Smart Low-Position Lighting System With Renewable Energy-Based Distributed Power Management And Composite Predictive Control, And Control Method Thereof

Abstract

The present invention relates to a smart street lighting system comprising: a plurality of lighting fixtures that distribute light in a horizontal direction toward the road surface of a road or pedestrian walkway; a renewable energy generation unit including at least one of a photovoltaic power generation module and a wind power generation module for supplying power to the plurality of lighting fixtures; an energy storage unit including individual battery units provided corresponding to each of the plurality of lighting fixtures and a central energy storage unit provided in common to the cluster unit; an energy management server that monitors the state of charge (SOC) of the individual battery units and distributes power from the central energy storage unit when the state of charge drops below a critical point; and a lighting control unit that controls lighting time and output by comprehensively analyzing weather information, battery status information, and radar sensor-based object detection information. The system has the effect of preventing light-out even on days with poor light, optimizing energy consumption, and meeting road lighting standards.

Description

Technology Field

[0001] The present invention relates to a smart street light system having renewable energy-based distributed power management and complex predictive control functions, and more specifically, to a smart street light system and an energy management method and a complex lighting control method that independently produces, stores, and distributes power using renewable energy sources such as solar and wind power for a plurality of lighting fixtures installed at a height of 1m or less that emit light in a horizontal direction toward the road surface of a road or pedestrian walkway, prevents light-out even on days without sunlight through a dual-layer structure of individual battery units and a central energy storage unit, and optimizes lighting output by comprehensively analyzing weather information, battery status information, and radar sensor-based object detection information. Background Technology

[0002] Various types of street lighting systems have been developed and are in use to ensure nighttime safety on roads and pedestrian walkways. In particular, with the depletion of fossil fuels and increasing social demand for carbon emission reduction, research on hybrid power generation systems that operate streetlights using renewable energy, such as solar and wind power, has been actively conducted.

[0003] In this regard, Registered Patent No. 10-1081323 (hereinafter referred to as the "prior art") discloses a hybrid power generation system for driving smart LED lighting, comprising a first street light including a solar power generation module and a wind power generation module, a lamp power supply device that detects the voltage or current of power charged to a battery to determine the range of equal voltage and generates equal voltage through a digital control method and provides it to the battery, and a second street light installed around the first street light and driven by power stored in the battery. The technology of the prior art is centered on improving system stability in response to output voltage fluctuations of solar and wind power generation through constant voltage and constant current conversion control using a decoder and a digital interface unit, and adopts a structure that requests power supply between adjacent street lights through a communication module.

[0004] However, conventional renewable energy-based street lighting systems, including the aforementioned prior art, have the following technical limitations.

[0005] First, conventional technology adopts a simple power distribution structure in which electricity produced by hybrid generation is stored in a single battery and supplied to adjacent streetlights; consequently, it fails to fundamentally resolve the problem of light-outs caused by the depletion of individual streetlight batteries during the rainy season or winter, when continuous days of poor lighting occur. Even in the aforementioned prior art, the power supply from the first streetlight to the second streetlight remains a simple request-based distribution method, and no function is disclosed to comprehensively manage the battery charge status at the cluster unit of multiple streetlights and to preemptively redistribute energy by predicting periods of poor lighting.

[0006] Second, conventional lighting control is limited primarily to time-schedule-based on / off control or single-parameter dimming control based on illuminance sensors. Consequently, there are limitations in dynamically optimizing lighting output by integrally analyzing multiple distinct information sources, such as rapid changes in weather conditions, real-time fluctuations in battery levels, and the presence of pedestrians and vehicles on the road. This leads to problems where energy efficiency is reduced because lighting is maintained at unnecessarily high output even in energy-deficient situations, or conversely, safety is compromised due to insufficient output in dangerous areas where pedestrians are present. The problem to be solved

[0007] The present invention has been devised to solve the problems of the prior art described above, and its first objective is to provide a smart street lighting system that independently supplies power to a lighting fixture from a renewable energy generation unit comprising at least one of a solar power generation module and a wind power generation module, and resolves power supply and demand imbalances within a cluster and prevents light-out even on days with poor sunlight through a dual-layer structure of an individual battery unit and a central energy storage unit.

[0008] Furthermore, the second objective of the present invention is to provide an energy management method for a smart street lighting system that can simultaneously achieve battery safety and extend lifespan by applying a range regression-based conservative prediction technique to predict power generation by season and time of day, and by calculating an optimal charging scenario in real time based on a mathematical model that reflects the non-linear charging characteristics of the battery.

[0009] Furthermore, the third objective of the present invention is to provide a complex lighting control method for a smart street lighting system that can provide a safe lighting environment that meets road lighting standards while minimizing energy consumption by dynamically optimizing lighting output through a complex analysis of weather forecast information received from an external weather agency server, real-time status information of individual battery units, and object detection information acquired from a radar sensor. means of solving the problem

[0010] To achieve the above objectives, a smart street lighting system having renewable energy-based distributed power management and complex predictive control functions according to one embodiment of the present invention comprises: a plurality of lighting fixtures that distribute light in a horizontal direction toward the road surface of a road or pedestrian walkway; a renewable energy generation unit for supplying power to the plurality of lighting fixtures, comprising at least one of a photovoltaic power generation module and a wind power generation module; an energy storage unit comprising a battery that stores power produced by the renewable energy generation unit, wherein the energy storage unit comprises an individual battery unit individually provided corresponding to each of the plurality of lighting fixtures and a central energy storage unit commonly provided for the cluster unit to which the plurality of lighting fixtures belong; an energy management server that monitors the state of charge (SOC) of each of the individual battery units and distributes power from the central energy storage unit to the corresponding individual battery unit when the state of charge of each individual battery unit falls below a preset threshold; and a lighting control unit that controls the lighting time and output of each of the plurality of lighting fixtures by comprehensively analyzing weather information, the state information of the batteries, and object detection information obtained from a radar sensor.

[0011] The energy management server can calculate the charge amount deviation between the individual battery units within the cluster to which the plurality of lighting fixtures belong, and perform cluster SOC balancing by redistributing power from the individual battery unit with a high charge amount to the individual battery unit with a low charge amount through the central energy storage unit when the charge amount deviation exceeds a preset allowable range.

[0012] The above-mentioned renewable energy generation unit may be selectively arranged in any one of the configurations of the above-mentioned solar power generation module alone, the above-mentioned wind power generation module alone, or a hybrid combination of the above-mentioned solar power generation module and the above-mentioned wind power generation module, and the charging interface of the above-mentioned energy storage unit may be configured as an integrated standard so as to commonly accept power for any of the three arrangement configurations.

[0013] The energy storage unit exchanges charge status information through a self-constructed Sub-1G private wireless communication network between the individual battery unit and the central energy storage unit, and the energy management server can collect the SOC, SOH (State of Health), and charge / discharge history of each individual battery unit in real time through the Sub-1G private wireless communication network.

[0014] The above-mentioned renewable energy generation unit includes at least one of an individual generation unit individually provided corresponding to each of the plurality of lighting fixtures and a clustered generation unit commonly provided by grouping the plurality of lighting fixtures into a single cluster, and the energy management server can control charging to the energy storage unit by integrally managing the power generation amount of the individual generation unit and the power generation amount of the clustered generation unit.

[0015] Meanwhile, an energy management method for a smart street lighting system including a plurality of lighting fixtures according to an embodiment of the present invention comprises: a state of charge (SOC) precision tracking step for monitoring charging and discharging patterns by tracking the state of charge (SOC) of each individual battery unit in a preset first precision unit; a power generation prediction step for predicting the starting point of power generation by day and time and automatically reflecting seasonal changes in sunrise and sunset times, and predicting patterns of power generation increase, maximum power generation, and power generation decrease by time by applying a range-regression based conservative prediction; and a charging scenario calculation step for calculating an optimal charging scenario in real time by applying a mathematical model that reflects non-linear charging characteristics that vary according to the remaining battery capacity, based on the SOC information monitored in the state of charge tracking step and the power generation information predicted in the power generation prediction step. The method is characterized by including a dynamic charging control step that dynamically controls the charging current and charging time from the renewable energy generation unit to the individual battery unit according to the optimal charging scenario calculated in the charging scenario calculation step, and dynamically adjusts the lower discharge threshold value to prevent over-discharge of the individual battery unit in conjunction with the predicted period of mis-weather.

[0016] The above power generation prediction step makes it possible to calculate a predicted power generation amount that is conservatively estimated compared to the actual power generation amount by performing regression analysis on the time series change patterns of the power generation start time, peak power generation time, and power generation end time from daily power generation data accumulated over a predetermined past period, and applying a predetermined safety margin to the results of the regression analysis.

[0017] The mathematical model reflecting the non-linear charging characteristics in the charging scenario calculation step models the characteristic that charging efficiency decreases non-linearly in the range where the remaining battery level is above a preset first ratio, and it is possible to minimize energy loss by applying different charging current limit values ​​for each remaining battery level range.

[0018] The dynamic charge control step described above may further include a supplementary power supply step that prevents light-out occurring on a non-light day by supplying supplementary power from a central energy storage unit commonly provided in the cluster unit to the individual battery unit when the SOC of the individual battery unit approaches the discharge lower limit threshold.

[0019] The above SOC precision tracking step analyzes the battery charging pattern by time period to estimate the battery state of health (SOH) in parallel, and based on the SOH estimation result, it is possible to adaptively update the lower discharge threshold and upper charge threshold of the dynamic charging control step in the direction of extending battery life.

[0020] Meanwhile, a composite lighting control method for a smart street lighting system having renewable energy-based distributed power management and composite predictive control functions according to one embodiment of the present invention is a composite lighting control method for a smart street lighting system that controls the output of a plurality of low-lighting fixtures supplied with power from a renewable energy generation unit, comprising: a weather-based output scheduling step of receiving weather forecast information for an installation area from an external weather agency server, calculating an expected period of solar radiation reduction for a predetermined future period based on at least one weather element among temperature, humidity, rainfall, snowfall, cloudy, and clear included in the weather forecast information, and preemptively generating a lighting output reduction schedule corresponding to the expected period of solar radiation reduction; and a battery-based output limiting step of collecting battery status information including SOC, charge amount, power generation amount, and discharge amount of an individual battery unit corresponding to each of the plurality of lighting fixtures, calculating a time point at which a light-out is expected when a non-light day persists based on the battery status information, and dynamically setting an upper limit for lighting output by time period considering the remaining energy up to the expected time point of the light-out. The method is characterized by comprising: an object detection-based output switching step that detects in real time whether a person or means of transportation is approaching through a radar sensor installed on the plurality of lighting fixtures, lights up the lighting fixture in the section where an object is detected at a predetermined standard output, and switches the lighting fixture in the section where an object is not detected to a standby output reduced from the standard output; and a composite output optimization step that determines a final output value for each of the plurality of lighting fixtures by integrally calculating the lighting output reduction schedule of the weather-based output scheduling step, the lighting output upper limit value by time of the battery-based output limiting step, and the output switching result of the object detection-based output switching step.

