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 sufcient 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. Tis 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 feld 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 efcient 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. Te results of the evaluations show that the fresh method is successful in dimming, prediction, and control. Tis conclusively demonstrates the efcacy 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 efciency and sustainability of the urban lighting system.


Introduction
Since urban lighting is such an integral part of smart cities, it is experiencing rapid, intelligent, and environmentally friendly growth.Cities are also looking for strategies to optimize energy consumption in light of rising concerns about sustainability and the need to reduce energy use.As a result, it is important for communities to work towards creating a more sophisticated method of controlling their streetlights.Tere is a growing recognition of the importance of urban lighting control systems to the health, safety, and attractiveness of metropolitan areas [1].Public, street, park, and building lighting can all beneft from these systems, which regulate and manage lighting settings to maximize efciency while maintaining enough illumination.Although urban lighting control systems help improve city life, they are still constrained in their efectiveness.Traditional urban lighting control systems, for instance, frequently rely on predetermined schedules or manual adjustments that ignore dynamic factors such as pedestrian trafc, weather conditions, and natural light, leading to systems that lack fexibility and adaptability to meet the changing lighting needs of a city.Terefore, it is crucial to create a smart and logical lighting control system as cities grow and their lighting requirements alter.
Technology advancements have allowed for the creation of better and more efcient lighting control systems, which is particularly signifcant given the signifcance of urban lighting.Te urban lighting industry is currently on board with the idea that digital and intelligent technologies can be used to better utilize lighting facilities and energy usage, all while satisfying public trafc and avoiding excessive energy consumption and simple and brutal control means [2].Researchers are, therefore, focusing more of their eforts on street lighting control systems, with the goal of creating more practical intelligent lighting management systems.Literature [3] relies on natural-light-sensing sensors to measure illumination levels.Te streetlight comes on when there is not enough daylight, combining the benefts of both artifcial and natural illumination while reducing power use.Infrared sensors are used to track passing vehicles and people in the published works [4].Tese experts devised and constructed a smart lighting system that can swiftly detect pedestrian and vehicle movements and activate street lighting fxtures to improve safety and convenience.Intelligent control algorithms for intelligent lighting systems were proposed in literature [5], with researchers studying and analyzing the microcontroller system's algorithm in order to determine the best way to process wireless control signals for use in regulating the functioning of intersection trafc lights.Tey demonstrated that the algorithm was able to fairly disperse trafc and signifcantly cut down on the waiting time.
Previous research has shown that sensor technology is essential in creating and deploying urban lighting management systems.However, it has proven challenging for a single type of sensor to meet the needs of intelligent lighting for urban roads due to the variability of external natural light (sunny, cloudy, etc.).New opportunities for investigating better and more efcient urban lighting control systems have emerged with the rapid growth of sensor technology, notably optical sensor technology [6].Furthermore, this paper also focuses on the reasonable and intelligent control strategy needed for the intelligent lighting management system for urban highways, in addition to the assistance of sensor technology.In light of these needs, the purpose of this paper is to investigate the design of optical multisensor technology and the GM model-based urban lighting control system, with the goal of making urban lighting systems more energy efcient and environmentally friendly in the aim to better the quality of life in urban areas.Te novel components of this paper's research are, frst, the integration of various optical sensors to collect data of the road lighting environment in real time (light intensity sensor, color sensor, and motion sensor).Intelligent and responsive control of the lighting system is possible with the help of data collection on aspects such as light level, pedestrian fow, trafc fow, and weather conditions.Second, this paper uses an enhanced version of the GM (1,1) model for lighting prediction control, namely, the polynomial discrete gray model GM (1,1,K), to solve the inadequacies of the traditional model.Te GM (1,1,K) model predicts future lighting demand and aids the system in making educated lighting control decisions by assessing environmental data acquired by optical multisensors.In the end, the strategy presented in this paper is applied to improving the management of an industrial park lighting system's energy consumption.Te results of the trials show that the precision and consistency of system lighting measurements are enhanced by the use of optical multisensor technology, while the GM (1,1,K) model ofers useful insights for lighting prediction and optimization.In this way, the new technology succeeds in three respects at once: dimming efect, prediction performance, and control performance.

Related Theory and Technology
2.1.Optical Sensors.Urban lighting control systems rely heavily on optical sensors, which are devices that can detect and quantify light or electromagnetic waves [7]. Figure 1 depicts the primary uses for four diferent types of optical sensors that are frequently employed: light intensity sensors, color sensors, motion sensors, and occupancy sensors.Roadway illumination factors including brightness, color temperature, occupancy levels, and ambient light may be tracked in real time thanks to the strategic placement of these sensors in metropolitan highways.Lighting control techniques, energy optimization, and user happiness can all beneft from this information gathered by the system.For instance, the system can reduce the brightness of artifcial lighting or turn it of altogether if a light intensity sensor detects a drop in ambient light owing to daylight saving time [8].When a motion detector senses movement, the system can brighten the lights in that area for further security and visibility [9].In parks, the system can light specifc paths based on detected occupancy, while dimming other areas to save energy [10].

Multisensor Data Fusion Technology.
In the 1980s, advancements in data processing led to the creation of data fusion technology.With the purpose to improve the accuracy of the data gathered by sensors, this technology performs operations such as analysis, fltering, and synthesis [11].Data fusion technology has become increasingly popular as a result of the growing sophistication of computing and communication systems.Tere are now three distinct forms of data fusion architecture in use today: centralized, distributed, and hybrid [12].Te sensor of the centralized kind, shown in Figure 2(a), cannot perform any analysis or make any decisions on its own and must instead transmit the raw data it has collected to a central processor for processing.Figure 2(b) depicts a distributed architecture in which the sensor transfers the raw data it has collected to the fusion node for preprocessing and then transfers the results of that preprocessing to the processor for analysis and decision-making.Te hybrid structure combines elements of both the centralized and decentralized models.It combines their benefts but at the expense of a high computational and communication cost.
Because lamps are spread out along diferent routes, and the terminal sensors in this system will detect the values of many environmental parameters, including light intensity, color, and motion, a great deal of data will be generated on the system.Tis system uses a distributed data fusion architecture to improve analysis and judgment.This sensor measures the intensity or brightness of light in a given area.It provides quantitative data about the amount of light, allowing the control system to adjust lighting levels accordingly.
The sensor is able to detect and distinguish between different colors in the lighting environment, providing information about the color temperature or chroma of the light.It is important to achieve the desired lighting aesthetics or to meet specific requirements.
The sensor detects movement or changes in the surrounding environment.In urban lighting control, they are used to detect the presence of pedestrians or vehicles.This information can be used to activate or adjust lighting in specific areas as needed, enhancing safety.
Occupancy sensors determine whether an area or space is occupied by detecting the presence or absence of an individual.They can be used to optimize energy consumption by activating or deactivating lighting based on occupancy, ensuring that the lights are only on when they are needed.Journal of Electrical and Computer Engineering consumption, can be captured by this method, making it an efective tool for lighting demand analysis and forecasting based on limited data.

System Design
Management agencies have spent a lot of time and money on fxing various issues plaguing the current urban lighting system, such as the unreasonableness of its control methods and accompanying massive energy waste.Te system's main goal is to design a scientifc, reasonable, and environmentally friendly urban intelligent lighting control system, and it does so by adhering to the principles of safety and reliability, high applicability, and scalability.In order to increase energy efciency, decrease operating costs, and boost user satisfaction in urban lighting environments, the system collects and processes data of environmental factors surrounding streetlights through optical multisensors and also uses the GM model to control and predict the collected data to achieve a more efcient and intelligent lighting control strategy.
3.1.General Architecture of the Control System.Tis paper proposes a "sensing-processing-decision-control" lighting system for roads that relies on optical multisensors to detect environmental changes, process data in real time, and generate control strategies.Tere are two main components to the system: software and hardware.Te bulk of the hardware system consists of terminal devices, controllers, and a server platform, with sensors detecting the intensity and color of natural light strategically positioned across the city and communicating with a central hub controller.Every streetlight has motion detectors built in to check if there are any passing vehicles or pedestrians.Te controller for the road's lights can be found in the streetlight distribution box.Te streetlight monitoring system may be remotely monitored in real time thanks to Ethernet-based connection between the primary controller and the database management center.Figure 3 depicts the system's general design, which is primarily mirrored in the software platform system of the control room.Tis system includes the communication interface software, data processing software, and operation management software.Te control system is the brains of the operation, processing data from optical multisensors in the feld to determine the optimal brightness level for streetlights and assigning diferent lighting strategies based on the state of the road.Because of this, the upper and lower networks make up the lighting control system architecture presented in this paper.Ethernet ties the city's central controller to regional monitoring stations across the metropolis.Each district's central light intensity and color sensor wirelessly transmit data about the district's natural illumination to the district's main controller, which then activates or deactivates the district's streetlights and sends data about the district's portion of the city's roads to the monitoring center, where it is displayed.Each road in the city is equipped with a controller for the city's streetlights, and the streetlights on both sides of the road are wired together.Each motorway streetlight or sidewalk street light is equipped with a motion sensor, and the information detected is sent to the road light controller, which then activates appropriate streetlights based on the information sent by the motion sensor.Trough the streetlight controller, data from the lower network are relayed to the higher network's master controller.Tis allows the entire lighting control system to communicate across a network.Figure 4 depicts the overall system network architecture.

Light Intensity Sensor Module.
By measuring exterior ambient brightness, the light intensity sensor module may determine if the streetlight has to be activated [15].Te light intensity sensor is built as an analog-to-digital converter so that the collected amplifed signal can be converted into a digital signal that can be read using a computer.LM393 is useful for a wide variety of tasks, including switch control, alarms, and temperature monitoring, thanks to its ability to compare and output high-and low-level signals based on a variety of input signals.It is well suited for both long-term operation and quick response thanks to its low power consumption, high-speed response, and other qualities.As a result, the light intensity sensor module in this research was developed using the LM393 chip.Figure 5 depicts the light intensity sensor's internal workings.
In this research, a light intensity sensor measures the level of illumination from streetlights and sends that information through an analog-to-digital converter for use by the system.When conditions on the road alter, the streetlight must adapt its settings accordingly.When the streetlight is too bright, the system adjusts the driver circuit's output current to lower the intensity of the light.However, when ambient light levels are low, the system takes action to modify the driving circuit's output current, keeping the streetlight illuminated.

Color Sensor.
Te results from a color sensor can be utilized to change the streetlight's intensity or hue to match the ambient light level or desired efect [16].In order to determine the color of the observed light, the system works by detecting the intensity of the light across a spectrum of wavelengths.If the sensor senses that it is getting dark, for instance, it can instruct the streetlight to shine brighter.On the other hand, the streetlight can be dimmed or turned of if the sensor determines that there is already enough light outside.In sum, color sensors play a crucial role in street lighting management systems by providing real-time feedback on the illumination conditions to allow for efcient and adaptive regulation of streetlights.In this study, an RGB color sensor is utilized to accomplish intelligent lighting system control by detecting and analyzing the color of light in the surrounding environment through the measurement of light intensity across three color channels: red, green, and blue.Figure 6 depicts the inner workings of an RGB color sensor.

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Journal of Electrical and Computer Engineering

Motion Sensor Module.
Te motion sensor module in this system uses an infrared pyroelectric module to detect oncoming pedestrians and vehicles [17].After a short period, the streetlight installed on the terminal module returns to its dim setting after being activated by a high-level signal if a person or vehicle is spotted passing by.Te circuit layout of the infrared sensor is depicted in Figure 7. Pyroelectric element PIR is shown in Figure 7. Changes in road temperature cause an increase in charge density at the pyroelectric exterior's electrodes, which causes a discharge of static electricity in all directions.Light density resistor (LDR) and temperature correction resistor (TCR) interfaces make it simple to add new capabilities to the sensor.Te connector for the jumper cap is labeled "LH."We would not be able to keep setting it of while it is in L. Contrarily, when set to H, it might be activated multiple times while waiting.

Data Processing Module Design.
Once data have been acquired from the aforementioned optical sensors, it will be processed using data fusion-based algorithms to improve the accuracy of the data and streamline the system's operation.Optical multisensor data fusion can be thought of as a fourstep process: In the frst step, sensors are used to collect data on the lighting conditions near the streetlight, including the intensity and color of the light, as well as the trafc fow in and out of the area.Te second step involves removing potential sources of error from the raw data that were obtained.In the fnal phase, all of the data that passed the frst two flters are combined.Te ideal fusion results are achieved in the fourth stage by fusing the fndings from the fusion at the data level with the information at the decision level.Te whole fusion process is depicted in Figure 8.
One of the features that the lighting control system must have is data processing, as the controller is more interested in summary statistics than in the raw readings from each sensor's terminal.An adaptive weighted fusion algorithm is used in the system's processing link for data-level fusion, allowing for the fusion of data from several sensors [18].Te system will next employ the polynomial discrete gray model G (1,1,K) for lighting prediction control in the decision-level fusion phase.

GM (1,1) Prediction Model.
Te gray GM (1,1) model is the GM model's simplest and most common variant.Its underlying premise is that for a given data series, a new set of data series with a discernible trend can be formed through cumulative addition, and a model can then be constructed to forecast based on the growing trend of the new data series.Te cumulative reduction approach is then used to do an inverse reduction, recovering the predicted values of the original dataset [19].
Assume that the original data series a (0) has C observations, i.e., a (0) � {a (0) (1), a (0) (2) ...a (0) (C)}.Te original data series a (0) is cumulated to generate a new series a (1) , which weakens the irregularity and randomness of the data series and the efect of random perturbation on the data.Te cumulative generation reveals the development of the gray volume accumulation process and brings out the patterns contained in the cluttered raw data.
For the series A (1) , Z (1) establishes the whitening differential formula and constructs the GM (1,1) model as shown in the following formula: da (1)  dt + ka (1) � b. ( Its whitening formula is as follows: where k and b denote the system development coefcient and the driving term coefcient, respectively, which are the parameters to be determined for the model.Te traditional GM models are based on data using the least squares method to solve for the parameters as shown in the following formula: where M and N are parameters whose defnitions are given in the following formulas: Te two formula parameters are derived from this.Formula (8) shows the solution of formula (3): Formula ( 9) shows the temporal response series of the GM (1,1) model: Predicted values are calculated by reducing the aforementioned results cumulatively, as indicated in the following formula: Journal of Electrical and Computer Engineering  a (0) (i + 1) �  a (1) (i + 1) −  a (1) (i) Te prediction accuracy of the GM (1,1) model is highly dependent on the model parameters k and b.Te optimality of the solution of the sought parameters directly afects the prediction accuracy of the model.It is shown that when the data series changes smoothly (i.e., |k| < 0.5), the error of the GM (1,1) model is small and the prediction efect is very satisfactory.However, for high growth data series (i.e., |k| > 0.5), the error is large and the prediction results are not satisfactory [20].

