Efficient Photodetector Placement Using Linear Optimization Fuzzy C-Means and Artificial Neural Networks
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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。Abstract: An efficient photodetector placement method using Linear Optimization Fuzzy C-Means (LOFCM) and Artificial Neural Networks (ANN) is proposed for the problem that the current photodetector placement method is computationally intensive, energy intensive, susceptible to human factors and difficult to predict accurately indoor daylight illuminance. In this method, working surface photodetector layout is obtained by the LOFCM algorithm that using Fuzzy C-Means (FCM) to filter data after using Linear Optimization (LO) to sparse weight matrix. Subsequently, a non-linear mathematical model is trained between the working surface photodetector layouts and the four sets of auxiliary photodetector layouts using the ANN respectively. The experimental results show that the proposed LOFCM-based algorithm can reduce the number of working surface photodetector by 37.5% compared to other methods while ensuring the accuracy of calculating the average illumination and uniformity of the working surface. In addition, the wall and window auxiliary photodetector layout obtain a high prediction performance.
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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)3 4 2.488 2.326 528 0.89 17 7.325 
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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 Z1 L1, L2, L5, L6 Z2 L3, L4, L7, L8 Z3 L5, L6, L9, L10 Z4 L7, L8, L11, L12
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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
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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 $
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表 6 辅助光照传感器分组表
分组 传感器 G1 Wd1，Wd2，Wd3，Wd4 G2 W1，W2，W3，W4 G3 Wd1，Wd2，Wd3，Wd4，W1，W2 G4 Wd1，Wd2，Wd3，Wd4，W3，W4
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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；15:00-18:00 2 min 晴天与阴天
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表 8 收集数据特征表(lx)
分组 Wd1-Wd4 W1-W4 Z1 Z2 Z3 Z4 最小值 19.2 51.5 182.0 167.0 111.0 108.0 最大值 192 175 1068 1106 425 359 平均值 45.86 122.28 474.19 505.78 244.40 245.18 标准偏差 18.09 24.85 176.94 223.98 45.91 55.90
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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_G1 3.930 1.239 0.99397 0.99419 0.98667 ANN_G2 4.727 1.369 0.99259 0.99481 0.98459 ANN_G3 4.151 1.601 0.99170 0.99114 0.98194 ANN_G4 6.228 1.974 0.98847 0.99006 0.98686
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