Data Center Energy Management

Latest papers

22 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Grid-Oriented Probabilistic Load Forecasting for AI Data Centers
Nov 6, 2026Qingyi Yao, Ding Lin, Tianqiao Zhao +2Electricity Load Forecasting Probabilistic Time Series Forecasting
Southern Methodist University, Dallas, TX, USA · The University of Texas at Arlington, Arlington, TX, USA · Brookhaven National Laboratory, Upton, NY, USA
Creating Power Flexibility in Datacenters via Query Routing
Nov 3, 2026Shivam Patel, Bingqing Chen, Jared Fernandez +2Data Center Energy Management Energy-Aware LLM Inference
Carnegie Mellon University · Bosch Center for AI · Cornell University
Locked In: Multi-Objective RL for the AI Data Center Build-Out
Oct 26, 2026Satyapragnya Kar, Rajarshi Das, PR PanigrahiMulti-Objective Reinforcement Learning Data Center Energy Management
New York University · AlphaInfra.ai, MQube Cognition · Envana Software Solutions
Modeling Liquid-Cooled Data Centers in EnergyPlus
Oct 23, 2026Yun-Dam Ko, Dipashreya A. Sur, Cade Napier +2Building Energy Modeling Data Center Energy Management
Stanford University, Stanford, California, USA
Heterogeneous Space Data Centers: A Key Paradigm for Future Non-Terrestrial and In-Orbit Services
Oct 23, 2026Peng Hu, Mustafa Alhassan, Amjad IqbalHeterogeneous Computing LEO Satellite Networks
Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada
Computing at Sea: Floating and Offshore Data Centres as a Pathway to Sustainable AI Infrastructure
Sep 11, 2026Cheng Siong Chin, Jianhua Zhang, M. VenkateshkumarData Center Energy Management
Artificial Intelligence for Energy Optimization in Data Centers
Sep 3, 2026Mohammed Basharath Ullah, Summaiya Unnisa Begum, Mohammed Nadeem UllahEnergy-Efficient ML Data Center Energy Management
Hyderabad, India
LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations
Aug 19, 2026Hanzhao Wang, Jingxuan Wu, Yumeng Li +2Data Center Energy Management Energy-Efficient ML
The University of Sydney Business School, The University of Sydney · Department of Statistics and Operations Research, University of North Carolina at Chapel Hill · Department of Industrial Engineering, Purdue University
InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers
Aug 13, 2026Nicoletta Tsiopani, Moysis Symeonides, George Pallis +1Energy-Efficient ML LLM Inference Serving
Department of Computer Science University of Cyprus
Cutting AI Datacenter Energy with Reinforcement Learning: Measured Power Control of LLM Training from One GPU to the Fleet
Jul 27, 2026Eliseo CurcioRL Control Energy-Efficient ML
Advanced Department of Artificial Intelligence and Energy – New York
A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures
Jul 19, 2026Soham Ghosh, Nabil Mohammed, Mohammad Ashraf Hossain SadiData Center Energy Management Power Systems
Black & Veatch, Overland Park, KS 66211, USA · Centre for New Energy Transition Research (CfNETR) at Federation University, Ballarat, Australia · University of Central Missouri, Warrensburg, MO 64093 USA
Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems
Jul 3, 2026Hyunsoo Lee, Panggah Prabawa, Dae-Hyun Choi +1Energy-Efficient ML Data Center Energy Management
LG Electronics, 17709, Pyeongtaek, Gyeonggi-do, Republic of Korea · School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea · Department of Electrical and Computer Engineering, Korea University, Seoul 02841, Republic of Korea
Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning
Jun 29, 2026Jan Stenner, Alexander Kilian, Sebastian Peitz +1RL Control Data Center Energy Management
Software Innovation Campus Paderborn, Paderborn University, Germany · Chair of Computer Networks and Computer Communications, University of Passau, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Technical University of Dortmund, Germany
Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
Jun 23, 2026Chris Williams, Philip Colangelo, Ayse Coskun +15Demand Response Carbon-Aware Computing
Emerald AI · Electric Power Research Institute (EPRI) · National Grid +2
A Scalable Digital Twin Framework for Energy Optimization in Data Centers
May 7, 2026Raphael Hendrigo de Souza Gonçalves, Wendel Marcos dos SantosData Center Energy Management Digital Twins
Federal University of São João del-Rei (UFSJ) · Federal Institute of Education, Science and Technology of São Paulo (IFSP)
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
May 6, 2026Jae-Won Chung, Zhirui Liang, Yanyong Mao +3Demand Response Power Systems
University of Michigan
Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach
May 3, 2026Yang Fu, Peng Qin, Liming Chen +3Reward Shaping Data Center Energy Management
Yang Fu, Peng Qin, Member, IEEE, Liming Chen, Zihao Zhang, Hao Yu, and Yifei Wang
DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling
Apr 17, 2026Nilavra Pathak, Samadrita Biswas, Nirmalya RoyModel Predictive Control Physics-Based Simulation
Marketing Data Science, Expedia Group · Analytical Development, Curia Global · Department of Information Systems, University of Maryland, Baltimore County
A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
Apr 14, 2026Mohammad AlShaikh Saleh, Sanjay Chawla, Sertac Bayhan +2Physics-Informed Forecasting Time Series Forecasting
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar · Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar · Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, Qatar +1
End-to-End Learning-based Operation of Integrated Energy Systems for Buildings and Data Centers
Mar 31, 2026Zhenyu Pu, Yu Yang, Liang Yu +1Data Center Energy Management Learning to Optimize
School of Automation Science and Engineering, Xi’an Jiaotong University, Shaanxi, China · College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China · Center for Intelligent and Networked Systems, Department of Automation, Tsinghua University, Beijing 100084, China +1
Energy Estimation for Virtual Servers Using Resource Utilization and Machine Learning
Sep 12, 2025Amandip SanghaData Center Energy Management Cross-Domain Generalization
The Climate and Environmental Institute NILU , Postboks 100, 2027 Kjeller, Norway
Power- and Fragmentation-aware Online Scheduling for GPU Datacenters
Dec 23, 2024Francesco Lettich, Emanuele Carlini, Franco Maria Nardini +2Online Resource Allocation Data Center Energy Management
Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo”, Consiglio Nazionale delle Ricerche, Pisa, Italy

