Light pollution prediction system and light pollution prediction method

Abstract

This system provides a light pollution prediction system capable of accurately predicting the occurrence of light pollution. [Solution] A light pollution prediction system 100 predicts the occurrence of light pollution caused by light reflected by a target member installed on the roof of a target building, comprising: a target member information acquisition unit 112 that acquires a first glossiness, which is the glossiness of the target member when the angle of incidence is a first angle, and a second glossiness, which is the glossiness of the target member when the angle of incidence is a second angle different from the first angle; and a prediction unit 115 that performs a light pollution prediction to predict whether or not light pollution is likely to occur based on the acquisition results of the first glossiness and the second glossiness.

Description

Technical Field

[0001] The present invention relates to a technology of a light damage prediction system and a light damage prediction method for predicting the occurrence of light damage caused by light reflected by a target member provided on the roof of a target building.

Background Art

[0002] Conventionally, technologies related to various members provided on the roofs of buildings are well-known. For example, it is as described in Patent Document 1.

[0003] Patent Document 1 describes a building material (roof forming body) including a roofing material and a cover body placed so as to cover the upper surface of the roofing material. The cover body is formed of a metal plate material (stainless steel plate) having a high reflectance of heat and light. When snow accumulates on such a roof forming body, in addition to the sunlight directly irradiating the snow, the sunlight reflected by the cover body can effectively promote snow melting.

[0004] Not limited to the technology described in Patent Document 1, various members are used for the roofs of buildings. Also, depending on the members provided on the roof of a building, the ease of reflecting sunlight varies. Here, when a member that easily reflects sunlight is provided on the roof as in Patent Document 1, the sunlight reflected by the member may irradiate surrounding buildings, which may cause discomfort (light damage) to users (residents, etc.) of the buildings. However, conventionally, the factors leading to the occurrence of light damage have been complex and unclear, and it has been difficult to appropriately predict the occurrence of light damage in advance.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] This invention was made in view of the above circumstances, and the problem it aims to solve is to provide a light pollution prediction system and a light pollution prediction method that can appropriately predict the occurrence of light pollution. [Means for solving the problem]

[0007] The problems that this invention aims to solve are as described above, and the means for solving these problems will now be explained.

[0008] In other words, claim 1 is a light pollution prediction system that predicts the occurrence of light pollution caused by light reflected by a target member installed on the roof of a target building, comprising: a glossiness acquisition unit that acquires a first glossiness, which is the glossiness of the target member when the angle of incidence is a first angle, and a second glossiness, which is the glossiness of the target member when the angle of incidence is a second angle different from the first angle; and a prediction unit that performs a light pollution prediction based on the acquisition results of the first glossiness and the second glossiness to predict whether or not light pollution is likely to occur.

[0009] In claim 2, the target member is a solar power generation module.

[0010] In claim 3, the prediction unit predicts that there is no possibility of light pollution occurring when the acquisition result of the first glossiness is below a first threshold and the acquisition result of the second glossiness is below a second threshold.

[0011] Claim 4 further comprises a building information acquisition unit that acquires information about surrounding buildings other than the target building in the vicinity of the target building, and the prediction unit predicts whether or not light pollution may occur based on whether or not the surrounding buildings exist in a first range to which reflected light can reach when sunlight is incident on the target member at a first angle, if the acquisition result of the first glossiness exceeds the first threshold in the light pollution prediction.

[0012] In claim 5, the prediction unit predicts whether or not light pollution may occur in the light pollution prediction, based on whether or not the surrounding buildings exist in the second range to which reflected light can reach when sunlight is incident on the target member at the second angle, if the acquisition result of the second glossiness exceeds the second threshold.

[0013] In claim 6, the prediction unit predicts that light pollution may occur if at least one of the following conditions is met in the light pollution prediction: first condition that the acquisition result of the first glossiness exceeds the first threshold and the surrounding buildings are located within the first range, or second condition that the acquisition result of the second glossiness exceeds the second threshold and the surrounding buildings are located within the second range.

[0014] In claim 7, the first angle is set to 60° and the second angle is set to 85°.

[0015] Claim 8 provides a light pollution prediction method for predicting the occurrence of light pollution caused by light reflected by a target member installed on the roof of a target building, comprising: a gloss acquisition step of acquiring a first gloss level, which is the gloss level of the target member when the angle of incidence is a first angle, and a second gloss level, which is the gloss level of the target member when the angle of incidence is a second angle different from the first angle; and a prediction step of predicting whether or not light pollution is likely to occur based on the acquisition results of the first gloss level and the second gloss level. [Effects of the Invention]

[0016] The present invention provides the following effects:

[0017] Claim 1 allows for the appropriate prediction of the occurrence of light pollution.

[0018] Claim 2 makes it possible to predict whether or not light pollution will occur due to the solar power generation module.

[0019] In claim 3, it is possible to appropriately predict that no light damage will occur.

[0020] In claim 4, when the first glossiness is relatively large, it is possible to appropriately predict whether light damage will occur.

[0021] In claim 5, when the second glossiness is relatively large, it is possible to appropriately predict whether light damage will occur.

[0022] In claim 6, it is possible to appropriately predict that incident light damage will occur.

[0023] In claim 7, it is possible to make it easier to obtain the first glossiness and the second glossiness.

[0024] In claim 8, it is possible to appropriately predict the occurrence of light damage.

Brief Description of the Drawings

[0025] [Figure 1] A side schematic view showing how sunlight incident on a roof at an incident angle of 60° is reflected. [Figure 2] A side schematic view showing how sunlight incident on a roof at an incident angle of 85° is reflected. [Figure 3] A graph showing the results of measuring glossiness. [Figure 4] A block diagram showing the configuration of a light damage prediction system according to an embodiment of the present invention. [Figure 5] A flowchart showing the processing until it is determined whether the glossiness when the incident angle is 60° is below the threshold value in the light damage occurrence prediction process. [Figure 6] A flowchart showing the processing when the glossiness when the incident angle is 60° exceeds the threshold value in the light damage occurrence prediction process. [Figure 7] A flowchart showing the processing when the glossiness when the incident angle is 60° is below the threshold value in the light damage occurrence prediction process. [Figure 8](a) Plan view showing area A. (b) Section view of A1-A1. [Figure 9] (a) Plan view showing area B. (b) Section view of A2-A2. [Figure 10] A diagram showing the relationship between predicted light pollution occurrences and gloss levels. [Figure 11] A side cross-sectional view showing area A when solar power generation modules are laid with a slope. [Modes for carrying out the invention]

[0026] The following describes a light pollution prediction system 100 according to one embodiment of the present invention.

