:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Muneer, Saad, Muhammadziad, Raziqullah
Natura:	Recurso digital
Lingua:
Pubblicazione:	Zenodo 2025
Accesso online:	https://doi.org/10.5281/zenodo.16149754
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management
di: Amiri, Mohammad Hossein Nejati, et al.
Pubblicazione: (2025)

AutoGrid AI: Deep Reinforcement Learning Framework for Autonomous Microgrid Management
di: Guo, Kenny, et al.
Pubblicazione: (2025)

Topologies for Battery and Supercapacitor Interconnection in Residential Microgrids with Intermittent Generation
di: Alexander Narvaez
Pubblicazione: (2020)

A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management
di: Liu, M. Vivienne, et al.
Pubblicazione: (2023)

A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning
di: Yao, Fulong, et al.
Pubblicazione: (2024)

Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling
di: Li, Yang, et al.
Pubblicazione: (2024)

Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
di: Li, Yuanzheng, et al.
Pubblicazione: (2022)

Advancing Energy Performance Efficiency in Residential Buildings for Sustainable Design: Integrating Machine Learning and Optimized Explainable AI (AIX)
di: Badr Saad Alotaibi
Pubblicazione: (2024)

Improvement of Frequency Stability in Shipboard Microgrids Based on MPC‐Reinforcement Learning
di: Farhad Amiri, et al.
Pubblicazione: (2025)

Autonomous and Distributed Synchronization and Restoration of an Islanded Network of Microgrids
di: Al-Karsani, Ahmed Saad, et al.
Pubblicazione: (2025)

Offline Reinforcement Learning for Microgrid Voltage Regulation
di: Yang, Shan, et al.
Pubblicazione: (2025)

A New Error Temporal Difference Algorithm for Deep Reinforcement Learning in Microgrid Optimization
di: Yao, Fulong, et al.
Pubblicazione: (2025)

Cooperative Energy Scheduling of Multi-Microgrids Based on Risk-Sensitive Reinforcement Learning
di: Zhang, Rongxiang, et al.
Pubblicazione: (2025)

A Reinforcement Learning Approach for Optimal Control in Microgrids
di: Salaorni, Davide, et al.
Pubblicazione: (2025)

Multiobjective Model Predictive Control for Residential Demand Response Management Under Uncertainty
di: Lin, Guan-Ting, et al.
Pubblicazione: (2026)

Go-Explore for Residential Energy Management
di: Lu, Junlin, et al.
Pubblicazione: (2024)

Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
di: da Silva, Caio Fabio Oliveira, et al.
Pubblicazione: (2024)

Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning
di: Zhang, Ruichang, et al.
Pubblicazione: (2024)

Mapping Research Productivity of BRICS Countries with Special Reference to Coronary Artery Disease (CAD): A Scientometric Study
di: Ahmad, Muneer
Pubblicazione: (2025)

Identifying Appropriately-Sized Services with Deep Reinforcement Learning
di: Fabiha, Syeda Tasnim, et al.
Pubblicazione: (2025)

Optimal Protection Coordination of DOCRs for Microgrids Using a Hybrid Deep Reinforcement Learning and Metaheuristic Optimization Framework
di: Yuxuan, Chen
Pubblicazione: (2026)

A Connectively Stable and Robust DAPI Control Scheme for Islanded Networks of Microgrids
di: Al-Karsani, Ahmed Saad, et al.
Pubblicazione: (2025)

A Battery‐Based Energy Management Approach for Weak Microgrid System
di: Waseem Akram, et al.
Pubblicazione: (2025)

Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization
di: Zhao, Yunyi, et al.
Pubblicazione: (2025)

Uncertainty-Aware Federated Learning for Cyber-Resilient Microgrid Energy Management
di: Babayomi, Oluleke, et al.
Pubblicazione: (2025)

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
di: Mulayim, Ozan Baris, et al.
Pubblicazione: (2025)

Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models
di: Maddineni, Vinod Kumar, et al.
Pubblicazione: (2024)

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics
di: Alqithami, Saad
Pubblicazione: (2025)

Portfolio Management using Deep Reinforcement Learning
di: Pawar, Ashish Anil, et al.
Pubblicazione: (2024)

Bridging Simulation and Reality: A 3D Clustering-Based Deep Learning Model for UAV-Based RF Source Localization
di: Masrur, Saad, et al.
Pubblicazione: (2025)

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning
di: Mukherjee, Sayak, et al.
Pubblicazione: (2022)

Multiagent Reinforcement Learning in Enhancing Resilience of Microgrids under Extreme Weather Events
di: Wu, Yin, et al.
Pubblicazione: (2026)

Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning
di: Liu, Changrui, et al.
Pubblicazione: (2025)

Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning for Cyber-Physical Systems Security
di: Alqithami, Saad
Pubblicazione: (2025)

Demand Response Optimization MILP Framework for Microgrids with DERs
di: Babu, K. Victor Sam Moses, et al.
Pubblicazione: (2025)

Real-Time Energy Management Strategies for Community Microgrids
di: Uddin, Moslem, et al.
Pubblicazione: (2025)

Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance
di: Qin, Zhaoming, et al.
Pubblicazione: (2021)

The Economic Impact and Carbon Footprint Dependence of Energy Management Strategies in Hydrogen-Based Microgrids
di: Segura Manzano, Francisca, et al.
Pubblicazione: (2025)

Efficient MPC-Based Energy Management System for Secure and Cost-Effective Microgrid Operations
di: He, Hanyang, et al.
Pubblicazione: (2025)

Deep Reinforcement Learning-Based Precoding for Multi-RIS-Aided Multiuser Downlink Systems with Practical Phase Shift
di: Chou, Po-Heng, et al.
Pubblicazione: (2025)