[0021] The above composite output optimization step determines the lowest output value among the weather-based output reduction rate calculated from the weather-based output scheduling step, the battery-based output upper limit value calculated from the battery-based output limiting step, and the detection-based output switching value calculated from the object detection-based output switching step as the final output value, and it is possible to apply lower limit clipping so that the final output value does not fall below the lower limit value of the minimum road surface luminance or minimum road surface illuminance specified in the road lighting standards.

[0022] The weather-based output scheduling step further includes a color temperature preemptive change step in which, when fog or rain is forecasted based on the weather forecast information, the color temperature of the plurality of lighting fixtures is preemptively changed from a preset first color temperature to a preset second color temperature or a preset third color temperature range, and the color temperature change in the color temperature preemptive change step can be corrected in real time in conjunction with visibility distance information received from a visibility meter.

[0023] The object detection-based output switching step described above can perform prior lighting control, in which, when the radar sensor detects an object, not only the corresponding lighting fixture but also adjacent lighting fixtures on a predicted path based on the object's direction of movement and speed of movement are illuminated at standard output in advance.

[0024] The above weather-based output scheduling step can perform season-adaptive color temperature control by setting the color temperature of the plurality of lighting fixtures to a preset fourth color temperature or lower to reduce the attraction of flying insects when it is determined that it corresponds to the summer season based on the weather forecast information, and by setting the color temperature to a preset fifth color temperature or higher to ensure visibility when it is determined that it corresponds to the winter season. Effects of the invention

[0025] According to an embodiment of the present invention, by applying renewable energy-based distributed power management and complex predictive control to lighting fixtures installed on roads or pedestrian walkways, lane recognition performance during rain can be significantly improved compared to conventional pole lighting, particularly when applied to a low lighting method that distributes light horizontally toward the road surface from a low position of 1m or less. In the event of fog, visibility can be improved by preemptively changing the color temperature to a low color temperature range, and light pollution can be fundamentally reduced as upward and backward luminous flux is almost non-existent. Furthermore, through a dual-layer energy storage structure of individual battery units and a central energy storage unit, SOC balancing within the cluster and critical point-based supplementary power supply are possible, thereby preventing light-out even in adverse weather conditions with continuous poor lighting, thereby ensuring stable night road safety.

[0026] In addition, the energy management method according to the present invention conservatively estimates power generation through range regression-based conservative prediction, minimizes energy waste by applying a mathematical model that reflects non-linear charging characteristics, and achieves optimal energy efficiency while extending battery life by adaptively updating the lower discharge threshold and the upper charge threshold by concurrently estimating the battery state of health (SOH).

[0027] Furthermore, the composite lighting control method according to the present invention determines the final output value by integrally calculating three control elements: preemptive output scheduling based on weather forecasts, setting an upper limit on output based on battery status, and output switching based on object detection by a radar sensor. Therefore, it can provide a safe lighting environment that meets road lighting standards while minimizing energy consumption even in distributed system environments where energy resources are limited, and has the effect of simultaneously achieving the safety of pedestrians and vehicles and the protection of the ecosystem through preemptive lighting control using a radar sensor and seasonally adaptive color temperature control. Brief explanation of the drawing

[0028] FIG. 1 is a schematic diagram showing the installation environment of a smart street light system according to an embodiment of the present invention. FIG. 2 is a block diagram showing the overall configuration of a smart street light system according to an embodiment of the present invention. FIG. 3 is a detailed diagram showing the dual-layer structure of an energy storage unit according to an embodiment of the present invention. FIG. 4 is a conceptual diagram illustrating a battery charge balancing operation within a cluster according to an embodiment of the present invention. FIG. 5 is a graph showing the lighting output profile by time period in the composite output optimization process according to an embodiment of the present invention. FIG. 6 is a conceptual diagram illustrating a radar sensor-based pre-lighting control operation according to an embodiment of the present invention. FIG. 7 is a flowchart illustrating an energy management method for a smart street light system according to an embodiment of the present invention. FIG. 8 is a flowchart illustrating a composite lighting control method of a smart street light system according to an embodiment of the present invention. Specific details for implementing the invention

[0029] Hereinafter, various embodiments and / or aspects are disclosed with reference to the drawings. For illustrative purposes, numerous specific details are disclosed in the following description to aid in a general understanding of one or more aspects. However, it will also be recognized by those skilled in the art that these aspects may be practiced without such specific details. The following description and the accompanying drawings describe specific exemplary aspects of one or more aspects in detail. However, these aspects are exemplary, and some of the various methods in the principles of the various aspects may be used, and the description is intended to include all such aspects and their equivalents.

[0030] As used herein, terms such as "examples," "examples," "aspects," "examples," etc., may not be interpreted as implying that any aspect or design described is better or more advantageous than other aspects or designs.

[0031] Additionally, the terms “comprising” and / or “comprising” should be understood to mean that the relevant feature and / or component is present, but not to exclude the presence or addition of one or more other features, components and / or groups thereof.

[0032] Additionally, terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but said components are not limited by said terms. Such terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0033] Furthermore, in the embodiments of the present invention, all terms used herein, including technical or scientific terms, unless otherwise defined, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in the embodiments of the present invention.

[0034] FIG. 1 is a schematic diagram showing the installation environment of a smart street light system according to an embodiment of the present invention; FIG. 2 is a block diagram showing the overall configuration of a smart street light system according to an embodiment of the present invention; FIG. 3 is a detailed diagram showing the dual-layer structure of an energy storage unit according to an embodiment of the present invention; FIG. 4 is a conceptual diagram showing the operation of balancing the battery charge amount within a cluster according to an embodiment of the present invention; FIG. 5 is a graph showing the lighting output profile by time period in the complex output optimization process according to an embodiment of the present invention; FIG. 6 is a conceptual diagram showing the radar sensor-based pre-lighting control operation according to an embodiment of the present invention; FIG. 7 is a flowchart showing the energy management method of a smart street light system according to an embodiment of the present invention; and FIG. 8 is a flowchart showing the complex lighting control method of a smart street light system according to an embodiment of the present invention.

[0035] Meanwhile, in the following description, some components described in the drawings may be omitted or excessively enlarged or reduced in order to explain the function of each component of the present invention, but it will be understood that such illustrated components do not limit the technical features and scope of rights of the present invention.

[0036] In addition, in the following description, multiple drawings will be referred to simultaneously to explain a single technical feature or a component constituting the invention.

[0037] With reference to FIGS. 1 to 8, a smart street light system having renewable energy-based distributed power management and complex predictive control functions and a control method thereof according to an embodiment of the present invention will be described in detail.

[0038] A smart street light system according to an embodiment of the present invention comprises a plurality of lighting fixtures installed on a road or pedestrian walkway to emit light toward the road surface. The lighting fixtures include both conventional street lights (pole lights) installed on the upper part of a pole at a height of 5m to 12m and low lighting fixtures installed at a height of 1m or less to emit light horizontally toward the road surface of the road or pedestrian walkway. Energy management and lighting control technologies, such as a dual-layer energy storage structure, conservative power generation prediction based on range regression, calculation of charging scenarios reflecting non-linear charging characteristics, and complex output optimization, which are core technical features of the present invention, can be commonly applied to the entire renewable energy-based standalone street light system regardless of the installation height of the lighting fixtures.

[0039] However, in the following description of the embodiments, low-lighting fixtures are described primarily as the main embodiments. This is because the low-lighting method is a configuration that maximizes energy efficiency in a renewable energy-based distributed system. Specifically, low-lighting has significantly lower power consumption per fixture compared to conventional pole lighting (100W to 250W for pole lighting compared to 10W to 24W for road use), allowing for stable nighttime lighting even with limited renewable energy generation. Furthermore, due to the low installation height, the physical integration of the fixture, battery unit, and photovoltaic module is facilitated, reducing wiring losses and installation costs. Additionally, because the direct illumination method on the road surface generates almost no upward luminous flux, the energy contribution rate to lighting is maximized, thereby demonstrating the most significant utility of the energy management and complex predictive control technology of the present invention. Therefore, the technical details described in the following embodiments, focusing on low-lighting fixtures, should be understood as a description of the smart street lighting system as a whole, including general streetlights (pole lighting), except for parts specifically described as unique to the low-lighting method.

[0040] In the present invention, "distributed power management" refers to a power management method in which the production, storage, and distribution of power for a plurality of lighting fixtures (100) are not concentrated at a single point, but rather local energy management at the individual fixture (100) level and central energy management at the cluster level are hierarchically distributed and operated cooperatively. The distributed power management of the present invention includes a plurality of operational forms as follows, depending on the installation target and site conditions.

[0041] The first operational mode is suitable for general streetlights (pole lights), in which power is primarily produced in individual power generation units (230) equipped in each streetlight and stored and used in individual battery units (310) of the streetlights, and separately, power is produced in a centralized solar power generation system, which is a cluster power generation unit (240) equipped in a cluster unit and stored in a central energy storage unit (320), and when the SOC of an individual battery unit (310) of a specific streetlight drops below a preset threshold due to the influence of adverse weather conditions, power is distributed from the central energy storage unit (320) to the individual battery unit (310).

[0042] The second operational mode is a form suitable for low lighting, in which the low lighting fixture (100) itself is not equipped with an individual power generation unit (230), but power is produced by a centralized solar power generation system, which is a cluster power generation unit (240) equipped in a cluster unit, stored in a central energy storage unit (320), and then distributed from the central energy storage unit (320) to each of the individual battery units (310) of a plurality of low lighting fixtures (100) connected thereto. In the second operational mode, the central energy storage unit (320) plays a central role in power production and distribution, but the basic structure of distributed power management is maintained as the individual battery units (310) of each low lighting fixture (100) store and manage the distributed power at a local level.

[0043] The two aforementioned operational forms differ only in the degree of energy generation distribution, but the core technical features of the present invention, such as a dual-layer energy storage structure, power distribution based on SOC monitoring, conservative power generation prediction based on range regression, calculation of charging scenarios reflecting non-linear charging characteristics, and complex output optimization, are applied identically. In addition, depending on the energy source configuration of the renewable energy generation unit (200), it may be classified into a solar street light system, a wind street light system, a hybrid street light system, a solar low lighting system, a hybrid low lighting system, etc., but regardless of such classification, the technical configuration of distributed power management and complex predictive control of the present invention is applied commonly.

[0044] Referring to FIG. 1, a smart street light system according to an embodiment of the present invention includes a plurality of low lighting fixtures (100) installed at a height of 1m or less on both sides or one side of a road (900) or pedestrian walkway and distributing light in a horizontal direction toward the road surface (900). Conventional street light systems adopt a pole lighting method in which lighting fixtures are installed on top of a support pole at a height of 5m to 12m. However, light sources at such high positions have problems such as a rapid decrease in lane recognition performance due to light source scattering by raindrops during rain, and a significant reduction in visibility distance due to the light source at high positions being blocked by a fog layer during fog. In contrast, the low lighting fixtures (100) of the present invention are installed at a height of 1m or less, preferably in the range of 0.3m to 1.0m, thereby fundamentally solving the above problems. The method of distributing light horizontally toward the road surface (900) from a low position significantly reduces scattering caused by raindrops during rainy weather along the road surface illumination path, thereby improving lane recognition performance by more than 5 times compared to conventional pole lighting, and also improves visibility distance by directly illuminating the road surface from below the fog layer even during fog. In addition, horizontal light distribution from a low position has the advantage of fundamentally reducing light pollution by generating almost no upward light and suppressing back light to the level of 0.0lx. FIG. 1 shows a plurality of low lighting fixtures (100) installed at equal intervals on both sides of a road (900), along with solar power generation modules (210) and wind power generation modules (220) installed above or adjacent to each fixture (100), and a central energy storage unit (320) placed on one side of the road (900), and an external weather station server (700) and a visibility meter (800) connected via wireless communication.

[0045] Referring to FIG. 2, the overall configuration of a smart street light system according to an embodiment of the present invention is illustrated in a block diagram. A smart street light system according to an embodiment of the present invention comprises: a plurality of low-lighting fixtures (100) installed at a height of 1m or less and distributing light horizontally toward the road surface of a road or pedestrian walkway; a renewable energy generation unit (200) for supplying power to the plurality of low-lighting fixtures (100), comprising at least one of a solar power generation module (210) and a wind power generation module (220); an energy storage unit (300) comprising a battery for storing power produced by the renewable energy generation unit (200), wherein the energy storage unit (300) comprises an individual battery unit (310) individually provided corresponding to each of the plurality of low-lighting fixtures (100), and a central energy storage unit (320) commonly provided for a cluster unit to which the plurality of low-lighting fixtures (100) belong; and a system for monitoring the charge amount (SOC) of each of the individual battery units (310), and when the charge amount of the individual battery unit (310) falls below a preset threshold, the corresponding individual battery from the central energy storage unit (320). It includes an energy management server (400) that distributes power to units (310), and a lighting control unit (500) that controls the lighting time and output of each of the plurality of low lighting fixtures (100) by comprehensively analyzing weather information, the status information of the battery, and object detection information obtained from the radar sensor (130).

[0046] The above components are described in detail below.

[0047] A low-lighting fixture (100) includes a light distribution lens (110), an LED light source (120), and a radar sensor (130). The light distribution lens (110) performs the function of horizontally distributing light emitted from the LED light source (120) toward the road surface (900), and the material of the light distribution lens (110) may be PMMA (Poly Methyl Methacrylate), polycarbonate (PC), or optical glass. PMMA is widely used as a lens material for outdoor lighting because it has excellent light transmittance of about 92% or more and good weather resistance, while polycarbonate is suitable for roadside installation environments with frequent vehicle traffic because it has superior impact resistance compared to PMMA. Optical glass has excellent scratch resistance and optical precision, but its weight and cost are relatively high, so it may be selectively applied in special environments. The light distribution lens (110) can be designed with an asymmetric freeform shape to ensure horizontal illumination of the road surface (900) while suppressing glare for drivers and pedestrians at a low installation height (1m or less), and this corresponds to a lens-type light distribution technology that is distinguished from the conventional reflector method. The reflector method distributes light by reflecting it from a reflective surface placed behind the light source, which has significant limitations on the size of the light source and low light distribution precision, whereas the lens method controls light distribution by using refraction in front of the light source, making it suitable for small LED light sources and having the advantage of enabling precise control of the light distribution profile.

[0048] The LED light source unit (120) includes a plurality of LED elements, and the LED elements are configured as tunable white LEDs, so that the color temperature can be varied in the range of 1500K to 5000K. The tunable white LED operates by arranging LED elements of low color temperature (warm white, e.g., 1500K to 2000K) and high color temperature (cool white, e.g., 4000K to 5000K) in parallel on a single substrate, and by independently controlling the current applied to each group of elements, it operates in a manner that freely implements an intermediate color temperature (e.g., 2000K, 3000K, etc.). As for the package form of the LED elements, an SMD (Surface Mount Device) type, a COB (Chip on Board) type, or a CSP (Chip Scale Package) type may be applied. SMD type allows for easy replacement of individual components and has good heat dissipation performance, COB type has high luminous efficiency per unit area and enables uniform light distribution, and CSP type is advantageous for miniaturization and is suitable for small lighting fixtures. The power consumption of the LED light source (120) can be set in the range of 10W to 24W for road use and in the range of 3W to 12W for walking paths and pedestrian walkways. As an example of a specific product configuration, the lighting fixture (100) for road use can be classified into a 24W class applied to M2 and M3 grade roads, a 15W class applied to M4 and M5 grade roads, and a 10W class applied to ramps and sharp turns. The lighting fixture (100) for pedestrian walkways can be classified into a 3W to 8W class attached type applied to deck paths and sidewalks, a 12W class general attached type applied to wide walking paths, and a 12W class independent type applied to perimeter paths and bicycle paths.

[0049] A radar sensor (130) is installed in each low-light fixture (100) and performs the role of detecting in real time whether a person or means of transportation is approaching. As the radar sensor (130), a microwave Doppler radar, an FMCW (Frequency Modulated Continuous Wave) radar, or an UWB (Ultra Wide Band) radar may be applied. A microwave Doppler radar transmits radio waves in the 24 GHz or 60 GHz band and detects the presence and speed of a moving object by analyzing the Doppler frequency shift of the reflected wave; it has the advantages of a simple structure, low power consumption, and being hardly affected by weather conditions such as rain or fog. An FMCW radar transmits waves by linearly modulating the frequency over time and analyzes the frequency difference (beat frequency) with the reflected wave to simultaneously measure the distance and speed to the object, thus enabling precise tracking of the object's direction of movement and speed of movement. A UWB radar achieves high distance resolution by using extremely short pulses (less than a few nanoseconds) and has excellent object classification performance at close range. In a preferred embodiment of the present invention, the FMCW radar method is adopted to track the direction and speed of movement of an object together, thereby enabling the pre-lighting control described later. The detection range of the radar sensor (130) can be set to a range of 5m to 30m depending on the road width and installation spacing, and the detection angle can be set to a range of 90 to 120 degrees horizontally and 15 to 30 degrees vertically. Meanwhile, as an alternative or supplementary means to the radar sensor (130), a laser sensor, an infrared sensor, an ultrasonic sensor, or a webcam-based image recognition module may be applied together or as a substitute.In the case of a pedestrian path lighting fixture (100), a pedestrian recognition system combining a laser sensor and a webcam may be applied, and in this case, a dual detection structure may be formed in which the laser sensor measures the distance to the pedestrian and the webcam confirms the presence of the pedestrian based on video.

[0050] The housing of the low lighting fixture (100) can be made of aluminum die-casting, stainless steel (SUS 304 or SUS 316), or a corrosion-resistant alloy. Aluminum die-casting is lightweight and has excellent heat dissipation performance, which is advantageous for thermal management of LED elements, while stainless steel has excellent corrosion resistance in coastal or humid environments. It is preferable that the dustproof and waterproof rating of the fixture (100) be set to IP65 or higher, and when installed near a road, impact resistance against vehicle impact (IK08 or higher) must be ensured. An SMPS (Switched Mode Power Supply) is built inside the fixture (100) to convert DC power supplied from the outside into a constant current suitable for driving LEDs, and the input voltage range can be DC 12V to 48V, and if AC power is used as an auxiliary power source, commercial power of 210V to 240V and 50Hz to 60Hz can also be accepted.

[0051] The installation spacing of low lighting fixtures (100) is determined according to the road grade and lane width. For road use, a spacing of 8m is preferred based on a 2-lane standard, and for walking paths and pedestrian walkways, they can be installed at intervals of 3m to 8m. The installation method of the lighting fixtures (100) may include a buried type that is directly embedded in the road surface, an attached type that is attached to a guardrail or sound barrier, or an independent type that is installed on an independent support. The buried type has a low risk of physical damage from vehicle traffic but requires road surface excavation for maintenance; the attached type reduces installation costs by utilizing existing road facilities; and the independent type has a high degree of freedom in installation location but requires separate foundation work. For road lighting fixtures (100), they must meet the road lighting standards according to the Korean Industrial Standard KS A 3701, and when installed at 8m intervals for a 2-lane road, an average road surface luminance of 1.0 cd / m² or more, an overall uniformity of 0.4 or more, and a lane axis uniformity of 0.5 or more can be achieved. In the case of a lighting fixture (100) for a pedestrian walkway, it must meet the P grade (P1 to P6) standards, and can achieve a level where the recognition of the road surface step and obstacles is normal (4) or higher at a horizontal illuminance of 0.5lx or higher, and can satisfy domestic and international pedestrian walkway standards (2.0lx to 3.0lx), while being able to operate flexibly at 2.0lx or less in an ecological conservation area.

[0052] Meanwhile, according to another embodiment of the present invention, the lighting fixture (100) may be implemented in the form of a general street light (pole light) installed on the upper part of a support pole at a height of 5m to 12m. In this case, the LED light source unit (120) of the lighting fixture (100) is composed of a high-output LED module of 100W to 250W class and includes an asymmetric light distribution optical system to secure road surface brightness and uniformity corresponding to road lighting standards M1 to M5 grades. For the lighting fixture (100) in the form of a general street light, a solar power generation module (210) and a wind power generation module (220) may be installed on the upper or side of the support pole, and an individual battery unit (310) may be housed in the lower part or base of the support pole. Since general streetlights have a higher power consumption per fixture compared to low-lighting lights, it is desirable to increase the capacity of individual battery units (310) to a range of 500Wh to 2,000Wh and, in response, increase the output of solar power generation modules (210) to a range of 100W to 400W. Even in the form of general streetlights, the dual-layer energy storage structure, SOC balancing, range regression-based conservative prediction, non-linear charging model, and composite output optimization of the present invention are applied in the same way, and especially for high-output fixtures, the precision of energy management has a greater impact on preventing light-out, so the utility of the energy management method of the present invention is manifested more significantly.

[0053] However, the improvement in lane recognition performance during rain due to horizontal light distribution, the improvement in visibility distance due to illumination below the fog layer during fog, and the reduction of light pollution due to zero upward luminous flux, which are technical features unique to the low lighting method, are limited to the low lighting method. It should be understood that in the form of a general street light, it has the same light distribution characteristics as a conventional pole light, but with the energy management and complex predictive control functions of the present invention added. Furthermore, depending on the arrangement of the solar power generation module (210) and the wind power generation module (220), it can be classified into a solar street light system, a wind street light system, or a hybrid street light system. Depending on the form of the light fixture, it can also be classified into a solar low lighting system or a hybrid low lighting system. However, regardless of the energy generation method or the form of the light fixture, the lighting control method and the power distribution method are applied commonly according to the technical features of the present invention.

