Intelligent Sensor Detection Technology in Lighting Design and Application
Sensors are an important part of intelligent lighting. They can sense the sound and brightness of the environment, the movement and presence of the human body, and sense the temperature, humidity and air quality of the environment. After sensor detection, it can further control the movement of lighting products, switch light and shade, color temperature, switch and scene changes, achieve the lighting effects of functional lighting, scene lighting, and environmental protection requirements for energy saving and emission reduction. The application of sensors and light sensors in intelligent lighting control has increased the level of intelligence in lighting control and saved a lot of energy.
KeywordsSensor Lighting Smart Smart lighting
The Key Disciplines for Operational Research and Cybernetics of the Education Department of Guangxi Province & Project of improving the basic ability of young and middle-aged teachers in Colleges and universities in Guangxi in 2018 (the application of wireless sensor network in agriculture) (2018KY0698).
- 1.R.F. Hughes et al., Substantial energy savings through adaptive lighting. IEEE Electr. Power Energy Conf. 9(12), 215–218 (2008)Google Scholar
- 2.S. Onaygil, O. Guler et al., Determination of the energy saving by daylight responsive lighting control systems with an example from Istanbul, Elsevier Sci. Ltd 1, 6(12), 215–219 (2003)Google Scholar
- 3.S.F.S. Fadzil et al., Improved illumination levels and energy savings by uplamping technology for office Buildings. IEEE Conf. Publ. 5(9), 599–603 (2009)Google Scholar
- 4.A. Guillemin et al., An innovative lighting controller integrated in a self-adaptive building control system. Elsevier Sci. B.V 6(10), 477–487 (2001)Google Scholar
- 5.X. Suiru, Z. Yan, L. Guojun et al., Illumination automatic control system based on PID control. Comput. Digit. Eng. 5(38), 70–73 (2010)Google Scholar
- 6.L. Huai, C. Yifei et al., Research on indoor illumination control system based on fuzzy neural network. Light. Design 1(10), 27–30 (2008)Google Scholar
- 7.G. Xiaojing et al., Indoor lighting level prediction in smart lighting systems. Low Volt. Apparat. 2(6), 12–14 (2004)Google Scholar
- 8.X. Xiangdong, Y. Lihua et al., Natural lighting and indoor artificial lighting (PSALI) control. J. Light. Eng. 3(16), 23–26 (2005)Google Scholar
- 9.W. Jinguang, X. Hui et al., Intelligent integration strategy of natural lighting and artificial lighting. Artif. Intell. Recogn. Technol. 1(21), 829–832 (2007)Google Scholar