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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]
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Deep Reinforcement Learning-Based Demand Response for Smart Facilities Energy Management
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“Here, we compare the proposed Q-SDRM scheme with multiagent RL (MARL) approach [38] and HEM using RL and artificial neural network (RL-ANN) [8].”
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LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers
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Implementation of home energy management system based on reinforcement learning
Haq, E. U., Lyu, C., Xie, P., et al. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.11.170 · Cited by 39
Self-citationSecure Multi-Party Household Load Scheduling Framework for Real-Time Demand-Side Management
Cheng, L., Zang, H., Wei, Z., & Sun, G. (2022) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2022.3221081 · Cited by 39
“Due to the high online computation speed, RL methods are considered feasible for decentralized real-time DSM [36], [37].”
MethodMulti-agent DRL-based data-driven approach for PEVs charging/discharging scheduling in smart grid
Wan, Y., Qin, J., Ma, Q., Fu, W., & Wang, S. (2022) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.01.016 · Cited by 38
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Decentralized energy management system for smart microgrids using reinforcement learning
Darshi, R., Shamaghdari, S., Jalali, A., et al. (2023) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12796 · Cited by 35
Next Generation of Multi-Agent Driven Smart City Applications and Research Paradigms
Arora, A., Jain, A., Yadav, D., Hassija, V., Chamola, V., & Sikdar, B. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3310528 · Cited by 32
Multi-Agent Reinforcement Learning for Smart Community Energy Management
Wilk, P., Wang, N., & Li, J. (2024) · Energies · doi:10.3390/en17205211 · Cited by 36
Rehman, U. U., Faria, P., Gomes, L., et al. (2025) · Process Integration and Optimization for Sustainability · doi:10.1007/s41660-025-00506-x · Cited by 35
“For instance, Xu et al. (2020) focus on the technical aspects of HEMS models but overlook user behavior modeling entirely.”
A real-time automated scheduling algorithm with PV integration for smart home prosumers
Sharda, S., Sharma, K., & Singh, M. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102828 · Cited by 35
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Meta-Reinforcement Learning-Based Transferable Scheduling Strategy for Energy Management
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“algorithms, some of which are also considered in scheduling problem of energy storage system and shiftable loads [28], [29].”
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“In [15], with extreme learning machine for uncertainty prediction, a multi-agent RL method for decision making under multiple uncertainties is developed.”
MethodEfficient and Private Scheduling of Wireless Electric Vehicles Charging Using Reinforcement Learning
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“The values of GA and ML parameters are chosen (based on successful empirical findings [30], [37]) for tractability and to guarantee the maximization of the cumulative reward of the underlying dynamical system.”
MethodContrastive Identity-Aware Learning for Multi-Agent Value Decomposition
Liu, S., Zhou, Y., Song, J., Zheng, T., Chen, K., Zhu, T., Feng, Z., & Song, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i10.26370 · Cited by 36
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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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