relevant configurations. A performance gap to the optimizer remains, which is expected: the optimizer plans offline with full-day foresight, whereas Reinforcement Learning must decide online from current observations without future realizations. The benchmark and ablation results provide a transparent basis for extending the approach toward richer multi-site and continuous-time scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 34 references that are in the map. First posted on arXiv 29 Jun 2026.

Builds on

9 of 34 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2021Carbon-Aware Computing for Datacenterscited by 41
  5. 2020Guided Exploration with Proximal Policy Optimization using a Single Demonstrationcited by 19
  6. 2023On the Limitations of Carbon-Aware Temporal and Spatial Workload Shifting in the Cloudcited by 8
  7. 2023Adapting Datacenter Capacity for Greener Datacenters and Gridcited by 6
  8. 2024A Reinforcement Learning Based Backfilling Strategy for HPC Batch Jobscited by 3
  9. 2024A Framework for Carbon-aware Real-Time Workload Management in Clouds using Renewables-driven Corescited by 1

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  1. 2025Data Center Cooling System Optimization Using Offline Reinforcement Learning
  2. 2026Offline Reinforcement-Learning-Based Power Control for Application-Agnostic Energy Efficiency
  3. 2026Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
  4. 2024Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
  5. 2025Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
  6. 2025Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
  7. 2026Cutting AI Datacenter Energy with Reinforcement Learning: Measured Power Control of LLM Training from One GPU to the Fleet
  8. 2025Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning