Cited by 420Smart Grid Energy ManagementElectric Vehicles and InfrastructureIoT-based Smart Home Systems

The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.

A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management

Open access

Xu, Xu, Jia, Youwei, Xu, Yan, Xu, Zhao, Chai, Songjian, Lai, Chun Sing·2020·IEEE Transactions on Smart Grid

DOI 10.1109/tsg.2020.2971427

Open this paper in CoChat — free account Open PDF

Abstract

This paper proposes a novel framework for home energy management (HEM) based on reinforcement learning in achieving efficient home-based demand response (DR). The concerned hour-ahead energy consumption scheduling problem is duly formulated as a finite Markov decision process (FMDP) with discrete time steps. To tackle this problem, a data-driven method based on neural network (NN) and Q-learning algorithm is developed, which achieves superior performance on cost-effective schedules for HEM system. Specifically, real data of electricity price and solar photovoltaic (PV) generation are timely processed for uncertainty prediction by extreme learning machine (ELM) in the rolling time windows. The scheduling decisions of the household appliances and electric vehicles (EVs) can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV and several PV panels. The test results demonstrate the effectiveness of the proposed data-driven based HEM framework.

Integrity signals

Registry

Crossref · 10.1109/tsg.2020.2971427

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Q-learning
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Solar cells—Operating principles, technology and system applications
(1982) · Solar Energy · doi:10.1016/0038-092x(82)90265-1 · Cited by 1,730
Reinforcement learning for demand response: A review of algorithms and modeling techniques
Vázquez-Canteli, J. R., & Nagy, Z. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.11.002 · Cited by 883
Evaluating MPPT Converter Topologies Using a Matlab PV Model
Walker, G. R. (2001) · QUT ePrints (Queensland University of Technology) · Cited by 871
Genetic Programming II
Koza, J. R. (1992) · Medical Entomology and Zoology · Cited by 825
Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility
Rezvanizaniani, S. M., Liu, Z., Chen, Y., & Lee, J. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.01.085 · Cited by 714
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2834219 · Cited by 649
Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning
Wan, Z., Li, H., He, H., & Prokhorov, D. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2879572 · Cited by 548 · cited 1×
“[14], Wan et al. use deep RL algorithms to determine the optimal solutions of the EV charging/discharging scheduling problem.”

Show all 37 references

Sorted by citations

Items 201–300 of 419

Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning
May, D. C., Taylor, M. E., & Musı́lek, P. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100446 · Cited by 12
Exploring the role of Energy Communities: A Comprehensive Review
Amin, M., Procopio, R., Invernizzi, M., Bonfiglio, A., & Jia, Y. (2025) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2025.100883 · Cited by 9
Self-citation
Adaptive Energy Management for MATTER-Enabled Smart Homes
Bhardwaj, N., & Joshi, P. (2025) · IEEE Access · doi:10.1109/access.2025.3584381 · Cited by 9
“To identify irregularities in energy consumption, we use the Long Short-Term Memory (LSTM) networks – a type of deep learning model [32].”
Gradient Play in Stochastic Games: Stationary Points and Local Geometry
Zhang, R. C., Ren, Z., & Li, N. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.11.031 · Cited by 8
Behavioral Modeling and Prediction in Social Perception and Computing: A Survey
Lv, Z., Cheng, C., Guerrieri, A., & Fortino, G. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3230211 · Cited by 9
“Finally, the test results proved the proposed data-driven HEM framework’s advantage [76].”
MethodResult
WyNDA: A method to discover mathematical models of dynamical systems from data
Hasan, A. (2024) · MethodsX · doi:10.1016/j.mex.2024.102625 · Cited by 9
RETRACTED: Optimization of the home energy system in presence of price fluctuation and intermittent renewable energy sources in grid-connected and islanded modes
Ma, J., & Poursoleiman, R. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102875 · Cited by 9
Retracted
A multi-agent deep reinforcement learning based energy management for behind-the-meter resources
Wilk, P., Wang, N., & Li, J. (2022) · The Electricity Journal · doi:10.1016/j.tej.2022.107129 · Cited by 9
“In (Xu et al., 2020) the author proposed a fully distributed multi-agent Q-learning algorithm with no observation for hour-ahead energy consumption and EV charging decisions.”
The Importance of AI-Enabled Internet of everything Services for Smart Home Management
Bajahzar, A. (2024) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2024-0026 · Cited by 8
Distributed Deep Deterministic Policy Gradient Agents for Real-Time Energy Management of DC Microgrid
Buraimoh, E., Özkan, G., Timilsina, L., et al. (2024) · IEEE International Conference on DC Microgrids · doi:10.1109/icdcm60322.2024.10665114 · Cited by 8
“This flexibility has ensured the successful application of learning-based methods across various power system applications [9] [10] [11] and [12].”
BackgroundMethod
Low-carbon energy management for networked multi-energy microgrids using multi-agent soft actor-critic algorithm
Song, D., Yan, L., Dai, X., et al. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101821 · Cited by 8
Designing personalized incentive-based demand response services based on smart meter data and NSGA-III-DE algorithm
Qin, C., Liu, W., Yan, Y., Guan, Q., & Wang, Q. (2025) · Energy · doi:10.1016/j.energy.2025.137454 · Cited by 10
Layered Temporal Spatial Graph Attention Reinforcement Learning for Multiplex Networked Industrial Chains Energy Management
Jiang, Y., Di, K., Wu, X., Hu, Z., Chen, F., Li, P., & Jiang, Y. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010111 · Cited by 8
Output Feedback H∞ Control for Linear Discrete-Time Multi-Player Systems With Multi-Source Disturbances Using Off-Policy Q-Learning
Xiao, Z., Li, J., & Li, P. (2020) · IEEE Access · doi:10.1109/access.2020.3038674 · Cited by 8
Real-Time Optimization of Microgrid Energy Management Using Double Deep Q-Network
Rokh, S. B., Zhang, R., Ravishankar, J., et al. (2023) · IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) · doi:10.1109/isgt51731.2023.10066355 · Cited by 8
“In [6], the authors applied Q-learning technique in conjunction with extreme learning machine to predict uncertainties and to minimize the operation cost.”
Method
Gradient Play in Multi-Agent Markov Stochastic Games: Stationary Points and ConvergencePreprint
Zhang, R. C., Ren, Z., & Li, N. (2021) · arXiv (Cornell University) · Cited by 8
Multi-Agent DRL-based Multi-Objective Demand Response Optimization for Real-Time Energy Management in Smart Homes
Abishu, H. N., Seid, A. M., Márquez-Sánchez, S., Fernandez, J. H., Corchado, J. M., & Erbad, A. (2024) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc61514.2024.10592515 · Cited by 7
“In this section, we present the performance evaluation of our proposed MADRL-based MODR optimization algorithm with three state-of-the-art algorithms: DQN [7], DDPG [5], and GA [17].”
Method
Real-time optimal energy management of microgrid based on multi-agent proximal policy optimization
Wang, D., Sun, Q., & Su, H. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10654-9 · Cited by 7
“Similarly, a Q-table-based RL is proposed in [8] to solve home energy management considering demand response (DR).”
Evolutionary Game-Based Battery Scheduling: A Comparative Study for Prosumers in Smart Grids
Karaki, A., Abedrabboh, K., & Al‐Fagih, L. (2025) · IEEE Access · doi:10.1109/access.2025.3549053 · Cited by 7
“In [35], a multi-agent off-policy DRL is applied to a residential EMS scenario, utilizing a neural network to predict RES production and electricity prices.”
Optimization methods in fully cooperative scenarios: a review of multiagent reinforcement learning
Yang, T., Shi, X., Zeng, Q., Yang, Y., Xu, C., & Liu, H. (2025) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2400259 · Cited by 7
“…2024; Ren Y et al., 2024), drone collaboration (Jia et al., 2023; Nian et al., 2024; Wang BL et al., 2024), smart cities (Wu T et al., 2020; Qiao et al., 2024), smart grids (Xu X et al., 2020; Wang JH et al., 2021a; Gou et al., 2022), and robotic cooperation (Chen HB et al., 2023; Gu et al., 2023).”
Real-Time Price Forecasting Combined Deep Reinforcement Learning for Predictive Home Energy Management System
Kim, H. J., & Kim, M.-K. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3585069 · Cited by 7
“For rated power mismatch, a modulus term is employed to reﬂect the linear cost of imbalances, providing the DRL agent with constant-magnitude gradients that stabilize actor–critic training and prevent rare, large spikes from overwhelming the reward signal [18], [27].”
Influential
Resilient coordination of energy storage and renewable generation in residential systems using deep reinforcement learning considering extreme weather conditions
Salehpour, M. J., Mj, H., & Shafie‐khah, M. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119868 · Cited by 7
A Transactive-Based Control Scheme for Minimizing Real-Time Energy Imbalance in a Multiagent Microgrid: A CVaR-Based Model
Fattaheian–Dehkordi, S., Tavakkoli, M., Abbaspour, A., et al. (2022) · IEEE Systems Journal · doi:10.1109/jsyst.2022.3185033 · Cited by 7
“To minimize the electricity and dis-satisfaction costs, a multiagent home energy management is presented in [8] by considering electricity price and solar photovoltaic (PV) uncertainties.”
Background
Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes
Amer, A. A., Shaban, K. B., & Massoud, A. M. (2022) · Energies · doi:10.3390/en15218235 · Cited by 7
Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning
Ismail, A., & Baysal, M. (2023) · Energies · doi:10.3390/en16145469 · Cited by 7
Decentralized Multiagent Reinforcement Learning Based State-of-Charge Balancing Strategy for Distributed Energy Storage System
Xiong, Z., Luo, B., Wang, B.-C., et al. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3424522 · Cited by 7
Self-citation
An Adaptive Energy Orchestrator for Cyberphysical Systems Using Multiagent Reinforcement Learning
Robles-Enciso, A., Robles-Enciso, R., & Skármeta, A. (2024) · Smart Cities · doi:10.3390/smartcities7060125 · Cited by 7
Multi-agent System for Management of Data from Electrical Smart Meters
Yacouba, Y. H., Diabagaté, A., Maïga, A., et al. (2021) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2021.01.02 · Cited by 7
“This presentation highlights the flow of exchanges between the different agents of the system [39, 40].”
Background
Day-ahead scheduling based on reinforcement learning with hybrid action space
Cao, J., Dong, L., & Sun, C. (2022) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2022.000064 · Cited by 7
Data-Driven Reliability Evaluation of the Integrated Energy System Considering Optimal Service Restoration
Dai, P., Li, Y., Zeng, Y., et al. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.934774 · Cited by 7
Output synchronization of multi-agent systems via reinforcement learning
Liu, Y., & Wang, Z. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.006 · Cited by 7
Demand response considering user behaviour differences for load serving entity: A multi‐agent deep reinforcement learning approach
Zeng, K., Wang, H., Liu, J., et al. (2022) · IET Energy Systems Integration · doi:10.1049/esi2.12059 · Cited by 7
Adaptive home energy management based on PI-DRA-PPO for integrated electricity, hydrogen, and heat storage systems
Liu, M., Bu, S., Zhu, Z., & Zhou, B. (2026) · Applied Energy · doi:10.1016/j.apenergy.2025.127257 · Cited by 6
Novel artificial intelligence driven model-based control framework for solar–electric vehicle home energy optimization
Irfan, M., Tahir, T., Deilami, S., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114868 · Cited by 6
A Nowcasting Central Controller with Continuous RTP for Residential Device Scheduling using Swap-Based BFPSO
