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基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法

孙科学 渠吉庆

孙科学, 渠吉庆. 基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法[J]. 电子与信息学报, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320
引用本文: 孙科学, 渠吉庆. 基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法[J]. 电子与信息学报, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320
SUN Kexue, QU Jiqing. Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320
Citation: SUN Kexue, QU Jiqing. Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1766-1773. doi: 10.11999/JEIT220320

基于线性优化模糊C均值算法和人工神经网络的光照传感器布局方法

doi: 10.11999/JEIT220320
基金项目: 江苏省研究生科研创新计划(KYCX20_0803),南京邮电大学自然科学基金(NY220013)
详细信息
    作者简介:

    孙科学:男,博士,教授,硕士生导师,主要研究方向为智能信号处理与通信软件设计

    渠吉庆:男,硕士生,研究方向为智能信号处理、智能优化与控制

    通讯作者:

    渠吉庆 qjq0806@163.com

  • 中图分类号: TN911.74; TP181

Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks

Funds: The Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX20_0803), The Natural Science Foundation of Nanjing University of Posts and Telecommunications (NY220013)
  • 摘要: 针对目前光照传感器的布局方式计算量大、能耗高,易受人为因素的影响,难以准确地预测室内日光强度等问题,该文提出一种基于线性优化模糊C均值算法(LOFCM)和人工神经网络(ANN)的光照传感器布局方法。LOFCM算法利用线性优化(LO)稀疏权重矩阵后,使用模糊C均值(FCM)筛选数据,确定工作面光照传感器布局。随后,使用ANN分别训练工作面光照传感器测量值与4组辅助光照传感器测量值之间的非线性数学模型。实验结果表明,该文提出的基于LOFCM算法在保证计算工作面平均照度和均匀度准确的情况下,工作面光照传感器的数量比对比方法减少了37.5%。此外,在4组辅助光照传感器布局中,墙壁和窗户布局方式具有较好的预测精度,为预测室内日光强度提供了更加准确的预测方式。
  • 图  1  LOFCM算法流程图

    图  2  实验房间3维视图

    图  3  工作面光照传感器布局示意图

    图  4  ANN_G1误差频率分布图

    图  5  ANN_G2误差频率分布图

    图  6  ANN_G3误差频率分布图

    图  7  ANN_G4误差频率分布图

    表  1  办公室房间细节

    参数
    空间大小6 m×8 m×3 m
    有效反射率天花板: 0.8; 墙壁: 0.8; 地板: 0.2
    工作面高度0.75 m
    灯具占用高度0.1 m
    下载: 导出CSV

    表  2  优化后的室内光源布局

    $ {N_a} $$ {N_b} $$ {L_a} $(m)$ {L_b} $(m)平均照度
    (lx)
    均匀度眩光度功率密度
    (W/m2)
    342.4882.3265280.89177.325
    下载: 导出CSV

    表  3  不同工作区对传感器有重要影响的灯具表

    区域对于传感器影响较大的灯具
    Z1L1, L2, L5, L6
    Z2L3, L4, L7, L8
    Z3L5, L6, L9, L10
    Z4L7, L8, L11, L12
    下载: 导出CSV

    表  4  工作区Z1结果对比表

    文献选择的光照传感器$ V_{{\text{rec}}}^1 $
    本文$ p_3^1 $,$ p_4^1 $,$ p_{18}^1 $,$ p_{21}^1 $,$ p_{22}^1 $0.078
    文献[19]$ p_3^1 $,$ p_4^1 $,$ p_8^1 $,$ p_{11}^1 $,$ p_{14}^1 $,$ p_{17}^1 $,$ p_{21}^1 $,$ p_{22}^1 $0.074
    下载: 导出CSV

    表  5  工作区Z2-Z4选择的光照传感器

    工作区选择的传感器
    Z2$ p_3^2 $,$ p_4^2 $,$ p_{18}^2 $,$ p_{21}^2 $,$ p_{22}^2 $
    Z3$ p_3^3 $,$ p_4^3 $,$ p_{13}^3 $,$ p_{21}^3 $,$ p_{22}^3 $
    Z4$ p_3^4 $,$ p_4^4 $,$ p_{13}^4 $,$ p_{21}^4 $,$ p_{22}^4 $
    下载: 导出CSV

    表  6  辅助光照传感器分组表

    分组传感器
    G1Wd1,Wd2,Wd3,Wd4
    G2W1W2W3W4
    G3Wd1,Wd2,Wd3,Wd4W1W2
    G4Wd1,Wd2,Wd3,Wd4,W3,W4
    下载: 导出CSV

    表  7  数据采集详细信息表

    采集地点采集时间采集时间间隔天气
    上海6月21-23日7:00-10:00;15:00-18:009月22-24日7:00-10:00;15:00-18:002 min晴天与阴天
    下载: 导出CSV

    表  8  收集数据特征表(lx)

    分组Wd1-Wd4W1-W4Z1Z2Z3Z4
    最小值19.251.5182.0167.0111.0108.0
    最大值19217510681106425359
    平均值45.86122.28474.19505.78244.40245.18
    标准偏差18.0924.85176.94223.9845.9155.90
    下载: 导出CSV

