Literature Database Entry

lau2013energyefficient


Sei Ping Lau, G.V. Merrett and N.M. White, "Energy-efficient street lighting through embedded adaptive intelligence," Proceedings of 2013 International Conference on Advanced Logistics and Transport (ICALT), May 2013, pp. 53-58.

Abstract

Streetlights place a heavy demand on electricity usage, providing significant financial and environmental burdens. Consequently, initiatives to reduce energy consumption have been proposed, usually by turning off or dimming the streetlight. In this paper, we propose an adaptive lighting scheme based on traffic sensing, which adaptively adjusts streetlight brightness based on current traffic conditions. The algorithm has been validated through simulation using the SUMO and OMNeT++ tools and, for two different geographical locations, the energy consumption evaluated with respect to traffic speed and state-of-the-art., volume. The simulation results presented indicate that the proposed lighting scheme can consume up to 30\% less energy when compared to the

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Sei Ping Lau
G.V. Merrett
N.M. White

BibTeX reference

@inproceedings{lau2013energyefficient,
    author = {Lau, Sei Ping and Merrett, G.V. and White, N.M.},
    title = {{Energy-efficient street lighting through embedded adaptive intelligence}},
    booktitle = {2013 International Conference on Advanced Logistics and Transport (ICALT)},
    pages = {53-58},
    keywords = {embedded systems;energy conservation;street lighting;traffic engineering computing;OMNeT++ tools;SUMO tools;adaptive lighting scheme;electricity usage;embedded adaptive intelligence;energy consumption;energy consumption reduction;energy-efficient street lighting;geographical locations;streetlight brightness;streetlight dimming;traffic sensing;Brightness;Energy consumption;Energy efficiency;Lighting;Roads;Sensors;Simulation;adaptive lighting;energy efficient lighting;streetlight},
    year = {2013},
    month = {May},
    publisher = {IEEE},
    doi = {10.1109/ICAdLT.2013.6568434},
   }
   
   

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