Ghole, M. S., Paliwal, P., & Thakur, T. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-07696-x · Cited by 6
“A HEMS framework based on RL to achieve an efficient home-based DR is proposed in [13,14] where data-driven, model-free adaptive framework is presented considering uncertainties that may come in the future.”
Method
Poster Abstract: Residential Energy Management System Using Personalized Federated Deep Reinforcement learning
Gao, J., Wang, W., & Campbell, B. (2022) · ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) · doi:10.1109/ipsn54338.2022.00071 · Cited by 6
Reinforcement learning-based scheduling of multi-battery energy storage system
Cheng, G., Dong, L., Yuan, X., & Sun, C. (2023) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2023.000036 · Cited by 6
FastInformer-HEMS: A Lightweight Optimization Algorithm for Home Energy Management Systems
Chen, X., & Ning, D. (2023) · Energies · doi:10.3390/en16093897 · Cited by 6
“In this regard, some scholars have introduced reinforcement learning [10–13] and machine learning [14–17] methods.”
Co-optimizing Consumption and EV Charging under Net Energy Metering
Jeon, M.-J., Tong, L., & Zhao, Q. Q. (2023) · IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesgm52003.2023.10252707 · Cited by 6
Data-Efficient MADDPG Based on Self-Attention for IoT Energy Management Systems
Al-Saffar, M., & Gül, M. (2023) · IEEE Access · doi:10.1109/access.2023.3322193 · Cited by 8
“[11] proposed an hour-ahead home energy consumption scheduling system that uses a finite Markov decision process.”
Method
Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study
Wang, H., & Xu, Y. (2024) · Energies · doi:10.3390/en17112543 · Cited by 6
Self-citation
CIBECS: Consumer Input Based Electric Vehicle Charge Scheduling for a Residential Home
Shuvo, S. S., & Yılmaz, Y. (2021) · North American Power Symposium (NAPS) · doi:10.1109/naps52732.2021.9654607 · Cited by 6
“So, even state-of-the-art Artiﬁcial Intelligence (AI) based techniques [12], [13] may suffer to manage their experimental success in real-life implementation.”
BackgroundMethodInfluential
Agent-Based Energy Sharing Mechanism Using Deep Deterministic Policy Gradient Algorithm
Kuang, Y., Wang, X., Zhao, H., Huang, Y., Chen, X., & Wang, X. (2020) · Energies · doi:10.3390/en13195027 · Cited by 6
“For home energy management, a decentralized method based on multi-agent RL is developed to address the decision-making issue with incomplete environmental information [28].”
Method
Optimizing TSCH Scheduling for IIoT Networks Using Reinforcement Learning
Yaala, S. B., Yaala, S. B., Yaâla, S. B., et al. (2025) · Technologies · doi:10.3390/technologies13090400 · Cited by 5
Offline Deep Reinforcement Learning-Based Home Energy Management Systems With Heterogeneous EV Charging Load Models
Xiong, L., Tang, Y. S., Kankar Bhattacharya, B., et al. (2025) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2025.3600455 · Cited by 5
“W ITH the evolution of Internet-of-Things technologies, smart metering infrastructures, and 6G communications, scheduling household appliances has become increasingly popular through intelligent Home Energy Management Systems (HEMS) [1], [2].”
EdgeHEM: Sparse Federated Reinforcement Learning for Home Energy Management at the Edge
Li, Y., Chen, X., & Wang, Y. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3598438 · Cited by 5
“On this foundation, [15] embedded the price and solar generation predicted by an extreme learning machine into the multi-agent deep Q-learning (DQN) algorithm-based energy schedule.”
Demand response for residential appliances using multi-agent reinforcement learning with price and solar power uncertainty
Shantanu, K., Choudhary, N. K., Singh, N., & Kumar, K. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.10.047 · Cited by 6
Emerging Efficiency Enhancement Strategies for Electric Motor Drives in Electric Vehicles: A Comprehensive Review
Mohammed, S. A. Q., Almuhaini, M. M., & Abido, M. A. (2026) · IEEE Access · doi:10.1109/access.2026.3668367 · Cited by 5
“Never-theless, ML provides substantial advantages by facilitating more intelligent, customized, and energy-efficient vehicle operating [125], [126], [127], [128].”
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling Under Data Governance
Qin, Z., Dong, N., Liu, D., Wang, Z., & Cao, J. (2024) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2024.3501278 · Cited by 5
Energy Management System in Residential Sector with Renewable Energy Source Under Demand Response Consideration
Waseem, M., Abdullah, M., Akram, F., et al. (2021) · International Conference on Emerging Technologies · doi:10.1109/icet54505.2021.9689862 · Cited by 5
An automated energy management framework for smart homes
Kanso, H., Noureddine, A., & Expósito, E. (2023) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-220482 · Cited by 5
“A multi-agent RL for home-based demand response was proposed in [57].”
Locational marginal price forecasting using Transformer-based deep learning network
Liao, S., Wang, Z., Luo, Y., & Liang, H. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9549619 · Cited by 5
“…example, some studies are based on data-driven methods, using neural networks to predict electricity prices, so as to provide computational information for reinforcement learning, and use reinforcement learning algorithms to achieve real-time optimization of household energy management [7, 9, 10].”
BackgroundMethod
Optimal Coordination of Distributed Energy Resources Using Deep Deterministic Policy Gradient
Das, A., & Wu, D. X. (2022) · doi:10.1109/eesat55007.2022.9998046 · Cited by 5
“An RLbased smart home energy management system is proposed in [7], considering stochastic electricity price and photovoltaics (PV) generation to minimize electricity bills and demand response-induced dissatisfaction.”
Background
Control Method of Buses and Lines Using Reinforcement Learning for Short Circuit Current Reduction
Han, S. (2020) · Sustainability · doi:10.3390/su12229333 · Cited by 5
Sim-PowerCS: An extensible and simplified open-source energy simulator
Robles-Enciso, A., Robles-Enciso, R., & Skármeta, A. (2023) · SoftwareX · doi:10.1016/j.softx.2023.101467 · Cited by 5
Reinforcement-Learning-Based V2G Scheduling: Peak Load Mitigation and Financial Benefits
Xiao, Y., Tang, J., Lin, X., et al. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00959-5 · Cited by 4
Smart Home Energy Management using Reinforcement Learning
Reddy, V. S. S., Akshaya, C. S., Devi, V. S. K., & Raj, K. V. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11252860 · Cited by 4
“Also, multiple agent RL frameworks can offer efficient means for energy trading among smart homes for maximizing energy sharing and improving system robustness [8].”
A Novel Predictive Strategy for Daily Energy Cost Reduction in Solar “PV + EV” System with V2G and V2H Modes
Irfan, M., Tahir, T., Deilami, S., et al. (2025) · IEEE International Conference on Energy Technologies for Future Grids (ETFG) · doi:10.1109/etfg61999.2025.11401228 · Cited by 4
Constraint-aware multi-agent reinforcement learning with adversarial training for resilience enhancement in multi-microgrid voltage control
Hu, P., Ma, Q., & Ukil, A. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112747 · Cited by 4
A Two-Layer User Energy Management Strategy for Virtual Power Plants Based on HG-Multi-Agent Reinforcement Learning
Tian, S., Xiao, Q., Li, T., et al. (2025) · Applied Sciences · doi:10.3390/app15126713 · Cited by 4
Leader-Following Output Feedback H∞ Consensus of Fractional-Order Multi-Agent Systems with Input Saturation
Xing, H.-S., Boutat, D., & Wang, Q. (2024) · Fractal and Fractional · doi:10.3390/fractalfract8110667 · Cited by 4
Operational Challenges to Accommodate High Penetration of Electric Vehicles: A Comparison between UK and China
Lai, C. S., Chen, D., Xu, X., et al. (2021) · IEEE International Conference on Compatibility, Power Electronics and Power Engineering · doi:10.1109/cpe-powereng50821.2021.9501197 · Cited by 4
“RL has been successfully applied to tackle complex energy management systems in the residential sector [26].”
MethodSelf-citation
Optimal Management of Active and Reactive Power of Smart Inverters of Solar Units to Prevent Over-Voltage
Haghighian, S. K., Talebi, M. H., Rahimi, T., et al. (2021) · IEEE Green Energy and Smart Systems Conference (IGESSC) · doi:10.1109/igessc53124.2021.9618688 · Cited by 4
Model-Based Reinforcement Learning with Automated Planning for Network Management
Ordóñez, A., Rendón, O. M. C., Jácome, W. F. V., Rodriguez-Vivas, A., & da Fonseca, N. L. S. (2022) · Sensors · doi:10.3390/s22166301 · Cited by 4
Energy Storage and Forecasting Error Impact Analysis In Photovoltaic Equipped Residential Nano-Grids
Hokmabad, H. N., Husev, O. O., Belikov, J., et al. (2023) · IEEE International Conference on Compatibility, Power Electronics and Power Engineering · doi:10.1109/cpe-powereng58103.2023.10227406 · Cited by 4
Power Generation Optimization of the Combined Cycle Power-Plant System Comprising Turbo Expander Generator and Trigen in Conjunction with the Reinforcement Learning Technique
Kim, H. T., Song, G. S., & Han, S. (2020) · Sustainability · doi:10.3390/su12208379 · Cited by 4
Implementation optimal control model of smart grid engineering cluster construction based on Markov decision process
Gu, Y., Wang, H., & Zhang, M. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.04.046 · Cited by 3
Time-varying formation control for heterogeneous multi-agent systems in the presence of actuator faults and deception attacks