    表  9  神经网络的结果数据对比

    分组MSE(×10–4)Min MSE(×10–4)训练集R2验证集R2测试集R2
    ANN_G13.9301.2390.993970.994190.98667
    ANN_G24.7271.3690.992590.994810.98459
    ANN_G34.1511.6010.991700.991140.98194
    ANN_G46.2281.9740.988470.990060.98686
    下载: 导出CSV
  • [1] VAN DUIJNHOVEN J, AARTS M P J, and KORT H S M. Personal lighting conditions of office workers: An exploratory field study[J]. Lighting Research & Technology, 2021, 53(4): 285–310. doi: 10.1177/1477153520976940
    [2] BOYCE P R. Light, lighting and human health[J]. Lighting Research & Technology, 2021, 54(2): 101–144. doi: 10.1177/14771535211010267
    [3] MONTOYA F G, PEÑA-GARCÍA A, JUAIDI A, et al. Indoor lighting techniques: An overview of evolution and new trends for energy saving[J]. Energy and Buildings, 2017, 140: 50–60. doi: 10.1016/j.enbuild.2017.01.028
    [4] BAEZA MOYANO D, SAN JUAN FERNANDEZ M, and GONZALEZ LEZCANO R A. Towards a sustainable indoor lighting design: Effects of artificial light on the emotional state of adolescents in the classroom[J]. Sustainability, 2020, 12(10): 4263. doi: 10.3390/su12104263
    [5] ZHANG Shenqiu and BIRRU D. An open-loop venetian blind control to avoid direct sunlight and enhance daylight utilization[J]. Solar Energy, 2012, 86(3): 860–866. doi: 10.1016/j.solener.2011.12.015
    [6] HWANG T and KIM J T. Effects of indoor lighting on occupants' visual comfort and eye health in a green building[J]. Indoor and Built Environment, 2011, 20(1): 75–90. doi: 10.1177/1420326X10392017
    [7] AFSHARI S and MISHRA S. A plug-and-play realization of decentralized feedback control for smart lighting systems[J]. IEEE Transactions on Control Systems Technology, 2016, 24(4): 1317–1327. doi: 10.1109/TCST.2015.2487880
    [8] GAO Yingming, CHENG Yukai, ZHANG Huanyue, et al. Dynamic illuminance measurement and control used for smart lighting with LED[J]. Measurement, 2019, 139: 380–386. doi: 10.1016/j.measurement.2019.03.003
    [9] LI Shuai, PANDHARIPANDE A, and WILLEMS F M J. Daylight sensing LED lighting system[J]. IEEE Sensors Journal, 2016, 16(9): 3216–3223. doi: 10.1109/JSEN.2016.2520495
    [10] CAICEDO D, LI Shuai, and PANDHARIPANDE A. Smart lighting control with workspace and ceiling sensors[J]. Lighting Research & Technology, 2017, 49(4): 446–460. doi: 10.1177/1477153516629531
    [11] MAYHOUB M S and CARTER D J. The costs and benefits of using daylight guidance to light office buildings[J]. Building and Environment, 2011, 46(3): 698–710. doi: 10.1016/j.buildenv.2010.09.014
    [12] REINHART C F and WIENOLD J. The daylighting dashboard–a simulation-based design analysis for daylit spaces[J]. Building and Environment, 2011, 46(2): 386–396. doi: 10.1016/j.buildenv.2010.08.001
    [13] LI D H W and TSANG E K W. An analysis of daylighting performance for office buildings in Hong Kong[J]. Building and Environment, 2008, 43(9): 1446–1458. doi: 10.1016/j.buildenv.2007.07.002
    [14] QU Jiqing, XU Qilin, and SUN Kexue. Optimization of indoor luminaire layout for general lighting scheme using improved particle swarm optimization[J]. Energies, 2022, 15(4): 1482. doi: 10.3390/en15041482
    [15] PANDHARIPANDE A and CAICEDO D. Smart indoor lighting systems with luminaire-based sensing: A review of lighting control approaches[J]. Energy and Buildings, 2015, 104: 369–377. doi: 10.1016/j.enbuild.2015.07.035
    [16] BONOMOLO M, BECCALI M, BRANO V L, et al. A set of indices to assess the real performance of daylight-linked control systems[J]. Energy and Buildings, 2017, 149: 235–245. doi: 10.1016/j.enbuild.2017.05.065
    [17] BECCALI M, BONOMOLO M, CIULLA G, et al. Assessment of indoor illuminance and study on best photosensors' position for design and commissioning of Daylight Linked Control systems. A new method based on artificial neural networks[J]. Energy, 2018, 154: 466–476. doi: 10.1016/j.energy.2018.04.106
    [18] SEYEDOLHOSSEINI A, MASOUMI N, MODARRESSI M, et al. Daylight adaptive smart indoor lighting control method using artificial neural networks[J]. Journal of Building Engineering, 2020, 29: 101141. doi: 10.1016/j.jobe.2019.101141
    [19] SEYEDOLHOSSEINI A, MODARRESSI M, MASOUMI N, et al. Efficient photodetector placement for daylight-responsive smart indoor lighting control systems[J]. Journal of Building Engineering, 2021, 42: 103013. doi: 10.1016/j.jobe.2021.103013
    [20] MAI T A, DANG T S, DUONG D T, et al. A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2021, 43(3): 156. doi: 10.1007/s40430-020-02767-8
    [21] MISHRA M and SRIVASTAVA M. A view of artificial neural network[C]. 2014 International Conference on Advances in Engineering & Technology Research (ICAETR-2014), Unnao, India, 2014: 1–3.
    [22] CHEN Limin, JAGOTA V, and KUMAR A. Research on optimization of scientific research performance management based on BP neural network[J]. International Journal of System Assurance Engineering and Management, 2023, 14(1): 489. doi: 10.1007/s13198-021-01263-z
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出版历程
  • 收稿日期:  2022-03-24
  • 修回日期:  2022-08-16
  • 录用日期:  2022-08-19
  • 网络出版日期:  2022-08-24
  • 刊出日期:  2023-05-10

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