Author:
S. Dr. Smys,Basar Dr. Abul,Wang Dr. Haoxiang
Abstract
The modern ways to empower the ecofriendly people also insists the necessity to cutting back of energy consumption. The minimizing the energy consumption would in turn reduce the rate of carbon emission, resulting in a cleaner air quality and higher standard of living by paving way for a cleaner planet. The increasing demand on power requirement is also one of the important reason for minimizing the energy consumption. The paper tries to decrease the energy usage of the street light system as the lighting systems in the street does not have an efficient way of managing and controlling the power flow in them as they are incapable of taking into consideration the prevailing demands on the intensity of light. So the paper puts forwards the idea of power management in the smart street lighting to control efficiently the power consumption by comparing the intensity of the light with the weather conditions. The artificial neural networks is used in power management of the street lighting in the proposed method. The evaluation of the method show up with the results that produce the better management of the power and the reduced power usage in street lights.
Publisher
Inventive Research Organization
Reference15 articles.
1. [1] Canca, D., J. Larrañeta, S. Lozano, and L. Onieva. "Traffic intensity forecast in urban networks using a multilayer perceptron." In Joint International Meeting EURO XV-INFORMS XXXIV. Barcelona, Spain. 1997.
2. [2] Ishak, Sherif, Prashanth Kotha, and Ciprian Alecsandru. "Optimization of dynamic neural network performance for short-term traffic prediction." Transportation Research Record 1836, no. 1 (2003): 45-56.
3. [3] Volosencu, Constantin, Daniel Ioan Curiac, Ovidiu Banias, Cristian Ferent, Dan Pescaru, and Alexa Doboli. "Hierarchical approach for intelligent lighting control in future urban environments." In 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, vol. 1, pp. 158-163. IEEE, 2008.
4. [4] Rea, Mark S., John D. Bullough, and Yukio Akashi. "Several views of metal halide and high-pressure sodium lighting for outdoor applications." Lighting Research & Technology 41, no. 4 (2009): 297-320.
5. [5] .Pamuła, Teresa. "Road traffic parameters prediction in urban traffic management systems using neural networks." Transport Problems 6 (2011): 123-128.
Cited by
41 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献