Cao, S., Yin, Y., Li, W., et al. (2025) · ISA Transactions · doi:10.1016/j.isatra.2025.06.004 · Cited by 3
Reinforcement Learning Approaches to PV System Maximum Power Point Tracking Control Under Partial Shading Conditions: A Review
Nwachukwu, S. E., Folly, K. A., Awodele, K. O., & Chen, Q. (2025) · IEEJ Transactions on Power and Energy · doi:10.1541/ieejpes.145.508 · Cited by 4
Optimized multi agent reinforcement learning algorithms with hybrid BiLSTM for cost efficient EV charging scheduling
Khekare, U., & Vedaraj, I. S. R. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1700664 · Cited by 6
“Xu et al. (2020) recommended a neural network-based Q-learning algorithm for home energy management.”
Optimal appliance scheduling in a residential building using an improved binary algorithm with an inverse hyperbolic tangent function
Manoharan, P., Sinha, G., Sowmya, R., et al. (2026) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.111489 · Cited by 3
Cascade Failure Management in Distributed Smart Grid Using Multi-Agent Control
Ikram, M., Ahmed, S., & Marwat, S. N. K. (2022) · International Conference on Emerging Technologies · doi:10.1109/icet56601.2022.10004671 · Cited by 3
Solar Powered EV Charging Electric Farm Vehicles for Sustainable Agriculture Areas
Ramkumar, G., Subalya, S., Priyadharsini, E. J., et al. (2024) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i61595.2024.10829026 · Cited by 3
Retracted
Deep Reinforcement Learning Based Energy Management for Smart Buildings with Heat Pump and Electric Vehicles
Jiang, X., & Xu, Q. (2024) · Proceedings of the Annual Conference of the IEEE Industrial Electronics Society · doi:10.1109/iecon55916.2024.10905415 · Cited by 3
“[7] used Q-Learning to schedule household appliances (excluding thermal loads) and electric vehicles (EVs).”
Hierarchical energy management and real-time optimization strategy for household based on multi-agent reinforcement learning
Liu, D., Zhong, S., Wu, L., et al. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101785 · Cited by 3
Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?
Qin, Z., Zhang, H., Zhao, Y., Xie, H., & Cao, J. (2021) · IEEE International Conference on Energy Internet (ICEI) · doi:10.1109/icei52466.2021.00009 · Cited by 3
PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services
Qiu, M., Najm, E., Sharrock, R., & Traverson, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-12426-6_13 · Cited by 3
Electric Vehicle Load Control Strategy Based on Improved Aquila Optimizer Algorithm
Meng, Z., Jin, X., Liu, J., et al. (2023) · Asia Conference on Energy and Electrical Engineering (ACEEE) · doi:10.1109/aceee58657.2023.10239561 · Cited by 3
Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Demand Management
Pei, Y., Yao, Y., Zhao, J., et al. (2023) · IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesgm52003.2023.10252567 · Cited by 3
“Then, the region, RE ti , can thus be deﬁned as: is the maximum real power of the i th PV inverter, and S i is the corresponding nameplate apparent power rating.”
Distributed Optimal Energy Dispatch for Networked Microgrids with Federated Reinforcement Learning
Wang, Y., & Xiao, M. (2023) · IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesgm52003.2023.10252453 · Cited by 3
“[5] further extends the work to energy management of multiple smart homes using federated learning (FL).”
Background
Sensitivity Analysis of Priority-Based Demand Response Metrics with Continuous Real-Time Pricing Scheme Using Swap-Based Butterfly Particle Swarm Optimization
Ghole, M. S., Paliwal, P., & Thakur, T. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08556-4 · Cited by 3
“A HEMS framework based on QL is proposed in [21] to minimize electricity bill and DR induced dissatisfaction cost.”
Deep Learning-Based Risk Prediction in Power Sector Financial Management Using Transformer and Actor-Critic Reinforcement Learning
Xu, Y., & Zhu, D. (2024) · IEEE Access · doi:10.1109/access.2024.3461474 · Cited by 3
“The study in [54] formulated a fundamental sequential decision-making problem as a Markov decision process and employed RL for energy management.”
MethodSelf-citation
Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving
Yan, S., König, L., & Burgard, W. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801946 · Cited by 3
“This approach has demonstrated success across various domains, including gaming [10], traffic light control [11], and resource scheduling optimization [12].”
Bi-level optimal planning of voltage regulator in distribution system considering maximization of incentive-based photovoltaic energy integration
Xu, X., Jia, Y., Lai, C., et al. (2020) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2020.01230 · Cited by 3
