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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
527-532,539

Event-driven reinforcement learning thermal comfort control for residential buildings

Li Zhu1a,1b
Fu Qiming1a,1b
Ding Zhengkai1a,1b
Liu Lu1a,1b
Zhang Ying1a,1b
Chen Jianping1b,1c,2
1. a. School of Electronic & Information Engineering, b. Jiangsu Provincial Key Laboratory of Intelligent Energy Saving in Buildings, c. College of Architecture & Urban Planning, Suzhou University of Science & Technology, Suzhou Jiangsu 215009, China
2. Chongqing Industrial Big Data Innovation Center Co. , Ltd. , Chongqing 400707, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0273

Abstract

Residential HVAC systems typically constitute a substantial portion of energy consumption and exert a significant influence on occupants' thermal comfort. At present, reinforcement learning is widely employed to optimize HVAC systems; however, this approach necessitates a substantial investment of time and data resources. To address this issue, this paper proposed a novel framework based on an event-driven Markov decision process(ED-MDP) and further introduce an event-driven deep deterministic policy gradient(ED-DDPG) method. This approach amalgamated reinforcement learning algorithms to deduce optimal control policies through event-triggered optimization. The experimental results demonstrate that ED-DDPG excels in enhancing learning speed and reducing decision frequency compared to the benchmark method. Furthermore, it attains notable accomplishments in energy conservation and sustaining thermal comfort. Following comprehensive testing and validation, the method showcases robustness and adaptability in optimizing residential HVAC control.

Foundation Support

国家重点研发计划资助项目(2020YFC2006602)
国家自然科学基金资助项目(62102278,62172324,61876217,61876121)
江苏省高等学校自然科学研究项目(21KJA520005)
江苏省重点研发计划资助项目(BE2020026)
江苏省自然科学基金资助项目(BK20190942)
江苏省研究生教育教学改革项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0273
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: System Development & Application
Pages: 527-532,539
Serial Number: 1001-3695(2024)02-031-0527-06

Publish History

[2023-09-01] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

李竹, 傅启明, 丁正凯, 等. 基于事件驱动深度强化学习的建筑热舒适控制 [J]. 计算机应用研究, 2024, 41 (2): 527-532,539. (Li Zhu, Fu Qiming, Ding Zhengkai, et al. Event-driven reinforcement learning thermal comfort control for residential buildings [J]. Application Research of Computers, 2024, 41 (2): 527-532,539. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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