mlsanityData to 14 Oct 2026

Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning

arXiv 2507.21153 ↗First posted 24 Jul 2025cs.LG cs.AI cs.SYcited by 2

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The exponential growth of digital services has positioned data centers among the most energy-intensive infrastructures in the modern economy, raising critical concerns regarding operational costs, carbon emissions, and the sustainable integration of renewable energy sources. This paper proposes a novel Deep Reinforcement Learning (DRL)-based energy management framework for data centers, designed to dynamically coordinate solar photovoltaic generation, wind power, battery storage systems, and conventional grid electricity under highly stochastic operational conditions. The proposed framework formulates the energy management problem as a Markov Decision Process and employs a Proximal Policy Optimization (PPO) agent augmented with a hybrid Long Short-Term Memory and temporal attention architecture, enabling accurate modeling of workload dynamics and renewable generation variability. A multi-objective reward function jointly minimizes energy costs, carbon emissions, and service-level agreement (SLA) violations while promoting efficient storage utilization. Extensive experiments conducted on three datasets demonstrate that the proposed framework achieves a 38% reduction in energy costs compared to rule-based heuristics and outperforms the strongest DRL baseline by 4.6%, while maintaining an SLA violation rate as low as 1.5% and an energy efficiency of 83.7%. Ablation studies confirm the individual contribution of each architectural component, and hyperparameter sensitivity analysis validates the robustness of the approach across a range of configurations.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 77 references that are in the map. First posted on arXiv 24 Jul 2025.

Builds on

6 of 77 references.

References · 6

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2020A machine learning approach for forecasting hierarchical time seriescited by 29
  4. 2021Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learningcited by 8
  5. 2020Efficient Privacy-Preserving Electricity Theft Detection with Dynamic Billing and Load Monitoring for AMI Networkscited by 3
  6. 2025NaviGNN: Multi-Agent Reinforcement Learning and Graph Neural Network for Sustainable Mobility in Futuristic Smart Citiescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farmscited by 0
  2. 2026Environmental Impact of Generative and Agentic AI: An in-Depth Analysis and Green Solutionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
  2. 2026Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
  3. 2025Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
  4. 2024A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning
  5. 2024Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
  6. 2025Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning
  7. 2026Reinforcement Learning-based Home Energy Management with Heterogeneous Batteries and Stochastic EV Behaviour
  8. 2024Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets