Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems
Fuente:
arXiv
Guardado en:
| Autores principales: | Alsheikhi, Abeer, Farhadi, Amirfarhad, Zamanifar, Azadeh |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
TA-RNN-Medical-Hybrid: A Time-Aware and Interpretable Framework for Mortality Risk Prediction
por: Jafari, Zahra, et al.
Publicado: (2026)
por: Jafari, Zahra, et al.
Publicado: (2026)
Deep Reinforcement Learning for System-on-Chip: Myths and Realities
por: Sung, Tegg Taekyong, et al.
Publicado: (2022)
por: Sung, Tegg Taekyong, et al.
Publicado: (2022)
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge
por: Mounesan, Motahare, et al.
Publicado: (2024)
por: Mounesan, Motahare, et al.
Publicado: (2024)
Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
por: Wang, Yuqing, et al.
Publicado: (2025)
por: Wang, Yuqing, et al.
Publicado: (2025)
Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization
por: Xu, Zheng, et al.
Publicado: (2024)
por: Xu, Zheng, et al.
Publicado: (2024)
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
por: Li, Boyang, et al.
Publicado: (2024)
por: Li, Boyang, et al.
Publicado: (2024)
Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
por: Hogade, Ninad, et al.
Publicado: (2024)
por: Hogade, Ninad, et al.
Publicado: (2024)
Hierarchical Federated Learning for Crop Yield Prediction in Smart Agricultural Production Systems
por: Abouaomar, Anas, et al.
Publicado: (2025)
por: Abouaomar, Anas, et al.
Publicado: (2025)
Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Survey
por: Liu, Zhihong, et al.
Publicado: (2024)
por: Liu, Zhihong, et al.
Publicado: (2024)
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
por: Li, Xiaoya, et al.
Publicado: (2025)
por: Li, Xiaoya, et al.
Publicado: (2025)
Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning
por: Turazza, Fabio, et al.
Publicado: (2026)
por: Turazza, Fabio, et al.
Publicado: (2026)
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning
por: Holen, Martin, et al.
Publicado: (2023)
por: Holen, Martin, et al.
Publicado: (2023)
ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning
por: Xiao, Bangjun, et al.
Publicado: (2026)
por: Xiao, Bangjun, et al.
Publicado: (2026)
Measuring Heterogeneity in Machine Learning with Distributed Energy Distance
por: Fan, Mengchen, et al.
Publicado: (2025)
por: Fan, Mengchen, et al.
Publicado: (2025)
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
por: He, Jialuo, et al.
Publicado: (2024)
por: He, Jialuo, et al.
Publicado: (2024)
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
por: Mei, Zhiyu, et al.
Publicado: (2023)
por: Mei, Zhiyu, et al.
Publicado: (2023)
Gradient Correction in Federated Learning with Adaptive Optimization
por: Chen, Evan, et al.
Publicado: (2025)
por: Chen, Evan, et al.
Publicado: (2025)
An Advanced Reinforcement Learning Framework for Online Scheduling of Deferrable Workloads in Cloud Computing
por: Dong, Hang, et al.
Publicado: (2024)
por: Dong, Hang, et al.
Publicado: (2024)
The Big Send-off: Scalable and Performant Collectives for Deep Learning
por: Singh, Siddharth, et al.
Publicado: (2025)
por: Singh, Siddharth, et al.
Publicado: (2025)
Adaptive Approach to Enhance Machine Learning Scheduling Algorithms During Runtime Using Reinforcement Learning in Metascheduling Applications
por: Alshaer, Samer, et al.
Publicado: (2025)
por: Alshaer, Samer, et al.
Publicado: (2025)
Optimizing Federated Learning by Entropy-Based Client Selection
por: Lutz, Andreas, et al.
Publicado: (2024)
por: Lutz, Andreas, et al.
Publicado: (2024)
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems
por: Stehle, Franz Kevin, et al.
Publicado: (2024)
por: Stehle, Franz Kevin, et al.
Publicado: (2024)
Scaling Deep Learning Research with Kubernetes on the NRP Nautilus HyperCluster
por: Hurt, J. Alex, et al.
Publicado: (2024)
por: Hurt, J. Alex, et al.
Publicado: (2024)
Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections
por: Wagenländer, Marcel, et al.
Publicado: (2023)
por: Wagenländer, Marcel, et al.
Publicado: (2023)
Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach
por: Husnoo, Muhammad Akbar, et al.
Publicado: (2024)
por: Husnoo, Muhammad Akbar, et al.
Publicado: (2024)
SmartMem: Layout Transformation Elimination and Adaptation for Efficient DNN Execution on Mobile
por: Niu, Wei, et al.
Publicado: (2024)
por: Niu, Wei, et al.
Publicado: (2024)
RLinf: Flexible and Efficient Large-scale Reinforcement Learning via Macro-to-Micro Flow Transformation
por: Yu, Chao, et al.
Publicado: (2025)
por: Yu, Chao, et al.
Publicado: (2025)
COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training
por: Kadiyala, Divya Kiran, et al.
Publicado: (2022)
por: Kadiyala, Divya Kiran, et al.
Publicado: (2022)
Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM
por: Zhang, Biyao, et al.
Publicado: (2025)
por: Zhang, Biyao, et al.
Publicado: (2025)
Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning
por: Wang, Yuqing, et al.
Publicado: (2025)
por: Wang, Yuqing, et al.
Publicado: (2025)
Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management
por: Zhang, Yifan, et al.
Publicado: (2024)
por: Zhang, Yifan, et al.
Publicado: (2024)
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
por: Fourati, Fares, et al.
Publicado: (2023)
por: Fourati, Fares, et al.
Publicado: (2023)
FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning
por: Chen, Tan, et al.
Publicado: (2025)
por: Chen, Tan, et al.
Publicado: (2025)
Enhancing Cluster Scheduling in HPC: A Continuous Transfer Learning for Real-Time Optimization
por: Sliwko, Leszek, et al.
Publicado: (2025)
por: Sliwko, Leszek, et al.
Publicado: (2025)
Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters
por: Dongare, Shruti, et al.
Publicado: (2025)
por: Dongare, Shruti, et al.
Publicado: (2025)
Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
por: Malhotra, Shubham, et al.
Publicado: (2025)
por: Malhotra, Shubham, et al.
Publicado: (2025)
Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks
por: Kaplan, Caelin, et al.
Publicado: (2024)
por: Kaplan, Caelin, et al.
Publicado: (2024)
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
por: Zhang, Pengyu, et al.
Publicado: (2024)
por: Zhang, Pengyu, et al.
Publicado: (2024)
Accelerating Privacy-Preserving Federated Learning in Large-Scale LEO Satellite Systems
por: Guo, Binquan, et al.
Publicado: (2025)
por: Guo, Binquan, et al.
Publicado: (2025)
HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
por: Lin, Zheng, et al.
Publicado: (2025)
por: Lin, Zheng, et al.
Publicado: (2025)
Ejemplares similares
-
TA-RNN-Medical-Hybrid: A Time-Aware and Interpretable Framework for Mortality Risk Prediction
por: Jafari, Zahra, et al.
Publicado: (2026) -
Deep Reinforcement Learning for System-on-Chip: Myths and Realities
por: Sung, Tegg Taekyong, et al.
Publicado: (2022) -
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge
por: Mounesan, Motahare, et al.
Publicado: (2024) -
Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
por: Wang, Yuqing, et al.
Publicado: (2025) -
Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization
por: Xu, Zheng, et al.
Publicado: (2024)