Polynomial Discrete Gray Model.
Te conventional GM (1,1) model solely employs the sequence of system behavior without the external action sequence because it is a single series prediction model.Because of this, issues such as high data distribution performance needs, weak antiinterference ability, and limited application arise [21].An improved solution is provided by the proposed polynomial discrete gray model (GM (1,1,K) model) [22].Te model is commonly used to ft data series with high unpredictability and uncertainty because it incorporates the best features of the nonfush gray model, the power exponential gray model, and the discrete gray model.In light of this, the predictive control model for the urban lighting system in this paper is the GM ( , the frst-order cumulative sequence of a (0) (i) is shown in the following formula: a (1) (i) � a (1) (1), a (1) (2), • • • , a (1) Let us assume that the discrete polynomial model is GM (1,1,K), defned as follows: a (1) (i) � λa (1) Let the parameters of the GM (1,1,K) model be then, the least squares estimate of P is shown in the following formula: where M and N are parameters whose defnitions are given in the following formulas: a (1) (1) M � a (1) (2) a (1) (3) where r is the conditioning operator and K is the number of polynomials.
If the initial condition  a (1) (1) � a (1) (1) is given, the estimate of GM (1,1,K) is obtained as shown in the following formula: If the initial condition  a (0) (1) �  a (0) (1) is given, the predicted value of the original series is obtained as shown in the following formula: New GM (1,1,K) incorporates the best features of multiple gray models into a single framework, and its two independently adjustable parameters, r and K, allow for greater fexibility in dealing with real-world issues.Tis makes the model more useful as a predictive control model for the urban lighting system under investigation in this paper.efcacy of the lighting control system described in the present paper.First, the house model, PLC controller, PC upper computer (Simulink module), LED driver, light intensity sensor, color sensor, and infrared sensor make up the major components of the intelligent lighting experiment platform upon which the frst and second sets of experiments are based.A municipal W industrial park was chosen as the experimental object for the third round of experiments.Te park spans 3,000 square meters across two stories above ground.In 2021, the industrial park will need a total of 117,356.48Kw•h of electricity.Te paper presents a method for regulating the park's lighting system's energy use in such a way as to achieve maximum efciency.

Experiment 1: Dimming Efect Analysis.
A fxed illuminance tracking experiment was frst developed to test the control system's dimming efect after the optical multisensor and the GM model had been validated.In Figure 9(a), we see the dimming efect achieved by operating the lighting control system with illuminance set to R � 100lx.We also ran tests comparing the enhanced GM model to the VAE model and the GAN model to prove that it is superior to these other methods when it comes to the dimming impact.To test the anti-interference capabilities of various models, a 20lx-strong disturbance is superimposed on the measured light level for 60 seconds.Figure 9(a) demonstrates how well the system follows the desired brightness level without straying too far from the target.As can be seen from Figures 9(b)-9(d), when the system was disturbed in 60 s, the control system using the GM model can swiftly recover from disturbances and resume following the programmed illuminance value.Te response time of the systems using the VAE and GAN models is longer.Tis demonstrates that the GM-based lighting control system can generate reliable predictions by analyzing data from optical multisensors about the lighting environment in relation to variables such as time of day, weather conditions, and pedestrian fow.As a result, the urban lighting system is better able to adapt to shifting environmental circumstances and user preferences with this method's adaptive lighting management, which features a good dimming efect and excellent interference immunity.

Experiment 2:
Predictive Performance Analysis.On a typical workday in the park, we conducted studies to predict future performance.Te energy consumption of the industrial park was predicted using the lighting control technology described in this paper.In the experiment, the optical multisensor developed in this paper is used to collect data on parameters such as light intensity, temperature, and pedestrian trafc fow, and the data from these sensors are then combined to gain a more holistic understanding of the lighting environment, which in turn increases the precision and reliability of the lighting measurements.Te predicted and actual energy consumption of the new lighting system is shown as a function of time in Figure 10.
Figure 10 shows that when the method presented in this paper was applied to the park lighting management system, the projected energy consumption fgures were quite close to the actual values.Tis demonstrates the high prediction performance of the optical multisensor and the GM modelbased lighting control system and the extremely satisfactory prediction accuracy of campus lighting energy usage.Tis is because the optical multisensor improves the accuracy and reliability of lighting measurements by combining data from several sensors to provide a more thorough picture of the lighting environment.Te GM model can make reliable forecasts because it considers a wide range of variables, including time of day, weather, and foot trafc.Terefore, combining these two technologies not only helps reduce energy waste but also boosts the reliability of lighting control systems' forecasts.

Experiment 3: Optimal Control Performance Analysis.
Tere is a clearer distinction between slow and busy times at the city's W industrial park.Terefore, the power consumption of the park's lighting system may be predicted using the street lighting management system proposed in this paper, which can then be utilized to give data standards for subsequent energy consumption control and aid frms in saving energy and reducing emissions.Te experiment involved creating a column chart from the monitored lighting system's monthly energy consumption data.Te approach of entering a single number is used to make calculations easier, and the outcomes of eforts to optimize energy use are shown in Figure 11.
Figure 11 illustrates that after implementing the energy consumption optimization control strategy proposed in this research, the lighting system at this industrial park uses an estimated annual total of 40,099 Kw•h of power.Tis industrial park's lighting system has reduced its electricity use by approximately two thirds compared to what it was in 2021.Tis is due to the fact that optical multisensors are used to collect data about the lighting environment, and the GM model is then used to analyze and forecast the lighting demand based on these limited data, analyze the pattern and trend of lighting consumption, and come up with a reliable prediction of the lighting demand in the future.We can deduce that the industrial park's lighting system has a lower annual average power consumption during the months of 1 and 3, when business volume is greatly reduced and working hours are correspondingly shorter.In addition, the lighting system uses less energy at this time of day.Power usage increases and decreases in tandem with fuctuating order volumes in other months.Te paper's approach has been shown to greatly boost the park's lighting system's energy Journal of Electrical and Computer Engineering consumption optimization control performance; therefore, it is worth spreading the word about and putting into practice.
In conclusion, the aforementioned tests proved the usefulness of combining optical multisensor technologies and GM models in municipal lighting management systems.Terefore, it is worthwhile to promote and apply the lighting control system developed in this study since it helps improve energy efciency, reduce operating costs, and boost user happiness in urban lighting environments.

Conclusion
Te smart city's urban lighting sector is growing rapidly, intelligently, and sustainably, and urban lighting management systems are becoming increasingly important to urban functionality, safety, and aesthetics.City planners and administrators in urban lighting today prioritize centralized control, uniform administration, and energy conservation and environmental protection.In this study, we investigate real-world urban lighting requirements and propose a management system using optical multisensors and the GM model to achieve them.Te system continuously monitors and records lighting conditions using optical sensors such as light intensity, motion, and color sensors.Tis information lets lights be controlled and altered in real time for various situations.However, adding GM models to urban lighting control systems improves their functionality.Te GM model analyzes past data and forecasts future lighting needs to optimize energy usage and meet lighting needs in various urban environments.Simulation tests confrmed the system's dimming efect, prediction performance, and control performance, demonstrating that this paper's lighting control system can optimize energy consumption and implement precise lighting controls.
In conclusion, this paper's investigation into optical multisensor techniques and GM models for urban lighting control has shown their potential to enhance the performance and utility of urban lighting systems, and it has provided new avenues for the development of such systems.However, the urban lighting control system described in this work still confronts signifcant constraints due to the enormous quantity of information involved and the wide coverage of the street lighting system, and more research is needed to solve these issues and limits.First, additional optimization of the optimal parameters of the GM model is required.Te prediction accuracy of the model is adversely afected because the parameters derived in this approach are not optimal, especially when forecasting nonstationary data series, for which the parameter model calculated using conventional mathematical methods will generate substantial mistakes.Te artifcial intelligence algorithms built by humans in recent years have perfectly solved several highly complicated optimization issues and presented a fresh notion for the solution of this problem by emulating the evolutionary mechanism of real creatures.Te gray model accuracy will be improved by exploring the use of an ant colony algorithm to solve the GM model parameters.Second, the Internet of things, big data, and cloud computing have all found widespread use in a variety of industries; furthermore, the integration of these new technologies can be investigated in the future to further improve the efectiveness of urban lighting control systems.Tird, given the urban nature of streetlights, the collected data can be supplemented with information about ambient noise, exhaust levels, and particulate matter 2.5 (PM2.5)levels to inform not only lighting strategy but also air pollution prevention and control initiatives.

Figure 2 :
Figure 2: Overall structure of data fusion technology.(a) Centralized processing method.(b) Distributed processing method.
Figures 9(b)-9(d) depict the outcomes of the tests.

Figure 9 :
Figure 9: Dimming efect of the system controller.(a) Actual illuminance L-time curve.(b) Actual illuminance L-time curve when disturbed.(c) Actual illuminance L-time curve of GAN when disturbed.(d) Actual illuminance L-time curve of VAE when disturbed.

Figure 10 :
Figure 10: Relationship between predicted and actual energy consumption of the system.