Full Text:   <563>

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CLC number: TP181

On-line Access: 2023-06-21

Received: 2022-12-27

Revision Accepted: 2023-09-21

Crosschecked: 2023-04-09

Cited: 0

Clicked: 904

Citations:  Bibtex RefMan EndNote GB/T7714


Luolin XIONG






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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.9 P.1261-1272


A home energy management approach using decoupling value and policy in reinforcement learning

Author(s):  Luolin XIONG, Yang TANG, Chensheng LIU, Shuai MAO, Ke MENG, Zhaoyang DONG, Feng QIAN

Affiliation(s):  Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; more

Corresponding email(s):   tangtany@gmail.com, fqian@ecust.edu.cn

Key Words:  Home energy system, Electric vehicle, Reinforcement learning, Generalization

Luolin XIONG, Yang TANG, Chensheng LIU, Shuai MAO, Ke MENG, Zhaoyang DONG, Feng QIAN. A home energy management approach using decoupling value and policy in reinforcement learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1261-1272.

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author="Luolin XIONG, Yang TANG, Chensheng LIU, Shuai MAO, Ke MENG, Zhaoyang DONG, Feng QIAN",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T A home energy management approach using decoupling value and policy in reinforcement learning
%A Luolin XIONG
%A Yang TANG
%A Chensheng LIU
%A Shuai MAO
%A Zhaoyang DONG
%A Feng QIAN
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200667

T1 - A home energy management approach using decoupling value and policy in reinforcement learning
A1 - Luolin XIONG
A1 - Yang TANG
A1 - Chensheng LIU
A1 - Shuai MAO
A1 - Ke MENG
A1 - Zhaoyang DONG
A1 - Feng QIAN
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200667

Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles (EVs), an improved mathematical model integrating driver’s experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic (DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally, comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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