[0054] Next, the renewable energy generation unit (200) will be described. The renewable energy generation unit (200) is intended to supply power to the plurality of low-light fixtures (100) and includes at least one of a photovoltaic power generation module (210) and a wind power generation module (220). The photovoltaic power generation module (210) includes a solar cell that converts solar light energy into electrical energy, and the types of solar cells may include crystalline silicon solar cells, thin film solar cells, perovskite solar cells, or organic solar cells. Crystalline silicon solar cells are classified into monocrystalline and polycrystalline types, and monocrystalline silicon solar cells have a high conversion efficiency of 20% to 24% and excellent durability, making them the most widely used in outdoor installation environments. Polycrystalline silicon solar cells have a slightly lower conversion efficiency compared to monocrystalline ones (16% to 20%), but they have the advantage of reduced manufacturing costs. Thin-film solar cells are manufactured using CIGS (copper indium gallium selenide), CdTe (cadmium telluride), or amorphous silicon materials. Since they can be formed on a flexible substrate, they allow for curved installation and exhibit relatively superior power generation efficiency in low-light environments. Perovskite solar cells are next-generation solar cell technologies that offer high theoretical efficiency and significant potential for cost reduction as the manufacturing process can be performed at low temperatures; however, ensuring long-term durability remains a challenge. The installation location of the photovoltaic power generation module (210) can be a separate support above the light fixture (100), a wall or roof surface of an adjacent structure, the top of a sound barrier, the top of a guardrail, etc., and the installation azimuth and tilt angles are optimized to match the latitude and sunlight conditions of the installation area.

[0055] The wind power generation module (220) includes a small wind generator that converts wind energy into electrical energy, and the wind generator can be of the form of a horizontal axis wind generator or a vertical axis wind generator. The horizontal axis wind generator includes a propeller-type rotor, and since it requires a head-on wind receiving motion, it is common to have a yaw mechanism. The vertical axis wind generator is classified into Savonius type, Darrieus type, or helical type, and has the advantage of being able to generate power regardless of wind direction and having a low starting wind speed, making it suitable for urban terrain. In a preferred embodiment of the present invention, an AFPM (Axial Flux Permanent Magnet) 3-phase output generator, which is a coreless generator with an armature coil without an iron core, may be applied. Since there is no cogging torque when starting power generation, the wind power generator can be started smoothly, and the starting wind speed is low at 1.0 m / s, enabling efficient power generation even in low-wind-speed urban environments. The installation location of the wind power generation module (220) may be a separate mast adjacent to the lighting fixture (100), the top of a sound barrier, a bridge railing, etc., and it is preferable to install it in a location with favorable wind direction and wind speed conditions.

[0056] In one embodiment of the present invention, the renewable energy generation unit (200) may be selectively arranged in any one of the configurations of the solar power generation module (210) alone, the wind power generation module (220) alone, or a hybrid combination of the solar power generation module (210) and the wind power generation module (220), wherein the charging interface (250) of the energy storage unit (300) is configured with an integrated standard so as to be able to accept power in common for any of the three arrangement configurations. The charging interface (250) includes multiple input channels and a Maximum Power Point Tracking (MPPT) charging controller so as to be able to accept DC power output from the solar power generation module (210) and DC power rectified from AC power output from the wind power generation module (220) separately or simultaneously. The MPPT charging controller maximizes power generation efficiency by tracking the maximum power point of the solar power generation module (210) in real time, and may apply the Perturb and Observe (P&O) method, the Incremental Conductance method, or an intelligent MPPT method based on an Artificial Neural Network. By adopting a charging interface (250) of such an integrated standard, the placement of the solar power generation module (210) alone, the wind power generation module (220) alone, or a hybrid of both can be flexibly selected according to the site conditions, depending on the sunlight and wind conditions of the installation site. Furthermore, it provides scalability that allows for the addition or change of power generation modules in the future without changing the hardware on the energy storage unit (300).For example, in inland plain areas with abundant sunlight and low wind speed, the sole placement of solar power generation modules (210) is efficient, and in areas where wind speed is stably secured, such as coastal areas, the sole placement of wind power generation modules (220) is advantageous. In areas with large seasonal variations in sunlight and wind speed, a hybrid configuration in which solar power generation modules (210) and wind power generation modules (220) are placed simultaneously is advantageous for securing a stable annual power generation amount.

[0057] In addition, in one embodiment of the present invention, the renewable energy generation unit (200) includes at least one of an individual generation unit (230) that is individually provided corresponding to each of the plurality of low-lighting fixtures (100) and a cluster generation unit (240) that is commonly provided by grouping the plurality of low-lighting fixtures (100) into a single cluster, and the energy management server (400) controls charging to the energy storage unit (300) by managing the power generation amount of the individual generation unit (230) and the power generation amount of the cluster generation unit (240) in an integrated manner. The individual generation unit (230) is configured to directly supply power to the individual battery unit (310) of the corresponding fixture (100) by installing a small-scale solar panel or a small wind turbine at the top or adjacent location of each fixture (100), and the cluster generation unit (240) is configured to supply power to the central energy storage unit (320) by installing a large-capacity solar array or a large-capacity wind generator in the common space of the cluster to which the plurality of fixtures (100) belong. The energy management server (400) collects and manages the power generation of each individual power generation unit (230) and the power generation of the cluster power generation unit (240) in real time. If the power generation of the individual power generation unit (230) exceeds the power consumption of the corresponding light fixture (100), the surplus power can be reverse charged to the central energy storage unit (320). Conversely, if the power generation of the individual power generation unit (230) is insufficient, supplementary power can be supplied from the cluster power generation unit (240).

[0058] Next, the dual-layer structure of the energy storage unit (300) will be described with reference to FIG. 2 and FIG. 3. FIG. 3 illustrates in detail the dual-layer structure of the energy storage unit (300). The energy storage unit (300) includes individual battery units (310) individually provided corresponding to each of the plurality of low-lighting fixtures (100), and a central energy storage unit (320) commonly provided for the cluster unit to which the plurality of low-lighting fixtures (100) belong. The individual battery units (310) are primary energy storage means that store the power required for night lighting of each low-lighting fixture (100) on their own, and the central energy storage unit (320) functions as a secondary energy storage means to supply supplementary power when there is a power shortage in the individual battery units (310) within the cluster. This dual-layer structure is intended to solve the problem of the conventional single battery structure, namely the problem of the battery of an individual street light being depleted and causing a light-out during continuous non-lighting.

[0059] The types of batteries used in the individual battery unit (310) may include lithium iron phosphate (LiFePO4, LFP) batteries, lithium ion (Li-ion) batteries, lithium polymer (Li-Po) batteries, lithium titanate (LTO) batteries, or solid-state batteries. Lithium iron phosphate batteries have excellent thermal stability and a long cycle life of 2,000 to 5,000 cycles or more, and have high resistance to overcharging and over-discharging, making them most suitable for long-term use in outdoor installation environments. Lithium ion batteries have high energy density, which allows for a reduction in size and weight relative to the same capacity, but thermal management is essential and performance may degrade in extreme low-temperature environments. Lithium titanate batteries have fast charging and discharging speeds, excellent low-temperature characteristics, and an extremely long cycle life of 10,000 cycles or more, which can minimize maintenance cycles, but they have low energy density, which has the disadvantage of increasing volume when implementing large capacity. All-solid-state batteries use a solid electrolyte instead of a liquid electrolyte, so there is almost no risk of ignition and they have high energy density, but they are currently in the early stages of commercialization and are therefore expensive. In a preferred embodiment of the present invention, a lithium iron phosphate battery is applied to an individual battery unit (310), and the battery capacity per unit can be set to a range of 100Wh to 500Wh based on a road lighting fixture (100), and the nominal voltage can be configured to 12.8V (4 cells in series) or 25.6V (8 cells in series).

[0060] The central energy storage unit (320) can be configured in the form of an Energy Storage System (ESS) and includes a battery bank with a large capacity compared to individual battery units (310). The capacity of the central energy storage unit (320) is determined by the number of lighting fixtures (100) in the cluster and the number of days for continuous non-lighting. For example, when 3 days for continuous non-lighting are considered for 20 lighting fixtures (100) in the cluster, the total energy storage capacity can be set in the range of 15 kWh to 30 kWh. The central energy storage unit (320) can be installed in the form of a distribution box along a roadside, an underground buried container, or an interior space of a soundproof wall, and it is preferable to have a dustproof / waterproof rating of IP55 or higher and a temperature management system (a heater and fan with a built-in BMS).

[0061] In one embodiment of the present invention, the energy storage unit (300) exchanges charging status information through a self-established WiFi-based wireless communication network between the individual battery unit (310) and the central energy storage unit (320), and the energy management server (400) collects the SOC, SOH (State of Health), and charge / discharge history of each individual battery unit (310) in real time through the WiFi-based wireless communication network.

[0062] The communication unit (600) includes a communication module for establishing a wireless communication network for data exchange between the individual battery unit (310) and the central energy storage unit (320), and between the energy management server (400) and each component. In a preferred embodiment of the present invention, a Sub-1G self-network wireless communication method is applied as the basic communication method. Sub-1G self-network wireless communication refers to a communication method using a self-organizing wireless mesh network that uses a frequency band of less than 1 GHz, preferably the 920 MHz band, which is a domestic unlicensed band. The Sub-1G band has superior radio wave diffraction characteristics compared to the 2.4 GHz band, resulting in fewer communication shadows caused by obstacles such as buildings, street trees, and terrain; the communication range is 2 to 5 times longer than the same transmission output; and power consumption is significantly lower, making it optimized for battery-operated environments. In addition, the self-organizing mesh method supports multi-hop routing in which each node (lighting fixture) receives data and relays it to an adjacent node simultaneously. This aligns with the topological characteristics of streetlights arranged in a line along a road, allowing for communication coverage of the entire cluster extending for several kilometers without the need for separate relay equipment.

[0063] Sub-1G private network communication modules may utilize Sub-GHz RF SoCs (System on Chip) such as Texas Instruments’ CC1312R, Silicon Labs’ EFR32FG series, or Semtech’s LoRa SX126x series. These SoCs integrate a 920 MHz RF transceiver, an ARM Cortex-M series microprocessor, and sufficient peripheral interfaces (GPIO, ADC, UART, etc.) onto a single chip, enabling battery status sensing, data processing, and wireless transmission and reception to be implemented on a single chip. As for communication protocols, Wi-SUN (Wireless Smart Utility Network) protocols based on the IEEE 802.15.4g standard or custom-defined lightweight mesh protocols may be applied. Wi-SUN is a standardized Sub-1G mesh network protocol in the smart grid and smart city fields that offers high interoperability and security, while custom-defined lightweight mesh protocols have the advantage of enabling low-overhead communication specialized for street lighting networks.

[0064] Meanwhile, a WiFi (IEEE 802.11 b / g / n, 2.4GHz) based wireless communication method may be applied in conjunction with or as an alternative to the above Sub-1G private network wireless communication method. The WiFi method provides high data transmission speeds (up to 150Mbps or more), making it advantageous for communications requiring bandwidth, such as large-capacity charge / discharge history data or firmware updates. It also has excellent compatibility with existing network infrastructure through a standardized protocol stack and has the advantage of being able to be directly incorporated into the existing network without a separate gateway in environments where existing WiFi infrastructure is established, such as densely installed sections where the distance between light fixtures is short, inside buildings, or underground parking lots. As a WiFi communication module, a WiFi / BLE dual-purpose microcontroller of the ESP32 WROOM series can be used. The ESP32 WROOM simultaneously supports 2.4GHz WiFi communication of the IEEE 802.11 b / g / n standard and Bluetooth 4.2 / BLE communication, and has a dual-core processor and sufficient GPIO pins built in, allowing battery status sensing, data processing, and wireless transmission and reception to be implemented on a single chip.

[0065] Furthermore, it is possible to configure a dual communication path by mounting a Sub-1G private network communication module and a WiFi communication module in parallel within a single lighting fixture (100). In this case, normal SOC monitoring and control command transmission are performed through the low-power Sub-1G private network, and high-capacity data transmission or firmware updates are performed by switching to the WiFi path, thereby enabling the coexistence of low-power operation and high-speed data transmission. Additionally, communication reliability can be improved by implementing communication path redundancy, which automatically switches to another communication path when a failure occurs in one communication path. Furthermore, for remote communication with a control server, a low-power wide area network (LPWAN) method such as an LTE-based cellular communication protocol or LTE-M / NB-IoT can be applied, thereby enabling bidirectional data exchange between the energy management server (400) and the central control center. The LoRa (Long Range) method can also be applied for inter-cluster communication or remote control purposes, as it enables long-distance communication using an unlicensed band.

[0066] Next, the energy management server (400) is described. The energy management server (400) monitors the state of charge (SOC) of each of the individual battery units (310) and performs the function of distributing power from the central energy storage unit (320) to the individual battery unit (310) when the state of charge of the individual battery unit (310) falls below a preset threshold. The energy management server (400) includes an SOC monitoring module (410), a power distribution module (420), a power generation prediction module (430), a charging scenario calculation module (440), and an SOC balancing module (450). The SOC monitoring module (410) calculates the SOC in real time based on voltage, current, and temperature data of each individual battery unit (310) received through the communication unit (600), using a Coulomb counting method, an Open Circuit Voltage (OCV) method, or an estimation method based on a Kalman filter. The Coulomb counting method tracks changes in charge amount by integrating the current flowing into and out of the battery over time; while it is simple to implement, it may result in cumulative errors during long-term operation. The OCV method estimates the SOC by utilizing the correspondence between the open circuit voltage and the SOC during battery idle; while it offers high precision, continuous real-time estimation is difficult. The Kalman filter method combines Coulomb counting and OCV estimation, which has the advantage of simultaneously ensuring real-time precision and long-term stability.

[0067] The power distribution module (420) detects individual battery units (310) whose charge level falls below a preset threshold based on SOC information of each individual battery unit (310) received from the SOC monitoring module (410), and controls the operation of distributing power from the central energy storage unit (320) to the corresponding unit. The threshold may be set as a fixed value or may be dynamically adjusted in conjunction with weather conditions. In the case of a fixed threshold method, for example, when the SOC falls below 20%, power distribution from the central energy storage unit (320) to the corresponding unit begins, and when the SOC reaches 50%, distribution is stopped. In the winter or rainy season when the days of poor sunlight are prolonged, the threshold can be raised to 30%. In the case of a prediction-linked dynamic threshold method, if a lack of solar radiation is predicted in the upcoming 72-hour weather forecast, the threshold is dynamically raised from the default 20% to 35% to preemptively begin distribution, and when a return to normal solar radiation is predicted, the threshold is returned to the default value. In addition, in the case of a priority queue method, if multiple individual battery units (310) within a cluster are simultaneously below a threshold, distribution is prioritized starting from the unit with the lowest SOC, but if the remaining amount of the central energy storage unit (320) itself is less than a preset reserve rate (e.g., 15%), distribution is withheld and the lighting output of the entire cluster is reduced collectively.

[0068] Referring to FIG. 4, an SOC balancing operation within a cluster according to an embodiment of the present invention is illustrated conceptually. In one embodiment of the present invention, the energy management server (400) calculates the charge amount deviation between the individual battery units (310) within a cluster to which the plurality of low lighting fixtures (100) belong, and if the charge amount deviation exceeds a preset allowable range, performs SOC balancing within the cluster by redistributing power from the individual battery units (310) with a high charge amount to the individual battery units (310) with a low charge amount through the central energy storage unit (320). FIG. 4 illustrates a process in which power is redistributed from high-SOC units to low-SOC units via a central energy storage unit (320) when multiple light fixture-battery pairs (e.g., 100a-310a, 100b-310b, 100c-310c, 100d-310d, 100e-310e) form a cluster and the SOCs of each individual battery unit (310a to 310e) are different from each other (e.g., 310a's SOC 78%, 310b's SOC 45%, 310c's SOC 92%). The SOC balancing module (450) controls the process of transitioning from an unbalanced state before balancing to a balanced state after balancing.

[0069] The following embodiments may be applied as specific algorithms for SOC balancing within a cluster. In the case of a deviation-based balancing method, the average SOC of all individual battery units (310) within the cluster is calculated, and power is transferred from a high-SOC unit to a low-SOC unit via a central energy storage unit (320) for units that have a deviation of more than a predetermined ratio (e.g., ±15%) relative to the average, and the balancing cycle is set to every hour and executed once additionally before charging begins after sunrise. In the case of a min-max convergence method, balancing is triggered when the difference between the highest SOC unit and the lowest SOC unit within the cluster is greater than a predetermined ratio (e.g., 20%), and is executed repeatedly until the difference in SOC between the two units converges within a predetermined convergence range (e.g., 5%). In the case of the weighted priority balancing method, weights are assigned to the importance of each installation location of each light fixture (100) (e.g., adjacent to an intersection, adjacent to a crosswalk, sharp turn section, etc.), and asymmetric balancing is performed so that the individual battery unit (310) of the light fixture (100) with the higher weight preferentially maintains a high SOC.

[0070] Next, the lighting control unit (500) is described. The lighting control unit (500) controls the lighting time and output of each of the plurality of low-light fixtures (100) by comprehensively analyzing weather information, the status information of the battery, and object detection information obtained from the radar sensor (130). The lighting control unit (500) includes a weather information receiving module (510), a battery status analysis module (520), an object detection processing module (530), a composite output optimization module (540), and a color temperature control module (550). The weather information receiving module (510) periodically receives weather forecast information for the installation area from an external weather agency server (700), and the receiving cycle can be set at intervals of 1 to 6 hours. The battery status analysis module (520) collects and analyzes real-time status information of each individual battery unit (310) from the SOC monitoring module (410) of the energy management server (400). The object detection processing module (530) receives an object detection signal from a radar sensor (130) installed in each low-light fixture (100) and determines the presence, direction of movement, and speed of movement of an object. The composite output optimization module (540) determines the final output value for each fixture (100) by integrally calculating the three pieces of information received from the weather information receiving module (510), the battery status analysis module (520), and the object detection processing module (530). The color temperature control module (550) variably controls the color temperature of the LED light source (120) of each fixture (100) according to weather conditions and the season.

[0071] Hereinafter, with reference to FIG. 7, an energy management method of a smart street light system according to an embodiment of the present invention will be described in detail.

[0072] FIG. 7 illustrates a flowchart of an energy management method according to an embodiment of the present invention. An energy management method according to an embodiment of the present invention is an energy management method for a smart street light system comprising a plurality of low-lighting fixtures (100) each having a corresponding individual battery unit (310) that stores power produced from a renewable energy generation unit (200), and comprises a SOC precision tracking step (S10), a power generation amount prediction step (S20), a charging scenario calculation step (S30), and a dynamic charging control step (S40), and may further include a SOC threshold determination step (S50), a central EMS supplementary power supply step (S60), and an SOH estimation and threshold adaptive update step (S70).

[0073] In the SOC precision tracking step (S10), the charge amount (SOC) of each individual battery unit (310) is tracked in a preset first precision unit to monitor the charging and discharging pattern. The first precision can be set to 0.1%, which corresponds to a precision 10 to 50 times higher than conventional SOC tracking in units of 1% to 5%. SOC tracking in units of 0.1% can detect minute changes in the charge and discharge of the battery, thereby precisely capturing the non-linear change interval of the charging efficiency, and contributes to improving the accuracy of the non-linear charging characteristic model described later based on this. For SOC precision tracking, a hybrid estimation method combining Coulomb counting and a Kalman filter may be applied, and a calibration routine may be included to correct accumulated errors by measuring the OCV during battery idle at regular intervals (e.g., 24 hours).

[0074] In one embodiment of the present invention, the SOC precision tracking step (S10) analyzes the battery charging pattern over time to estimate the battery degradation state (SOH) in parallel, and adaptively updates the discharge lower limit threshold and the charge upper limit threshold of the dynamic charging control step (S40) in the direction of extending battery life based on the SOH estimation result. SOH estimation is performed by comprehensively analyzing the number of battery cycles, the trend of decreasing charge capacity, the trend of increasing internal resistance, etc., and if the SOH drops below a predetermined standard (e.g., 80%), the discharge lower limit threshold is raised (e.g., from 20% to 30%) and the charge upper limit threshold is lowered (e.g., from 100% to 90%) to reduce the stress applied to the battery, thereby extending the remaining life. Such SOH estimation and adaptive updating of thresholds correspond to the step (S70) of FIG. 7.

[0075] In the power generation prediction step (S20), a range-regression based conservative prediction is applied to predict the starting point of power generation by day and time period, automatically reflect changes in sunrise and sunset times by season, and predict the pattern of power generation increase, maximum power generation, and power generation decrease by time period. A range-regression based conservative prediction refers to a technique that performs regression analysis on past accumulated power generation data and applies a predetermined safety margin to the result to calculate a predicted power generation amount that is conservatively estimated compared to the actual power generation amount. This conservative prediction is intended to prevent the risk of nighttime lights-out caused by overestimating power generation, thereby contributing to ensuring battery safety and extending its lifespan.

[0076] In one embodiment of the present invention, the power generation prediction step (S20) performs regression analysis on the time series change pattern of the power generation start time, peak power generation time, and power generation end time from daily power generation data accumulated over a predetermined past period, and calculates a predicted power generation amount estimated more conservatively than the actual power generation amount by applying a predetermined safety margin to the result of the regression analysis. The following embodiments may be applied as specific algorithms for conservative prediction based on range regression. In the case of the moving average regression and lower margin method, a 7-day moving average is calculated for the daily power generation data of the past 30 days, and the predicted power generation amount is set by multiplying the moving average value by a concession factor (e.g., 0.8, i.e., a safety margin of 20%), and during the seasonal transition period, the concession factor is lowered to 0.7 by referring to historical data of the same month in the past. In the case of the polynomial regression and lower confidence interval method, the daily power generation curve is modeled by regressing the hourly power generation of the past 90 days using a second-order polynomial, the lower bound of the 95% confidence interval is adopted as the predicted power generation, and the start and end times of power generation are calculated by applying installation azimuth correction to the sunrise and sunset times. In the case of the segment-based linear regression method, the day is divided into three segments: a power generation rising segment (from sunrise to noon), a peak segment (a predetermined period around noon, e.g., noon ± 2 hours), and a power generation falling segment (from noon to sunset). A separate linear regression model is applied to each segment to predict the hourly power generation, and different return coefficients (e.g., 0.85 for the rising segment, 0.80 for the peak segment, 0.75 for the falling segment) are applied to the predicted values ​​of each segment.

[0077] In the charging scenario calculation step (S30), based on the SOC information monitored in the SOC precision tracking step (S10) and the power generation information predicted in the power generation prediction step (S20), an optimal charging scenario is calculated in real time by applying a mathematical model that reflects non-linear charging characteristics that vary according to the remaining battery capacity. In one embodiment of the present invention, the mathematical model reflecting the non-linear charging characteristics in the charging scenario calculation step (S30) models the characteristic that charging efficiency decreases non-linearly in the section where the remaining battery capacity is greater than or equal to a preset first ratio, and minimizes energy loss by applying different charging current limit values ​​for each remaining battery capacity section. The first ratio may be set to, for example, 80%, and since a phenomenon in which charging efficiency decreases non-linearly occurs due to the electrochemical characteristics of lithium-based batteries in the section where the SOC is 80% or higher, energy loss is reduced by mathematically modeling this.

[0078] The following examples may be applied as specific algorithms for a non-linear charging characteristic model. In the case of a CC-CV model method for each section, the maximum charging current is applied as a constant current (CC) charging in the SOC 0% to 80% section, the charging current is linearly reduced by a predetermined ratio (e.g., 5%) for every 1% increase in SOC in the SOC 80% to 95% section, and the charging is switched to constant voltage (CV) charging in the SOC 95% to 100% section, and charging is completed when the charging current reaches a preset minimum current value (e.g., 0.02C). In the case of the exponential decay charging efficiency model method, the charging efficiency η is modeled as a function of SOC as η(SOC) = η_max Х exp(-k Х (SOC - SOC_threshold)) (provided that SOC is greater than or equal to SOC_threshold), and energy waste is prevented by suspending charging when the charging efficiency drops below a preset minimum efficiency (e.g., 60%). Here, η_max represents the maximum charging efficiency, k represents the decay constant, and SOC_threshold represents the SOC reference point at which non-linear decay begins. In the case of the lookup table method, the maximum allowable charging current and charging efficiency for each 5% SOC interval are stored in a lookup table based on actual charge / discharge data provided by the battery manufacturer, the charging current is determined by referencing the table value corresponding to the real-time SOC, and the table is periodically (e.g., once a month) corrected by reflecting the SOH value according to battery degradation.

[0079] In the dynamic charging control step (S40), the charging current and charging time from the renewable energy generation unit (200) to the individual battery unit (310) are dynamically controlled according to the optimal charging scenario calculated in the charging scenario calculation step (S30), and the lower discharge threshold value for preventing over-discharge of the individual battery unit (310) is dynamically adjusted in conjunction with the predicted period of poor sunlight. For example, if three consecutive days of poor sunlight are predicted in the power generation amount prediction step (S20), the lower discharge threshold value is raised from the usual 20% to 35% to maintain a higher remaining battery capacity when lighting up at night, thereby preventing light-out during the period of poor sunlight.

[0080] Next, in the SOC threshold determination step (S50) of FIG. 7, it is determined whether the SOC of each individual battery unit (310) is below the discharge lower limit threshold after passing through the dynamic charge control step (S40). As a result of the determination, if the SOC is close to or below the discharge lower limit threshold, the process proceeds to the central EMS supplementary power supply step (S60). In one embodiment of the present invention, the dynamic charge control step (S40) further includes a supplementary power supply step (S60) which supplies supplementary power from a central energy storage unit (320) commonly provided in the cluster unit to the individual battery unit (310) when the SOC of the individual battery unit (310) is close to the discharge lower limit threshold, thereby preventing a light-out occurring on an unfavorable day. The central energy storage unit (320) stores power produced through solar and wind power generation in a centralized energy management system (EMS), and when the charge amount of the individual battery unit (310) of each street light reaches a critical point, it distributes power from the centralized EMS to the street light. When the SOC is above the discharge lower limit threshold, it proceeds directly to the SOH estimation and threshold adaptive update step (S70), thereby estimating the battery degradation state in parallel as described above and adaptively updating the discharge lower limit threshold and the charge upper limit threshold in the direction of extending battery life.

[0081] Hereinafter, with reference to FIGS. 5, 6, and 8, a composite lighting control method of a smart street light system according to an embodiment of the present invention will be described in detail.

[0082] FIG. 8 illustrates a flowchart of a composite lighting control method according to an embodiment of the present invention. A composite lighting control method according to an embodiment of the present invention is a composite lighting control method of a smart street light system that controls the output of a plurality of low lighting fixtures (100) that receive power from a renewable energy generation unit (200), and includes a weather-based output scheduling step (S100), a battery-based output limiting step (S110), an object detection-based output switching step (S120), a composite output optimization step (S130), and a final output value application step (S140).

[0083] In the weather-based output scheduling step (S100), weather forecast information for the installation area is received from an external weather agency server (700), and based on at least one weather element among temperature, humidity, rainfall, snowfall, cloudiness, and clearness included in the weather forecast information, an expected period of solar radiation reduction for a predetermined period is calculated, and a lighting output reduction schedule corresponding to the expected period of solar radiation reduction is preemptively generated. Here, the weather forecast information received from the weather agency server (700) may include weather elements such as temperature, humidity, rainfall amount, snowfall amount, wind speed, wind direction, cloudiness, and visibility, and the reception cycle may be set at intervals of 1 to 3 hours in accordance with the weather agency's forecast update cycle. The predetermined period may be set to a range of 24 to 72 hours in the future, and it is preferable to apply a short-term forecast (within 24 hours) with high weather forecast accuracy first and refer to a medium-term forecast (72 hours) as a supplementary reference.

[0084] The following embodiments may be applied as specific algorithms for calculating the expected period of solar radiation reduction and generating a corresponding lighting output reduction schedule. In the case of the attenuation coefficient method by weather element, a solar radiation attenuation coefficient by weather element is predefined (e.g., clear 1.0, cloudy 0.6, overcast 0.3, rain 0.15, snow 0.1), and the corresponding attenuation coefficient is applied to the weather elements of the time period in the weather forecast to calculate the expected power generation. If the expected power generation is less than a preset ratio (e.g., 70%) of the standard power consumption for the corresponding night, the output reduction schedule is activated. In the case of the continuous non-sunlight count method, if it is forecasted that continuous non-sunlight days (days when the daily power generation is less than 50% of the standard power generation) will continue for 2 days or more, the night lighting output is reduced in stages in proportion to the expected number of continuous non-sunlight days (e.g., 90% for 2 consecutive days, 80% for 3 consecutive days, 70% for 4 consecutive days or more). In the case of the energy balance simulation method, an energy balance curve is generated by simulating the hourly expected power generation and hourly expected power consumption for the next 72 hours, and a schedule is generated to start output reduction before that point by calculating inversely the time when the energy balance turns negative. Weather-based output scheduling constitutes a core part of a complex lighting control algorithm that minimizes and optimizes the amount of power consumed by streetlights to prevent light-out situations from occurring when days without sunlight are prolonged.

[0085] In one embodiment of the present invention, the weather-based output scheduling step (S100) further includes a color temperature preemptive change step in which, when fog or rain is forecasted based on the weather forecast information, the color temperature of the plurality of low-lighting fixtures (100) is preemptively changed from a preset first color temperature to a preset second color temperature or a preset third color temperature range, and the color temperature change in the color temperature preemptive change step is corrected in real time in conjunction with visibility distance information received from a visibility meter (800). The first color temperature may be set to 5000K as the basic color temperature in a normal state, the second color temperature may be set to 1500K as the lower limit of the low color temperature range, and the third color temperature may be set to 2000K as the upper limit of the low color temperature range. Changing the color temperature from 5000K to a range of 1500K to 2000K during fog or rain is because a lower color temperature (warm light) has less scattering by fog particles and raindrops compared to a higher color temperature (cold light), thereby improving visibility. According to the results of an empirical experiment, it has been confirmed that when the color temperature is automatically changed to 1500K during fog, visibility is improved by approximately 67%. The visibility meter (800) is an optical type (transmittance measuring type or scattered light measuring type) visibility observation device that provides real-time visibility data to the lighting control unit (500) and corrects the discrepancy between the weather forecast and the actual data by correcting the degree of color temperature change in real time based on the visibility data.

[0086] In addition, in one embodiment of the present invention, the weather-based output scheduling step (S100) performs season-adaptive color temperature control by setting the color temperature of the plurality of low-light fixtures (100) to a preset fourth color temperature or lower when it is determined that it is summer based on the weather forecast information to reduce the attraction of flying insects, and by setting the color temperature to a preset fifth color temperature or higher when it is determined that it is winter to ensure visibility. The fourth color temperature may be set to 3000K, and the fifth color temperature may be set to 4000K. Setting the color temperature to 3000K or lower during the summer is because flying insects have phototaxis characteristics in which they are strongly attracted to light of short wavelengths (blue light region), so the density of flying insect attraction is significantly reduced in light of low color temperature (mainly long wavelengths). Conversely, since there is almost no activity of flying insects during the winter season, it is desirable to set the color temperature to a high color temperature of 4000K or higher to improve the Color Rendering Index (CRI) of the road surface and maximize visibility. The season can be determined based on temperature data included in weather forecast information, with periods when the daily average temperature is above a predetermined threshold value (e.g., 20°C) being identified as summer, and periods when it is below a predetermined threshold value (e.g., 10°C) being identified as winter. For the spring and autumn seasons, an intermediate color temperature (e.g., 3000K to 4000K) can be applied.