Self-citation
Data-driven Method of Renewable Energy Based on Generative Adversarial Networks and EnergyPLAN
Liu, Y., Zhang, H., & Mu, Y. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9601975 · Cited by 3
“And the Home Energy Management (HEM) framework [3] is constructed by the data-driven method of neural network and reinforcement learning in the indicator prediction of smart grid meets the demand of electricity consumption scheduling.”
Method
Evolutionary computing assisted deep reinforcement learning for multi-objective integrated energy system management
Huang, C., Wang, L., Luo, X., et al. (2021) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai52525.2021.00082 · Cited by 3
Data-Driven Smart Home Energy Management Based on Proximal Policy Optimization
Ma, A., Cao, J. H., & Rodríguez, P. (2023) · International Conference on Industrial Artificial Intelligence (IAI) · doi:10.1109/iai59504.2023.10327581 · Cited by 2
“Xu et al. [3] introduced an ELM-based method with a multi-agent Q-learning algorithm for HEMS.”
Method
Learning general multi-agent decision model through multi-task pre-training
Wang, J., Xu, L., & Sun, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129524 · Cited by 3
Optimization skill: How good is your energy management strategy really?
Crespo, C. B., Silva, R. A. E., & Brito, M. C. (2025) · Renewable and Sustainable Energy Transition · doi:10.1016/j.rset.2025.100133 · Cited by 2
Computationally efficient smart building energy management via deep reinforcement learning-enhanced model predictive control
Li, B., Si, G., Xu, M., Fan, D., Wang, Q., & Wang, X. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117128 · Cited by 2
Advancing energy storage systems through machine learning: Current developments, technical challenges, and future directions
Kumar, R., Chauhan, P. R., Rathod, M. K., Kumar, K., Sharma, P., Said, Z., Choudhary, P. K., Lamba, R., & Tyagi, P. K. (2026) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2026.131023 · Cited by 3
Role of machine learning and deep learning in optimizing smart home energy management systems: Advancements and technical factors
Mathangi, N., & Malathi, S. (2026) · Energy Efficiency · doi:10.1007/s12053-026-10439-0 · Cited by 2
“The technologies advance energy efficiency and cost reduction and involve the use of smart devices and adaptation techniques related to changing consumption patterns and factors (Xu et al., 2020).”
The Double Readiness Gap in Machine Learning for Building Energy Management: A Scoping Review of Deployment Maturity, Trustworthy AI, and EU AI Act Alignment
Malvoni, M. (2026) · Sustainability · doi:10.3390/su18126107 · Cited by 2
Optimization Strategies in Residential Energy Management Systems: A Comprehensive Review
Alsultan, A. M. T., Bani, N. A., Muhtazaruddin, M. N., Muhammad‐Sukki, F., Usman, S., Ahmad, N., Kamarudin, M. K. A., & Zulkipli, M. (2026) · International Journal of Energy Research · doi:10.1155/er/6685065 · Cited by 2
Optimal continuous control of refrigerator for electricity cost minimization—Hierarchical reinforcement learning approach
Kim, B.-S., An, J., & Sim, M. K. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.101177 · Cited by 2
Optimization of Logistics Information System based on Multi-Agent Reinforcement Learning
Lu, C., & Wu, Y. (2024) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi61536.2024.00066 · Cited by 2
A peer-and self-group competitive behavior-based socio-inspired approach for household electricity conservation
Ramnath, G. S., Harikrishnan, R., Muyeen, S. M., Kukker, A., Pohekar, S. D., & Kotecha, K. V. (2024) · Scientific Reports · doi:10.1038/s41598-024-56926-1 · Cited by 2
“The sociology approach helps to understand the reasons for variations in the consumption behavior of society, culture, technology, and history 31 .”
Background
Non-Intrusive Load Monitoring-based Fuzzy Actor-Critic Reinforcement Learning Home Energy Management
Hamedifar, S., Liu, S., & Xiao, G. (2024) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps59941.2024.10639994 · Cited by 2
“Authors of [1], [4]–[8] use Deep Q-learning Networks (DQN) to design HEMSs with discrete actions.”
Background
Data Analytics Challenges and Needs in Smart Grid for Smart Energy Management
Deepa, K. R., & Kodabagi, M. M. (2024) · doi:10.1109/icait61638.2024.10690834 · Cited by 2
“[5] 2016), who compared them to fuzzy classification, rough sets, ANN, SVM, and fuzzy logic paired with SVM. (The results showed that the approach worked well, with 92.5% accuracy and a false-positive rate of 5.12”
Influential
Adaptive Machine Learning for Automatic Load Optimization in Connected Smart Green Townhouses
Moghimi, S. M., Gulliver, T. A., Chelvan, I. T., et al. (2025) · Algorithms · doi:10.3390/a18030132 · Cited by 2

