Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning
Energy efficient control of thermal comfort has been already an important part of residential heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of energy saving control for thermal comfort is not an easy task due to the complex dynamics of HVAC systems, the dynamic...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2120598 |
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| author | Zheng-Kai Ding Qi-Ming Fu Jian-Ping Chen Hong-Jie Wu You Lu Fu-Yuan Hu |
| author_facet | Zheng-Kai Ding Qi-Ming Fu Jian-Ping Chen Hong-Jie Wu You Lu Fu-Yuan Hu |
| author_sort | Zheng-Kai Ding |
| collection | DOAJ |
| description | Energy efficient control of thermal comfort has been already an important part of residential heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of energy saving control for thermal comfort is not an easy task due to the complex dynamics of HVAC systems, the dynamics of thermal comfort and the trade-off between energy saving and thermal comfort. To solve the above problem, we propose a deep reinforcement learning-based thermal comfort control method in multi-zone residential HVAC. In this paper, firstly we design a SVR-DNN model, consisting of Support Vector Regression and a Deep Neural Network to predict thermal comfort value. Then, we apply Deep Deterministic Policy Gradient (DDPG) based on the output of the SVR-DNN model to achieve an optimal HVAC thermal comfort control strategy. This method can minimise energy consumption while satisfying occupants' thermal comfort. The experimental results show that our method can improve thermal comfort prediction performance by 20.5% compared with DNN; compared with deep Q-network (DQN), energy consumption and thermal comfort violation can be reduced by 3.52% and 64.37% respectively. |
| format | Article |
| id | doaj-art-37ea10ca5cd845f78370d6eb74f69878 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-37ea10ca5cd845f78370d6eb74f698782025-08-20T01:56:53ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013412364239410.1080/09540091.2022.21205982120598Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learningZheng-Kai Ding0Qi-Ming Fu1Jian-Ping Chen2Hong-Jie Wu3You Lu4Fu-Yuan Hu5SuZhou University of Science and TechnologySuZhou University of Science and TechnologySuzhou University of Science and TechnologySuZhou University of Science and TechnologySuZhou University of Science and TechnologySuZhou University of Science and TechnologyEnergy efficient control of thermal comfort has been already an important part of residential heating, ventilation, and air conditioning (HVAC) systems. However, the optimisation of energy saving control for thermal comfort is not an easy task due to the complex dynamics of HVAC systems, the dynamics of thermal comfort and the trade-off between energy saving and thermal comfort. To solve the above problem, we propose a deep reinforcement learning-based thermal comfort control method in multi-zone residential HVAC. In this paper, firstly we design a SVR-DNN model, consisting of Support Vector Regression and a Deep Neural Network to predict thermal comfort value. Then, we apply Deep Deterministic Policy Gradient (DDPG) based on the output of the SVR-DNN model to achieve an optimal HVAC thermal comfort control strategy. This method can minimise energy consumption while satisfying occupants' thermal comfort. The experimental results show that our method can improve thermal comfort prediction performance by 20.5% compared with DNN; compared with deep Q-network (DQN), energy consumption and thermal comfort violation can be reduced by 3.52% and 64.37% respectively.http://dx.doi.org/10.1080/09540091.2022.2120598reinforcement learningdeep reinforcement learningmulti-zone residential hvacenergy consumptionthermal comfort |
| spellingShingle | Zheng-Kai Ding Qi-Ming Fu Jian-Ping Chen Hong-Jie Wu You Lu Fu-Yuan Hu Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning Connection Science reinforcement learning deep reinforcement learning multi-zone residential hvac energy consumption thermal comfort |
| title | Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning |
| title_full | Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning |
| title_fullStr | Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning |
| title_full_unstemmed | Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning |
| title_short | Energy-efficient control of thermal comfort in multi-zone residential HVAC via reinforcement learning |
| title_sort | energy efficient control of thermal comfort in multi zone residential hvac via reinforcement learning |
| topic | reinforcement learning deep reinforcement learning multi-zone residential hvac energy consumption thermal comfort |
| url | http://dx.doi.org/10.1080/09540091.2022.2120598 |
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