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

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

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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

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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.

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Retracted
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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

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

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