Frontiers in Energy Research | |
Application of smart grid and non-dominated sorting genetic algorithm in adaptive energy-saving control of building lighting | |
Energy Research | |
Yingrui Wang1  Shengjie Huang2  Chong Guo2  | |
[1] Nanjing Vocational Institute of Railway Technology, Institute of Architecture and Art Design, Nanjing, China;Nari Technology Co., Ltd., Nanjing Electric Control Branch, Nanjing, China; | |
关键词: smart lighting; digital buildings; non-dominating sorting genetic algorithms; augmented reality; smart grid; | |
DOI : 10.3389/fenrg.2023.1202090 | |
received in 2023-04-07, accepted in 2023-05-15, 发布年份 2023 | |
来源: Frontiers | |
【 摘 要 】
Introduction: In the wave of urbanization, the increase of public lighting equipment in buildings has brought about more prominent problems of energy saving and consumption reduction.Methods: In order to solve the above problems, this paper designs a set of intelligent lighting solutions for digital buildings by combining the smart grid and non-dominant sorting genetic algorithms. Firstly, an intelligent lighting monitoring solution is constructed through ZigBee ad hoc network and sensor technology to monitor the relevant environment and lighting control of the laboratory building. Secondly, this paper uses the DIALux software network to build a public lighting light distributiona public lighting light distribution model in the building, and deeply studies the dimming control strategy of the system under the principle of making full use of sunlight and natural light.Results: The purpose ofself-adaptive intelligent control of desktop illuminance, finally using this scheme to achieve the optimal balance of desktop lighting.Discussion: The simulation experiment counts the power data of the intelligent lighting system under different weather conditions. The experimental results verify that the intelligent lighting control scheme can effectively reduce the output luminous flux of the lamps, thereby reducing power consumption.
【 授权许可】
Unknown
Copyright © 2023 Wang, Huang and Guo.
【 预 览 】
Files | Size | Format | View |
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RO202310101876420ZK.pdf | 37755KB | download | |
fenrg-09-801398-fx4.tif | 29KB | Image | download |
【 图 表 】
fenrg-09-801398-fx4.tif