Lighting Control System Research Articles

Potentials of radar sensor detecting the presence of an imitated user for optimising short-range presence-sensing lighting in homes

Current presence-sensing technologies for energy-efficient lighting control and building optimisation are (i) catered to commercial and institutional environments, and (ii) focused on lamp technology and occupancy detection. They often ignore user behaviour characteristics, which significantly influence energy consumption. Therefore, this study aims to identify alternative sensing techniques as part of a lighting control system that can energy-efficiently support user’s behavioural needs in mixed-function residential spaces. An exploratory study investigated the optimal placement of a non-wearable radar sensor to detect an imitated user’s breathing frequency at varying pre-set horizontal distance positions, and the sensor’s performance was validated with a spirometer. The procedure measured a balloon’s radar-detected distance, radar-detected breathing frequency, and spirometer-registered breathing frequency at each pre-set position. The radar sensor detected all simulated breathing frequencies with almost 100% data accuracy but was not comparable in detecting all distances. The radar offers a less intrusive short-range presence-sensing for homes, accurately detecting breathing frequencies in a contactless way between 0.2m to 0.8m. Further investigations are recommended to develop radar sensing that could predict lighting options based on user’s objective feedback.

Intelligent Personalized Lighting Control System for Residents

The demand for personalized lighting environments based on households is steadily increasing among users. This article proposes a novel intelligent control system for personalized lighting in home environments, aiming to automatically capture user information, such as homecoming time and light switching behavior, in order to train a model that intelligently regulates the lights for users. Facial recognition technology is employed by this system to identify users and record their lighting data. Subsequently, nine commonly used machine learning models were evaluated, revealing that the error back-propagation neural network algorithm exhibits excellent performance in time-series analysis. The BPNN weights were optimized using genetic algorithms, resulting in an improved coefficient of determination (R2) of 0.99 for turn-on time and 0.82 for turn-off time test sets. Furthermore, testing was conducted on data collection duration which demonstrated that even with only 20 time-series data collected from new users, the model still displayed exceptional performance in training prediction tasks. Overall, this system effectively identifies users and automatically adjusts the lighting environment according to their preferences, providing comfortable and convenient lighting conditions tailored to individual needs. Consequently, a broader goal of energy conservation and environmental sustainability can be achieved.

Visual comfort and energy use reduction comparison for different shading and lighting control strategies in a small office building

Automated shading and lighting control systems can be used to control solar radiation and daylight entering the space and reduce the lighting requirement, providing both energy savings and better visual comfort for occupants. Past studies that have explored automated lighting and shading control strategies typically report energy savings and visual comfort improvements over their respective baselines. However, across different studies, the assumptions for baseline models differ, as many daylighting studies use an experimental setup to measure various daylighting-based metrics to evaluate the performance of their control strategies in a real building. For instance, these actual buildings usually have different room sizes (depth and width), different window locations (height, width, and WWR (with/without split windows), window orientation (simultaneous windows in two directions), glazing properties and climate zones of the building location. As these input variables affect the result of daylighting studies, comparing percent improvement in energy savings due to the implemented control strategies across different papers can be challenging. This research uses a common baseline to evaluate the differences between a broad diversity of existing lighting and shading control strategies. The author also proposes an Integrated Control Strategy (IGS), which accounts for variables such as occupancy, HVAC state, solar radiation entering the space, and time of day for control. This study uses a multi-step modeling process, including daylighting and energy simulations using RADIANCE and EnergyPlus, respectively, in a Ladybug and Honeybee plugin environment. The results are used to quantify the differences in the existing control strategies to a common baseline model. The results suggest that complex rule based shading and lighting strategies such as an IGS performs only slightly better (6–12 % decrease in view to the outside, no glare, 82–88% lighting energy savings and 7.5–14.5% total energy savings compared shades fully open) when compared to existing glare metric based control strategy (4.5–9.5 % decrease in view to the outside, no glare, 63–72% lighting energy savings and 7–13% total laod savings compared shades fully open. Thus, if the addition of sensors is needed to support complex shading and lighting controls strategies, these sensors may benefit from being used for multiple purposes in order to justify their use.