[0027] One embodiment of the present invention, the light pollution prediction system 100, predicts the occurrence of light pollution caused by sunlight reflected by various components (such as photovoltaic power generation modules 11) installed on the roof of a building irradiating other buildings. Below, we will first explain the type of light pollution that the light pollution prediction system 100 is intended to predict, using Figure 1.

[0028] Figure 1 shows a building on which a solar power generation module 11 is installed on the roof (hereinafter referred to as "Target Building 10"), and other buildings built around the Target Building (hereinafter referred to as "Surrounding Buildings 20"). The solar power generation module 11 generates electricity by converting solar energy into electrical energy. The solar power generation module 11 comprises a plurality of cells (not shown). The surface of the solar power generation module 11 in this embodiment is formed of a glass-based material to protect the cells. Note that the surface refers to the surface on which sunlight is incident.

[0029] When sunlight reflected from the roof (photovoltaic modules 11) of the target building 10 shines onto the surrounding building 20, it may cause discomfort (glare) to the residents of the surrounding building 20. In this embodiment, this discomfort to the residents of the surrounding building 20 caused by sunlight reflected from the roof (photovoltaic modules 11) of the target building 10 is assumed to be "light pollution."

[0030] One piece of information that can be used to determine the light reflection characteristics of various materials is glossiness. Glossiness is an index that indicates the degree of gloss on an object's surface. Specifically, glossiness is expressed as a percentage of how much light is specularly reflected on the object's surface, using the specularly reflected light from a reference surface (a glass surface with a refractive index of 1.567) as a reference. The higher the glossiness of a material, the more easily light is reflected from its surface.

[0031] Note that the imaginary line L shown in Figure 1 is a line perpendicular to the surface of the photovoltaic power generation module 11. Figure 1 also shows how sunlight irradiated onto the surface of the photovoltaic power generation module 11 at an angle D1 of 60° (incident angle of 60°) relative to the imaginary line L is specularly reflected.

[0032] Figure 2 also shows how sunlight irradiated onto the surface of the photovoltaic module 11 at an angle D2 of 85° with respect to the imaginary line L is reflected. The glossiness of various materials varies depending on the angle of incidence. Furthermore, how the glossiness changes depending on the angle of incidence differs for each material. For example, some materials have a higher glossiness (light is reflected more easily) at an angle of incidence of 60° (Figure 1) than at an angle of incidence of 85° (Figure 2), while others have a higher glossiness at an angle of incidence of 85° than at an angle of incidence of 60°.

[0033] Figure 3 is a graph showing the results of verifying the relationship between the angle of incidence and glossiness. The details of this verification are explained below. In this verification, the glossiness of the surfaces of the solar power generation module 11 and roofing materials 1 to 4 was measured. In this measurement, the glossiness was measured at angles of incidence of 20°, 60°, and 85°, respectively.

[0034] Roofing materials 1-4 are tiles, slates, etc., laid on the roofs of houses. Roofing materials 1-4 are made of materials other than glass-based materials on their surfaces. Roofing materials 1-4 differ from each other in surface color, etc.

[0035] The graph shown in Figure 3 plots the gloss measurement results from this verification on a graph with the angle of incidence on the horizontal axis and gloss on the vertical axis.

[0036] The gloss level of roofing material 1 differed from that of roofing material 1 at incident angles of 20°, 60°, and 85°, but was 10% or less at all incident angles. The measurement results for roofing materials 2-4 were similar to those for roofing material 1.

[0037] As can be seen from these measurement results, the glossiness of the surfaces of roofing materials 1-4 varies depending on the angle of incidence, but the difference is not significant (less than 10%). Therefore, it is thought that even by measuring the glossiness of the surfaces of roofing materials 1-4 at just one angle of incidence (for example, 60°), it is possible to get a certain degree of understanding of how easily sunlight is reflected by roofing materials 1-4. In this case, even if we predict whether or not light pollution will occur based on the glossiness (ease of light reflection) of roofing materials 1-4 at one angle of incidence, it is thought that there will be no major problems with the accuracy of the prediction.

[0038] On the other hand, the glossiness of the solar power generation module 11 increased with increasing angle of incidence, and the degree of increase (degree of variation) was greater than that of the roofing materials 1-4. In particular, the variation in glossiness was significant when the angle of incidence was 60° and 85° (a variation of nearly 50%). This is thought to be due to the fact that the surface of the solar power generation module 11 is formed of a glass-based material.

[0039] Thus, some components installed on roofs (such as the photovoltaic power generation module 11) exhibit a degree of glossiness that easily varies depending on the angle of incidence. For such components, measuring the glossiness at only one angle of incidence may not adequately convey how easily they reflect sunlight, and therefore may not be able to accurately predict whether or not light pollution will occur.

[0040] Therefore, the light pollution prediction system 100 predicts whether or not light pollution will occur based on multiple gloss levels with different incident angles. This makes it possible to appropriately predict whether or not light pollution will occur when materials (such as photovoltaic power generation modules 11) whose gloss level is easily affected by the incident angle are installed on the roof.

[0041] The configuration of the light pollution prediction system 100 will be described below using Figure 4. The light pollution prediction system 100 mainly comprises a control device 110, an input device 120, and a display device 130.

[0042] The control device 110 is responsible for storing and calculating various types of information. The control device 110 is mainly composed of a processing unit such as a CPU, a storage device such as RAM or ROM, and input / output devices such as I / O. Functionally, the control device 110 includes a target building information acquisition unit 111, a target component information acquisition unit 112, a surrounding building information acquisition unit 113, a storage unit 114, and a prediction unit 115.

[0043] The target building information acquisition unit 111 acquires information about the target building 10. This information includes the location, shape, and other details of the target building 10. The target building information acquisition unit 111 acquires information about the target building 10 that is input using the input device 120, which will be described later.

[0044] The target component information acquisition unit 112 acquires information about components installed on the roof of the target building 10, particularly components that reflect sunlight and may cause light pollution (hereinafter referred to as "target components"). In this embodiment (see Figure 1), the solar power generation module 11 of the target building 10 is assumed to be the target component. Information about the target component (solar power generation module 11) includes the glossiness of the surface of the solar power generation module 11, the inclination angle of the solar power generation module 11 with respect to the horizontal plane, and so on.

[0045] Furthermore, the glossiness of the surface of the solar power generation module 11 includes multiple glossiness levels with different incident angles. In this embodiment, this includes glossiness when the incident angle is 60° (see Figure 1) and glossiness when the incident angle is 85° (see Figure 2). Hereinafter, the glossiness when the incident angle is 60° will be referred to as "60° gloss," and the glossiness when the incident angle is 85° will be referred to as "85° gloss." The target component information acquisition unit 112 acquires information about the solar power generation module 11 that has been input using the input device 120, which will be described later.

[0046] The surrounding building information acquisition unit 113 acquires information about surrounding buildings 20 built around the target building 10. The information about surrounding buildings 20 includes information such as the location and shape of the surrounding buildings 20. The surrounding building information acquisition unit 113 acquires information about surrounding buildings 20 that is input using the input device 120, which will be described later.

[0047] The memory unit 114 stores various types of information. The memory unit 114 can store information acquired by the target building information acquisition unit 111, the target component information acquisition unit 112, and the surrounding building information acquisition unit 113. In addition, the memory unit 114 stores various control programs related to the operation of the light pollution prediction system 100 and other necessary information in advance (or acquired by an appropriate method).

[0048] The prediction unit 115 predicts the occurrence of light pollution by taking various types of information into consideration. More specifically, the prediction unit 115 predicts the occurrence of light pollution based on information acquired by the target building information acquisition unit 111, the target component information acquisition unit 112, and the surrounding building information acquisition unit 113 (stored in the storage unit 114).

[0049] The input device 120 is for inputting various types of information into the control device 110. Specifically, the input device 120 can input information about the target building 10, the solar power generation module 11 (target component), and surrounding buildings 20. The input device 120 can be configured with, for example, a keyboard, touch panel, operation buttons, a scanner, etc. It is also possible to use a gloss sensor for measuring the glossiness of the target building 10, the solar power generation module 11 (target component), etc., as the input device 120.

[0050] The display device 130 is capable of displaying various types of information. Specifically, the display device 130 can display prediction results from the prediction unit 115 of the control device 110. The display device 130 can be configured as, for example, a liquid crystal monitor, a touch panel, a portable terminal, or the like.

[0051] Next, referring to Figures 5 to 7, the procedure for predicting the occurrence of light pollution using the light pollution prediction system 100 (light pollution prediction method) will be described. In this embodiment, the prediction unit 115 performs the processes shown in Figures 5 to 7 to predict whether or not light pollution will occur due to the solar power generation module 11. For the sake of explanation, the processes shown in Figures 5 to 7 will be referred to as the "light pollution occurrence prediction process" below. The light pollution occurrence prediction process is performed at appropriate timings.

[0052] In this embodiment, the light pollution prediction process is performed before the installation of the photovoltaic power generation module 11. The photovoltaic power generation module 11 is planned to be installed on the roof of the target building 10 shown in Figure 8. Figure 8(a) is a plan view of the target building 10, and Figure 8(b) is a side cross-sectional view of the target building 10. As shown in Figure 8, the target building 10 is a building with a flat roof, and the photovoltaic power generation module 11 is planned to be installed on a part of this flat roof (area R11 shown in Figure 8(a)). The photovoltaic power generation module 11 is planned to be installed facing upwards (angle of inclination with respect to the horizontal plane is 0°). Hereafter, the area in which the photovoltaic power generation module 11 is planned to be installed will be referred to as "installation area R11". The contents of the light pollution prediction process will be explained below.

[0053] As shown in Figure 5, when the light pollution prediction process is started, various information necessary for predicting the occurrence of light pollution is input to the control device 110. In this embodiment, information regarding the target building 10 and surrounding buildings 20 (architectural structures), and information regarding the solar power generation modules 11 (installation area R11) are input to the control device 110 via the input device 120. For example, the arrangement and height of the target building 10, surrounding buildings 20, and solar power generation modules 11 (installation area R11), as well as the glossiness and tilt angle of the solar power generation modules 11 relative to the horizontal direction, are input. The information input to the control device 110 is stored in the storage unit 114.

[0054] When the light pollution prediction process is started, the prediction unit 115 moves to step S10. In step S10, the prediction unit 115 grasps information about the building (target building 10, surrounding buildings 20) and the installation conditions for the solar power generation module 11. At this time, the prediction unit 115 acquires the layout of the target building 10 based on the information about the target building 10 stored in the memory unit 114. The prediction unit 115 also acquires the layout of the surrounding buildings 20 based on the information about the surrounding buildings 20 stored in the memory unit 114. The prediction unit 115 also acquires the layout and inclination angle of the solar power generation module 11 (installation range R11) based on the information about the solar power generation module 11 stored in the memory unit 114. After performing the processing in step S10, the prediction unit 115 moves to step S20.

[0055] In step S20, the prediction unit 115 determines the 60° gloss and 85° gloss (glossiness when the incident angle is 60° and 85°) of the solar power generation module 11. At this time, the prediction unit 115 obtains the 60° gloss, etc., based on the information about the solar power generation module 11 stored in the storage unit 114. After performing the processing in step S20, the prediction unit 115 proceeds to step S30.

[0056] In step S30, if the prediction unit 115 determines that the 60° gloss obtained in step S20 is less than or equal to a predetermined threshold (7) (step S30: YES), it proceeds to step S110 shown in Figure 7. On the other hand, if the prediction unit 115 determines that the 60° gloss exceeds a predetermined threshold (step S30: NO), it proceeds to step S40 shown in Figure 6.

[0057] Note that the threshold used in step S30 is assumed to be 7, but this is just an example and can be set as appropriate.

[0058] The process in steps S40 to S100 shown in Figure 6 predicts whether or not light pollution will occur when the 60° gloss exceeds the threshold (7). In steps S40 to S100, the prediction unit 115 determines whether or not light pollution will occur due to the influence of 60° gloss. If the prediction unit 115 determines that light pollution will not occur due to the influence of 60° gloss, it then determines whether or not light pollution will occur due to the influence of 85° gloss. This will be explained in detail below.

[0059] In step S40, the prediction unit 115 determines whether there are any surrounding buildings 20 that can routinely view the solar power generation module 11 (installation area R11) within the range A shown in Figure 8. The prediction unit 115 sets range A based on the information (arrangement, etc.) about the target building 10 and the solar power generation module 11 acquired in step S20. For example, the prediction unit 115 sets range A according to the arrangement of the target building 10, the inclination angle of the solar power generation module 11 (installation area R11), etc. Then, the prediction unit 115 determines whether there are surrounding buildings 20 within range A based on the information (arrangement, etc.) about the surrounding buildings 20 acquired in step S20. In this embodiment, the prediction unit 115 determines whether any part of the surrounding building 20 that is visible to the outside is within range A. Examples of parts that are visible to the outside include windows, balconies, and exterior corridors. The range A will be explained below using Figure 8.

[0060] Range A is the range to which reflected light can reach a solar power generation module 11 when sunlight is incident at an incident angle of 60°, provided that the 60° gloss exceeds the threshold (7). If there are surrounding buildings 20 (windows, etc.) within Range A, it may cause discomfort to residents of those surrounding buildings 20. In this embodiment, it is assumed that sunlight is incident on the solar power generation module 11 at an incident angle of 60° when the solar altitude is 30°. This is just an example, and the solar intensity when sunlight is incident on the solar power generation module 11 at an incident angle of 60° is not limited to this embodiment. For example, the solar altitude may be set including the roof slope. If the solar power generation module 11 is installed on a roof with a roof slope of 10°, it may be assumed that sunlight is incident on the solar power generation module 11 at an incident angle of 60° when the solar intensity is 40°.

[0061] First, let's refer to Figure 8(a) and explain the area A in the plan view. Area A is set to surround the target building 10 in the plan view. Also, the southern side of area A is set to be a narrower area than the northern side. In this embodiment, area A in the plan view is the area enclosed by the dashed lines X1 to X5. Note that in Figure 8(a), the area enclosed by the diagonal lines is shown to be area A. Area A in the plan view originally includes the target building 10, but for the sake of explanation, the diagonal lines are omitted in the area that overlaps with the target building 10.

[0062] The virtual line X1 is a straight line drawn east-west from the northern end of the laying area R11, at a predetermined distance to the north, in a plan view. This predetermined distance may be set appropriately based on empirical rules or other factors. In this embodiment, the predetermined distance is set to 300m.

[0063] Virtual line X2 is a straight line drawn north-south from the eastern end of the laying area R11, at a distance of 300m (a predetermined distance) to the east, in a plan view. Virtual line X3 is a straight line drawn north-south from the western end of the laying area R11, at a distance of 300m to the west, in a plan view.

[0064] Virtual line X4 is a straight line drawn from the southeast corner of the laying area R11 towards the southwest (a direction tilted 30° south relative to west) in a plan view. Virtual line X5 is a straight line drawn from the southwest corner of the laying area R11 towards the southeast (a direction tilted 30° south relative to east) in a plan view. The direction (angle) of these virtual lines X4 and X5 is determined based on the position of the sun at sunrise and sunset on the summer solstice in Japan (more specifically, Sapporo, Tokyo, Osaka, and Okinawa).

[0065] Area A is the area enclosed by virtual lines X1 to X5 in a plan view, and is the area above the installation area R11 (the surface of the solar power generation module 11). Furthermore, the vertical range of Area A is set so as to cover the trajectory of sunlight reflected by the installation area R11 at a solar altitude of 30°.

[0066] For example, as shown in Figure 8(b), in a side cross-sectional view (A1-A1 cross-sectional view), range A is set to extend upward as it moves away from the laying range R11. In Figure 8(b), range A is the area enclosed by the imaginary lines X11 to X15.

[0067] Virtual line X11 is a straight line in a side view that passes through the installation area R11 (the top surface of the solar power generation module 11) and is parallel to the installation area R11. Virtual line X12 is a straight line drawn vertically from the eastern end of the installation area R11 at a predetermined distance (300m) to the east in a side view. Virtual line X13 is a straight line drawn vertically from the western end of the installation area R11 at a predetermined distance (300m) to the west in a side view.

[0068] The virtual line X14 is a straight line that extends upward (tilted by 30° with respect to the horizontal) from the eastern end of the installation area R11 towards the west in a side view. The virtual line X15 is a straight line that extends upward (tilted by 30° with respect to the horizontal) from the western end of the installation area R11 towards the east in a side view. The direction (angle) of these virtual lines X14 and X15 is determined based on the position of the sun when sunlight is incident on the solar power generation module 11 at an incidence angle of 60°. Specifically, it is determined based on the height of the sun when the solar altitude is 30°.

[0069] In step S40 shown in Figure 6, the prediction unit 115 checks the positional relationship between the arrangement of surrounding buildings 20 (windows, etc.) and the above-mentioned range A, and determines whether or not surrounding buildings 20 are located within range A. If surrounding buildings 20 are located within range A, when the sunlight intensity is 30°, the sunlight reflected by the solar power generation module 11 may cause discomfort to residents of surrounding buildings 20 (light pollution may occur due to the effect of 60° gloss).