[0087] In the battery-based output limiting step (S110), battery status information including the SOC, charge amount, power generation amount, and discharge amount of an individual battery unit (310) corresponding to each of the plurality of low-lighting fixtures (100) is collected, and based on the battery status information, a time point at which a light-out is expected to occur when the day of non-lighting persists is calculated, and an upper limit for lighting output by time period is dynamically set by considering the remaining energy up to the expected time point of the light-out. The following embodiment may be applied as an algorithm for calculating the expected time point of the light-out. In the case of a simple consumption ratio method relative to remaining capacity, the remaining possible lighting time is calculated by dividing the current remaining battery capacity (Wh) by the currently set lighting output (W), and if the remaining possible lighting time is shorter than the required remaining lighting time for today's night, it is determined to be a risk of a light-out. In the case of the time-based variable consumption profile application method, a variable output profile (e.g., early evening 100%, late night 60%, dawn 40%) based on the expected traffic and pedestrian volume by time is applied to calculate the total cumulative power consumption for the entire night, and the time of light-out is predicted by comparing this with the current remaining amount and expected supplementary power (including distribution by the central energy storage unit (320)). In the case of the Monte Carlo simulation method, multiple scenarios (e.g., 1,000 times) are simulated by reflecting weather uncertainty and the probabilistic fluctuation of the radar sensor (130) detection frequency, and output reduction is triggered when the ratio of scenarios in which light-out occurs exceeds a preset risk probability (e.g., 10%).

[0088] In the object detection-based output switching step (S120), the approach of a person or means of transportation is detected in real time through a radar sensor (130) installed on the plurality of low-lighting fixtures (100), and the fixture (100) in the section where an object is detected is lit at a predetermined standard output, while the fixture (100) in the section where an object is not detected is switched to a standby output reduced from the standard output. The standard output is set to an output value that satisfies the minimum road surface luminance or minimum road surface illuminance for each grade specified in the road lighting standards, and the standby output can be set to a level of 10% to 30% of the standard output. Maintaining the standby output at a predetermined minimum output rather than completely off (0%) is because sudden lighting in a completely off state can impede the driver's visual adaptation, and minimal background lighting contributes to the recognition of the road's outline.

[0089] Referring to FIG. 6, in one embodiment of the present invention, the object detection-based output switching step (S120) performs a pre-lighting control that, when the radar sensor (130) detects an object, lights up not only the corresponding light fixture (100) but also adjacent light fixtures (100) on a predicted path based on the object's direction of movement and speed of movement, in advance with standard output. FIG. 6 is a plan view in which light fixtures (100a to 100g) are arranged in a line at equal intervals on a road, and the process of the radar sensor (130) detecting a moving object (vehicle or pedestrian) and pre-lighting adjacent light fixtures (100) on a predicted path based on the direction of movement and speed of movement is illustrated in chronological order (t1, t2, t3). For example, when the radar sensor (130) of the light fixture (100c) detects a vehicle approaching at 60 km / h at time t1, the light fixture (100d) and light fixture (100e) in the direction predicted to be reached by the vehicle are illuminated in advance at standard output to provide a continuous lighting environment to the driver. The number of light fixtures (100) to be illuminated in advance is dynamically determined according to the moving speed and the spacing between light fixtures; for example, when the spacing between light fixtures is 8 m and the vehicle speed is 60 km / h (approx. 16.7 m / s), it is preferable to illuminate 3 to 5 light fixtures in advance. The light fixtures (100) that are illuminated in advance return to standby output after a predetermined delay time (e.g., 10 to 30 seconds) has elapsed after the moving object has passed.

[0090] In the composite output optimization step (S130), the lighting output reduction schedule of the weather-based output scheduling step (S100), the lighting output upper limit value per time period of the battery-based output limiting step (S110), and the output switching result of the object detection-based output switching step (S120) are integrated to determine the final output value for each of the plurality of low-lighting fixtures (100). In one embodiment of the present invention, the composite output optimization step (S130) determines the lowest output value among the weather-based output reduction rate calculated from the weather-based output scheduling step (S100), the battery-based output upper limit value calculated from the battery-based output limiting step (S110), and the detection-based output switching value calculated from the object detection-based output switching step (S120) as the final output value, and applies lower limit clipping so that the final output value does not fall below the lower limit value of the minimum road surface luminance or minimum road surface illuminance specified in the road lighting standards.

[0091] The following embodiments may be applied as specific algorithms for composite output optimization. In the case of the minimum value selection and lower limit clipping method, the minimum value among the three values—weather-based output value (P_weather), battery-based output upper limit value (P_battery), and detection-based output value (P_detect)—is determined as the final output, and if the final output is less than the lower limit output (P_min) corresponding to the minimum luminance of the road lighting standard, it is clipped to P_min. Expressed as a formula, this is P_final = max(min(P_weather, P_battery, P_detect), P_min). In the case of the weighted average method, situational weights are assigned to the three control values ​​to determine the final output as a weighted average, and the basic weights are 0.3 for weather, 0.4 for battery, and 0.3 for detection. However, if the battery SOC is less than a predetermined standard (e.g., 30%), the battery weight is increased to 0.6 and the remainder is redistributed equally, while the lower limit clipping is applied equally. In the case of the hierarchical priority method, the first priority is to apply a battery-based output upper limit to set an absolute upper limit, the second priority is to apply a weather-based reduction schedule, and the third priority is to apply a detection-based output switching. The output value derived from each layer cannot exceed the upper limit of the upper layer, and finally, a lower limit clipping method is applied.

[0092] Referring to FIG. 5, the lighting output profile by time period during the composite output optimization process is graphed. In FIG. 5, the x-axis represents the night time period (e.g., 18:00 to 06:00 the next day), and the y-axis represents the lighting output ratio (0% to 100%). The weather-based output value (P_weather), the battery-based output upper limit value (P_battery), the detection-based output value (P_detect), and the final output value (P_final) are superimposed by time period. Additionally, the lower limit output (P_min), corresponding to the minimum luminance of the road lighting standard, is indicated by a horizontal dashed line. As can be seen in FIG. 5, when an object is detected by the radar sensor (130), the detection-based output value (P_detect) shows a pulse shape in which it momentarily rises from standby output to standard output, the battery-based output upper limit value (P_battery) shows a tendency to gradually decrease over time in conjunction with the decrease in remaining capacity, and the weather-based output value (P_weather) shows a pattern of being adjusted stepwise according to changes in the weather forecast. The final output value (P_final) is the result of the integrated calculation of these three control values ​​and forms an optimal profile that minimizes energy consumption while always maintaining a lower limit output (P_min).

[0093] In the final output value application step (S140), the final output value determined in the composite output optimization step (S130) is applied to the LED light source part (120) of each low lighting fixture (100) to control the actual lighting output. Output control can be implemented using a Pulse Width Modulation (PWM) dimming method or a constant current dimming method. PWM dimming has the advantage of supporting a wide dimming range (0% to 100%) without flicker being perceived visually when the frequency is sufficiently high (e.g., 1 kHz or higher), while constant current dimming has the advantage of having less color temperature variation and higher efficiency because it directly controls the current applied to the LED element.

[0094] Meanwhile, regarding installation examples of the smart street light system of the present invention, domestic installation examples of low lighting for roads include Naegok Bridge in Gangneung (300m section), Guhwang Bridge in Gyeongju (400m section), and installations totaling approximately 13km across national and provincial road sections in Gangwon-do, Gyeongbuk, Jeonnam, Busan, and Daejeon. Overseas installation examples include the entrance to Angkor Wat in Siem Reap, Cambodia (320m section, solar-powered independent type), Da Nang, Vietnam (255m section), and Hanoi, Vietnam (160m section). Installation examples of low lighting for walking paths include the deck at Children's Grand Park in Busan (power consumption reduced from 3.3W / m to 0.7W / m), Bansong Park in Changwon (user satisfaction over 90%), Gwangchi Stream in Namwon, Geumgang Reservoir in Haenam-gun, and the sidewalk in Jinyeong-eup, Gimhae-si. These demonstration examples support the fact that the low lighting fixture (100) of the present invention is effective in various installation environments.

[0095] In terms of economic efficiency, the unidirectional installation cost for a 1km section of the low-lighting system for roads is approximately 165 million won, which is significantly lower than that of conventional LED smart streetlights (approx. 285 million won) or weather-sensitive streetlights (approx. 310 million won). Additionally, the monthly power consumption is approximately 750kW, which is about 40% lower than that of competing products (1,188kW to 1,237kW). Furthermore, the power consumption per meter is 3.0W / m, achieving superior energy efficiency compared to Europe's ES SYSTEM (3.9W / m) and Thorn Lighting ORUS (8.7W / m), as well as Japan's PANASONIC (8.2W / m) and IWASAKI LEDioc ROAD (6.3W / m). The cost of the lighting fixture per meter is also 89,375 won, which has the advantage of being significantly lower than that of European products (195,000 won to 309,000 won).

[0096] The parts of the functions of the energy management server (400) and the lighting control unit (500) of the smart street light system according to the embodiment of the present invention described above that are implemented in software may be performed by a computing device including a processor and memory, and each step of the energy management method and the complex lighting control method may be implemented by the processor of the computing device executing a program command stored in memory.

[0097] FIG. 9 illustrates an example of the internal configuration of a computing device according to an embodiment of the present invention. In the following description, descriptions of unnecessary embodiments that overlap with the descriptions of FIG. 1 to 8 described above will be omitted.

[0098] As illustrated in FIG. 9, the computing device (10000) may include at least one processor (11100), memory (11200), peripheral interface (11300), input / output subsystem (I / O subsystem) (11400), power circuit (11500), and communication circuit (11600). In this case, the computing device (10000) may correspond to a user terminal (A) connected to a haptic interface device or the aforementioned computing device (B).

[0099] The memory (11200) may include, for example, high-speed random access memory, a magnetic disk, SRAM, DRAM, ROM, flash memory, or non-volatile memory. The memory (11200) may include software modules, instruction sets, or various other data required for the operation of the computing device (10000).

[0100] At this time, access to memory (11200) from other components, such as the processor (11100) or peripheral device interface (11300), can be controlled by the processor (11100).

[0101] The peripheral device interface (11300) can connect input and / or output peripheral devices of the computing device (10000) to the processor (11100) and memory (11200). The processor (11100) can perform various functions for the computing device (10000) and process data by executing software modules or instruction sets stored in the memory (11200).

[0102] The input / output subsystem (11400) can connect various input / output peripherals to the peripheral interface (11300). For example, the input / output subsystem (11400) may include a controller for connecting peripherals such as a monitor, keyboard, mouse, printer, or, if necessary, a touchscreen or sensor to the peripheral interface (11300). According to another aspect, input / output peripherals may be connected to the peripheral interface (11300) without passing through the input / output subsystem (11400).

[0103] The power circuit (11500) can supply power to all or part of the components of the terminal. For example, the power circuit (11500) may include one or more power sources such as a power management system, a battery or alternating current (AC), a charging system, a power failure detection circuit, a power converter or inverter, a power status indicator, or any other components for power generation, management, and distribution.

[0104] The communication circuit (11600) can enable communication with another computing device using at least one external port.

[0105] Alternatively, as described above, the communication circuit (11600) may enable communication with other computing devices by including an RF circuit and transmitting and receiving an RF signal, also known as an electromagnetic signal.

[0106] The embodiment of FIG. 9 is merely an example of a computing device (10000), and the computing device (11000) may have some components shown in FIG. 9 omitted, additional components not shown in FIG. 9 added, or a configuration or arrangement that combines two or more components. For example, a computing device for a communication terminal in a mobile environment may include, in addition to the components shown in FIG. 9, a touchscreen or sensor, etc., and the communication circuit (1160) may include a circuit for RF communication of various communication methods (WiFi, 3G, LTE, Bluetooth, NFC, Zigbee, etc.). The components that can be included in the computing device (10000) may be implemented as hardware, software, or a combination of both hardware and software, including one or more integrated circuits specialized for signal processing or applications.

[0107] Methods according to embodiments of the present invention may be implemented in the form of program instructions that can be executed through various computing devices and recorded on a computer-readable medium. In particular, the program according to the present embodiment may be configured as a PC-based program or an application dedicated to a mobile terminal. An application to which the present invention is applied may be installed on a user terminal through a file provided by a file distribution system. For example, the file distribution system may include a file transmission unit (not shown) that transmits the file upon a request from the user terminal.

[0108] The device described above may be implemented as a hardware component, a software component, and / or a combination of a hardware component and a software component. For example, the device and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include a plurality of processing elements and / or a plurality of types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0109] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium, or device so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed across networked computing devices and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0110] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0111] Although the embodiments have been described above with reference to limited embodiments and drawings, those skilled in the art can make various modifications and variations from the description above. For example, appropriate results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents. Therefore, other implementations, other embodiments, and equivalents to the claims below also fall within the scope of the claims.

Claims

Claim 1 A smart street lighting system having renewable energy-based distributed power management and complex predictive control functions, characterized by comprising: a plurality of lighting fixtures that distribute light in a horizontal direction toward the road surface of a road or pedestrian walkway; a renewable energy generation unit for supplying power to the plurality of lighting fixtures, comprising at least one of a solar power generation module and a wind power generation module; an energy storage unit comprising a battery that stores power produced by the renewable energy generation unit, wherein the energy storage unit comprises individual battery units individually provided corresponding to each of the plurality of lighting fixtures and a central energy storage unit commonly provided for the cluster unit to which the plurality of lighting fixtures belong; an energy management server that monitors the state of charge (SOC) of each of the individual battery units and distributes power from the central energy storage unit to the corresponding individual battery unit when the state of charge of the individual battery unit falls below a preset threshold; and a lighting control unit that controls the lighting time and output of each of the plurality of lighting fixtures by comprehensively analyzing weather information, the state information of the batteries, and object detection information obtained from a radar sensor. Claim 2 A smart street light system having renewable energy-based distributed power management and complex predictive control functions, wherein, in claim 1, the energy management server performs SOC balancing within the cluster by distributing power from the central energy storage unit to individual battery units with a charge level lower than the average charge level of individual battery units within the cluster. Claim 3 A smart street light system having renewable energy-based distributed power management and complex predictive control functions, wherein, in claim 1, the renewable energy generation unit can be selectively arranged in any one of the configurations of the solar power generation module alone, the wind power generation module alone, or a hybrid combination of the solar power generation module and the wind power generation module, and the charging interface of the energy storage unit is configured with an integrated standard so as to commonly accept power for any of the three arrangement configurations. Claim 4 A smart street light system having renewable energy-based distributed power management and complex predictive control functions, wherein, in claim 1, the energy storage unit exchanges charge status information through a self-constructed Sub-1G private network wireless communication network between the individual battery unit and the central energy storage unit, and the energy management server collects the SOC, SOH (State of Health), and charge / discharge history of each individual battery unit in real time through the Sub-1G private network wireless communication network. Claim 5 A smart street light system having renewable energy-based distributed power management and complex predictive control functions, wherein, in claim 1, the renewable energy generation unit comprises at least one of an individual generation unit individually provided corresponding to each of the plurality of lighting fixtures and a cluster generation unit commonly provided by grouping the plurality of lighting fixtures into one cluster, and the energy management server manages the power generation amount of the individual generation unit and the power generation amount of the cluster generation unit in an integrated manner to control charging to the energy storage unit. Claim 6 An energy management method for a smart street lighting system comprising a plurality of lighting fixtures, each corresponding to an individual battery unit that stores power produced from a renewable energy generation unit, comprising: a state of charge (SOC) precision tracking step for monitoring charging and discharging patterns by tracking the state of charge (SOC) of each individual battery unit in a preset first precision unit; a power generation prediction step for predicting the starting point of power generation by day and time and automatically reflecting seasonal changes in sunrise and sunset times, and predicting patterns of power generation increase, maximum power generation, and power generation decrease by time by applying a range-regression based conservative prediction; and a charging scenario calculation step for calculating a charging scenario in real time to reduce energy loss by applying different charging current limit values ​​for each battery remaining capacity range, based on the SOC information monitored in the state of charge tracking step and the power generation information predicted in the power generation prediction step, while applying a mathematical model that reflects non-linear charging characteristics that vary according to the remaining battery capacity. An energy management method for a smart street light system having renewable energy-based distributed power management and complex predictive control functions, characterized by including: a dynamic charging control step that dynamically controls the charging current and charging time from the renewable energy generation unit to the individual battery unit according to the charging scenario calculated in the charging scenario calculation step, and dynamically adjusts the discharge lower limit threshold value to prevent over-discharge of the individual battery unit in conjunction with the predicted period of non-light; Claim 7 In claim 6, the power generation prediction step is characterized by performing regression analysis on the time series change patterns of the power generation start time, peak power generation time, and power generation end time from daily power generation data accumulated over a predetermined past period, and calculating a predicted power generation amount estimated more conservatively than the actual power generation amount by applying a predetermined safety margin to the result of the regression analysis. This describes an energy management method for a smart street light system having renewable energy-based distributed power management and complex prediction control functions. Claim 8 In claim 6, the mathematical model reflecting the non-linear charging characteristics in the charging scenario calculation step models the characteristic that the charging efficiency decreases non-linearly in the section where the remaining battery capacity is greater than or equal to a preset first ratio, and is characterized by minimizing energy loss by applying different charging current limit values ​​for each remaining battery capacity section, thereby providing an energy management method for a smart street light system having renewable energy-based distributed power management and complex predictive control functions. Claim 9 In claim 6, the smart street light system further comprises a central energy storage unit commonly provided for a cluster unit to which the plurality of lighting fixtures belong, and the dynamic charging control step further comprises a supplementary power supply step that prevents light-out occurring on a non-lighting day by supplying supplementary power from the central energy storage unit commonly provided for the cluster unit to the individual battery unit when the SOC of the individual battery unit approaches the discharge lower limit threshold. Energy management method of a smart street light system having renewable energy-based distributed power management and complex predictive control functions. Claim 10 An energy management method for a smart street lighting system having renewable energy-based distributed power management and complex predictive control functions, wherein, in claim 6, the SOC precision tracking step analyzes the battery charging pattern by time period to estimate the battery degradation state (SOH) in parallel, and adaptively updates the discharge lower limit threshold and the charge upper limit threshold of the dynamic charging control step in the direction of extending battery life based on the SOH estimation result. Claim 11 A composite lighting control method for a smart street lighting system that controls the output of a plurality of low-lighting fixtures supplied with power from a renewable energy generation unit, comprising: a weather-based output scheduling step of receiving weather forecast information for an installation area from an external weather agency server, calculating a section expected to reduce solar radiation for a predetermined period based on at least one weather element among temperature, humidity, rainfall, snowfall, cloudiness, and clearness included in the weather forecast information, and preemptively generating a lighting output reduction schedule corresponding to the section expected to reduce solar radiation; a battery-based output limiting step of collecting battery status information including the SOC, charge amount, generation amount, and discharge amount of an individual battery unit corresponding to each of the plurality of lighting fixtures, calculating a time point when a light-out is expected during a prolonged period of non-light based on the battery status information, and dynamically setting an upper limit for lighting output by time period considering the remaining energy up to the expected time point of the light-out; and detecting in real time whether a person or means of transportation is approaching through a radar sensor installed on the plurality of lighting fixtures, illuminating the fixtures in the section where an object is detected at a predetermined standard output, and illuminating the fixtures in the section where an object is not detected at the standard output A composite lighting control method for a smart street lighting system having renewable energy-based distributed power management and composite predictive control functions, characterized by comprising: an object detection-based output switching step that switches to a reduced standby output; and a composite output optimization step that determines the lowest output value among the lighting output reduction schedule of the weather-based output scheduling step, the time-based lighting output upper limit of the battery-based output limiting step, and the output switching result of the object detection-based output switching step as the final output value for each of the plurality of lighting fixtures, wherein lower limit clipping is applied so that the final output value does not fall below the lower limit of the minimum road surface luminance or minimum road surface illuminance specified in the road lighting standards. Claim 12 delete Claim 13 In claim 11, the weather-based output scheduling step further includes a color temperature preemptive change step in which, when fog or rain is forecasted based on the weather forecast information, the color temperature of the plurality of lighting fixtures is preemptively changed from a preset first color temperature to a preset second color temperature or a preset third color temperature range, and the color temperature change in the color temperature preemptive change step is corrected in real time in conjunction with visibility distance information received from a visibility meter, characterized in that the combined lighting control method of a smart street lighting system having renewable energy-based distributed power management and combined predictive control functions. Claim 14 A composite lighting control method for a smart street lighting system having renewable energy-based distributed power management and composite predictive control functions, wherein, in claim 11, the object detection-based output switching step performs a pre-lighting control that, when the radar sensor detects an object, lights up adjacent lighting fixtures on a predicted path based on the object's direction of movement and speed of movement, in addition to the corresponding lighting fixture, in advance at a standard output. Claim 15 A method for controlling combined lighting in a smart street lighting system having renewable energy-based distributed power management and combined predictive control functions, wherein, in claim 11, the weather-based output scheduling step performs season-adaptive color temperature control by setting the color temperature of the plurality of lighting fixtures to a preset fourth color temperature or lower to reduce the attraction of flying insects when it is determined that it is a summer season based on the weather forecast information, and setting the color temperature to a preset fifth color temperature or higher to ensure visibility when it is determined that it is a winter season.

Images