Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning-Reference-Cited by-同舟云学术

Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning

Published:2022-09-14 Issue:1 Volume:34 Page:2364-2394
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Ding Zheng-Kai¹²,Fu Qi-Ming¹²,Chen Jian-Ping²³⁴,Wu Hong-Jie¹,Lu You¹²,Hu Fu-Yuan¹

Affiliation:

1. School of Electronic and Information Engineering, SuZhou University of Science and Technology, Suzhou, Jiangsu, People's Republic of China

2. Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, Jiangsu, People's Republic of China

3. School of Archirecture and Urban Planning, SuZhou University of Science and Technology, Suzhou, Jiangsu, People's Republic of China

4. Chongqing Industrial Big Data Innovation Center Co., Ltd., Chongqing, People's Republic of China

Funder

Primary Research and Development Plan of China

National Natural Science Foundation of China

University Natural Science Foundation of Jiangsu Province

Primary Research and Development Plan of Jiangsu Province

Natural Science Foundation of Jiangsu Province

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2022.2120598

Reference45 articles.

1. Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach

2. Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings

3. Bureau, C. M. (2005). China standard weather data for analyzing building thermal conditions (pp. 90–105). China Building Industry Publishing House.

4. Hierarchical-fuzzy allocation and multi-parameter adjustment prediction for industrial loading optimisation

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