[0070] Therefore, if the prediction unit 115 determines in step S40 that there are surrounding buildings 20 within range A (step S40: YES), it proceeds to step S50 and predicts that there is a risk of light pollution occurring. The prediction unit 115 then displays the prediction result on the display device 130. After performing the processing in step S50, the prediction unit 115 terminates the light pollution prediction process.

[0071] On the other hand, if the prediction unit 115 determines in step S40 that there are no surrounding buildings 20 within range A (step S40: NO), it proceeds to step S60. The prediction unit 115 then determines whether or not light pollution will occur due to the influence of 85° gloss.

[0072] More specifically, in step S60, the prediction unit 115 proceeds to step S70 if the 85° gloss obtained in step S20 is less than or equal to a predetermined threshold (10) (step S60: YES). On the other hand, if the 85° gloss exceeds a predetermined threshold (step S60: NO), the prediction unit 115 proceeds to step S80.

[0073] Note that the gloss threshold in step S60 is set to 10, but this is just an example and can be set as appropriate.

[0074] In step S70, the prediction unit 115 predicts that there is no risk of light pollution occurring and displays the prediction result on the display device 130. After performing the process in step S70, the prediction unit 115 terminates the light pollution prediction process.

[0075] In step S80, the prediction unit 115 determines whether there are any surrounding buildings 20 that can routinely view the solar power generation module 11 (installation area R11) within the range B shown in Figure 9. In Figure 9, the range B as seen from above is shown in Figure 9(a), and the side cross-section of the range B is shown in Figure 9(b). The range B will be explained below using Figure 9.

[0076] Range B is the range to which reflected light can reach a solar power generation module 11 when sunlight is incident at an incident angle of 85°, provided that the 85° gloss exceeds the threshold (10). If there are surrounding buildings 20 (windows, etc.) within range B, it may cause discomfort to residents of those surrounding buildings 20. In this embodiment, it is assumed that sunlight is incident on the solar power generation module 11 at an incident angle of 85° when the solar altitude is 5°. This is just an example, and the solar intensity when sunlight is incident on the solar power generation module 11 at an incident angle of 85° is not limited to this embodiment. For example, the solar altitude may be set including the roof slope. If the solar power generation module 11 is installed on a roof with a roof slope of 10°, it may be assumed that sunlight is incident on the solar power generation module 11 at an incident angle of 85° when the solar intensity is 15°.

[0077] Range B is set to cover the trajectory of sunlight reflected by the laid-out range R11 at a solar altitude of 5°. When the solar altitude is 5° (see Figure 2), the sun is located lower than when the solar altitude is 30° (see Figure 1). Also, the azimuth angle of the sun is different when the solar altitude is 5° and 30°. Range B at a solar altitude of 5° is set to be a narrower version of range A shown in Figure 8, taking into account this difference in the sun's position compared to when the solar altitude is 30°. Below, we will explain range B, focusing on the differences from range A.

[0078] First, let's refer to Figure 8(a) and explain the area B in the plan view. The northern side of area B is set in the same way as the southern side of area B in the plan view (symmetrical in the north-south direction). Area B is the area enclosed by the virtual lines X2~X5 (see Figure 8(a)) used in the case of area A, and the virtual lines X6·X7.

[0079] Virtual line X6 is a straight line drawn from the northeast corner of the laying area R11 towards the northwest (a direction tilted 30° north relative to west) in a plan view. Virtual line X7 is a straight line drawn from the northwest corner of the laying area R11 towards the northeast (a direction tilted 30° north relative to east) in a plan view. By defining the northern side of area B with virtual lines X6 and X7, area B can be appropriately set according to the position (azimuth angle) of the sun when the solar altitude is 5°.

[0080] Range B is the area enclosed by the virtual lines X2 to X7 in a plan view, and is the area above the installation area R11 (the surface of the solar power generation module 11). Furthermore, the vertical range of Range B is set so as to cover the trajectory of sunlight reflected from the installation area R11 at a solar altitude of 5°.

[0081] For example, as shown in Figure 9(b), in a side cross-sectional view (A2-A2 cross-sectional view), range B is set to extend upward as it moves away from the laying range R11. In Figure 9(b), range A is the area enclosed by the imaginary lines X11~X13 (see Figure 8(b)) used in the case of range A, and the imaginary lines X16·X17.

[0082] The imaginary line X16 is a straight line that extends upward from the eastern end of the laying area R11 towards the west in a side view. The inclination of imaginary line X16 with respect to the horizontal is smaller than the inclination of imaginary line X14 (see Figure 8(b)) which defines area A (30°). The inclination of imaginary line X16 with respect to the horizontal is 5°.

[0083] The imaginary line X17 is a straight line that extends upward from the western end of the laying area R11 towards the east in a side view. The angle of inclination of imaginary line X17 with respect to the horizontal is smaller than the angle of inclination of imaginary line X15 (see Figure 8(b)) which defines area A (30°). The angle of inclination of imaginary line X17 with respect to the horizontal is 5°.

[0084] In this way, the prediction unit 115 can appropriately set the range to which sunlight reflected by the solar power generation module 11 can reach, taking into account the height and azimuth of the sun, by setting ranges A and B according to the altitude of the sun.

[0085] In step S80 shown in Figure 6, if the prediction unit 115 determines that there are surrounding buildings 20 within the aforementioned range B (step S80: YES), it proceeds to step S90 and predicts that there is a risk of light pollution occurring. The prediction unit 115 then displays the prediction result on the display device 130. After performing the processing in step S90, the prediction unit 115 terminates the light pollution prediction process.

[0086] On the other hand, if the prediction unit 115 determines that there are no surrounding buildings 20 within range B (step S90: NO), it proceeds to step S100 and predicts that there is no risk of light pollution occurring. The prediction unit 115 then displays the prediction result on the display device 130. After performing the processing in step S100, the prediction unit 115 terminates the light pollution prediction process.

[0087] The processes described in steps S40 to S100 are for when the 60° gloss of the photovoltaic module 11 obtained in step S20 exceeds a predetermined threshold (7) (step S30: NO shown in Figure 5). On the other hand, if the 60° gloss is less than or equal to the predetermined threshold (7) (step S30: YES), the prediction unit 115 proceeds to step S110 shown in Figure 7.

[0088] Here, if the 60° gloss is below a predetermined threshold, sunlight is less likely to be reflected even when sunlight is incident on the photovoltaic power generation module 11 at an incident angle of 60°. In this case, even if there are surrounding buildings 20 within the range A shown in Figure 8, at an incident angle of 60° (solar altitude of 30°), it is considered unlikely that sunlight reflected by the photovoltaic power generation module 11 will cause discomfort to the residents of the surrounding buildings 20 (no light pollution will occur). For this reason, if the 60° gloss is below a predetermined threshold, the prediction unit 115 checks whether there is a possibility of light pollution occurring due to the influence of 85° gloss in the processing from step S110 onwards shown in Figure 7. At this time, the prediction unit 115 performs steps S110 to S150, which correspond to the steps S60 to S100 (see Figure 6) described above.

[0089] Note that the processing content in steps S110 to S150 is the same as that in steps S60 to S100 described above. Therefore, the detailed explanation of the processing content will be omitted below, and the flow of the light pollution prediction processing (processing from step S110 onwards) will be briefly explained.

[0090] In step S110 shown in Figure 7, if the prediction unit 115 determines that the 85° gloss obtained in step S20 is less than or equal to a predetermined threshold (10) (step S110: YES), it proceeds to step S120 and predicts that there is no risk of light pollution occurring.

[0091] On the other hand, if the 85° gloss exceeds a predetermined threshold (10) in step S110 (step S110: NO), the prediction unit 115 determines whether or not there are surrounding buildings 20 within range B (see Figure 9) (step S130). If the prediction unit 115 determines that there are surrounding buildings 20 within range B, it predicts that there is a risk of light pollution occurring (step S130: YES, step S140). On the other hand, if the prediction unit 115 determines that there are no surrounding buildings 20 within range B, it predicts that there is no risk of light pollution occurring (step S130: NO, step S150).

[0092] Figure 10 shows the processing details (prediction results, etc.) of the prediction unit 115 in the light pollution prediction process, and the relationship between the 60° gloss and 85° gloss of the solar power generation module 11. In Figure 10, the processing details of the prediction unit 115 are shown on a graph with 60° gloss on the horizontal axis and 85° gloss on the vertical axis.

[0093] The prediction unit 115 predicts that there is no risk of light pollution occurring if the 60° gloss and 85° gloss are both below the threshold (7, 10) through light pollution prediction processing (step S120 shown in Figure 7). By confirming that multiple gloss levels are relatively low, the prediction unit 115 can appropriately understand that the photovoltaic power generation module 11 does not reflect sunlight easily. Therefore, it can appropriately predict that there is no risk of light pollution occurring due to the photovoltaic power generation module 11.

[0094] Furthermore, if the 60° gloss exceeds the threshold (step S30:NO shown in Figure 5), the prediction unit 115 checks whether there are surrounding buildings 20 within the range A corresponding to the 60° gloss (step S40 shown in Figure 6).

[0095] Furthermore, the prediction unit 115 checks whether there are surrounding buildings 20 within the range B corresponding to the 85° gloss if the 85° gloss exceeds the threshold (step S60:NO shown in Figure 6, step S110:NO shown in Figure 7) (steps S80~S100, S130~S150).

[0096] The prediction unit 115 can evaluate whether light pollution is likely to occur when sunlight is incident on the photovoltaic module 11 at a relatively high incident angle, by checking range A or range B corresponding to the gloss level that exceeds the threshold. This makes it possible to appropriately predict whether light pollution will occur due to a component whose gloss level is prone to fluctuation (such as the photovoltaic module 11).

[0097] For example, if the glossiness is prone to fluctuations depending on the angle of incidence, light pollution may occur only within a certain range of the angle of incidence of sunlight on the photovoltaic power generation module 11. For example, light pollution may occur only within the range of an angle of incidence of 85° or more.

[0098] The prediction unit 115 evaluates whether light pollution is likely to occur at each of several incident angles (60°, 85°), making it possible to predict that light pollution will occur only within a certain range of incident angles. For example, the prediction unit 115 can predict that light pollution will not occur at an incident angle of 60°, but will occur at an incident angle of 85°. Therefore, it is possible to appropriately predict whether or not light pollution will occur due to a material whose glossiness is prone to fluctuation.

[0099] In this case, glossiness is generally measured at incident angles of 60° and 85°. For this reason, many commercially available glossiness sensors are capable of measuring 60° gloss and 85° gloss. In this embodiment, whether or not light pollution will occur is predicted based on 60° gloss and 85° gloss (steps S30 to S150). By predicting the occurrence of light pollution based on glossiness corresponding to common incident angles (60°, 85°), the glossiness necessary for predicting the occurrence of light pollution can be obtained without using a special glossiness sensor.

[0100] Furthermore, if the prediction unit 115 predicts that light pollution may occur (steps S50, S90, S140), it may notify the user of measures to suppress the occurrence of light pollution in addition to the prediction result. For example, if the prediction unit 115 predicts that light pollution may occur due to the effect of 60° gloss (step S50), it may display information on the display device 130 recommending that the 60° gloss be set to 7 or less. By notifying the user of information indicating the angle of incidence and glossiness as measures to suppress the occurrence of light pollution, appropriate measures against light pollution can be taken.

[0101] As described above, the light pollution prediction system 100 according to this embodiment is a light pollution prediction system 100 that predicts the occurrence of light pollution caused by light reflected by a target member (photovoltaic power generation module 11) installed on the roof of a target building 10, and comprises a target member information acquisition unit 112 (glossiness acquisition unit) that acquires a first glossiness (60° gloss), which is the glossiness of the target member when the angle of incidence is a first angle (60°), and a second glossiness (85° gloss), which is the glossiness of the target member when the angle of incidence is a second angle (85°) different from the first angle, and a prediction unit 115 that performs a light pollution prediction (light pollution occurrence prediction processing) that predicts whether or not light pollution is likely to occur based on the acquisition results of the first glossiness and the second glossiness.

[0102] By configuring it in this way, it is possible to evaluate whether or not light pollution may occur based on multiple gloss levels with different incident angles (60° gloss and 85° gloss). For this reason, even when installing a component (photovoltaic module 11) on a roof whose gloss level easily changes depending on the incident angle, it is possible to appropriately predict whether or not light pollution will occur.

[0103] Furthermore, the target component is a solar power generation module 11.

[0104] By configuring it in this way, it is possible to predict whether or not light pollution will occur due to the solar power generation module 11.

[0105] Furthermore, in the light pollution prediction (light pollution occurrence prediction process), the prediction unit 115 predicts that there is no possibility of light pollution occurring if the acquisition result of the first glossiness is less than or equal to the first threshold (7) (step S30: YES) and the acquisition result of the second glossiness is less than or equal to the second threshold (10) (step S110: YES).

[0106] By configuring it in this way, it is possible to understand that the target component (photovoltaic power generation module 11) is a component that does not easily reflect sunlight, and to appropriately predict that no light pollution will occur.

[0107] Furthermore, the light pollution prediction system 100 further comprises a surrounding building information acquisition unit 113 (building information acquisition unit) that acquires information about surrounding buildings 20 different from the target building 10 in the vicinity of the target building 10, and the prediction unit 115, in the light pollution prediction (light pollution occurrence prediction processing), predicts whether or not light pollution may occur based on whether or not the surrounding buildings 20 are located in a first range (range A) to which reflected light can reach when sunlight is incident on the target member (photovoltaic power generation module 11) at a first angle (60°) (steps S40 to S100).

[0108] By configuring it in this way, it is possible to appropriately predict whether or not light pollution will occur due to the influence of the first glossiness (60° gloss) when the first glossiness is relatively high.

[0109] Furthermore, in the light pollution prediction (light pollution occurrence prediction process), if the acquisition result of the second glossiness (85° gloss) exceeds the second threshold (10) (step S60: NO, step S110: NO), the prediction unit 115 predicts whether or not light pollution may occur based on whether or not the surrounding buildings 20 exist in the second range (range B) to which reflected light can reach when sunlight is incident on the target member (photovoltaic power generation module 11) at the second angle (85°) (steps S80~S100, S130~S150).

[0110] By configuring it in this way, it is possible to appropriately predict whether or not light pollution will occur due to the influence of the second glossiness (85° gloss) when the second glossiness is relatively high.

[0111] Furthermore, in the light pollution prediction (light pollution occurrence prediction process), the prediction unit 115 predicts that light pollution may occur if at least one of the following conditions is met: a first condition (step S30: NO, step S40: YES) where the acquisition result of the first glossiness (60° gloss) exceeds the first threshold and the surrounding buildings 20 are located in the first range (range A), or a second condition (step S60: NO, step S80: YES, step S110: NO, step S130: YES) where the acquisition result of the second glossiness (85° gloss) exceeds the second threshold and the surrounding buildings 20 are located in the second range (range B) (see Figures 8 and 9).

[0112] By configuring it in this way, it is possible to accurately predict when light pollution will occur. For example, if light pollution occurs when sunlight is incident on the target component at only one of the two angles, the first angle (60°) or the second angle (80°), then it is possible to predict that light pollution may occur.

[0113] Furthermore, the first angle is set to 60°, and the second angle is set to 85°.

[0114] By configuring it in this way, it becomes easier to obtain the first and second gloss levels.

[0115] Furthermore, the present invention relates to a light pollution prediction method for predicting the occurrence of light pollution caused by light reflected by a target component (photovoltaic power generation module 11) installed on the roof of a target building 10, comprising: a glossiness acquisition step (step S20) in which a first glossiness (60° gloss) of the target component is obtained when the angle of incidence is a first angle (60°), and a second glossiness (85° gloss) of the target component is obtained when the angle of incidence is a second angle (85°) different from the first angle; and a prediction step (steps S30 to S150) in which light pollution is predicted to occur based on the acquisition results of the first glossiness and the second glossiness.

[0116] By configuring it in this way, it is possible to accurately predict whether or not light pollution will occur.

[0117] Furthermore, the target member information acquisition unit 112 in this embodiment is one form of the glossiness acquisition unit according to the present invention. Furthermore, the surrounding building information acquisition unit 113 according to this embodiment is one form of the building information acquisition unit according to the present invention.

[0118] Although embodiments of the present invention have been described above, the present invention is not limited to the above configuration, and various modifications are possible within the scope of the invention as described in the claims.

[0119] For example, in this embodiment, a solar power generation module 11 is given as an example of a target component (a component that reflects sunlight and can cause light pollution), but the target components envisioned by the present invention are not limited to this. That is, various components installed on the roof of the target building 10 can be used as target components. Specifically, it is also possible to predict the occurrence of light pollution by using roofing materials and the like as target components. It is considered that for components whose glossiness does not change easily with respect to the angle of incidence, it is often meaningless to evaluate the possibility of light pollution occurring at multiple angles of incidence (when the judgment result for the occurrence of light pollution is the same regardless of the angle of incidence). For this reason, it is desirable to predict whether or not light pollution will occur for components whose glossiness changes easily with respect to the angle of incidence. For example, it is desirable to predict whether or not light pollution will occur for components whose surface is made of a glass-like material.

[0120] In this embodiment, the presence or absence of light pollution is predicted based on the glossiness when the incident angle is 60° (60° gloss) and when the incident angle is 85° (85° gloss). However, the magnitude of the incident angle is not limited to this and can be appropriately changed depending on the target member, etc.

[0121] Furthermore, although the prediction unit 115 predicts whether or not light pollution will occur based on two gloss levels with different incident angles, the number of gloss levels used in predicting the occurrence of light pollution is not limited to this embodiment, as long as there are two or more.

[0122] Furthermore, the prediction unit 115 checks whether the 60° gloss and 85° gloss exceed the threshold, and then checks whether there are surrounding buildings 20 within ranges A and B. However, this is just one example, and the order in which ranges A and B and the threshold are checked is not limited to this embodiment. For example, the prediction unit 115 may check whether there are surrounding buildings 20 within ranges A and B, and then check whether the 60° gloss and 85° gloss exceed the threshold.

[0123] Furthermore, the prediction unit 115 predicts that light pollution may occur if there are surrounding buildings 20 within range A (step S40: YES, step S50), but the processing when there are surrounding buildings 20 within range A is not limited to this embodiment. For example, even if there are surrounding buildings 20 within range A, the prediction unit 115 may check whether the 85° gloss is below the threshold (10) and whether there are surrounding buildings 20 within range B. This allows the prediction unit 115 to determine at which angle, 60° or 85°, there is a risk of light pollution occurring. The prediction unit 115 may also display the result of this determination on the display device 130.

[0124] Furthermore, the prediction unit 115 checks whether there are surrounding buildings 20 within range A or range B when both the 60° gloss and 85° gloss exceed a threshold (see Figure 10), but the processing when both gloss levels exceed a threshold is not limited to this. For example, the prediction unit 115 may predict that there is a risk of light pollution occurring when both gloss levels exceed a threshold, regardless of whether there are surrounding buildings 20 within ranges A and B.

[0125] In this embodiment, range A is set assuming that the solar power generation module 11 is laid in an upward-facing position (see Figure 8), but range A can be set appropriately depending on the position of the solar power generation module 11. For example, when the solar power generation module 11 is laid in an inclined position with respect to the horizontal direction, as shown in the laying range R11 in Figure 11, the lower end of range A may be defined by a virtual line X18 that is inclined with respect to the horizontal direction. The virtual line X18 shown in Figure 11 is parallel to the laying range R11 in a side view and passes through the laying range R11. By setting the lower end of range A by the virtual line X18 in this way, range A is set three-dimensionally above the plane that is inclined with respect to the horizontal plane (the extended plane that reflects the inclination angle of the laying range R11).

[0126] In this embodiment, range B is set assuming that the solar power generation module 11 is installed in an upward-facing position (see Figure 9), but range B can be set appropriately according to the orientation of the solar power generation module 11, similar to range A.

[0127] Furthermore, in this embodiment, the presence or absence of surrounding buildings 20 within ranges A and B is determined by considering the height of ranges A and B (see Figures 8(b) and 9(b)). However, this is merely an example, and the method for determining whether or not surrounding buildings 20 are within ranges A and B is not limited to this embodiment. For example, it is also possible to omit setting the height of ranges A and B and determine whether or not surrounding buildings 20 are within ranges A and B in a plan view.

[0128] Furthermore, in this embodiment, light pollution prediction is mainly performed based on various information input using the input device 120, but the present invention is not limited to this, and it is also possible to perform light pollution prediction using information automatically acquired via the Internet (for example, map information, etc.). [Explanation of Symbols]

[0129] 10 Target Buildings 11 Solar power generation modules 100 Light Pollution Prediction System 112 Target component information acquisition unit 115 Prediction Section

Claims

1. A light pollution prediction system that predicts the occurrence of light pollution caused by light reflected by a target component installed on the roof of a target building, A glossiness acquisition unit that acquires a first glossiness, which is the glossiness of the target member when the angle of incidence is a first angle, and a second glossiness, which is the glossiness of the target member when the angle of incidence is a second angle different from the first angle, A prediction unit that performs light pollution prediction based on the acquisition results of the first glossiness and the second glossiness predicts whether or not light pollution may occur, Equipped with, Light pollution prediction system.

2. The aforementioned target member is Solar power generation module, The light pollution prediction system according to claim 1.

3. The prediction unit, In the light pollution prediction, if the acquisition result of the first glossiness is below the first threshold and the acquisition result of the second glossiness is below the second threshold, it is predicted that there is no possibility of light pollution occurring. A light pollution prediction system according to claim 1 or claim 2.

4. The system further comprises a building information acquisition unit that acquires information about surrounding buildings other than the target building in the vicinity of the aforementioned target building. The prediction unit, In the light pollution prediction, if the acquisition result of the first glossiness exceeds the first threshold, the system predicts whether or not light pollution may occur based on whether or not there are surrounding buildings in the first range to which reflected light can reach when sunlight is incident on the target member at a first angle. The light pollution prediction system according to claim 3.

5. The prediction unit, In the light pollution prediction, if the acquisition result of the second glossiness exceeds the second threshold, the system predicts whether or not light pollution may occur based on whether or not there are surrounding buildings in the second range to which reflected light can reach when sunlight is incident on the target member at the second angle. The light pollution prediction system according to claim 4.

6. The prediction unit, In the light pollution prediction, if at least one of the following conditions is met, it is predicted that light pollution may occur: first condition that the acquisition result of the first glossiness exceeds the first threshold and the surrounding buildings are located within the first range, or second condition that the acquisition result of the second glossiness exceeds the second threshold and the surrounding buildings are located within the second range. The light pollution prediction system according to claim 5.

7. The first angle is, Set to 60°, The aforementioned second angle is, Set to 85° A light pollution prediction system according to claim 1 or claim 2.

8. A light pollution prediction method that predicts the occurrence of light pollution caused by light reflected by a target component installed on the roof of a target building, A glossiness acquisition step that acquires a first glossiness, which is the glossiness of the target member when the angle of incidence is a first angle, and a second glossiness, which is the glossiness of the target member when the angle of incidence is a second angle different from the first angle, A prediction step that predicts whether or not light pollution may occur based on the acquisition results of the first gloss and the second gloss, Equipped with, Light pollution prediction methods.

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