Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm

The aim of this work is to propose an artificial intelligence algorithm that maintains thermal comfort and air quality within optimal levels while consuming the least amount of energy from air-conditioning units and ventilation fans. The proposed algorithm is first trained with 10 years of simulated...

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Veröffentlicht in:	Building and environment Jg. 155; S. 105 - 117
Hauptverfasser:	Valladares, William, Galindo, Marco, Gutiérrez, Jorge, Wu, Wu-Chieh, Liao, Kuo-Kai, Liao, Jen-Chung, Lu, Kuang-Chin, Wang, Chi-Chuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 15.05.2019 Elsevier BV
Schlagworte:	Air conditioners Air conditioning Air conditioning equipment Air quality Algorithms Artificial intelligence Carbon dioxide Computer simulation Control methods Control systems Deep reinforcement learning Energy consumption Indoor air pollution Indoor air quality Indoor environments Machine learning Optimization Subtropical zones Thermal comfort Ventilation Ventilation fans Air conditioning Deep reinforcement learning Ventilation Thermal comfort Optimization Indoor air quality
ISSN:	0360-1323, 1873-684X
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Abstract	The aim of this work is to propose an artificial intelligence algorithm that maintains thermal comfort and air quality within optimal levels while consuming the least amount of energy from air-conditioning units and ventilation fans. The proposed algorithm is first trained with 10 years of simulated past experiences in a subtropical environment in Taiwan. The simulations are carried out in a laboratory room having around 2–10 occupants and a classroom with up to 60 occupants. The proposed agent was first selected among different configurations of itself, with the 10th-year of training data set, then it was tested in real environments. Finally, a comparison between the current control methods and this new strategy is performed. It was found that the proposed AI agent can satisfactorily control and balance the needs of thermal comfort, indoor air quality (in terms of CO2 levels) and energy consumption caused by air-conditioning units and ventilation fans. For both environments, the AI agent can successfully manipulate the indoor environment within the accepted PMV values, ranging from about −0.1 to +0.07 during all the operating time. In regards to the indoor air quality, in terms of the CO2 levels, the results are also satisfactory. By utilizing the agent, the average CO2 levels fall below 800 ppm all the time. The results show that the proposed agent has a superior PMV and 10% lower CO2 levels than the current control system while consuming about 4–5% less energy. •An artificial intelligence algorithm (AI) is developed for thermal comfort, air quality, and energy consumption.•The developed algorithm is applicable for subtropical environment with cooling-only demand.•The simulations are in line with the experimental results in a laboratory room and a classroom.•The AI agent shows better thermal comfort and much lower CO2 levels than those without AI agent.•AI reveals 4–5% lower energy consumption with superior thermal comfort and 10% lower CO2 levels.
AbstractList	The aim of this work is to propose an artificial intelligence algorithm that maintains thermal comfort and air quality within optimal levels while consuming the least amount of energy from air-conditioning units and ventilation fans. The proposed algorithm is first trained with 10 years of simulated past experiences in a subtropical environment in Taiwan. The simulations are carried out in a laboratory room having around 2–10 occupants and a classroom with up to 60 occupants. The proposed agent was first selected among different configurations of itself, with the 10th-year of training data set, then it was tested in real environments. Finally, a comparison between the current control methods and this new strategy is performed. It was found that the proposed AI agent can satisfactorily control and balance the needs of thermal comfort, indoor air quality (in terms of CO2 levels) and energy consumption caused by air-conditioning units and ventilation fans. For both environments, the AI agent can successfully manipulate the indoor environment within the accepted PMV values, ranging from about −0.1 to +0.07 during all the operating time. In regards to the indoor air quality, in terms of the CO2 levels, the results are also satisfactory. By utilizing the agent, the average CO2 levels fall below 800 ppm all the time. The results show that the proposed agent has a superior PMV and 10% lower CO2 levels than the current control system while consuming about 4–5% less energy. •An artificial intelligence algorithm (AI) is developed for thermal comfort, air quality, and energy consumption.•The developed algorithm is applicable for subtropical environment with cooling-only demand.•The simulations are in line with the experimental results in a laboratory room and a classroom.•The AI agent shows better thermal comfort and much lower CO2 levels than those without AI agent.•AI reveals 4–5% lower energy consumption with superior thermal comfort and 10% lower CO2 levels. The aim of this work is to propose an artificial intelligence algorithm that maintains thermal comfort and air quality within optimal levels while consuming the least amount of energy from air-conditioning units and ventilation fans. The proposed algorithm is first trained with 10 years of simulated past experiences in a subtropical environment in Taiwan. The simulations are carried out in a laboratory room having around 2–10 occupants and a classroom with up to 60 occupants. The proposed agent was first selected among different configurations of itself, with the 10th-year of training data set, then it was tested in real environments. Finally, a comparison between the current control methods and this new strategy is performed. It was found that the proposed AI agent can satisfactorily control and balance the needs of thermal comfort, indoor air quality (in terms of CO2 levels) and energy consumption caused by air-conditioning units and ventilation fans. For both environments, the AI agent can successfully manipulate the indoor environment within the accepted PMV values, ranging from about −0.1 to +0.07 during all the operating time. In regards to the indoor air quality, in terms of the CO2 levels, the results are also satisfactory. By utilizing the agent, the average CO2 levels fall below 800 ppm all the time. The results show that the proposed agent has a superior PMV and 10% lower CO2 levels than the current control system while consuming about 4–5% less energy.
Author	Gutiérrez, Jorge Lu, Kuang-Chin Wu, Wu-Chieh Wang, Chi-Chuan Galindo, Marco Liao, Kuo-Kai Liao, Jen-Chung Valladares, William
Author_xml	– sequence: 1 givenname: William surname: Valladares fullname: Valladares, William email: williamvalladares@outlook.com organization: Department of Mechanical Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan – sequence: 2 givenname: Marco surname: Galindo fullname: Galindo, Marco email: marcodavidg@gmail.com organization: Department of Mechanical Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan – sequence: 3 givenname: Jorge surname: Gutiérrez fullname: Gutiérrez, Jorge email: ing.jorge.se@gmail.com organization: Department of Mechanical Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan – sequence: 4 givenname: Wu-Chieh surname: Wu fullname: Wu, Wu-Chieh email: wcwu@cht.com.tw organization: Internet of Things Laboratory, Teleco Labs, Chunghwa Telecom Co. Ltd, Taoyuan, Taiwan – sequence: 5 givenname: Kuo-Kai surname: Liao fullname: Liao, Kuo-Kai email: kai1027@cht.com.tw organization: Internet of Things Laboratory, Teleco Labs, Chunghwa Telecom Co. Ltd, Taoyuan, Taiwan – sequence: 6 givenname: Jen-Chung surname: Liao fullname: Liao, Jen-Chung email: renjong@cht.com.tw organization: Internet of Things Laboratory, Teleco Labs, Chunghwa Telecom Co. Ltd, Taoyuan, Taiwan – sequence: 7 givenname: Kuang-Chin surname: Lu fullname: Lu, Kuang-Chin email: gcl@cht.com.tw organization: Internet of Things Laboratory, Teleco Labs, Chunghwa Telecom Co. Ltd, Taoyuan, Taiwan – sequence: 8 givenname: Chi-Chuan orcidid: 0000-0002-4451-3401 surname: Wang fullname: Wang, Chi-Chuan email: ccwang@mail.nctu.edu.tw organization: Department of Mechanical Engineering, National Chiao Tung University, Hsinchu, 300, Taiwan
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Snippet	The aim of this work is to propose an artificial intelligence algorithm that maintains thermal comfort and air quality within optimal levels while consuming...
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SubjectTerms	Air conditioners Air conditioning Air conditioning equipment Air quality Algorithms Artificial intelligence Carbon dioxide Computer simulation Control methods Control systems Deep reinforcement learning Energy consumption Indoor air pollution Indoor air quality Indoor environments Machine learning Optimization Subtropical zones Thermal comfort Ventilation Ventilation fans
Title	Energy optimization associated with thermal comfort and indoor air control via a deep reinforcement learning algorithm
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