Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 52-59· 0 citations· 17 references
Abstract
The residential market has seen the rapid take-up of Internet of Things (IoT) devices, connected IIoT smart systems due to it. This situation gives rise to new issues and challenges for the energy management implemented in real time. The traditional centralized energy management mechanisms face great challenges like excessive latency, confidentiality vulnerability, scalability issues across heterogeneous smart home networks. The paper proposes a Federated Deep Reinforcement Learning (D-RLR) framework based on DQN agents and federated learning method. The framework allows for the decentralized training of DQN models in smart home nodes with the restriction that raw energy consumption data remain local. Each agent carries out the processing of grid price signal, usage data of appliances, observed amount and availability of local renewable energy to develop optimal load scheduling policy. The overall demand response policy is obtained by periodic federated aggregation of local models, helping to reduce communication latency. Experiments conducted on a simulated IIoT smart home environment, constructed using appliance consumption profiles derived from publicly available residential energy datasets and realistic solar irradiance-based renewable generation profiles, demonstrate that the proposed framework reduces peak energy consumption by 23% and improves demand response efficiency by 18% relative to centralized DRL baselines. Communication overhead generates roughly 65% lesser and user privacy is preserved throughout by design. The mechanism scales across nodes and supports real-time scheduling decisions under dynamically changing grid conditions. Therefore, it can be deployed practically for next-generation residential energy management.
Digital Twin-Based Low-Energy Reinforcement Learning for Multi-Cell IoV (DT-LERL) distributed collaborative training architecture in cellular-based IoV scenarios is designed, which allows the twin to replace the end-side vehicular entities by introducing digital twins to carry out scenario interactions and model training.
A. Alamoudi, Abdullah S. Almansouri· Journal of Big Data· 0 citations
Results confirm that reinforcement learning–based resource allocation provides a scalable and effective solution for IoT networks, particularly in environments characterized by large state spaces, dynamic network conditions, and stochastic traffic patterns.
L. Hoang, Van-Tam Hoang, Huu-Huy Ngo· International journal of Com...· 1 citation
This work designs a decision theory (DT)-guided transfer learning (TL) framework that unifying cyber resilience and energy adaptability in agricultural monitoring, advancing methodological innovation with DT-guided TL for stable DRL convergence, and providing design insights for sustainable agricultural cyber-physical systems.
Dian Chen, Zelin Wan, D. Ha et al.· ACM Transactions on Cyber-Ph...· 0 citations
The integration of photovoltaic generation, battery storage, electric vehicles, smart appliances, and dynamic electricity pricing has made residential energy management a challenging real-time optimization problem. Conventional demand-side management methods often depend on fixed rules and are less effective under uncertain solar generation, changing tariffs, and variable user demand. To address this issue, this paper proposes a Proximal Policy Optimization-based deep reinforcement learning framework for smart home energy management. The proposed PPO controller learns adaptive scheduling decisions using real-time PV output, electricity price, battery state of charge, EV charging status, and appliance operating conditions. The controller coordinates shiftable, controllable, and non-shiftable loads while reducing electricity cost and maintaining user comfort. The proposed method is compared with DDPG and TRPO. Simulation results show that PPO reduces the average daily energy cost by 4.7% compared with TRPO and 8.3% compared with DDPG. The results confirm that PPO is an effective and stable approach for real-time residential demand-side management.
Chittemma Yerra, Kiran Teeparthi, R. Naik et al.· Energies· 0 citations
The rapid growth of renewable energy integration has increased the complexity of smart grid operation due to the intermittent nature of distributed energy resources and continuously varying load demand. Existing approaches often rely on centralized control or combine only selected intelligent technologies, limiting scalability, data privacy, and autonomous decision-making. This paper proposes a digital twin-driven federated multi-agent intelligence (DT-FMAI) framework that integrates virtual system synchronization, privacy-preserving distributed learning, and cooperative multi-agent control within a unified architecture. Digital twins continuously mirror physical grid assets, federated learning enables collaborative forecasting without sharing raw data, and intelligent agents coordinate energy management in real time. The framework was implemented in MATLAB/Simulink with TensorFlow Federated and evaluated using renewable generation, weather, battery, and load datasets. Results demonstrate a 15-25% reduction in forecasting error, 10-18% improvement in voltage regulation, 92-96% load-matching efficiency, and 12-20% higher energy efficiency. These outcomes demonstrate the potential of the proposed framework for scalable, secure, and intelligent renewable-integrated smart grid operation.
Unknown authors· International Journal of Pow...· 0 citations
The rapid evolution of electric vehicles, smart grids, and energy storage infrastructures has exposed critical limitations in conventional Battery Management Systems (BMS), particularly in adaptive intelligence, privacy preservation, degradation awareness, and real-time decision autonomy. This paper proposes a novel Federated Physics-Informed Digital Twin Reinforcement Learning Battery Management System (FPD-RL BMS), an autonomous multi-layered framework that integrates Physics-Informed Neural Networks (PINNs), federated edge learning, deep reinforcement learning, and synchronized digital twin technology into a unified intelligent battery ecosystem. Unlike traditional AI-based BMS approaches that rely on centralized data training and isolated state estimation models, the proposed framework introduces a unique Adaptive Federated Physics-Informed Deep Reinforcement Learning (AFPIDRL) algorithm capable of jointly optimizing State-of-Charge (SOC), State-of-Health (SOH), thermal stability, charging efficiency, and battery lifespan under dynamic operating conditions. The methodology enables distributed vehicles or battery nodes to collaboratively learn degradation patterns without sharing raw data, thereby ensuring cybersecurity and privacy preservation. Additionally, a continuously evolving digital twin provides real-time electrochemical synchronization and predictive fault intelligence, while the reinforcement learning agent autonomously adapts charging and balancing strategies based on aging-aware reward functions. Experimental simulations demonstrate that the proposed architecture significantly enhances prediction accuracy, thermal safety, energy efficiency, and long-term battery sustainability compared with existing AI-driven BMS frameworks.
R. Ninitha Ashwini, Swetha Shekarappa· Journal of Intelligent Decis...· 0 citations
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