# Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2507.21153 · HTML: https://mlsanity.com/paper/2507.21153/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 6 of 6)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [A machine learning approach for forecasting hierarchical time series](https://mlsanity.com/paper/2006.00630/) (2020)
- [Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning](https://mlsanity.com/paper/2111.10898/) (2021)
- [Efficient Privacy-Preserving Electricity Theft Detection with Dynamic Billing and Load Monitoring for AMI Networks](https://mlsanity.com/paper/2005.13793/) (2020)
- [NaviGNN: Multi-Agent Reinforcement Learning and Graph Neural Network for Sustainable Mobility in Futuristic Smart Cities](https://mlsanity.com/paper/2507.15143/) (2025)

## Built on by (most cited first, 2 of 2)

- [Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms](https://mlsanity.com/paper/2601.08052/) (2026)
- [Environmental Impact of Generative and Agentic AI: An in-Depth Analysis and Green Solutions](https://mlsanity.com/paper/2609.32960/) (2026)

## 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.