Artificial Neural Network Based Power Management for Smart Street Lighting Systems
Smys S., Abul Basar, Haoxiang Wang
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.