计算机科学 ›› 2019, Vol. 46 ›› Issue (7): 327-332.doi: 10.11896/j.issn.1002-137X.2019.07.050
文俊浩1,万园1,曾骏1,王喜宾2,梁冠中1
WEN Jun-hao1,WAN Yuan1,ZENG Jun1,WANG Xi-bin2,LIANG Guan-zhong1
摘要： 传统路灯行业主要采用时间、经纬度、光照度等策略控制路灯开关。其中,光照度控制的理论节能效果最佳,但受采集数据误差、安装角度等环境因素影响,节能效果没有达到最大化。针对该问题,提出一种融合光照度聚类和支持向量机算法的路灯节能控制策略。该方法收集光照度、时间、安装角度数据,并使用K-means算法对光照度数据进行聚类,把原本变化剧烈的光照度数据变为5个等级(1－5),然后通过SVM对数据进行学习训练,在不考虑其他外在因素的情况下预测路灯的开关时间。实验研究结果表明,该算法可有效降低路灯的用电量。
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| [1]RODRIGUES C,ALMEIDA P S,SOARES G M,et al.An experimental comparison between different technologies arising for public lighting:LED luminaires replacing high pressure sodium lamps[C]∥IEEE International Symposium on Industrial Electronics.IEEE,2011:141-146. [2]BAE K Y,HAN S J,DAN K S.Hourly Solar Irradiance Prediction Based on Support Vector Machine and Its Error Analysis[J].IEEE Transactions on Power Systems,2017,PP(99):1-1. [3]KIM H C.Development of a stand-alone solar street light controller integrated[J].Ecological Entomology,2014,9(6):641-647. [4]AHMED S,ZENAN A H,TASNEEM N,et al.Design of a solar powered LED street light:Effect of panel’s mounting angle and traffic sensing[C]∥Sustainable Utilization and Development in Engineering and Technology.IEEE,2013:74-79. [5]ZHANG X H,KANG P H,ZHANG X,et al.Intelligent Street Lamp Energy Saving System Based on ZigBee[C]∥Fifth International Conference on Instrumentation and Measurement,Computer,Communication and Control.IEEE,2016:786-789. [6]LECCESE F.Remote-Control System of High Efficiency and Intelligent Street Lighting Using a ZigBee Network of Devices and Sensors[J].IEEE Transactions on Power Delivery,2012,28(1):21-28. [7]HAO G F,LI W C,LIU C L,et al.Research and Design of Intelligent Energy-Saving Control Device for Streetlamp[J].Applied Mechanics & Materials,2014,701-702:816-819. [8]NGUYEN H N,LEE H H.An Effective SVM Method for Matrix Converters With a Superior Output Performance[J].IEEE Transactions on Industrial Electronics,2018,65(9):6948-6958. [9]CHANDRASEKHAR A M,RAGHUVEER K.Intrusion detection technique by using k-means,fuzzy neural network and SVM classifiers[C]∥International Conference on Computer Communication and Informatics.IEEE,2013:1-7. [10]LIU H.Internet Public Opinion Hotspot Detection and Analysis Based on Kmeans and SVM Algorithm[C]∥International Conference of Information Science and Management Engineering.IEEE Computer Society,2010:257-261. [11]CUI G M,SUN T,ZHANG Y.Application of SVM in Prediction of Hot Metal Temperature in Blast Furnace[J].Control Engineering,2013,20(5):809-812.(in Chinese) 崔桂梅,孙彤,张勇.支持向量机在高炉铁水温度预测中的应用[J].控制工程,2013,20(5):809-812. [12]ORTEGA J L G,HAN L,WHITTACKER N,et al.A machine-learning based approach to model user occupancy and activity patterns for energy saving in buildings[C]∥Science and Information Conference.IEEE,2015:474-482. [13]WAN X C,LIANG L X,LU X J.Research and Design of Street Lamp Energy-Saving Control System Based on GPRS[J].Automation & Instrumentation,2012(2):62-63.(in Chinese) 万乡才,梁龙学,吕晓剑.基于GPRS的路灯节能控制系统的研究与设计[J].自动化与仪器仪表,2012(2):62-63. [14]KANG S H,SANDBERG B,YIP A M.A regularized k-means and multiphase scale segmentation[J].Inverse Problems & Imaging,2017,5(2):407-429. [15]KEERTHI S S,SHEVADE S K,BHATTACHARYYA C, et al.Improvements to Platt’s SMO Algorithm for SVM Classifier Design[J].Neural Computation,2014,13(3):637-649. [17]WANG Z W,SUN J J,YU Z Y,et al.Research on remote sen- sing image classification based on support vector machine[J].Computer Science,2016,43(9):11-17.(in Chinese) 王振武,孙佳骏,于忠义,等.基于支持向量机的遥感图像分类研究综述[J].计算机科学,2016,43(9):11-17. [18]WEN J H,SUN G H,LI S.Research on matrix decomposition recommendation algorithm based on user clustering and mobile context [J].Computer Science,2018,45(4):215-219.(in Chinese) 文俊浩,孙光辉,李顺.基于用户聚类和移动上下文的矩阵分解推荐算法研究[J].计算机科学,2018,45(4):215-219. |
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