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 accessAbstract
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.
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“When paired with fuzzy Q-learning adaptations [43], agents can navigate uncertain partner behaviors or incomplete operational data by adjusting policy confidence based on interaction outcomes.”
From Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review (2026) · cites this paper as [43]
Citations
Cites
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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.”
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Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112128 · Cited by 711
Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review
Cao, D., Hu, W., Zhao, J., Zhang, G., Zhang, B., Liu, Z., Chen, Z., & Blaabjerg, F. (2020) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000552 · Cited by 460
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MethodChen, X., Qu, G., Tang, Y., Low, S. H., & Li, N. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3154718 · Cited by 465
“Some Q -learning-based work [133] discretizes the continuous action space to fit the algorithm framework.”
BackgroundEnergetics Systems and artificial intelligence: Applications of industry 4.0
Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A. S., & Alshamrani, S. S. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.11.256 · Cited by 399
A Review of Technical Standards for Smart Cities
Lai, C. S., Jia, Y., Dong, Z., Wang, D., Tao, Y., Lai, Q. H., Wong, R. T. K., Zobaa, A. F., Wu, R., & Lai, L. L. (2020) · Clean Technologies · doi:10.3390/cleantechnol2030019 · Cited by 352
Self-citation
Metadata
- 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)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2020-07
- Volume
- 11
- Issue
- 4
- Pages
- 3201-3211
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- 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
- 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
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