EVATL: A novel framework for emergency vehicle communication with adaptive traffic lights for smart cities

AbstractFixed cycle traffic lights primarily regulate road traffic, in which traffic light control systems are for specific lanes or crossings in urban areas. Also, not being appropriately installed can prolong the congestion delay and unnecessarily long wait times for crossing intersections, which can cause emergency vehicles to become stuck at intersections. Adaptive signal timing management technique that is more computationally viable than current fixed cycle signal control systems and can improve network‐wide traffic operations by reducing traffic delay and energy consumption. Even though specific adaptive control systems exist, there is no mechanism to communicate with emergency vehicles, which is crucial for smart cities. Motivated by this problem, a novel framework, Emergency Vehicle Adaptive Traffic Light (EVATL), is proposed for smart cities where an adaptive mode of operation for traffic lights is employed with emergency vehicle communication, improving their functioning and reducing overall congestion delay. EVATL detects emergency vehicle location using GPS with the Internet of Things(IoT), which integrates with traffic signals and works adaptively according to vehicle density at the traffic signal using YOLOv8. So, the primary goal of the proposed EVATL is to prioritise an emergency vehicle while simultaneously integrating adaptive traffic signals for smart cities. A GUI is developed for evaluating the proposed model by creating different scenarios for an adaptive traffic light and emergency vehicle communication. While analysing the simulation results of the proposed model EVATL, a clear improvement can be seen in the wait time of vehicles at a traffic light with the timely detection of an emergency vehicle at a set distance.

Development of public lighting system with smart lighting control systems and internet of thing (IoT) technologies for smart city

This study proposes the design and development of public light systems integrated with Internet of Things (IoT) applications for smart cities. Smart public lighting systems are designed using LED light sources in combination with a controller based on light intensity and motion sensors to control the brightness in working areas as per standard requirements. The control of smart public lighting systems is designed to operate in three modes: manual, scheduled, and auto modes, which are tested and compared in terms of lighting and power quality. Other functions of IoT, such as air pollution detection, security system-based video surveillance, and warning systems for flood disasters, are integrated into smart public lighting systems. A prototype of the designed smart public lighting system is presented in this paper. The parameters of average illuminance, overall uniformity, and threshold increment were implemented using DIALux software to optimise the luminaire spacing. The results showed that the goal of energy saving could be achieved using the auto mode. Although the automatic mode caused current harmonic distortion to the electrical grid, the harmonic value was less than the specified standard. Furthermore, the precision of IoT system operation was dependent on environmental variations and the reliability of wireless network technology.

Design of the Urban Lighting Control System Based on Optical Multisensor Technology and the GM Model

Lighting has emerged as a central concern in the domain of city planning and design in recent decades. Better lighting does more than just make cities safer and more secure; it also makes them more aesthetically pleasing and easier to live in. A single type of optical sensor is no longer sufficient to meet the needs of intelligent lighting for urban roads, and as such, there is a growing demand for cutting-edge control systems that can adapt to the dynamic lighting needs in urban environments. This paper’s goal is to create an intelligent urban lighting control system by integrating optical multisensor technology and the gray model (GM model). Programmable logic controller (PLC) serves as the system’s central processing unit, with light intensity sensors and color sensor-detecting devices placed strategically throughout each city and linked directly to the controller. Each road streetlight is equipped with a motion sensor detection device that is tasked with identifying the presence of vehicles and pedestrians within its field of view. Data fusion technology is utilized to process the environmental data gathered by optical multisensors, the collected data are then used to control and predict outcomes using the robust prediction capability of the GM model, and the result is a lighting control strategy that is both efficient and intelligent. In the end, the strategy presented in this paper is applied to improving the management of an industrial park lighting system’s energy consumption. The results of the evaluations show that the fresh method is successful in dimming, prediction, and control. This conclusively demonstrates the efficacy of the paper’s proposed design solution, which integrates optical multisensor technology with sophisticated control algorithms and data analysis to improve the quality of life in urban areas by boosting the efficiency and sustainability of the urban lighting system.

Design of Health Healing Lighting in a Medical Center Based on Intelligent Lighting Control System

The intelligent lighting control system will unify the management of lighting equipment in outpatient buildings, inpatient buildings, and other buildings of the hospital, thereby improving the service quality of the hospital, providing a comfortable and relaxed working environment for medical staff, and providing a warm and comfortable treatment environment for patients. This project aims to develop a health rehabilitation environment lighting automation control system based on combining Zigbee Wide Ad Hoc Network and RFID (Radio Frequency Identification) technology. It can be customized according to the comfort needs of patients and can achieve lighting adjustments before the patient arrives. A PSO-based intelligent lighting control method has been proposed. To overcome the shortcomings of PSO such as being prone to local minima and premature convergence, a new PSO optimization method is proposed based on the inertia weight PSO and combined with genetic optimization theory. This method can not only learn experience from individuals in the group but also avoid the possibility of parent particles falling into local extremum. Compared with the original particle swarm optimization algorithm, the new particle swarm optimization algorithm can quickly find the optimal combination of lighting devices, improve computational efficiency by 6.208%, and also reduce the energy consumption of the calculated lighting devices, indicating the advantages of the new particle swarm optimization algorithm.

AI-based YOLO V4 Intelligent Traffic Light Control System

With the growing number of city vehicles, traffic management is becoming one of the most persistent challenges. Traffic bottlenecks cause more significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, the effective result of wait times for the Commuters at the traffic signal point is not reduced. The proposed methodology employs Artificial Intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in lower vehicle waiting times.

ADAPTIVE TRAFFIC CONTROL AT THE INTERSECTION

The solution to the problem is a typical technology of a smart cross-logic system. Due to the rapid growth in the number of cars in the city, various problems such as congestion and environmental pollution are increasingly emerging.
 Adaptive intersection management system, often implemented to solve the problem of congestion in the city. Constantly used control systems are usually considered illogical, that is, the traffic light signal changes statically. The purpose of this work is to present an adaptive system of logical control of the intersection, i.e. changing traffic light signals in real time depending on the type of vehicles.
 This system is an example of an adaptive automotive communication model that implements data representation between vehicles and traffic lights. When crossing with cars at an intersection, it sends an instant message to the traffic light, after which the traffic light corrects the information and adjusts the signal time in real time. Each traffic light in both directions has its own management strategy, without requiring orthogonality. This traffic light control system allows you to maximize the number of cars crossing the intersection and, as a result, reduce congestion and pollution. The algorithm of traffic light control based on the type of car and its modeling is proposed. The safety and reliability of this control system are also discussed.

Simulation model of an intelligent transportation system for the “smart city” with adaptive control of traffic lights based on fuzzy clustering

This article presents a new simulation model of an intelligent transportation system (ITS) for the “smart city” with adaptive traffic light control. The proposed transportation model, implemented in the AnyLogic, allows us to study the behavior of interacting agents: vehicles (V) and pedestrians (P) within the framework of a multi-agent ITS of the “Manhattan Lattice” type. The spatial dynamics of agents in such an ITS is described using the systems of finite-difference equations with the variable structure, considering the controlling impact of the “smart traffic lights.” Various methods of traffic light control aimed at maximizing the total traffic of the ITS output flow have been studied, in particular, by forming the required duration phases with the use of a genetic optimization algorithm, with a local (“weakly adaptive”) switching control and based on the proposed fuzzy clustering algorithm. The possibilities of optimizing the characteristics of systems for individual control of the behavior of traffic lights under various scenarios, in particular, for the ITS with spatially homogeneous and periodic characteristics, are investigated. To determine the best values of individual parameters of traffic light control systems, such as the phases’ durations, the radius of observation of traffic and pedestrian flows, threshold coefficients, the number of clusters, etc., the previously proposed parallel real-coded genetic optimization algorithm (RCGA type) is used. The proposed method of adaptive control of traffic lights based on fuzzy clustering demonstrates greater efficiency in comparison with the known methods of collective impact and local (“weakly adaptive”) control. The results of the work can be considered a component of the decision-making system in the management of urban services.

Modern Smart Street Light Monitoring Systems

Abstract: This study highlights the importance of Modern Street light control systems over traditional ones, with LED lamps emerging as the most suitable choice due to their advantagessuch as reduced heat production, lower electricity consumption, and extended lifespan. The main goal is to enhance system efficiency by minimizing unnecessary electricity loss during daylight and when lighting is not required. Automating street lights using LDR and PIR sensorsin conjunction with Arduino Uno offers a costeffective and secure solution, eliminating the drawbacks of manual switching and simplifying cost reduction and maintenance. Additionally,applying a ZnO coating to the street light casings provides effective protection against dust and moisture, resulting in efficient lighting. The process involves sol-gel deposition of ZnO nanoparticles on silica glass substrates, followed by preannealing and repeated dip coating. Annealing at temperatures between 450°C and 600°C further improves the results. The uniformand defect-free coatings are confirmed by SEM images, while transmission spectra reveal over80% penetration of visible light (400 nm to 900 nm). Lower precursor concentrations lead to better penetration of light below 300 nm wavelength.

Research on a fuzzy algorithm for intelligent power-saving control of urban street lamps

Urbanisation has increased the demand for energy-efficient and environmentally friendly street lighting systems. However, traditional urban lighting uses time interval control and cannot be intelligently controlled according to the environment. Therefore, an intelligent urban street lighting control system based on advanced fuzzy control technology was developed. The system uses a fuzzy control algorithm based on ZigBee technology to study the street lighting system. The fuzzy control system determines input and output variables and obtains demand instructions for intelligent control of street lights through fuzzy operation. The number of node visits and node degree were optimised to complete the construction of the street lamp intelligent control system. Experimental simulations showed that the proposed scheme could reasonably control the brightness of the street lighting according to ambient light. At 5.00 p.m., the proposed scheme adjusts the street lighting to 80 W. The system is illuminated at the rated power, and the current power consumption is 100 W. A test was carried out under a street lighting system, and the proposed scheme dynamically adjusted the lighting brightness reasonably considering various factors such as light intensity, noise intensity and vehicle speed. The proposed street lamp fuzzy control algorithm has good control performance and significantly reduces the system energy consumption. The research has important reference value for energy saving and intelligent control of urban lighting.

A Novel High-Priority Traffic Light System Based on a Smart Technique

In the world today, traffic congestion in most cities is a severe problem. Recently, the system used is based on a fixed-time system in traffic, which cannot provide incompetence if one or more lane has higher traffic than others. The higher traffic on one side lane needs a longer operation time for a green signal as compared to the fixed time-based systems. This paper proposed a High Priority Traffic Light system HPTL as a technique to compute the period time of green and red lights based on the density of traffic present at that time. To fix this problem an automatic traffic light control system should proposed. The proposed system will be achieved by using PIR proximity Infrared Sensors. When the density is computed, the luminous period of the green and red signal will be selected to help the microcontroller. On each roadside, the sensors can detect the traffic density and calculate the vehicles in a specific lane then forward the density value to the microcontroller. The microcontroller will select the free lane and the high-traffic lane and then assign the priority system. The proposed system is successful in investing time, reducing vehicle density, and increasing reliability.

Study on the effect of photovoltaic louver shading and lighting control system on building energy consumption and daylighting

ABSTRACT Photovoltaic louver shading (PLS) and lighting control system (LCS) can both reduce energy consumption during building operation, but when used in combination, PLS may reduce indoor daylighting and increase lighting energy consumption, which is not good for the LCS. In order to reduce the energy consumption of building operation, this paper adopts the parametric performance design method, uses the Grasshopper platform and Ladybug tools to establish a one-click simulation process, and analyzes the energy-saving potential and daylighting impact of the combined use of PLS and LCS in Qingdao. It was found that the annual energy efficiency of the PLS can reach 15%, and LCS can save about 50% energy of lighting in a year, producing an energy efficiency at 14%. In the meantime, using both the PLS and LCS throughout the year can save up to 22.8%. When using only LCS from December to February, and using both PLS and LCS from March to November can save up to 24.14%.

Efficient performability analysis of dynamic multi-state k-out-of-n: G systems

A dynamic multi-state k-out-of-n: G system, denoted by DMS(k, n, G) is a system where the system and its components exhibit multiple performance levels, and the system has different requirements on the number of working components in different states. DMS(k, n, G) abounds in both industrial and military applications. In this paper, a novel and efficient analytical method based on multi-valued decision diagrams (MDDs) is proposed for performability assessment of DMS(k, n, G) with non-identical components. Unlike existing approaches where multiple combination operations between models of lower performance levels are needed for constructing the model of a higher performance level, the proposed MDD generation algorithm constructs the system performability MDD in a top-down manner by considering multiple system state requirements simultaneously. A smart home lighting control system is analyzed to demonstrate the application of the proposed method. A detailed case study of an oil supply system is provided to verify the correctness of the proposed method and illustrate component sensitivity analysis. Complexity analysis and comprehensive empirical studies are performed to demonstrate that the efficiency of the proposed model construction is greatly improved as compared to the existing method, enabling fast model generation and efficient analysis of large-scale DMS(k, n, G).

Simulasi dan Analisis Tegangan Sensor LDR dengan OP-AMP Berbeda

The development of the accelerated pace of technology makes people motivated to make new innovations, as well as something that can be controlled automatically. Therefore, we need a sensor that can facilitate human activities, especially in the home light control system. The purpose of this study is to test and analyze the LDR sensor by simulating a software, namely Proteus with different op-amps. LDR (Light Dependent Resistor) is a type of resistor whose resistance value depends on the intensity of light. The method used in this research is an experimental method, namely by conducting repeated tests 10 times for different types of op-amps. The tests carried out here use three ICs that have different types, namely IC LM 358, IC LM741 and IC LM747. The results of this study are the average voltage value generated for the IC LM385 input 2.75 Volt and 4.99 Volt, IC LM 741 input 2.74 Volt and 2.62 Volt and IC OP07 2.77 Volt input and output 2.5 Volts. Of the three IC test results, the largest average voltage value for both input and output is the OP-AMP LM385 IC.

A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning

With the rapid development of human society, people’s requirements for lighting are also increasing. The amount of energy consumed by lighting systems in buildings is increasing, but most current lighting systems are inefficient and provide insufficient light comfort. Therefore, this paper proposes an intelligent lighting control system based on a distributed architecture, incorporating a dynamic shading system for adjusting the interior lighting environment. The system comprises two subsystems: lighting and shading. The shading subsystem utilizes fuzzy control logic to control lighting based on the room’s temperature and illumination, thereby achieving rapid control with fewer calculations. The lighting subsystem employs a Deep Deterministic Policy Gradient (DDPG) algorithm to optimize the luminaire dimming problem based on room illuminance in order to maximize user convenience while achieving uniform illumination. This paper also includes the construction of a prototype box on which the system is evaluated in two distinct circumstances. The results of the tests demonstrate that the system functions properly, has stability and real-time performance, and can adapt to complex and variable outdoor environments. The maximum relative error between actual and expected illuminance is less than 10%, and the average relative error is less than 5% when achieving uniform illuminance.

Design of Urban Public Landscape Lighting Control System Based on Dmx512 Protoco

Design an urban public landscape lighting control system based on DMX512 protocol to improve the lighting effect of urban public landscapes. The system uses the upper computer urban public landscape scene transformation software to edit the colour change data of urban public landscape lamps, set a reasonable number of sub-controllers according to the actual needs of urban public landscape scenes, and store relevant data in the SD card of the data storage layer. The Ethernet transmits the data in the SD card to the lighting control module, the main controller transmits the data in the SD card to the sub-controller, and the ARM of the sub-controller decodes the urban public landscape scene data, and uses the fireworks algorithm-fuzzy adaptive PID. The control algorithm solves the control data of urban public landscape lighting lamps, and the data that meets the DMX512 protocol is edited by FPGA and transmitted to the DMX512 controller. Through the DMX512 controller, the control data of urban public landscape lighting lamps is transmitted to the equipment module realize the control of LED lamps for urban public landscape lighting. The experimental results show that the response speed of the system is fast, at 0.60s it can quickly reach the illuminance value of the park landscape lighting LED light 7.5 lx set in the experiment, and its control ability is strong. At the same time, the system has high software and hardware compatibility and system stability. Better, in multiple operating systems, there will be no problems such as flashback, blue screen, crash, etc., ensuring the safe operation of the PC and improving the effect of park landscape lighting control.

Review on Traffic Light Control Systems Based on Single-Chip Microcomputers and Smart Traffic Light Control Systems

With the development of the economy and the improvement of living standards, the number of private cars is increasing, which brings about traffic congestion and increased traffic safety hazards. Although some cities have widened some urban roads, traffic congestion and traffic safety hazards have not been effectively alleviated, and the speed of road construction and urban construction is far from meeting the needs of citizens. Therefore, traffic congestion is the primary problem that needs to be solved at present. This paper introduces single-chip microcomputers with their functions and applications as well as designs for traffic light control systems based on microcomputers. By comparing previous research designs and models, this paper points out the comparisons between different models and provides research suggestions and future prospects.

Development of an automated lighting control system in agricultural premises to save energy resources

An automated lighting control system has been developed in the premises used for agricultural needs. An algorithm for the operation of the lighting system is presented. The setup of the light sensor in the OpenHAB is shown. The results of an experimental study are presented. The introduction of the developed control system will lead to saving energy resources and reducing economic costs.