Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (7): 327-332.doi: 10.11896/j.issn.1002-137X.2019.07.050

• Interdiscipline & Frontier • Previous Articles     Next Articles

Application of Illumination Clustering and SVM in Energy-saving Control Strategy of Street Lamps

WEN Jun-hao1,WAN Yuan1,ZENG Jun1,WANG Xi-bin2,LIANG Guan-zhong1   

  1. (School of Big Data & Software Engineering,Chongqing University,Chongqing 401331,China)1
    (School of Big Data,Guizhou Institute of Technology,Guiyang 550003,China)2
  • Received:2018-06-19 Online:2019-07-15 Published:2019-07-15

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Abstract: The traditional street lighting industry mainly adopts the strategy of time,latitude and longitude,illumination and so on to control street lights,and the theory of illumination control has the best energy saving effect.However,due to the error of light collection,installation angle and other environmental factors,the energy saving effect has not been maximized.Aiming at this problem,this paper proposed a street lighting energy saving control strategy based on illumination clustering and support vector machine algorithm.This method collects the light intensity,time and installation angle data,and uses K-means algorithm to cluster the illumination data and changes the original light illumination data into 5 grades (from 1 to 5).Then,the data is trained by SVM,and the switching time of the street lamp is predicted without considering other external factors.The experimental results show that the algorithm can effectively reduce the power consumption of street lamps.

Key words: Energy-saving of street lamp, Illumination, K-means, Support vector machine

CLC Number: 

  • TP391
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