Nov 6, 2026J/K move · Enter open · S save

Grid-Oriented Probabilistic Load Forecasting for AI Data Centers

Qingyi Yao, Ding Lin, Tianqiao Zhao, Meng Yue+1

Southern Methodist University, Dallas, TX, USA · The University of Texas at Arlington, Arlington, TX, USA · Brookhaven National Laboratory, Upton, NY, USA

Electricity Load Forecasting Probabilistic Time Series Forecasting Forecasting Benchmarks Uncertainty Calibration Data Center Energy Management Time Series Generation

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Probabilistic load forecasts for AI data centers need to represent both uncertainty under changing workloads and temporal dependence to support grid operations. This paper develops an open, grid-oriented benchmark using measured GPU power from a 448-GPU AI cluster. Forecasts are evaluated through four stages: probabilistic prediction, online uncertainty calibration, temporal scenario generation, and horizon-wide reserve sizing. Following a change in cluster utilization, a quantile LightGBM forecast achieves only 63.4% coverage for a nominal 90% interval, while online regime-conditioned calibration increases coverage to 87.3%. Even with well-calibrated marginal forecasts, independent sampling produces unrealistic ramp distributions and excessive reserve procurement. Reconstructing temporal dependence from historical forecast errors reduces the discrepancy in ramp statistics and lowers realized reserve cost by 7.4-10.6% across three forecasting tasks. These findings show that marginal forecast quality alone is insufficient for operational decisions and motivate joint evaluation of calibration, temporal structure, and decision outcomes.