Authors: Xu, X., Jia, Y., Xu, Y., Xu, Z., Chai, S., & Lai, C. S.
Affiliations: Nanyang Technological University (SG)
Southern University of Science and Technology (CN)
Hong Kong Polytechnic University (HK)
Guangdong University of Technology (CN)
ORCID: Xu, Xu
Jia, Youwei
Xu, Yan
Xu, Zhao
Chai, Songjian
Lai, Chun Sing
Venue: IEEE Transactions on Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-07
Volume: 11
Issue: 4
Pages: 3201-3211
ISSN: 1949-3053 (ISSN-L)
1949-3061
DOI: 10.1109/tsg.2020.2971427
OpenAlex: W3005335721
Semantic Scholar: 8d06a30a270ff6dc763458dcedeea500fbf249e0
Cited by: 420
References: 37
Citations by year: 2026: 46 · 2025: 77 · 2024: 70 · 2023: 71 · 2022: 74 · 2021: 61 · 2020: 21
FWCI: 19.50
Type: journal article
Language: en
Open access: green
bura.brunel.ac.uk
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Smart Grid (publishedVersion)
Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion)
Funders: SUSTech Faculty Startup Funding (Y01236135, Y01236235)
Young Talent Program (2018KQNCX223)
Nanyang Assistant Professorship from Nanyang Technological University, Singapore
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
Keywords: home energy management systems, demand response, multi-agent reinforcement learning, Q-learning, finite Markov decision processes, extreme learning machine, uncertainty prediction, solar photovoltaic generation, electric vehicle scheduling, home appliance scheduling, electricity price forecasting, cost minimization, user dissatisfaction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks