WO2013105244A1 - Shadow location predict system and shadow location predict method - Google Patents

Shadow location predict system and shadow location predict method Download PDF

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Publication number
WO2013105244A1
WO2013105244A1 PCT/JP2012/050473 JP2012050473W WO2013105244A1 WO 2013105244 A1 WO2013105244 A1 WO 2013105244A1 JP 2012050473 W JP2012050473 W JP 2012050473W WO 2013105244 A1 WO2013105244 A1 WO 2013105244A1
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Prior art keywords
cloud
shadow
cloud detection
prediction
detection devices
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PCT/JP2012/050473
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French (fr)
Japanese (ja)
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浩太 今井
鶴貝　満男
泰崇 木村
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株式会社日立製作所
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Priority to PCT/JP2012/050473 priority Critical patent/WO2013105244A1/en
Publication of WO2013105244A1 publication Critical patent/WO2013105244A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/12—Sunshine duration recorders

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  • the present invention relates to a shadow position prediction system and a shadow position prediction method for predicting the position of a cloud shadow.
  • the amount of power generation of the solar power generation device is determined by the amount of light incident on the solar panel. Therefore, the output of the photovoltaic power generator varies depending on the weather change. When the output of the solar power generation device fluctuates, the operation of the device using the power generation from the solar power generation device becomes unstable.
  • Patent Document 1 Patent Document 2
  • a camera capable of imaging the whole sky is installed, and cloud distribution and cloud movement are detected from an image captured by the camera.
  • the distribution of clouds at a predetermined future time point is predicted to predict the generated power of the solar panel.
  • a plurality of images are photographed using a camera capable of photographing most of the sky.
  • a cloud is recognized from a plurality of images, the position, velocity, and moving direction of the cloud are calculated, and the output of the solar panel after a predetermined time is predicted.
  • the prediction accuracy of the power generation amount is lowered to a degree that cannot be ignored.
  • the position of the cloud seen from the solar power generation device and the position of the cloud imaged by the all-sky camera may be greatly different.
  • FIG. 13 a plurality of solar power generation devices 3 (1) and 3 (2) and one all-sky camera 2 are arranged.
  • the cloud 4a exists at a relatively low position of the height ha
  • the cloud 4b exists at a position hb higher than that.
  • the cloud 4b is recognized as being in the position of the projection 4c of the cloud 4b.
  • the solar power generation device 3 (2) arranged near the all-sky camera 2 there is not much difference between the position of the cloud seen from the all-sky camera 2 and the position of the cloud seen from the solar power generation device 3 (2). Does not occur.
  • the predicted value of the power generation amount is greatly affected.
  • the position of the shadow 6c is calculated with the height of the cloud 4b being ha, the position of the shadow 6c is a place away from any of the solar power generation devices 3 (1) and 3 (2).
  • the actual height of the cloud 4b is hb higher than ha, and the actual position of the shadow 6b is greatly different from the predicted position (6c).
  • the nonexistent shadow 6c predicted based on the projection 4c is irrelevant to the solar power generation device 3 (1).
  • the actual shadow 6b covers almost the entire solar power generation device 3 (1). Therefore, the actual power generation amount of the solar power generation device 3 (1) is significantly lower than the predicted value.
  • a shadow position prediction system is a shadow position prediction system for predicting the position and size of a cloud shadow, and is a plurality of separately positioned shadow detection systems.
  • a prediction device that acquires a signal from a cloud detection device via a communication network, and the prediction device acquires cloud detection information related to cloud detection from a plurality of predetermined cloud detection devices among the plurality of cloud detection devices. Then, based on a plurality of cloud detection information, each calculates predetermined basic data including the height, spread, moving direction and moving speed of the cloud, and a cloud is formed on the ground based on the calculated predetermined basic data. The position and size of the shadow to be calculated are calculated.
  • the prediction device acquires cloud detection information about a cloud to be predicted from a plurality of predetermined cloud detection devices at a plurality of predetermined time points with a predetermined time interval set in advance, and acquires each at a plurality of predetermined time points.
  • Predetermined basic data can also be calculated by comparing and calculating a plurality of cloud detection information.
  • At least one solar-powered device that uses sunlight is installed at a distance of a predetermined distance or more from a plurality of cloud detection devices, and the prediction device includes the position and size of the shadow, and the position of the solar-powered device. Based on the above, it may be configured to calculate the influence of the shadow on the solar light utilization device.
  • At least a part of the configuration of the present invention can be realized as a computer program.
  • the computer program can be distributed, for example, via a communication medium such as the Internet, a recording medium such as a hard disk or a flash memory device.
  • FIG. 1 is an overall configuration diagram of a shadow position prediction system.
  • FIG. 2 is a configuration diagram of the prediction device.
  • FIG. 3 is a configuration example of the device data management table.
  • FIG. 4 is a configuration example of the imaging data management table.
  • FIG. 5 is a configuration example of the prediction history data management table.
  • FIG. 6 shows the overall operation flow of the shadow position prediction system.
  • FIG. 7 is a flowchart showing a process for calculating the position of a cloud shadow.
  • FIG. 8 is a flowchart showing a process for predicting the power generation amount.
  • FIG. 9 is an explanatory diagram showing the relationship between the shadow of the cloud and the solar power generation device.
  • FIG. 9 is an explanatory diagram showing the relationship between the shadow of the cloud and the solar power generation device.
  • FIG. 10 relates to the second embodiment and shows a prediction method when the camera, the cloud, and the solar power generation device do not exist on the same straight line.
  • FIG. 11 is an explanatory diagram illustrating a state in which a camera to be used is selected for each prediction target area according to the third embodiment.
  • FIG. 12 is a flowchart illustrating a process of predicting the power generation amount.
  • FIG. 13 is an explanatory diagram showing a problem of the prior art.
  • the altitude of the cloud is also calculated using a plurality of cameras.
  • size of the shadow which a cloud falls on the ground can be estimated comparatively accurately.
  • FIG. 1 is a configuration diagram of a photovoltaic power generation prediction system including a shadow position prediction system.
  • the cameras 2 (1), 2 (2), the cloud 4, and the solar power generation device 3 are arranged in a straight line.
  • Cameras 2 (1) and 2 (2) which are examples of “cloud detection devices”, are for capturing images of clouds 4 in the sky.
  • a plurality (two) of cameras 2 (1) and 2 (2) are respectively installed at locations away from the solar power generation device 3. Note that the camera 2 is referred to unless otherwise distinguished.
  • the solar power generation device 3 which is an example of a “solar power utilization device” receives light energy from the sun 5 and converts it into electric energy to generate power. Although illustration is omitted, a power storage device can be connected to the solar power generation device 3. The power storage device can accumulate at least a part of the power generation amount from the solar power generation device 3 or can discharge power that is not sufficient with the power generation amount of the solar power generation device 3 alone.
  • a plurality of clouds 4 may exist.
  • PV Photovoltaic
  • the PV prediction apparatus 1 includes, for example, a CPU (Central Processing Unit) 10, a storage unit 11, a sensor communication interface 12, and an external server communication interface 13. These circuits 10-13 are connected to the bus 14.
  • a CPU Central Processing Unit
  • storage unit 11 a storage unit 11
  • sensor communication interface 12 a sensor communication interface 13
  • external server communication interface 13 an external server communication interface 13.
  • the storage unit 11 includes, for example, a main memory.
  • the storage unit 11 can also be configured to include an auxiliary storage device such as a flash memory device or a hard disk drive.
  • the storage unit 11 stores various programs such as an operating system (not shown), a shadow position calculation program P10, and a power generation amount calculation program P11. Further, the storage unit 11 also stores various management information such as a device data management table T10, an imaging data management table T11, and prediction history data T12. Furthermore, a work area used by the CPU 10 is also set in the storage unit 11.
  • the sensor communication interface 12 is communicably connected to each camera 2 via the sensor communication network CN1.
  • the sensor communication interface 12 transmits an imaging command to each camera 2 and receives imaging data from each camera 2.
  • the PV device 3 may also be connected to the sensor communication network CN1.
  • the external server communication interface 13 is connected to the external servers 7 and 8 via the external server communication network CN2.
  • Examples of external servers include a weather data distribution server 7 and a CEMS (Community Energy Management System) 8.
  • the meteorological data distribution server 7 is a server that distributes meteorological data such as wind speed, wind direction, atmospheric pressure, weather, and sun position.
  • the CEMS 8 monitors the supply and demand state of power in the area in charge, and provides information to an electric utility that manages the power system.
  • the CEMS 8 can also collect information on the power generation amount and the power consumption amount by communicating with an EMS (Energy Management System) installed in each consumer (for example, general household, commercial facility, factory, hospital, etc.).
  • EMS Electronicgy Management System
  • the CEMS 8 may be connected to the PV device 3 via a communication network (not shown), for example.
  • the CEMS 8 may collect information such as the power generation amount and the operating state of the PV device 3 and transmit the information to the PV prediction device 1.
  • the PV prediction device 1 is configured to predict information such as the ratio and date / time when the PV device 3 is covered with the shadow 6, transmit the predicted values to the CEMS 8, and the CEMS 8 predicts the power generation amount of the PV device 3. It may be. That is, the PV prediction apparatus 1 does not need to predict the PV power generation amount, and may be configured to output a prediction value necessary for prediction of the PV power generation amount or a prediction value indicating a variation in the PV power generation amount to the outside.
  • the output destination is not limited to CEMS 8, and may be, for example, a power control system managed by a power system operator.
  • the communication networks CN1 and CN2 may be configured as a wired network or a wireless network. Further, the communication network CN1 and the communication network CN2 may be configured as one communication network.
  • the configuration of the device data management table T10 will be described with reference to FIG.
  • the device data management table T10 is a table for managing information related to the camera 2 and the PV device 3.
  • the device data management table T10 manages, for example, a device identifier (identifier is abbreviated as ID in the figure) C100, a position C101, a specification C102, and a state C103 in association with each other.
  • the device identifier C100 is information for the PV prediction device 1 to identify the device (camera 2, PV device 3).
  • the device identifier C100 may be configured as a combination of information indicating the device type and a serial number, for example.
  • the position C101 is information indicating the position where the camera 2 or the PV device 3 is installed.
  • the installation position C101 may be expressed by, for example, latitude and longitude, or may be expressed as a value in another coordinate system.
  • Spec C102 indicates information regarding the specifications of the camera 2 or information regarding the specifications of the PV device 3.
  • the specification information of the camera 2 includes, for example, a manufacturer name, model name, administrator name, serial number, contact information, lens performance, photographing sensitivity, and the like.
  • the specification information of the PV device 3 includes, for example, a manufacturer name, a model name, an administrator name, a manufacturing number, a contact number, a solar panel size, a power generation capacity, and the like.
  • the state C103 indicates the state of the camera 2 or the state of the PV device 3. For example, a value indicating a normal operation, a value indicating an abnormal state, a value indicating a failure, a value indicating a check, and the like are set in the state C103.
  • the imaging data management table T11 will be described with reference to FIG.
  • the imaging data management table T11 manages image data captured by the camera 2 as it is or after being processed.
  • the imaging data management table T11 manages, for example, a management number C110, an area number C111, imaging data C112, and date / time C113 in association with each other.
  • the area number C111 is a number for identifying the photographed area.
  • a pointer indicating the storage location of the imaging data is stored in the imaging data C112.
  • the raw image data captured by the camera 2 may be stored, or the captured data may be processed to store subsequent data.
  • Imaging data is not limited to still images. It may be moving image data shot for a predetermined time. In the case of still image data, a plurality of still image data can be stored.
  • the date and time C113 is the shooting date and time.
  • the prediction history data T12 will be described with reference to FIG.
  • the prediction history data T12 is a table for managing the prediction result of the position of the shadow 6 created by the cloud and the history of the prediction result of the power generation amount of the PV device 3.
  • the prediction history table T12 manages, for example, a management number C120, an area number C121, prediction data C122, and date / time C123 in association with each other.
  • the area number C121 is information for identifying an area in which a cloud shadow position or the like is predicted.
  • the prediction data C122 may include, for example, both basic data used for prediction and prediction result data, such as cloud moving direction and speed, shadow position and size, and power generation amount.
  • the date and time C123 is a predicted date and time.
  • FIG. 6 is a flowchart showing the entire operation of the PV prediction apparatus 1.
  • the PV prediction device 1 first updates the device data management table T10 (S1), next predicts the position of the shadow formed by the cloud on the ground (S2), and finally predicts the PV power generation amount (S2). S3).
  • the update process S1 will be described.
  • the PV prediction device 1 updates the content of the device data management table T10. Examples of the configuration change include addition or removal of the camera 2 or the PV device 3, replacement, and change of the installation position.
  • a user who is an administrator of the PV prediction apparatus 1 may manually rewrite the contents of the apparatus data management table T10, or based on the information acquired from an external management server or the like, the contents of the apparatus data management table T10 may be changed. You may update automatically.
  • the PV prediction apparatus 1 acquires imaging data from a plurality of cameras 2 (S20), and further acquires weather data from the weather data distribution server 7 (S21).
  • cloud image data captured by the camera 2 may be referred to as all-sky image data.
  • the PV prediction device 1 performs image processing on the captured data to extract clouds, and sets identification labels for all the extracted clouds (S22).
  • Each cloud can be extracted by classifying the cloud and the background sky based on the brightness of each pixel of the imaging data and calculating the outline of the cloud by differentiation processing or the like.
  • the identification label is identification information for identifying the cloud to be processed. For example, serial numbers may be assigned to the clouds in the order of extraction, and the numbers may be used as identification labels.
  • the PV prediction device 1 determines the determination interval ⁇ t as “predetermined predetermined time interval” (S23).
  • the determination interval ⁇ t is a parameter for detecting the movement amount ⁇ d (see FIG. 1) of the cloud 4 by photographing a plurality of times by a predetermined plurality of cameras 2.
  • the determination interval ⁇ t may be set in advance as a fixed value, for example, or a value corresponding to the wind speed may be selected. Alternatively, the minimum value that can detect the cloud movement amount ⁇ d may be selected as the determination interval ⁇ t based on past imaging data and weather data.
  • the determination interval ⁇ t It is relatively easy to set the determination interval ⁇ t longer. However, if the determination interval ⁇ t is set longer than necessary, the responsiveness of prediction decreases when the speed of the cloud 4 is high. In order to predict the change to the PV device 3 due to the shadow of the cloud 4 in advance and prepare for the change, it is preferable to predict as soon as possible. Therefore, in this embodiment, the minimum time during which the movement amount ⁇ d of the cloud 4 to be predicted can be detected is selected as the determination interval ⁇ t.
  • the PV prediction device 1 calculates the height, position, spread, and velocity vector of the cloud 4 based on the difference between the first sky image data and the second sky image data (S24). As described above, the shooting time of the first all-sky image data and the shooting time of the second all-sky image data differ by the determination interval ⁇ t.
  • h is the height of the cloud 4 to be predicted.
  • D is a length indicating the spread of the cloud 4.
  • ⁇ d is a distance traveled by the cloud 4 during the determination interval ⁇ t from time t1 to time t2.
  • d1 is a distance between the front end of the cloud 4 and the position of one camera 2 (1) at time t1.
  • L is the distance between one camera 2 (1) and the other camera 2 (2).
  • One camera 2 (1) is a camera closer to the cloud 4 to be predicted among a plurality of cameras used for prediction.
  • One camera 2 (1) may be referred to as a first camera 2 (1).
  • the other camera 2 (2) is a camera farther from the cloud 4 to be predicted among a plurality of cameras used for prediction.
  • the other camera 2 (2) may be referred to as a second camera 2 (2).
  • the angle ⁇ in FIG. 1 is an angle formed by a line connecting the rear end of the cloud 4 from the center of the optical axis of the camera 2 and a horizontal line.
  • the angle ⁇ is an angle formed by a line connecting the front end of the cloud 4 from the center of the optical axis of the camera 2 and a horizontal line.
  • a number for specifying the camera 2 and a number for specifying the time are attached to the angles ⁇ and ⁇ .
  • ⁇ 11 means the angle ⁇ at the time t1 of the first camera 2 (1).
  • ⁇ 12 means the angle ⁇ at the time t2 of the first camera 2 (1).
  • Other examples are omitted.
  • Expression (1b) Expression (1f)
  • Expression (1d) Expression (1h)
  • the PV prediction device 1 calculates the position of the shadow of the cloud 4 after time t (S25). Assuming that the moving speed v of the cloud 4 is constant, it can be predicted that the cloud 4 has moved by (v * t) after time t. In order to know whether or not the shadow on the ground formed by the cloud 4 after time t covers the PV device 3, a projection from the cloud 4 to the ground may be created.
  • FIG. 8 is a flowchart showing a process for predicting the power generation amount of the PV device 3.
  • the PV prediction apparatus 1 acquires the shadow position from step S25 described in FIG. 7 (S30). Furthermore, the PV prediction device 1 acquires the position data of the PV device 3 to be predicted from the device data management table T10 (S31).
  • the PV prediction device 1 calculates the relationship between the shadow created by the cloud 4 on the ground and the PV device 3 (S32).
  • the relationship between the shadow and the PV device 3 is how much the shadow covers the PV device 3 at the time of the prediction target.
  • the PV prediction device 1 predicts the power generation amount of the PV device 3 based on the relationship between the shadow and the PV device 3 (S33).
  • the PV prediction device 1 calculates the amount of power generation in the PV device 3 by multiplying, for example, the ratio of the area where the sunlight hits the PV device 3 and the power generation capacity per unit area of the PV device 3. Can do.
  • FIG. 9 is a conceptual diagram for determining whether or not the cloud 4 blocks sunlight incident on the PV device 3.
  • a point described as “0” on the left side of the figure is a reference point.
  • the height of the cloud 4 from the reference point is h, and the length indicating the cloud spread is D.
  • the front end of the cloud 4 and the PV device 3 (2) are separated by a distance d.
  • the angle when looking up at the sun 5 from the PV device 3 (2) is defined as ⁇ . Based on the above assumptions, the position of the shadow 6 is calculated.
  • the rear end (right end in the figure) and the front end (left end in the figure) of the shadow 6 of the cloud 4 are obtained.
  • the trailing edge of the cloud is separated from the reference point 0 by (d + D).
  • the height and speed of the cloud 4 are calculated by analyzing image data photographed multiple times by a plurality of cameras 2. Therefore, the PV prediction device 1 can predict the position and size of the shadow 6 created by the cloud 4 on the ground relatively accurately. Thereby, the PV prediction apparatus 1 of a present Example can estimate the electric power generation amount of the PV apparatus 3 comparatively correctly. For this reason, a present Example can be useful for the plan preparation for adjusting the supply-and-demand balance of an electric power grid
  • the PV power generation amount can be predicted with a smaller number of cameras 2 than the number of PV devices 3 installed, and the cost of the entire system can be reduced.
  • the second embodiment will be described with reference to FIG.
  • Each of the following embodiments, including the present embodiment, corresponds to a modification of the first embodiment, and therefore, differences from the first embodiment will be mainly described.
  • the present embodiment is a generalization of the first embodiment, and considers a case where the camera 2, the PV device 3, and the cloud 4 are not on the same straight line.
  • FIG. 10 shows a system layout according to the present embodiment.
  • the ground is expressed as a plane defined by the X axis and the Y axis.
  • Equation (7) is obtained from equation (2e), and equation (8) is obtained from equation (2f).
  • d'1 d1 * tan ( ⁇ 21) (7)
  • d'2 d2 * tan ( ⁇ 22) (8)
  • a third embodiment will be described with reference to FIGS.
  • three or more cameras 2 (1) to 2 (3) are used.
  • FIG. 11 is an explanatory diagram showing the relationship between the arrangement of the cameras 2 (1) to 2 (3) and the cameras used for each region.
  • the PV prediction apparatus 1 includes a management table in which contents as shown in FIG. 11 are defined.
  • the entire region to be predicted is divided into three areas 1 to 3.
  • the first camera 2 (1) is located at the boundary between area 1 and area 2.
  • the second camera 2 (2) is located at the boundary between area 2 and area 3.
  • the third camera 2 (3) is located at the boundary between the area 3 and the area 1.
  • the entire region to be predicted is divided into a plurality of areas 1 to 3 according to the positions of the plurality of cameras 2 (1) to 2 (3) to be dispersed.
  • Each area 1 to 3 is configured as a pentagonal area in the example of FIG.
  • the area 4 is formed as a triangular area with the cameras 2 (1) to 2 (3) as vertices. It is assumed that one PV device 3 is provided in each of the areas 1 to 4.
  • FIG. 12 shows a process for calculating the position of the shadow according to this embodiment.
  • step S20 in the process shown in FIG. 7 is changed to step S20A.
  • Step S20A characteristic of the present embodiment selects a plurality of cameras 2 to be used for the prediction process according to the position of the PV device 3 to be predicted.
  • the first camera 2 (1) and the third camera 2 (3) provided at the boundary of the area 1 are selected.
  • the second camera 2 (2) and the first camera 2 (1) provided at the boundary of the area 2 are selected.
  • the PV power generation amount is predicted for the PV device 3 (3) in the area 3
  • the second camera 2 (2) and the third camera 2 (3) provided at the boundary of the area 3 are selected.
  • the first camera 2 (1), the second camera 2 (2), and the third camera 2 located at the boundary of the area 4 are used. (3) is selected.
  • the area 4 may be omitted.
  • This embodiment configured as described above also has the same effect as the first embodiment. Furthermore, in this embodiment, a plurality of cameras 2 are selected according to the position of the PV device 3 to be predicted. Therefore, in this embodiment, it is possible to select a plurality of cameras 2 close to the cloud 4 that can affect the PV device 3 to be predicted. As a result, the altitude and speed of the cloud 4 can be calculated more accurately, and the PV power generation amount can be predicted according to the position of the shadow.
  • this invention is not limited to the Example mentioned above.
  • a person skilled in the art can make various additions and changes within the scope of the present invention.
  • the case where the PV power generation amount is predicted has been described, but instead of this, only the position and speed of the cloud and / or the position and speed of the shadow may be predicted. By inputting these predictions to another computer, the PV power generation amount and the like can be calculated.
  • the present invention can also be expressed as a computer program invention.
  • Photovoltaic power generation prediction device 2 Camera 3: Photovoltaic power generation device 4: Cloud 5: Sun 6: Shadow of cloud

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Abstract

An objective of the present invention is to comparatively accurately predict the location, speed, etc., of shadows cast by clouds. Provided is a shadow location predict system for predicting the location and size of cloud shadows, comprising: a plurality of cloud detection devices (2) which are positioned apart from one another, and which are for detecting clouds; and a predict device (1), which is connected to the plurality of cloud detection devices (2) via a communication network (CN1). The predict device (1) acquires cloud detection information relating to the detection of clouds from a prescribed plurality of the cloud detection devices (2) among the cloud detection devices (2). On the basis of a plurality of cloud detection information, the predict device (1) respectively computes prescribed base data including cloud height, spread, movement direction, and movement speed, and, on the basis of these prescribed base data, computes the location and size of shadows which clouds (4) cast on the ground.

Description

影位置予測システム及び影位置予測方法Shadow position prediction system and shadow position prediction method
　本発明は、雲の影の位置を予測するための影位置予測システム及び影位置予測方法に関する。 The present invention relates to a shadow position prediction system and a shadow position prediction method for predicting the position of a cloud shadow.
　近年では、太陽光を利用した発電装置の普及が期待されている。太陽光発電装置は、太陽光パネルに入射する光量によって、その発電量が定まる。従って、天候変化に応じて、太陽光発電装置の出力は変動する。太陽光発電装置の出力が変動すると、太陽光発電装置からの発電を利用する装置の動作が不安定になる。 In recent years, the spread of power generation devices using sunlight is expected. The amount of power generation of the solar power generation device is determined by the amount of light incident on the solar panel. Therefore, the output of the photovoltaic power generator varies depending on the weather change. When the output of the solar power generation device fluctuates, the operation of the device using the power generation from the solar power generation device becomes unstable.
　また、複数の太陽光発電装置が電力系統に連系している場合、太陽光発電装置の出力変動に応じて、電力系統の電力需給バランスを調整する必要がある。そこで、太陽光発電装置の発電量を予測する技術が提案されている（特許文献１、特許文献２）。 Also, when a plurality of solar power generation devices are connected to the power system, it is necessary to adjust the power supply / demand balance of the power system according to the output fluctuation of the solar power generation device. Therefore, techniques for predicting the power generation amount of the solar power generation apparatus have been proposed (Patent Document 1, Patent Document 2).
　第１の特許文献に記載の従来技術では、全天を撮像できるカメラを設置し、カメラで撮影した画像から雲の分布と雲の動きを検出する。その従来技術では、所定の未来の時点での雲の分布を予測して、太陽光パネルの発電電力を予測する。 In the prior art described in the first patent document, a camera capable of imaging the whole sky is installed, and cloud distribution and cloud movement are detected from an image captured by the camera. In the related art, the distribution of clouds at a predetermined future time point is predicted to predict the generated power of the solar panel.
　第２の特許文献に記載の従来技術では、天空の大半が撮影可能なカメラを用いて、複数の画像を撮影する。その従来技術では、複数の画像から雲を認識し、その雲の位置と速度及び移動方向を算出して、太陽光パネルの所定時間後の出力を予測する。 In the prior art described in the second patent document, a plurality of images are photographed using a camera capable of photographing most of the sky. In the prior art, a cloud is recognized from a plurality of images, the position, velocity, and moving direction of the cloud are calculated, and the output of the solar panel after a predetermined time is predicted.
特開２００７－１８４３５４号公報JP 2007-184354 A 特開２００５－３１２１６３号公報JP-A-2005-312163
　前記各文献に記載の従来技術は、全ての雲の高さを一定値とみなして予測しており、予測精度が低い。例えば、積雲と層雲が同時に全天に存在する際、その２種類の雲は必ずしも同じ高さには存在しない。 The prior art described in each of the above references predicts all the cloud heights as a constant value, and the prediction accuracy is low. For example, when cumulus clouds and stratus clouds are present all over the sky, the two types of clouds do not necessarily exist at the same height.
　太陽光発電装置の近傍に全天カメラが設置されている場合、全ての雲の高さを同一であると仮定して演算しても実質的に問題は生じない。全天カメラで撮像された雲の位置と、太陽光発電装置から見える雲の位置とは、ほぼ同一となるためである。 If there is an all-sky camera installed in the vicinity of the solar power generation device, there is virtually no problem even if it is calculated assuming that all the clouds have the same height. This is because the position of the cloud imaged by the all-sky camera and the position of the cloud seen from the solar power generation device are almost the same.
　これに対し、太陽光発電装置の位置と全天カメラの位置とが離れている場合、発電量の予測精度は無視できないほどに低下する。太陽光発電装置から見える雲の位置と、全天カメラで撮像される雲の位置とは、大きく異なる可能性がある。 On the other hand, when the position of the solar power generation device and the position of the all-sky camera are separated, the prediction accuracy of the power generation amount is lowered to a degree that cannot be ignored. There is a possibility that the position of the cloud seen from the solar power generation device and the position of the cloud imaged by the all-sky camera may be greatly different.
　図１３を参照して従来技術の問題を説明する。図１３では、複数の太陽光発電装置３（１）、３（２）と、１つの全天カメラ２とが配置されている。図１３では、高さｈａの比較的低い位置に雲４ａが存在し、それよりも高い位置ｈｂに雲４ｂが存在している。 The problem of the prior art will be described with reference to FIG. In FIG. 13, a plurality of solar power generation devices 3 (1) and 3 (2) and one all-sky camera 2 are arranged. In FIG. 13, the cloud 4a exists at a relatively low position of the height ha, and the cloud 4b exists at a position hb higher than that.
　もしも、全ての雲４ａ、４ｂの高さをｈａであると仮定すると、雲４ｂは、雲４ｂの射影４ｃの位置にあると認識されてしまう。全天カメラ２の近くに配置された太陽光発電装置３（２）の場合は、全天カメラ２から見える雲の位置と、太陽光発電装置３（２）から見える雲の位置とにあまりズレが生じない。 If it is assumed that the heights of all the clouds 4a and 4b are ha, the cloud 4b is recognized as being in the position of the projection 4c of the cloud 4b. In the case of the solar power generation device 3 (2) arranged near the all-sky camera 2, there is not much difference between the position of the cloud seen from the all-sky camera 2 and the position of the cloud seen from the solar power generation device 3 (2). Does not occur.
　図１３に示す例では、雲４ｂの射影４ｃから影６ｃの存在を予測したとしても、影６ｃと太陽光発電装置３（２）とは位置が離れており、全く関係がない。従って、太陽光発電装置３（２）の発電量予測に影響はない。このように、全天カメラ２と太陽光発電装置３（２）とが近い場合は、全ての雲４ａ、４ｂの高さを一定値ｈａとして計算しても、太陽光発電装置３（２）の発電量の予測値に誤差は生じない。 In the example shown in FIG. 13, even if the presence of the shadow 6c is predicted from the projection 4c of the cloud 4b, the shadow 6c and the solar power generation device 3 (2) are separated from each other and have no relation at all. Therefore, there is no influence on the power generation amount prediction of the solar power generation device 3 (2). Thus, when the all-sky camera 2 and the solar power generation device 3 (2) are close, even if the heights of all the clouds 4a and 4b are calculated as a constant value ha, the solar power generation device 3 (2) There is no error in the predicted value of power generation.
　これに対し、全天カメラ２から離れた場所に設置された他の太陽光発電装置３（１）では、発電量の予測値に大きな影響を与える。図１３の場合、雲４ｂの高さをｈａとして影６ｃの位置を算出すると、影６ｃの位置は、太陽光発電装置３（１）、３（２）のいずれからも離れた場所になる。 On the other hand, in the other solar power generation apparatus 3 (1) installed in a place away from the all-sky camera 2, the predicted value of the power generation amount is greatly affected. In the case of FIG. 13, when the position of the shadow 6c is calculated with the height of the cloud 4b being ha, the position of the shadow 6c is a place away from any of the solar power generation devices 3 (1) and 3 (2).
　しかし、雲４ｂの実際の高さは、ｈａよりも高いｈｂであり、実際の影６ｂの位置は予測された位置（６ｃ）と大きく異なる。射影４ｃに基づいて予測された実在しない影６ｃは、太陽光発電装置３（１）と無関係である。しかし、実際の影６ｂは、太陽光発電装置３（１）のほぼ全体を覆ってしまう。従って、太陽光発電装置３（１）の実際の発電量は、予測値よりも大幅に低下する。 However, the actual height of the cloud 4b is hb higher than ha, and the actual position of the shadow 6b is greatly different from the predicted position (6c). The nonexistent shadow 6c predicted based on the projection 4c is irrelevant to the solar power generation device 3 (1). However, the actual shadow 6b covers almost the entire solar power generation device 3 (1). Therefore, the actual power generation amount of the solar power generation device 3 (1) is significantly lower than the predicted value.
今、太陽光発電装置３（１）に加え、蓄電装置と、家庭および工場等の需要家とからなる小規模な電力系統を考える。太陽光発電装置３（１）の発電量が大幅に低下すると、その低下量を補うために、蓄電装置からの放電量を増やす、若しくは、電力系統から購入する電力量を増やす必要がある。蓄電装置からの放電量を増やす場合、計画していた蓄電残量を保てなくなり、最終的に小規模系統内での電力が不足する恐れがある。また、電力系統から購入する場合、予定外に電力を購入する事になり、コスト面で問題が生じる。 Now, consider a small-scale power system composed of a power storage device and consumers such as homes and factories in addition to the solar power generation device 3 (1). When the power generation amount of the solar power generation device 3 (1) is significantly reduced, it is necessary to increase the amount of discharge from the power storage device or increase the amount of power purchased from the power system in order to compensate for the reduction amount. When the amount of discharge from the power storage device is increased, the planned remaining power storage capacity cannot be maintained, and there is a risk that the power in the small-scale system will eventually become insufficient. In addition, when purchasing from the power system, power is purchased unscheduled, which causes a problem in terms of cost.
　そこで、本発明の目的は、複数の雲検出装置を用いて雲の高さも算出し、雲が地上に形成する影の位置及び大きさを予測することができるようにした、影位置予測システム及び影位置予測方法を提供することにある。本発明の他の目的は、雲の作る影の位置及び大きさを比較的正確に予測することで、太陽光を利用する装置への影響も予測できるようにした影位置予測システム及び影位置予測方法を提供することにある。 Accordingly, an object of the present invention is to calculate the height of a cloud using a plurality of cloud detection devices, and to predict the position and size of a shadow formed by the cloud on the ground, It is to provide a shadow position prediction method. Another object of the present invention is to predict the position and size of a shadow formed by a cloud relatively accurately, thereby predicting the influence on a device using sunlight, and a shadow position prediction system. It is to provide a method.
　上記課題を解決すべく、本発明に係る影位置予測システムは、雲の影の位置及び大きさを予測するための影位置予測システムであって、離間して配置され雲を検出するための複数の雲検出装置から通信ネットワークを介して信号を取得する予測装置と、を備え、予測装置は、複数の雲検出装置のうち所定の複数の雲検出装置から、雲の検出に関する雲検出情報を取得し、複数の雲検出情報に基づいて、雲の高さ、広がり、移動方向及び移動速度を含む所定の基礎データをそれぞれ算出し、算出された所定の基礎データに基づいて、雲が地上に形成する影の位置及び大きさを算出する。 In order to solve the above-described problem, a shadow position prediction system according to the present invention is a shadow position prediction system for predicting the position and size of a cloud shadow, and is a plurality of separately positioned shadow detection systems. A prediction device that acquires a signal from a cloud detection device via a communication network, and the prediction device acquires cloud detection information related to cloud detection from a plurality of predetermined cloud detection devices among the plurality of cloud detection devices. Then, based on a plurality of cloud detection information, each calculates predetermined basic data including the height, spread, moving direction and moving speed of the cloud, and a cloud is formed on the ground based on the calculated predetermined basic data. The position and size of the shadow to be calculated are calculated.
　予測装置は、予め設定される所定の時間間隔をおいた複数の所定時点で、所定の複数の雲検出装置から、予測対象の雲に関する雲検出情報をそれぞれ取得し、複数の所定時点でそれぞれ取得される複数の雲検出情報を比較して演算することで、所定の基礎データを算出することもできる。 The prediction device acquires cloud detection information about a cloud to be predicted from a plurality of predetermined cloud detection devices at a plurality of predetermined time points with a predetermined time interval set in advance, and acquires each at a plurality of predetermined time points. Predetermined basic data can also be calculated by comparing and calculating a plurality of cloud detection information.
　複数の雲検出装置から所定距離以上離れた場所に、太陽光を利用する太陽光利用装置が少なくとも一つ設置されており、予測装置は、影の位置及び大きさと、太陽光利用装置の位置とに基づいて、影が太陽光利用装置に与える影響を算出する構成でもよい。 At least one solar-powered device that uses sunlight is installed at a distance of a predetermined distance or more from a plurality of cloud detection devices, and the prediction device includes the position and size of the shadow, and the position of the solar-powered device. Based on the above, it may be configured to calculate the influence of the shadow on the solar light utilization device.
　本発明の構成の少なくとも一部は、コンピュータプログラムとして実現できる。コンピュータプログラムは、例えば、インターネットのような通信媒体、ハードディスクまたはフラッシュメモリデバイスのような記録媒体を介して、配布することができる。 At least a part of the configuration of the present invention can be realized as a computer program. The computer program can be distributed, for example, via a communication medium such as the Internet, a recording medium such as a hard disk or a flash memory device.
図１は、影位置予測システムの全体構成図である。FIG. 1 is an overall configuration diagram of a shadow position prediction system. 図２は、予測装置の構成図である。FIG. 2 is a configuration diagram of the prediction device. 図３は、装置データ管理テーブルの構成例である。FIG. 3 is a configuration example of the device data management table. 図４は、撮像データ管理テーブルの構成例である。FIG. 4 is a configuration example of the imaging data management table. 図５は、予測履歴データ管理テーブルの構成例である。FIG. 5 is a configuration example of the prediction history data management table. 図６は、影位置予測システムの全体動作の流れを示す。FIG. 6 shows the overall operation flow of the shadow position prediction system. 図７は、雲の影の位置を算出する処理を示すフローチャートである。FIG. 7 is a flowchart showing a process for calculating the position of a cloud shadow. 図８は、発電量を予測する処理を示すフローチャートである。FIG. 8 is a flowchart showing a process for predicting the power generation amount. 図９は、雲の影と太陽光発電装置との関係を示す説明図である。FIG. 9 is an explanatory diagram showing the relationship between the shadow of the cloud and the solar power generation device. 図１０は、第２実施例に係り、カメラ、雲及び太陽光発電装置が同一直線上に存在しない場合の、予測方法を示す。FIG. 10 relates to the second embodiment and shows a prediction method when the camera, the cloud, and the solar power generation device do not exist on the same straight line. 図１１は、第３実施例に係り、予測対象の領域毎に、使用するカメラを選択する様子を示す説明図である。FIG. 11 is an explanatory diagram illustrating a state in which a camera to be used is selected for each prediction target area according to the third embodiment. 図１２は、発電量を予測する処理を示すフローチャートである。FIG. 12 is a flowchart illustrating a process of predicting the power generation amount. 図１３は、従来技術の問題を示す説明図である。FIG. 13 is an explanatory diagram showing a problem of the prior art.
　以下、図面に基づいて、本発明の実施の形態を説明する。本実施形態では、以下に詳述するように、複数のカメラを用いて雲の高度も算出する。これにより、本実施形態では、雲が地上に落とす影の位置及び大きさを比較的正確に予測できる。その結果、太陽光発電装置の発電量の変化を比較的正確に予測することができる。以下に述べる実施形態は、本発明の理解及び実施のために作成されたものである。本発明の範囲は、実施形態に記載の構成に限定されない。 Hereinafter, embodiments of the present invention will be described with reference to the drawings. In this embodiment, as described in detail below, the altitude of the cloud is also calculated using a plurality of cameras. Thereby, in this embodiment, the position and magnitude | size of the shadow which a cloud falls on the ground can be estimated comparatively accurately. As a result, it is possible to predict a change in the power generation amount of the solar power generation apparatus relatively accurately. The embodiments described below have been created for the understanding and implementation of the present invention. The scope of the present invention is not limited to the configurations described in the embodiments.
　図１は、影位置予測システムを含む太陽光発電予測システムの構成図である。ここでは、説明を簡単にするために、カメラ２（１）、２（２）と、雲４と、太陽光発電装置３とが一直線上に並んでいるものとする。 FIG. 1 is a configuration diagram of a photovoltaic power generation prediction system including a shadow position prediction system. Here, in order to simplify the description, it is assumed that the cameras 2 (1), 2 (2), the cloud 4, and the solar power generation device 3 are arranged in a straight line.
　「予測装置」としての太陽光発電予測装置１の構成は、図２を参照して後述する。「雲検出装置」の一例であるカメラ２（１）、２（２）は、上空に有る雲４の画像を撮像するためのものである。本実施例では、複数の（２台の）カメラ２（１）、２（２）を、太陽光発電装置３から離れた場所にそれぞれ設置している。なお、特に区別しない場合、カメラ２と呼ぶ。 The configuration of the photovoltaic power generation prediction device 1 as the “prediction device” will be described later with reference to FIG. Cameras 2 (1) and 2 (2), which are examples of “cloud detection devices”, are for capturing images of clouds 4 in the sky. In the present embodiment, a plurality (two) of cameras 2 (1) and 2 (2) are respectively installed at locations away from the solar power generation device 3. Note that the camera 2 is referred to unless otherwise distinguished.
　「太陽光利用装置」の一例である太陽光発電装置３は、太陽５からの光エネルギを受光して電気エネルギに変換し、発電する。図示は省略するが、太陽光発電装置３には蓄電装置を接続することができる。蓄電装置は、太陽光発電装置３からの発電量の少なくとも一部を蓄積したり、太陽光発電装置３の発電量だけでは足りない分の電力を放電したりすることができる。 The solar power generation device 3 which is an example of a “solar power utilization device” receives light energy from the sun 5 and converts it into electric energy to generate power. Although illustration is omitted, a power storage device can be connected to the solar power generation device 3. The power storage device can accumulate at least a part of the power generation amount from the solar power generation device 3 or can discharge power that is not sufficient with the power generation amount of the solar power generation device 3 alone.
　なお、図１では、太陽光発電装置３を１つのみ示すが、複数設けられてもよい。また、図１では、一つの雲４が、時刻ｔ１から時刻ｔ２までの時間Δｔに、距離Δｄ（＝ｄ１－ｄ２）移動した様子を示している。雲４は複数存在してもよい。 In FIG. 1, only one photovoltaic power generation device 3 is shown, but a plurality of photovoltaic power generation devices 3 may be provided. Further, FIG. 1 shows a state in which one cloud 4 has moved a distance Δd (= d1−d2) at time Δt from time t1 to time t2. A plurality of clouds 4 may exist.
　図２を参照して、太陽光発電予測装置１の構成を説明する。以下の説明では、太陽光発電をＰＶ（Photovoltaic）と略記する場合がある。 Referring to FIG. 2, the configuration of the photovoltaic power generation prediction apparatus 1 will be described. In the following description, solar power generation may be abbreviated as PV (Photovoltaic).
　ＰＶ予測装置１は、例えば、ＣＰＵ（Central Processing Unit）１０と、記憶部１１と、センサ用通信インターフェース１２と、外部サーバ用通信インターフェース１３と、を備える。それら回路１０－１３はバス１４に接続されている。 The PV prediction apparatus 1 includes, for example, a CPU (Central Processing Unit) 10, a storage unit 11, a sensor communication interface 12, and an external server communication interface 13. These circuits 10-13 are connected to the bus 14.
　ＣＰＵ１０は、記憶部１１に記憶されている各種のコンピュータプログラムＰ１０、Ｐ１１を実行することで、後述の機能を実現する。 CPU10 implement | achieves the function mentioned later by executing the various computer programs P10 and P11 memorize | stored in the memory | storage part 11. FIG.
　記憶部１１は、例えば、メインメモリなどから構成される。記憶部１１は、フラッシュメモリデバイスまたはハードディスクドライブのような補助記憶装置を含んで構成することもできる。 The storage unit 11 includes, for example, a main memory. The storage unit 11 can also be configured to include an auxiliary storage device such as a flash memory device or a hard disk drive.
　記憶部１１には、例えば、オペレーティングシステム（図示せず）、影位置算出プログラムＰ１０、発電量算出プログラムＰ１１などの各種プログラムが格納される。さらに、記憶部１１には、装置データ管理テーブルＴ１０、撮像データ管理テーブルＴ１１、予測履歴データＴ１２などの各種管理情報も格納される。さらに、記憶部１１には、ＣＰＵ１０により使用される作業領域なども設定される。 The storage unit 11 stores various programs such as an operating system (not shown), a shadow position calculation program P10, and a power generation amount calculation program P11. Further, the storage unit 11 also stores various management information such as a device data management table T10, an imaging data management table T11, and prediction history data T12. Furthermore, a work area used by the CPU 10 is also set in the storage unit 11.
　センサ用通信インターフェース１２は、センサ用の通信ネットワークＣＮ１を介して、各カメラ２に通信可能に接続される。センサ用通信インターフェース１２は、各カメラ２に撮像指令を送信したり、各カメラ２からの撮像データを受信したりする。ＰＶ装置３もセンサ用通信ネットワークＣＮ１に接続してもよい。 The sensor communication interface 12 is communicably connected to each camera 2 via the sensor communication network CN1. The sensor communication interface 12 transmits an imaging command to each camera 2 and receives imaging data from each camera 2. The PV device 3 may also be connected to the sensor communication network CN1.
　外部サーバ用通信インターフェース１３は、外部サーバ用の通信ネットワークＣＮ２を介して、外部サーバ７、８に接続される。外部サーバとして、例えば、気象データ配信サーバ７と、ＣＥＭＳ（Community Energy Management System）８を挙げる。 The external server communication interface 13 is connected to the external servers 7 and 8 via the external server communication network CN2. Examples of external servers include a weather data distribution server 7 and a CEMS (Community Energy Management System) 8.
　気象データ配信サーバ７は、例えば、風速、風向、気圧、天候、太陽の位置などの気象データを配信するサーバである。ＣＥＭＳ８は、担当地域の電力の需給状態を監視し、電力系統を管理する電気事業者などに情報を提供する。ＣＥＭＳ８は、各需要家（例えば、一般家庭、商業施設、工場、病院など）に設置されたＥＭＳ（Energy Management System）と通信して、発電量及び電力消費量に関する情報を収集することもできる。 The meteorological data distribution server 7 is a server that distributes meteorological data such as wind speed, wind direction, atmospheric pressure, weather, and sun position. The CEMS 8 monitors the supply and demand state of power in the area in charge, and provides information to an electric utility that manages the power system. The CEMS 8 can also collect information on the power generation amount and the power consumption amount by communicating with an EMS (Energy Management System) installed in each consumer (for example, general household, commercial facility, factory, hospital, etc.).
　ＣＥＭＳ８は、例えば、ＰＶ装置３と図示せず通信ネットワークを介して接続されてもよい。ＣＥＭＳ８がＰＶ装置３の発電量及び稼働状態等の情報を収集して、それら情報をＰＶ予測装置１に送信してもよい。 The CEMS 8 may be connected to the PV device 3 via a communication network (not shown), for example. The CEMS 8 may collect information such as the power generation amount and the operating state of the PV device 3 and transmit the information to the PV prediction device 1.
　なお、ＰＶ予測装置１は、ＰＶ装置３が影６で覆われる比率及び日時等の情報を予測し、それら予測値をＣＥＭＳ８に送信し、ＣＥＭＳ８がＰＶ装置３の発電量を予測する、という構成にしてもよい。つまり、ＰＶ予測装置１は、ＰＶ発電量まで予測する必要はなく、ＰＶ発電量の予測に必要な予測値、または、ＰＶ発電量の変動を示す予測値を、外部に出力する構成でもよい。出力先はＣＥＭＳ８に限らず、例えば、電力系統の事業者が管理する電力制御システムでもよい。 The PV prediction device 1 is configured to predict information such as the ratio and date / time when the PV device 3 is covered with the shadow 6, transmit the predicted values to the CEMS 8, and the CEMS 8 predicts the power generation amount of the PV device 3. It may be. That is, the PV prediction apparatus 1 does not need to predict the PV power generation amount, and may be configured to output a prediction value necessary for prediction of the PV power generation amount or a prediction value indicating a variation in the PV power generation amount to the outside. The output destination is not limited to CEMS 8, and may be, for example, a power control system managed by a power system operator.
　通信ネットワークＣＮ１、ＣＮ２は、有線ネットワークとして構成してもよいし、無線ネットワークとして構成してもよい。さらに、通信ネットワークＣＮ１と通信ネットワークＣＮ２とを一つの通信ネットワークとして構成してもよい。 The communication networks CN1 and CN2 may be configured as a wired network or a wireless network. Further, the communication network CN1 and the communication network CN2 may be configured as one communication network.
　図３を参照して、装置データ管理テーブルＴ１０の構成を説明する。装置データ管理テーブルＴ１０は、カメラ２及びＰＶ装置３に関する情報を管理するテーブルである。装置データ管理テーブルＴ１０は、例えば、装置識別子（図中、識別子をＩＤと略記）Ｃ１００と、位置Ｃ１０１と、仕様Ｃ１０２と、状態Ｃ１０３とを対応付けて管理する。 The configuration of the device data management table T10 will be described with reference to FIG. The device data management table T10 is a table for managing information related to the camera 2 and the PV device 3. The device data management table T10 manages, for example, a device identifier (identifier is abbreviated as ID in the figure) C100, a position C101, a specification C102, and a state C103 in association with each other.
　装置識別子Ｃ１００は、装置（カメラ２、ＰＶ装置３）をＰＶ予測装置１が識別するための情報である。装置識別子Ｃ１００は、例えば、装置種別を示す情報と連続番号の組合せとして構成してもよい。 The device identifier C100 is information for the PV prediction device 1 to identify the device (camera 2, PV device 3). The device identifier C100 may be configured as a combination of information indicating the device type and a serial number, for example.
　位置Ｃ１０１は、カメラ２またはＰＶ装置３の設置された位置を示す情報である。設置位置Ｃ１０１は、例えば、緯度経度で表現してもよいし、他の座標系の値として表現してもよい。 The position C101 is information indicating the position where the camera 2 or the PV device 3 is installed. The installation position C101 may be expressed by, for example, latitude and longitude, or may be expressed as a value in another coordinate system.
　仕様Ｃ１０２は、カメラ２の仕様に関する情報、または、ＰＶ装置３の仕様に関する情報を示す。カメラ２の仕様情報としては、例えば、製造者名、機種名、管理者名、製造番号、連絡先、レンズ性能、撮影感度等がある。ＰＶ装置３の仕様情報としては、例えば、製造者名、機種名、管理者名、製造番号、連絡先、太陽光パネルの大きさ、発電能力等がある。 Spec C102 indicates information regarding the specifications of the camera 2 or information regarding the specifications of the PV device 3. The specification information of the camera 2 includes, for example, a manufacturer name, model name, administrator name, serial number, contact information, lens performance, photographing sensitivity, and the like. The specification information of the PV device 3 includes, for example, a manufacturer name, a model name, an administrator name, a manufacturing number, a contact number, a solar panel size, a power generation capacity, and the like.
　状態Ｃ１０３は、カメラ２の状態、または、ＰＶ装置３の状態を示す。状態Ｃ１０３には、例えば、正常稼働中であることを示す値、異常状態であることを示す値、故障中であることを示す値、点検中であることを示す値などが設定される。 The state C103 indicates the state of the camera 2 or the state of the PV device 3. For example, a value indicating a normal operation, a value indicating an abnormal state, a value indicating a failure, a value indicating a check, and the like are set in the state C103.
　図４を参照して、撮像データ管理テーブルＴ１１を説明する。撮像データ管理テーブルＴ１１は、カメラ２で撮影された画像データを、そのままの状態で、または、加工した状態で、管理する。 The imaging data management table T11 will be described with reference to FIG. The imaging data management table T11 manages image data captured by the camera 2 as it is or after being processed.
　撮像データ管理テーブルＴ１１は、例えば、管理番号Ｃ１１０と、エリア番号Ｃ１１１と、撮像データＣ１１２と、日時Ｃ１１３とを対応付けて管理する。エリア番号Ｃ１１１は、撮影されたエリアを識別するための番号である。撮像データＣ１１２には、例えば、撮像データの格納場所を示すポインタが格納される。カメラ２で撮影されたそのままの撮像データを保管してもよいし、撮像データを加工して後のデータを保管してもよい。撮像データは静止画像に限らない。所定時間流し撮りした動画像データでもよい。静止画像データの場合は、複数の静止画像データを保管できる。なお、日時Ｃ１１３は、撮影日時である。 The imaging data management table T11 manages, for example, a management number C110, an area number C111, imaging data C112, and date / time C113 in association with each other. The area number C111 is a number for identifying the photographed area. For example, a pointer indicating the storage location of the imaging data is stored in the imaging data C112. The raw image data captured by the camera 2 may be stored, or the captured data may be processed to store subsequent data. Imaging data is not limited to still images. It may be moving image data shot for a predetermined time. In the case of still image data, a plurality of still image data can be stored. The date and time C113 is the shooting date and time.
　図５を参照して、予測履歴データＴ１２を説明する。予測履歴データＴ１２は、雲の作る影６の位置の予測結果、及び、ＰＶ装置３の発電量の予測結果の履歴を管理するためのテーブルである。 The prediction history data T12 will be described with reference to FIG. The prediction history data T12 is a table for managing the prediction result of the position of the shadow 6 created by the cloud and the history of the prediction result of the power generation amount of the PV device 3.
　予測履歴テーブルＴ１２は、例えば、管理番号Ｃ１２０と、エリア番号Ｃ１２１と、予測データＣ１２２と、日時Ｃ１２３を対応付けて管理する。エリア番号Ｃ１２１は、雲の影の位置等を予測したエリアを識別する情報である。予測データＣ１２２には、例えば、雲の移動方向及び速度、影の位置及び大きさ、発電量などの、予測に用いた基礎データと、予測結果のデータの両方を含めてもよい。日時Ｃ１２３は、予測日時である。 The prediction history table T12 manages, for example, a management number C120, an area number C121, prediction data C122, and date / time C123 in association with each other. The area number C121 is information for identifying an area in which a cloud shadow position or the like is predicted. The prediction data C122 may include, for example, both basic data used for prediction and prediction result data, such as cloud moving direction and speed, shadow position and size, and power generation amount. The date and time C123 is a predicted date and time.
　図６は、ＰＶ予測装置１の動作の全体を示すフローチャートである。ＰＶ予測装置１は、最初に装置データ管理テーブルＴ１０を更新し（Ｓ１）、次に、雲が地上に形成する影の位置等を予測し（Ｓ２）、最後に、ＰＶ発電量を予測する（Ｓ３）。 FIG. 6 is a flowchart showing the entire operation of the PV prediction apparatus 1. The PV prediction device 1 first updates the device data management table T10 (S1), next predicts the position of the shadow formed by the cloud on the ground (S2), and finally predicts the PV power generation amount (S2). S3).
　更新処理Ｓ１を説明する。設置される装置の構成に変化が生じた場合に、ＰＶ予測装置１は、装置データ管理テーブルＴ１０の内容を更新する。構成の変化としては、例えば、カメラ２またはＰＶ装置３の、追加または撤去、交換、設置位置の変更等がある。 The update process S1 will be described. When a change occurs in the configuration of the installed device, the PV prediction device 1 updates the content of the device data management table T10. Examples of the configuration change include addition or removal of the camera 2 or the PV device 3, replacement, and change of the installation position.
　ＰＶ予測装置１の管理者であるユーザが、手動で、装置データ管理テーブルＴ１０の内容を書き換えてもよいし、外部の管理サーバ等から取得した情報に基づいて、装置データ管理テーブルＴ１０の内容を自動的に更新してもよい。 A user who is an administrator of the PV prediction apparatus 1 may manually rewrite the contents of the apparatus data management table T10, or based on the information acquired from an external management server or the like, the contents of the apparatus data management table T10 may be changed. You may update automatically.
　図７を参照して、雲の影の位置を算出する処理（Ｓ２）の詳細を説明する。ＰＶ予測装置１は、複数のカメラ２から撮像データを取得し（Ｓ２０）、さらに、気象データ配信サーバ７から気象データを取得する（Ｓ２１）。以下の説明では、カメラ２で撮影した雲の画像データを、全天画像データと呼ぶことがある。 Referring to FIG. 7, the details of the process of calculating the cloud shadow position (S2) will be described. The PV prediction apparatus 1 acquires imaging data from a plurality of cameras 2 (S20), and further acquires weather data from the weather data distribution server 7 (S21). In the following description, cloud image data captured by the camera 2 may be referred to as all-sky image data.
　ＰＶ予測装置１は、撮像データを画像処理して雲を抽出し、抽出された全ての雲に識別ラベルを設定する（Ｓ２２）。撮像データの各画素の輝度等に基づいて、雲と背景の空とを区分けし、微分処理等で雲の輪郭を算出することで、それぞれの雲を抽出することができる。識別ラベルとは、処理対象の雲を識別するための識別情報である。例えば、抽出された順番に雲に連続番号を付与して、その番号を識別ラベルとすればよい。 The PV prediction device 1 performs image processing on the captured data to extract clouds, and sets identification labels for all the extracted clouds (S22). Each cloud can be extracted by classifying the cloud and the background sky based on the brightness of each pixel of the imaging data and calculating the outline of the cloud by differentiation processing or the like. The identification label is identification information for identifying the cloud to be processed. For example, serial numbers may be assigned to the clouds in the order of extraction, and the numbers may be used as identification labels.
　ＰＶ予測装置１は、「予め設定される所定の時間間隔」としての判定用間隔Δｔを決定する（Ｓ２３）。判定用間隔Δｔは、所定の複数のカメラ２による複数回ずつの撮影によって、雲４の移動量Δｄ（図１参照）を検出するためのパラメータである。 The PV prediction device 1 determines the determination interval Δt as “predetermined predetermined time interval” (S23). The determination interval Δt is a parameter for detecting the movement amount Δd (see FIG. 1) of the cloud 4 by photographing a plurality of times by a predetermined plurality of cameras 2.
　判定用間隔Δｔは、例えば、固定値として予め設定してもよいし、もしくは、風速に応じた値を選択してもよい。または、過去の撮像データ及び気象データなどに基づいて、雲の移動量Δｄを検出可能な最小の値を、判定用間隔Δｔとして選択してもよい。 The determination interval Δt may be set in advance as a fixed value, for example, or a value corresponding to the wind speed may be selected. Alternatively, the minimum value that can detect the cloud movement amount Δd may be selected as the determination interval Δt based on past imaging data and weather data.
　判定用間隔Δｔを長く設定するのは比較的簡単である。しかし、判定用間隔Δｔを必要以上に長く設定すると、雲４の速度が大きい場合に、予測の応答性が低下する。雲４の影によるＰＶ装置３への変動を事前に予測し、その変動に備えるためには、できるだけ早く予測することが好ましい。そこで、本実施形態では、予測対象の雲４の移動量Δｄを検出可能な最小時間を、判定用間隔Δｔとして選択する。 It is relatively easy to set the determination interval Δt longer. However, if the determination interval Δt is set longer than necessary, the responsiveness of prediction decreases when the speed of the cloud 4 is high. In order to predict the change to the PV device 3 due to the shadow of the cloud 4 in advance and prepare for the change, it is preferable to predict as soon as possible. Therefore, in this embodiment, the minimum time during which the movement amount Δd of the cloud 4 to be predicted can be detected is selected as the determination interval Δt.
　ＰＶ予測装置１は、一回目の全天画像データと二回目の全天画像データとの差分に基づいて、雲４の高さ、位置、広がり、速度ベクトルを算出する（Ｓ２４）。なお、上述のように、一回目の全天画像データの撮影時刻と二回目の全天画像データの撮影時刻とは、判定用間隔Δｔだけ異なる。 The PV prediction device 1 calculates the height, position, spread, and velocity vector of the cloud 4 based on the difference between the first sky image data and the second sky image data (S24). As described above, the shooting time of the first all-sky image data and the shooting time of the second all-sky image data differ by the determination interval Δt.
　図１に示す配置関係を例に挙げると、図１から以下の関係式（１ａ）～（１ｈ）を得られる。図１において、ｈは、予測対象の雲４の高さである。Ｄは、雲４の広がりを示す長さである。Δｄは、時刻ｔ１から時刻ｔ２までの判定用間隔Δｔの間に、雲４が移動した距離である。ｄ１は、時刻ｔ１における雲４の前端と一方のカメラ２（１）の位置との間の距離である。ｄ２は、時刻ｔ２（＝ｔ１＋Δｔ）における雲４の前端と一方のカメラ２（１）の位置との間の距離である。 Taking the arrangement relationship shown in FIG. 1 as an example, the following relational expressions (1a) to (1h) can be obtained from FIG. In FIG. 1, h is the height of the cloud 4 to be predicted. D is a length indicating the spread of the cloud 4. Δd is a distance traveled by the cloud 4 during the determination interval Δt from time t1 to time t2. d1 is a distance between the front end of the cloud 4 and the position of one camera 2 (1) at time t1. d2 is a distance between the front end of the cloud 4 and the position of one camera 2 (1) at time t2 (= t1 + Δt).
　Ｌは、一方のカメラ２（１）と他方のカメラ２（２）との距離である。なお、一方のカメラ２（１）とは、予測に使用する複数のカメラのうち、予測対象の雲４に近い方のカメラである。一方のカメラ２（１）を第１カメラ２（１）と呼ぶことがある。他方のカメラ２（２）とは、予測に使用する複数のカメラのうち、予測対象の雲４に遠い方のカメラである。他方のカメラ２（２）を第２カメラ２（２）と呼ぶことがある。 L is the distance between one camera 2 (1) and the other camera 2 (2). One camera 2 (1) is a camera closer to the cloud 4 to be predicted among a plurality of cameras used for prediction. One camera 2 (1) may be referred to as a first camera 2 (1). The other camera 2 (2) is a camera farther from the cloud 4 to be predicted among a plurality of cameras used for prediction. The other camera 2 (2) may be referred to as a second camera 2 (2).
　図１中の角度αは、カメラ２の光軸の中心から雲４の後端を結ぶ線と水平線とが作る角度である。角度βは、カメラ２の光軸の中心から雲４の前端を結ぶ線と水平線とが作る角度である。角度α、角度βには、カメラ２を特定するための番号と、時刻を特定する番号とを添えている。例えば、α１１とは、第１カメラ２（１）の時刻ｔ１における、角度αであることを意味する。同様に、β１２とは、第１カメラ２（１）の時刻ｔ２における角度βであることを意味する。他の例は省略する。 The angle α in FIG. 1 is an angle formed by a line connecting the rear end of the cloud 4 from the center of the optical axis of the camera 2 and a horizontal line. The angle β is an angle formed by a line connecting the front end of the cloud 4 from the center of the optical axis of the camera 2 and a horizontal line. A number for specifying the camera 2 and a number for specifying the time are attached to the angles α and β. For example, α11 means the angle α at the time t1 of the first camera 2 (1). Similarly, β12 means the angle β at the time t2 of the first camera 2 (1). Other examples are omitted.
　　　(d1+D) * tan(α11) = h　・・・(1a)
　　　d1 * tan(β11) = h　　　・・・(1b)
　　　(d2+D) * tan(α12) = h　・・・(1c)
　　　d2 * tan(β12) = h　　　・・・(1d)
　　　(d1+D+L) * tan(α21) = h・・・(1e)
　　　(d1+L) * tan(β21) = h　・・・(1f)
　　　(d2+D+L) * tan(α22) = h・・・(1g)
　　　(d2+L) * tan(β22) = h　・・・(1h) (d1 + D) * tan (α11) = h ・ ・ ・ (1a)
d1 * tan (β11) = h ・ ・ ・ (1b)
(d2 + D) * tan (α12) = h ・ ・ ・ (1c)
d2 * tan (β12) = h ・ ・ ・ (1d)
(d1 + D + L) * tan (α21) = h ・ ・ ・ (1e)
(d1 + L) * tan (β21) = h ・ ・ ・ (1f)
(d2 + D + L) * tan (α22) = h ・ ・ ・ (1g)
(d2 + L) * tan (β22) = h ・ ・ ・ (1h)
　上記の式を整理する。式(1b)=式(1f)であるから、以下のように（１）式を得る。
　d1 * tan(β11) = (d1+L) *
tan(β21)
→d1 * {tan(β11)- tan(β21)} = L * tan(β21)
→d1 = L * (tanβ21)/(tanβ11-tanβ21)　・・・(1) Organize the above formula. Since Expression (1b) = Expression (1f), Expression (1) is obtained as follows.
d1 * tan (β11) = (d1 + L) *
tan (β21)
→ d1 * {tan (β11)-tan (β21)} = L * tan (β21)
→ d1 = L * (tanβ21) / (tanβ11-tanβ21) (1)
　式(1d)=式(1h)であるから、以下のように（２）式を得る。
　d2 * tan(β12) = (d2+L) *
tan(β22)
→d2 * {tan(β12)-tan(β22)} = L * tan(β22)
→d2 = L * (tanβ22)/(tanβ12-tanβ22)　・・・(2) Since Expression (1d) = Expression (1h), Expression (2) is obtained as follows.
d2 * tan (β12) = (d2 + L) *
tan (β22)
→ d2 * {tan (β12) -tan (β22)} = L * tan (β22)
→ d2 = L * (tanβ22) / (tanβ12-tanβ22) (2)
　式(1b)より、以下の（３）式を得る。
　h = d1 * tan(β11)　・・・(3) From the formula (1b), the following formula (3) is obtained.
h = d1 * tan (β11) (3)
　式(1a)より、以下のように（４）式を得る。
　(d1+D) * tan(α11)            = h
→d1+D = h/tanα11
→ D = h/tanα11 - d1　・・・(4) From equation (1a), equation (4) is obtained as follows.
(d1 + D) * tan (α11) = h
→ d1 + D = h / tanα11
→ D = h / tanα11-d1 (4)
　上記（１）～（４）から、高さｈで広がりＤを持つ雲の移動速度ｖを、v = Δｄ
/ Δｔ = (d2 -
d1) / Δｔとして、算出できる。 From the above (1) to (4), the moving speed v of the cloud having a height D and a spread D is represented by v = Δd
/ Δt = (d2-
d1) It can be calculated as / Δt.
　次に、ＰＶ予測装置１は、ｔ時間後の雲４の影の位置を算出する（Ｓ２５）。雲４の移動速度ｖが一定であると仮定することで、雲４は、ｔ時間後に（ｖ＊ｔ）だけ移動していると予測することができる。ｔ時間後の雲４が形成する地上の影が、ＰＶ装置３を覆い隠すか否かを知るためには、雲４から地面への射影を作成すればよい。 Next, the PV prediction device 1 calculates the position of the shadow of the cloud 4 after time t (S25). Assuming that the moving speed v of the cloud 4 is constant, it can be predicted that the cloud 4 has moved by (v * t) after time t. In order to know whether or not the shadow on the ground formed by the cloud 4 after time t covers the PV device 3, a projection from the cloud 4 to the ground may be created.
　図８は、ＰＶ装置３の発電量を予測する処理を示すフローチャートである。ＰＶ予測装置１は、図７で述べたステップＳ２５から、影の位置を取得する（Ｓ３０）。さらに、ＰＶ予測装置１は、装置データ管理テーブルＴ１０から、予測対象のＰＶ装置３の位置データを取得する（Ｓ３１）。 FIG. 8 is a flowchart showing a process for predicting the power generation amount of the PV device 3. The PV prediction apparatus 1 acquires the shadow position from step S25 described in FIG. 7 (S30). Furthermore, the PV prediction device 1 acquires the position data of the PV device 3 to be predicted from the device data management table T10 (S31).
　ＰＶ予測装置１は、雲４が地上に作る影とＰＶ装置３の関係を算出する（Ｓ３２）。影とＰＶ装置３との関係とは、予測対象の時点において影がＰＶ装置３をどの程度覆うかである。ＰＶ予測装置１は、影とＰＶ装置３の関係に基づいて、ＰＶ装置３の発電量を予測する（Ｓ３３）。ＰＶ予測装置１は、例えば、ＰＶ装置３に太陽光が当たる面積の割合と、ＰＶ装置３の単位面積当たりの発電能力とを乗算することで、そのＰＶ装置３での発電量を算出することができる。 The PV prediction device 1 calculates the relationship between the shadow created by the cloud 4 on the ground and the PV device 3 (S32). The relationship between the shadow and the PV device 3 is how much the shadow covers the PV device 3 at the time of the prediction target. The PV prediction device 1 predicts the power generation amount of the PV device 3 based on the relationship between the shadow and the PV device 3 (S33). The PV prediction device 1 calculates the amount of power generation in the PV device 3 by multiplying, for example, the ratio of the area where the sunlight hits the PV device 3 and the power generation capacity per unit area of the PV device 3. Can do.
　図９は、雲４が、ＰＶ装置３に入射する太陽光を遮るか否かを判定するための概念図である。図の左側の「０」と記載している点を基準点とする。基準点からの雲４の高さをｈとし、雲の広がりを示す長さをＤとする。ある時点において、雲４の前端とＰＶ装置３（２）とは距離ｄだけ離れている。ＰＶ装置３（２）から太陽５を見上げたときの角度をθとする。以上の前提で影６の位置を算出する。 FIG. 9 is a conceptual diagram for determining whether or not the cloud 4 blocks sunlight incident on the PV device 3. A point described as “0” on the left side of the figure is a reference point. The height of the cloud 4 from the reference point is h, and the length indicating the cloud spread is D. At some point, the front end of the cloud 4 and the PV device 3 (2) are separated by a distance d. The angle when looking up at the sun 5 from the PV device 3 (2) is defined as θ. Based on the above assumptions, the position of the shadow 6 is calculated.
　具体的には、雲４の影６の後端（図中、右端）と前端（図中、左端）を求める。雲の後端は、基準点０から（ｄ＋Ｄ）だけ離れている。雲４の後端位置までを影として計算すると、実際の影との間に、ｈ／ｔａｎ（θ）だけ、ずれが発生する。つまり、基準点から影６の後端位置までの距離をｄｓ１とすると、ｄｓ１は、（ｄ＋Ｄ）からｈ／ｔａｎ（θ）を差し引いた値として求められる（ds1 = d + D - h/tan(θ)）。 Specifically, the rear end (right end in the figure) and the front end (left end in the figure) of the shadow 6 of the cloud 4 are obtained. The trailing edge of the cloud is separated from the reference point 0 by (d + D). When calculation is performed up to the rear end position of the cloud 4 as a shadow, a deviation of h / tan (θ) occurs between the actual shadow and the actual shadow. That is, when the distance from the reference point to the rear end position of the shadow 6 is ds1, ds1 is obtained as a value obtained by subtracting h / tan (θ) from (d + D) (ds1 = d + D-h / tan ( θ)).
　影６の前端も同様に、基準点から距離ｄだけ離れている。基準点から影の前端位置までをの距離ｄｓ２とすると、ｄｓ２は、距離ｄからｈ／ｔａｎ（θ）を差し引いた値として求められる（ds2 = d - h/tan(θ)）。 Similarly, the front end of the shadow 6 is separated from the reference point by a distance d. If the distance ds2 from the reference point to the front edge position of the shadow is ds2, ds2 is obtained as a value obtained by subtracting h / tan (θ) from the distance d (ds2 = d-h / tan (θ)).
　このように構成される本実施例では、複数のカメラ２で複数回ずつ撮影される画像データを解析することで、雲４の高さ及び速度等を算出する。従って、ＰＶ予測装置１は、雲４が地上に作る影６の位置及び大きさを比較的正確に予測可能である。これにより、本実施例のＰＶ予測装置１は、ＰＶ装置３の発電量を比較的正確に予測できる。このため、本実施例は、例えば、電力系統の需給バランスを調整するための計画作成に役立てることができる。 In the present embodiment configured as described above, the height and speed of the cloud 4 are calculated by analyzing image data photographed multiple times by a plurality of cameras 2. Therefore, the PV prediction device 1 can predict the position and size of the shadow 6 created by the cloud 4 on the ground relatively accurately. Thereby, the PV prediction apparatus 1 of a present Example can estimate the electric power generation amount of the PV apparatus 3 comparatively correctly. For this reason, a present Example can be useful for the plan preparation for adjusting the supply-and-demand balance of an electric power grid | system, for example.
　本実施例では、ＰＶ装置３毎にカメラ２を設ける必要はなく、複数のカメラ２を地域に分散して配置し、それらカメラ２とＰＶ予測装置１とを通信ネットワークＣＮ１で接続すれば足りる。従って、ＰＶ装置３の設置数よりも少ない数のカメラ２で、ＰＶ発電量を予測でき、システム全体のコストを低減できる。 In this embodiment, it is not necessary to provide a camera 2 for each PV apparatus 3, and it is sufficient to disperse and arrange a plurality of cameras 2 in a region and connect the cameras 2 and the PV prediction apparatus 1 via a communication network CN1. Therefore, the PV power generation amount can be predicted with a smaller number of cameras 2 than the number of PV devices 3 installed, and the cost of the entire system can be reduced.
　図１０を参照して第２実施例を説明する。本実施例を含む以下の各実施例は、第１実施例の変形例に該当するため、第１実施例との相違を中心に説明する。本実施例は、第１実施例を一般化したものであり、カメラ２とＰＶ装置３及び雲４が同一直線上に無い場合を検討する。 The second embodiment will be described with reference to FIG. Each of the following embodiments, including the present embodiment, corresponds to a modification of the first embodiment, and therefore, differences from the first embodiment will be mainly described. The present embodiment is a generalization of the first embodiment, and considers a case where the camera 2, the PV device 3, and the cloud 4 are not on the same straight line.
　図１０は、本実施例によるシステム配置を示す。本実施例では、地面をＸ軸及びＹ軸で規定される面として表現する。 FIG. 10 shows a system layout according to the present embodiment. In this embodiment, the ground is expressed as a plane defined by the X axis and the Y axis.
　図１０に示す雲４（ｔ１）、４（ｔ２）の各位置において、以下の式が成り立つ。 The following equations hold at the positions of clouds 4 (t1) and 4 (t2) shown in FIG.
　　　d1/cos(γ21) * tan(α21) = h　　・・・(2a)
　　　d2/cos(γ22) * tan(α22) = h　　・・・(2b)
　　　(d1-L)/cos(γ11) * tan(α11) = h・・・(2c)
　　　(d2-L)/cos(γ12) * tan(α12) = h・・・(2d)
　　　d'1 = d1 * tan(γ21)　　　　　　・・・(2e)
　　　d'2 = d2 * tan(γ22)　　　　　　・・・(2f) d1 / cos (γ21) * tan (α21) = h ・ ・ ・ (2a)
d2 / cos (γ22) * tan (α22) = h ・ ・ ・ (2b)
(d1-L) / cos (γ11) * tan (α11) = h ... (2c)
(d2-L) / cos (γ12) * tan (α12) = h ... (2d)
d'1 = d1 * tan (γ21) (2e)
d'2 = d2 * tan (γ22) (2f)
　上記の式を整理すると、式(2a)=式(2c)より、以下のように（５）式を得られる。
　d1*tanα21*cosγ11 = (d1-L)*tanα11*cosγ21
→d1*(tanα11*cosγ21-tanα21*cosγ11) = L*tanα11*cosγ21
→d1 = L*tanα11*cosγ21/(tanα11*cosγ21-tanα21*cosγ11)・・・（５） By arranging the above equations, equation (5) can be obtained from equation (2a) = expression (2c) as follows.
d1 * tanα21 * cosγ11 = (d1-L) * tanα11 * cosγ21
→ d1 * (tanα11 * cosγ21-tanα21 * cosγ11) = L * tanα11 * cosγ21
→ d1 = L * tanα11 * cosγ21 / (tanα11 * cosγ21-tanα21 * cosγ11) (5)
　式(2b)=式(2d)より、以下のように（６）式を得られる。
d2*tanα22*cosγ12 = (d2-L)*tanα12*cosγ22
→d2*(tanα12*cosγ22-tanα22*cosγ12) = L*tanα12*cosγ22
→d2 = L*tanα12*cosγ22/(tanα12*cosγ22-tanα22*cosγ12)　・・・（６） From Expression (2b) = Expression (2d), Expression (6) is obtained as follows.
d2 * tanα22 * cosγ12 = (d2-L) * tanα12 * cosγ22
→ d2 * (tanα12 * cosγ22-tanα22 * cosγ12) = L * tanα12 * cosγ22
→ d2 = L * tanα12 * cosγ22 / (tanα12 * cosγ22-tanα22 * cosγ12) (6)
　式(2e)より（７）式を、式(2f)より（８）式を、それぞれ得られる。
　　　d'1 = d1 * tan(γ21)・・・（７）
　　　d'2 = d2 * tan(γ22)  ・・・（８） Equation (7) is obtained from equation (2e), and equation (8) is obtained from equation (2f).
d'1 = d1 * tan (γ21) (7)
d'2 = d2 * tan (γ22) (8)
　以上の式から、雲４の移動速度ベクトルｖは、v = (vx,
vy) = ((d2-d1)/Δｔ, (d'2-d'1)/Δｔ)として算出することができる。このように構成される本実施例も第１実施例と同様の効果を奏する。 From the above equation, the moving velocity vector v of the cloud 4 is v = (vx,
vy) = ((d2-d1) / Δt, (d'2-d'1) / Δt). Configuring this embodiment like this also achieves the same effects as the first embodiment.
　図１１及び図１２を参照して第３実施例を説明する。本実施例では、３台以上のカメラ２（１）～２（３）を用いる。本実施例では、予め設定される領域毎に、３台以上のカメラ２（１）～２（３）のうち、いずれの２台のカメラを使用するか決定する。 A third embodiment will be described with reference to FIGS. In this embodiment, three or more cameras 2 (1) to 2 (3) are used. In this embodiment, it is determined which of the two or more cameras 2 (1) to 2 (3) to use for each preset region.
　図１１は、カメラ２（１）～２（３）の配置と、領域毎に使用するカメラとの関係を示す説明図である。ＰＶ予測装置１は、図１１に示すような内容が定義された管理テーブルを備えている。 FIG. 11 is an explanatory diagram showing the relationship between the arrangement of the cameras 2 (1) to 2 (3) and the cameras used for each region. The PV prediction apparatus 1 includes a management table in which contents as shown in FIG. 11 are defined.
　図１１では、予測対象の全領域を、エリア１～エリア３の３つに区切っている。第１カメラ２（１）は、エリア１とエリア２の境界に位置する。第２カメラ２（２）は、エリア２とエリア３の境界に位置する。第３カメラ２（３）は、エリア３とエリア１の境界に位置する。換言すれば、予測対象の全領域は、分散する複数のカメラ２（１）～２（３）の位置に応じて、複数のエリア１～３に分割されている。 In FIG. 11, the entire region to be predicted is divided into three areas 1 to 3. The first camera 2 (1) is located at the boundary between area 1 and area 2. The second camera 2 (2) is located at the boundary between area 2 and area 3. The third camera 2 (3) is located at the boundary between the area 3 and the area 1. In other words, the entire region to be predicted is divided into a plurality of areas 1 to 3 according to the positions of the plurality of cameras 2 (1) to 2 (3) to be dispersed.
　各エリア１～３は、図１１の例では、５角形のエリアとして構成されている。エリア４は、各カメラ２（１）～２（３）を頂点とする三角形状のエリアとして形成される。各エリア１～４内にそれぞれ１つずつＰＶ装置３が設けられているものとする。 Each area 1 to 3 is configured as a pentagonal area in the example of FIG. The area 4 is formed as a triangular area with the cameras 2 (1) to 2 (3) as vertices. It is assumed that one PV device 3 is provided in each of the areas 1 to 4.
　図１２は、本実施例による、影の位置を算出する処理を示す。本処理は、図７に示す処理のうちステップＳ２０がステップＳ２０Ａに変更されている。本実施例に特徴的なステップＳ２０Ａは、予測対象のＰＶ装置３の位置に応じて、予測処理に使用するカメラ２を複数選択する。 FIG. 12 shows a process for calculating the position of the shadow according to this embodiment. In this process, step S20 in the process shown in FIG. 7 is changed to step S20A. Step S20A characteristic of the present embodiment selects a plurality of cameras 2 to be used for the prediction process according to the position of the PV device 3 to be predicted.
　エリア１のＰＶ装置３（１）についてＰＶ発電量を予測する場合、エリア１の境界に設けられている第１カメラ２（１）と第３カメラ２（３）とが選択される。エリア２のＰＶ装置３（２）についてＰＶ発電量を予測する場合、エリア２の境界に設けられている第２カメラ２（２）と第１カメラ２（１）とが選択される。エリア３のＰＶ装置３（３）についてＰＶ発電量を予測する場合、エリア３の境界に設けられている第２カメラ２（２）と第３カメラ２（３）とが選択される。 When the PV power generation amount is predicted for the PV device 3 (1) in the area 1, the first camera 2 (1) and the third camera 2 (3) provided at the boundary of the area 1 are selected. When the PV power generation amount is predicted for the PV device 3 (2) in the area 2, the second camera 2 (2) and the first camera 2 (1) provided at the boundary of the area 2 are selected. When the PV power generation amount is predicted for the PV device 3 (3) in the area 3, the second camera 2 (2) and the third camera 2 (3) provided at the boundary of the area 3 are selected.
　中央部のエリア４のＰＶ装置３（４）についてＰＶ発電量を予測する場合、エリア４の境界に位置する第１カメラ２（１）と、第２カメラ２（２）と、第３カメラ２（３）とが選択される。なお、エリア４を省略してもよい。 When the PV power generation amount is predicted for the PV device 3 (4) in the central area 4, the first camera 2 (1), the second camera 2 (2), and the third camera 2 located at the boundary of the area 4 are used. (3) is selected. The area 4 may be omitted.
　このように構成される本実施例も第１実施例と同様の効果を奏する。さらに、本実施例では、予測対象のＰＶ装置３の位置に応じて複数のカメラ２を選択する。従って、本実施例では、予測対象のＰＶ装置３に影響を与え得る雲４に近い、複数のカメラ２を選択することができる。この結果、雲４の高度及び速度等をより正確に算出することができ、影の位置に応じてＰＶ発電量を予測できる。 This embodiment configured as described above also has the same effect as the first embodiment. Furthermore, in this embodiment, a plurality of cameras 2 are selected according to the position of the PV device 3 to be predicted. Therefore, in this embodiment, it is possible to select a plurality of cameras 2 close to the cloud 4 that can affect the PV device 3 to be predicted. As a result, the altitude and speed of the cloud 4 can be calculated more accurately, and the PV power generation amount can be predicted according to the position of the shadow.
　なお、本発明は、上述した実施例に限定されない。当業者であれば、本発明の範囲内で、種々の追加や変更等を行うことができる。例えば、前記各実施例では、ＰＶ発電量の予測まで行う場合を述べたが、これに代えて、雲の位置及び速度等、及び／または、影の位置及び速度等を予測するだけでもよい。それらの予測を他のコンピュータに入力することで、ＰＶ発電量等を算出することができる。 In addition, this invention is not limited to the Example mentioned above. A person skilled in the art can make various additions and changes within the scope of the present invention. For example, in each of the above-described embodiments, the case where the PV power generation amount is predicted has been described, but instead of this, only the position and speed of the cloud and / or the position and speed of the shadow may be predicted. By inputting these predictions to another computer, the PV power generation amount and the like can be calculated.
　本発明は、コンピュータプログラムの発明として表現することもできる。
「コンピュータを、雲の影の位置及び大きさを予測するための装置として機能させるためのコンピュータプログラムであって、
　離間して配置され、雲を検出するための複数の雲検出装置の中から、所定の複数の雲検出装置を選択するステップと、
　選択された前記所定の複数の雲検出装置から、予め設定される所定の時間だけ離れた複数の時点で、雲の検出に関する雲検出情報をそれぞれ取得するステップと、
　複数の前記雲検出情報に基づいて、雲の高さ、広がり、移動方向及び移動速度を含む所定の基礎データをそれぞれ算出するステップと、
　算出された前記所定の基礎データに基づいて、雲が地上に形成する影の位置及び大きさを算出するステップと、
を前記コンピュータに実行させるためのコンピュータプログラム。」 The present invention can also be expressed as a computer program invention.
"A computer program for causing a computer to function as a device for predicting the position and size of a cloud shadow,
Selecting a plurality of predetermined cloud detection devices from a plurality of cloud detection devices that are spaced apart and detect clouds;
Respectively obtaining cloud detection information relating to cloud detection at a plurality of time points separated by a predetermined time from the selected plurality of selected cloud detection devices;
Calculating each predetermined basic data including the height, spread, moving direction and moving speed of the cloud based on the plurality of cloud detection information;
Calculating the position and size of a shadow formed by the cloud on the ground based on the calculated predetermined basic data;
A computer program for causing the computer to execute. "
　１：太陽光発電予測装置
　２：カメラ
　３：太陽光発電装置
　４：雲
　５：太陽
　６：雲の影 1: Photovoltaic power generation prediction device 2: Camera 3: Photovoltaic power generation device 4: Cloud 5: Sun 6: Shadow of cloud

Claims (8)

  1. 　雲の影の位置及び大きさを予測するための影位置予測システムであって、
    　離間して配置され雲を検出するための複数の雲検出装置から通信ネットワークを介して信号を取得する予測装置と、
    を備え、
    　前記予測装置は、
    　　複数の前記雲検出装置のうち所定の複数の雲検出装置から、雲の検出に関する雲検出情報を取得し、
    　　複数の前記雲検出情報に基づいて、雲の高さ、広がり、移動方向及び移動速度を含む所定の基礎データをそれぞれ算出し、
    　　算出された前記所定の基礎データに基づいて、雲が地上に形成する影の位置及び大きさを算出する、
    影位置予測システム。     A shadow position prediction system for predicting the position and size of a cloud shadow,
    A prediction device that acquires signals via a communication network from a plurality of cloud detection devices that are spaced apart to detect clouds;
    With
    The prediction device is
    Cloud detection information related to cloud detection is obtained from a plurality of predetermined cloud detection devices among the plurality of cloud detection devices,
    Based on the plurality of cloud detection information, each calculates predetermined basic data including cloud height, spread, moving direction and moving speed,
    Based on the calculated predetermined basic data, the position and size of the shadow formed by the cloud on the ground is calculated.
    Shadow position prediction system.
  2. 　前記予測装置は、
    　　予め設定される所定の時間間隔をおいた複数の所定時点で、前記所定の複数の雲検出装置から、予測対象の雲に関する前記雲検出情報をそれぞれ取得し、
    　　複数の前記所定時点でそれぞれ取得される複数の前記雲検出情報を比較して演算することで、前記所定の基礎データを算出する、
    請求項１に記載の影位置予測システム。     The prediction device is
    At a plurality of predetermined time points with a predetermined time interval set in advance, each of the cloud detection information regarding the cloud to be predicted is acquired from the predetermined plurality of cloud detection devices,
    By calculating and comparing the plurality of cloud detection information respectively obtained at a plurality of the predetermined time points, to calculate the predetermined basic data,
    The shadow position prediction system according to claim 1.
  3. 　複数の前記雲検出装置から所定距離以上離れた場所に、太陽光を利用する太陽光利用装置が少なくとも一つ設置されており、
    　前記予測装置は、前記影の位置及び大きさと、前記太陽光利用装置の位置とに基づいて、前記影が前記太陽光利用装置に与える影響を算出する、
    請求項２に記載の影位置予測システム。     At least one solar-powered device that uses sunlight is installed at a location more than a predetermined distance away from the plurality of cloud detection devices,
    The prediction device calculates the influence of the shadow on the sunlight utilization device based on the position and size of the shadow and the position of the sunlight utilization device.
    The shadow position prediction system according to claim 2.
  4. 　前記太陽光利用装置は、太陽光を受光するための受光面を有しており、
    　前記予測装置は、前記受光面を前記影が覆う比率及び時間を、前記影が前記太陽光利用装置に与える影響として算出する、
    請求項３に記載の影位置予測システム。     The solar light utilization device has a light receiving surface for receiving sunlight,
    The prediction device calculates a ratio and time that the shadow covers the light receiving surface as an influence of the shadow on the sunlight utilization device.
    The shadow position prediction system according to claim 3.
  5. 　前記予測装置は、
    　　地理的領域毎にどの雲検出装置を選択するかを予め定義する選択用情報を有し、
    　　前記太陽光利用装置の位置と前記選択用情報とに基づいて、複数の前記雲検出装置のうち、前記太陽光利用装置の位置に応じた複数の雲検出装置を、前記所定の複数の雲検出装置として選択する、
    請求項４に記載の影位置予測システム。     The prediction device is
    Selection information that predefines which cloud detection device to select for each geographic region;
    Based on the position of the sunlight utilization device and the information for selection, among the plurality of cloud detection devices, a plurality of cloud detection devices according to the position of the sunlight utilization device are detected as the predetermined plurality of clouds. Select as device,
    The shadow position prediction system according to claim 4.
  6. 　前記雲検出装置は、カメラ装置として構成されており、
    　前記雲検出情報は、雲を撮像した画像データであり、
    　前記太陽光利用装置は、太陽光発電装置として構成されている、
    請求項１～５のいずれかに記載の影位置予測システム。     The cloud detection device is configured as a camera device,
    The cloud detection information is image data obtained by imaging a cloud,
    The solar power utilization device is configured as a solar power generation device,
    The shadow position prediction system according to any one of claims 1 to 5.
  7. 　雲の影の位置及び大きさをコンピュータを用いて予測するための影位置予測方法であって、
    　前記コンピュータは、
    　　離間して配置され、雲を検出するための複数の雲検出装置の中から、所定の複数の雲検出装置を選択し、
    　　選択された前記所定の複数の雲検出装置から、予め設定される所定の時間だけ離れた複数の時点で、雲の検出に関する雲検出情報をそれぞれ取得し、
    　　複数の前記雲検出情報に基づいて、雲の高さ、広がり、移動方向及び移動速度を含む所定の基礎データをそれぞれ算出し、
    　　算出された前記所定の基礎データに基づいて、雲が地上に形成する影の位置及び大きさを算出する、
    影位置予測方法。     A shadow position prediction method for predicting the position and size of a cloud shadow using a computer,
    The computer
    A predetermined plurality of cloud detection devices are selected from among a plurality of cloud detection devices that are spaced apart and detect clouds,
    Obtaining cloud detection information related to cloud detection at a plurality of time points separated by a predetermined time from the selected plurality of selected cloud detection devices;
    Based on the plurality of cloud detection information, each calculates predetermined basic data including cloud height, spread, moving direction and moving speed,
    Based on the calculated predetermined basic data, the position and size of the shadow formed by the cloud on the ground is calculated.
    Shadow position prediction method.
  8. 　前記コンピュータは、さらに、前記影の位置及び大きさと、複数の前記雲検出装置から所定距離以上離れた場所に位置して太陽光を利用する太陽光利用装置の位置とに基づいて、前記影が前記太陽光利用装置に与える影響を算出する、
    請求項７に記載の影位置予測方法。     The computer further includes the shadow based on the position and size of the shadow and the position of a sunlight utilization device that is located at a predetermined distance or more away from the plurality of cloud detection devices and uses sunlight. Calculating the impact on the solar-powered device;
    The shadow position prediction method according to claim 7.
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