结合日光的室内自适应照明方法  

Daylight Adaptive Smart Indoor Lighting Method

作　　者：渠吉庆 孙科学 许海兵 QU Jiqing;SUN Kexue;XU Haibing(School of Medical Imaging,Jiangsu Vocational College of Medicine,Yancheng 224005,China;College of Electronic and Optical Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;Nation-Local Joint Project Engineering Lab of RF Integration&Micropackage,Nanjing 210023,China)

机构地区：[1]江苏医药职业学院医学影像学院,江苏盐城224005 [2]南京邮电大学电子与光学工程学院、微电子学院,江苏南京210023 [3]射频集成与微组装技术国家地方联合工程实验室,江苏南京210023

出　　处：《照明工程学报》2023年第1期16-21,共6页China Illuminating Engineering Journal

基　　金：江苏省大学生创新训练计划(SYB2021017);南京邮电大学国自孵化项目(NY220013)。

摘　　要：针对能源紧缺和高质量照明需求的问题,提出一种结合日光的室内自适应照明方法。首先建立以最小化能源消耗为目标,以平均照度和均匀度为约束条件的非线性约束数学模型。其次,使用粒子群优化算法(Particle Swarm Optimization,PSO)求解各个灯具的亮度,该方法考虑了室内光源布局和光照传感器布局因素。最后,将该方法与人工神经网络(Artificial Neural Networks,ANN)方法进行对比。结果显示,优化方法在能源消耗和照明质量上均胜于人工神经网络方法。In oder to address the problems of energy scarcity and the need for high quality lighting,a daylight adaptive smart indoor lighting method is proposed.Firstly,a non-linear constrained mathematical model is developed with the objective of minimizing energy consumption and with average illuminance and uniformity as constraints.Then,the lighting levels of luminaires are solved using Particle Swarm Optimization(PSO).The method takes into account the layout of the indoor light sources and the layout of the light sensors.Finally,the method is compared with the Artificial Neural Networks(ANN)method.The results show that the optimization method outperforms the ANN method in terms of energy consumption and lighting quality.

关 键 词：智能照明 自适应调节 优化方法 粒子群优化算法 

分 类 号：TU113.66[建筑科学—建筑理论]