An energy efficient street lighting framework: ANN-based approach
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An energy efficient street lighting framework: ANN-based approach Pragna Labani Sikdar1 · Parag Kumar Guha Thakurta1 Received: 15 March 2020 / Accepted: 3 November 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract An energy efficient street lighting framework is proposed in this paper to reduce energy consumption obtained from the street lights. It is determined for various possible inter-distances offered by International Commission on Illumination. An ANN model is approached to obtain such reduced energy consumption for various traffic volumes on the road with minimum mean square error. The results of the proposed approach show an improvement over existing works. Keywords Street lighting · Energy consumption · Illuminance · Artificial neural network · Mean square error
1 Introduction Street lighting system is an important component of a modern city that provides safety for the passersby and vehicles during night [19]. This lighting system is considered to be a major concern of the government for facilitating electricity. It is known that it consumes about 40% of the total electricity of a city [14] and about 114 TWh (TeraWatt Hour) annually [9]. In conventional street lighting system, lights are continuously on with 100% brightness for total operational hours that consumes an enormous amount of energy [10]. As a consequence, it results in huge CO2 emission and also economic costs. Even an emission of over 150 millions tons of CO2 is caused by the streetlights in USA also [5]. Therefore, this system needs to be utilized efficiently so that the energy consumption is reduced in case where lighting is unnecessarily kept ON. In order to resolve the issues in the field of street lighting, the International Commission on Illumination (CIE) [6] develops basic guidelines. Furthermore, improvements in lighting quality as per CIE can enhance safety conditions for both vehicles and pedestrians. An energy efficient street lighting system is proposed in this paper that reduces the energy consumption of the streetlights by reducing unnecessary wastage of energy. In this work, the street lights are assumed to be controlled by the
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Pragna Labani Sikdar [email protected] Parag Kumar Guha Thakurta [email protected]
1
Department of Computer Science and Engineering, NIT Durgapur, Durgapur, India
sensors so that an autonomous adjustment of brightness can be adopted by sensing the pedestrians and vehicles. Energy consumed by the streetlights is determined for various possible inter distance between two consecutive streetlights as per the recommendation of the CIE. In this context, an artificial neural network (ANN) [13] model is developed to predict the energy consumption for various traffic volumes and is trained several times for estimating the minimum mean square error (MSE) in connection to obtain reduced energy consumption. The results show an improvement over the existing work in terms of reduced energy consumption. Hence, the major contributions of the proposed work are hig
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