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...

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Bibliographic Details
Published in:Applied sciences 2023-08, Vol.13 (16), p.9057
Main Authors: Fang, Peixin, Wang, Ming, Li, Jingzheng, Zhao, Qianchuan, Zheng, Xuehan, Gao, He
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Building automation
Buildings
Control algorithms
Control systems
Deep learning
deep reinforcement learning
distributed systems
Emissions
Energy consumption
fuzzy control
intelligent lighting
Internet of Things
Light emitting diodes
Lighting systems
Optimization
Radiation
shading systems
User experience
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13169057