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.
arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.
By Hallah Shahid Butt, Qiong Huang, G\"okhan Demirel, Kevin F\"orderer, Erfan Tajalli-Ardekani, Simnon Waczowicz, Luigi Spatafora, Veit Hagenmeyer, Benjamin Sch\"afer
arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.
By Aleksandra Dmitruka, Karlis Freivalds
arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.
By Yang Fu, Peng Qin, Liming Chen, Zihao Zhang, Hao Yu, Yifei Wang
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.
By Takumi Shioda, Kohei Terashima, Tatsuo Nagai
arXiv:2501. 12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities.
By Zhuoran Li, Ruishuo Chen, Hai Zhong, Longbo Huang
arXiv:2605. 29032v2 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) agents typically learn world models by minimizing predictive loss.
By Christoph Dann, Yishay Mansour, Mehryar Mohri
arXiv:2608. 09453v1 Announce Type: cross Abstract: On--off cycling is the main cause of compressor wear in residential heat pumps, yet reinforcement learning (RL) controllers for buildings typically optimise only energy cost and thermal comfort, ignoring how much the learned policy cycles.
By Faizan Ahmed, Aniket Dixit, James Brusey
arXiv:2607. 03140v1 Announce Type: new Abstract: Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements.
By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.
By Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang
arXiv:2607. 12763v1 Announce Type: cross Abstract: Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior.
By Usman Haider, Karl Mason
arXiv:2510. 17709v2 Announce Type: replace-cross Abstract: Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when real-world interaction is expensive.
By Akhil S Anand, Shambhuraj Sawant, Paavo Parmas, Jasper Hoffmann, Dirk Reinhardt, Sebastien Gros