An energy efficient street lighting framework: ANN-based approach

Sikdar, Pragna Labani; Thakurta, Parag Kumar Guha

doi:10.1007/s11334-020-00375-2

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…Pragna et al [128] used an ANN to optimize SL brightness and energy consumption, based on traffic volume and inter-distance between luminaires. The ANN, consisting of three layers, took inputs like traffic volume and inter-distance to predict energy consumption and was trained using backpropagation with a variable learning rate.…”

Section: User-driven Controlmentioning

confidence: 99%

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Agramelal,

Sadik,

Moubarak

et al. 2023

Energies

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As urbanization increases, streetlights have become significant consumers of electrical power, making it imperative to develop effective control methods for sustainability. This paper offers a comprehensive review on control methods of smart streetlight systems, setting itself apart by introducing a novel light scheme framework that provides a structured classification of various light control patterns, thus filling an existing gap in the literature. Unlike previous studies, this work dives into the technical specifics of individual research papers and methodologies, ranging from basic to advanced control methods like computer vision and deep learning, while also assessing the energy consumption associated with each approach. Additionally, the paper expands the discussion to explore alternative functionalities for streetlights, such as serving as communication networks, environmental monitors, and electric vehicle charging stations. This multidisciplinary research aims to be a pivotal resource for both academics and industry professionals, laying the groundwork for future innovation and sustainable solutions in urban lighting.

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Section: User-driven Controlmentioning

confidence: 99%

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Agramelal,

Sadik,

Moubarak

et al. 2023

Energies

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“…The paper [16] focused on solving the issue of traffic using supervised feed-forward neural networks and Particle Swarm Optimization The results of the two methods was able to control the traffic light that allows the vehicles on each street to be moving or waiting its turn without focusing on the power consumption of traffic lights, the researcher main focus was on traffic lights of smart streets without tackling the issue of street lights or objects detection, hence the researcher didn't dim the lights according to the detected objects in the streets. The paper [17] used an ANN model to reduce energy consumption of street lights. It is determined for various possible inter-distances offered by International Commission on Illumination, the paper focused on volumes on the road with minimum mean square error.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Using deep learning to design an intelligent controller for street lighting and power consumption

Bakri

Abid

Ali

et al. 2022

EEJET

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Street lighting is very important now-days especially at dangerous areas and highways but it consume a lot of power and it became challenging for many researchers in the past few years. Enormous efforts have been placed on the issue of reducing power consumption in illuminating cities and streets, researchers had various approaches and methods in tackling this challenging matter, till now there is no ideal system that has been developed to reduce the electricity usage. In this paper intelligent controller based on deep learning proposed to control the light at the street from sunset to sunrise, the system will decrease the light used to illuminate the streets in the absence of movements, the network trained based on deep learning with several image of different objects to help the system detecting any moving objects in the street to provide the street with the exact amount of light needed in order to reduce the waste of electrical energy resulting from street lighting and to help reduce accidents hence high percentage of criminal activity and life threatening conditions occur in the absence of light. The system was trained with a vast and diverse dataset to assure the accuracy and efficiency of the proposed system, the trained system showed a result of 90 precision of detecting moving objects, the proposed system was tested with a new dataset to assure the reliability and dependency of the system and reducing the errors to the minimum, the system shows promising results in detecting movements and objects, after the detection being complete, the system will send a pulse width modulation causing a 20 % light dimming, leading to enormous reduction in the power consumption, adding to that the proposed system is easy to use

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“…It is important to maintain visibility across the entire path and prevent the lamp from dazzling people's vision. Therefore, illumination uniformity (Uo) and glare index (UGR) are included as performance parameters of lighting [23,25].…”

Section: Introductionmentioning

confidence: 99%

Optimal Selection of Distribution, Power and Type of Luminaires for Street Lighting Designs Using Multi-criteria Decision

Valencia Pavón,

Aguila Téllez,

Garcia

et al. 2024

Preprint

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This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

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An energy efficient street lighting framework: ANN-based approach

Cited by 11 publications

References 18 publications

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Smart Street Light Control: A Review on Methods, Innovations, and Extended Applications

Using deep learning to design an intelligent controller for street lighting and power consumption

Optimal Selection of Distribution, Power and Type of Luminaires for Street Lighting Designs Using Multi-criteria Decision

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