A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning
With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/16/9057 |
| _version_ | 1797585654606987264 |
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| author | Peixin Fang Ming Wang Jingzheng Li Qianchuan Zhao Xuehan Zheng He Gao |
| author_facet | Peixin Fang Ming Wang Jingzheng Li Qianchuan Zhao Xuehan Zheng He Gao |
| author_sort | Peixin Fang |
| collection | DOAJ |
| description | With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an intelligent lighting control system based on a distributed architecture, incorporating a dynamic shading system for adjusting the interior lighting environment. The system comprises two subsystems: lighting and shading. The shading subsystem utilizes fuzzy control logic to control lighting based on the room’s temperature and illumination, thereby achieving rapid control with fewer calculations. The lighting subsystem employs a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the luminaire dimming problem based on room illuminance in order to maximize user convenience while achieving uniform illumination. This paper also includes the construction of a prototype box on which the system is evaluated in two distinct circumstances. The results of the tests demonstrate that the system functions properly, has stability and real-time performance, and can adapt to complex and variable outdoor environments. The maximum relative error between actual and expected illuminance is less than 10%, and the average relative error is less than 5% when achieving uniform illuminance. |
| first_indexed | 2024-03-11T00:10:10Z |
| format | Article |
| id | doaj.art-0c6df26e88024accb31694467b6a944f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T00:10:10Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0c6df26e88024accb31694467b6a944f2023-11-19T00:03:33ZengMDPI AGApplied Sciences2076-34172023-08-011316905710.3390/app13169057A Distributed Intelligent Lighting Control System Based on Deep Reinforcement LearningPeixin Fang0Ming Wang1Jingzheng Li2Qianchuan Zhao3Xuehan Zheng4He Gao5School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, ChinaDepartment of Automation, Tsinghua University, Beijing 100018, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, ChinaWith the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an intelligent lighting control system based on a distributed architecture, incorporating a dynamic shading system for adjusting the interior lighting environment. The system comprises two subsystems: lighting and shading. The shading subsystem utilizes fuzzy control logic to control lighting based on the room’s temperature and illumination, thereby achieving rapid control with fewer calculations. The lighting subsystem employs a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the luminaire dimming problem based on room illuminance in order to maximize user convenience while achieving uniform illumination. This paper also includes the construction of a prototype box on which the system is evaluated in two distinct circumstances. The results of the tests demonstrate that the system functions properly, has stability and real-time performance, and can adapt to complex and variable outdoor environments. The maximum relative error between actual and expected illuminance is less than 10%, and the average relative error is less than 5% when achieving uniform illuminance.https://www.mdpi.com/2076-3417/13/16/9057intelligent lightingshading systemsfuzzy controldeep reinforcement learningdistributed systems |
| spellingShingle | Peixin Fang Ming Wang Jingzheng Li Qianchuan Zhao Xuehan Zheng He Gao A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning Applied Sciences intelligent lighting shading systems fuzzy control deep reinforcement learning distributed systems |
| title | A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning |
| title_full | A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning |
| title_fullStr | A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning |
| title_full_unstemmed | A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning |
| title_short | A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning |
| title_sort | distributed intelligent lighting control system based on deep reinforcement learning |
| topic | intelligent lighting shading systems fuzzy control deep reinforcement learning distributed systems |
| url | https://www.mdpi.com/2076-3417/13/16/9057 |
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