2023年2月第34卷第1期照明工程学报ZHAOMINGGONGCHENGXUEBAOFeb．2023Vol.34No.1结合日光的室内自适应照明方法渠吉庆1，孙科学2，3，许海兵1(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)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(ParticleSwarmOptimization，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(ArtificialNeuralNetworks，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。关键词:智能照明;自适应调节;优化方法;粒子群优化算法中图分类号:TU113.66文献标识码:ADOI:10.3969/j.issn.1004-440X.2023.01.004DaylightAdaptiveSmartIndoorLightingMethodQUJiqing1，SUNKexue2，3，XUHaibing1(1.SchoolofMedicalImaging，JiangsuVocationalCollegeofMedicine，Yancheng224005，China;2.CollegeofElectronicandOpticalEngineering，NanjingUniversityofPostsandTelecommunications，Nanjing210023，China;3.Nation-LocalJointProjectEngineeringLabofＲFIntegration＆Micropackage，Nanjing210023，China)Abstract:Inodertoaddresstheproblemsofenergyscarcityandtheneedforhighqualitylighting，adaylightadaptivesmartindoorlightingmethodisproposed．Firstly，anon-linearconstrainedmathematicalmodelisdevelopedwiththeobjectiveofminimizingenergyconsumptionandwithaverageilluminanceanduniformityasconstraints．Then，thelightinglevelsofluminairesaresolvedusingParticleSwarmOptimization(PSO)．Themethodtakesintoaccountthelayoutoftheindoorlightsourcesandthelayoutofthelightsensors．Finally，themethodiscomparedwiththeArtificialNeuralNetworks(ANN)method．TheresultsshowthattheoptimizationmethodoutperformstheANNmethodintermsofenergyconsumptionandlightingquality．Keywords:smartlighting;adaptiveadjustment;optimizationmethod;ParticleSwarmOptimization基金项目:江苏省大学生创新训练计划(SYB2021017);南京邮电大学国...
