WO2013105244A1 - Système de prédiction d'emplacement d'ombre et procédé de prédiction d'emplacement d'ombre - Google Patents

Système de prédiction d'emplacement d'ombre et procédé de prédiction d'emplacement d'ombre Download PDF

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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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English (en)
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/fr
Publication of WO2013105244A1 publication Critical patent/WO2013105244A1/fr

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

Definitions

  • 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

La présente invention vise à prédire avec précision, de manière comparative, l'emplacement, la vitesse, etc., d'ombres projetées par des nuages. La présente invention concerne un système de prédiction d'emplacement d'ombre pour prédire l'emplacement et la dimension d'ombres de nuage, comprenant : une pluralité de dispositifs de détection de nuage (2) qui sont positionnés à distance les uns des autres, et qui sont destinés à détecter des nuages ; et un dispositif de prédiction (1), qui est connecté à la pluralité de dispositifs de détection de nuage (2) par l'intermédiaire d'un réseau de communication (CN1). Le dispositif de prédiction (1) acquiert des informations de détection de nuage relatives à la détection de nuages à partir d'une pluralité prescrite des dispositifs de détection de nuage (2) parmi les dispositifs de détection de nuage (2). Sur la base d'une pluralité d'informations de détection de nuage, le dispositif de prédiction (1) calcule respectivement des données de base prescrites comprenant la hauteur, la propagation, la direction de déplacement et la vitesse de déplacement de nuage et, sur la base de ces données de base prescrites, calcule l'emplacement et la dimension d'ombres que des nuages (4) projettent sur le sol.
PCT/JP2012/050473 2012-01-12 2012-01-12 Système de prédiction d'emplacement d'ombre et procédé de prédiction d'emplacement d'ombre WO2013105244A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104181612A (zh) * 2014-08-13 2014-12-03 中国人民解放军理工大学 一种红外与激光联合的地基测云方法
JP2015171247A (ja) * 2014-03-07 2015-09-28 株式会社 日立産業制御ソリューションズ 発電電力急減予兆検知装置、太陽光発電システム、発電電力急減予兆検知方法、及びプログラム
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WO2016054112A1 (fr) 2014-09-29 2016-04-07 View, Inc. Detection de l'intensite de lumiere solaire ou de nuages avec capteur de distance variable
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WO2018067996A1 (fr) * 2016-10-06 2018-04-12 View, Inc. Systèmes et procédés de détection de nuage infrarouge
USD816518S1 (en) 2015-10-06 2018-05-01 View, Inc. Multi-sensor
US10520784B2 (en) 2012-04-17 2019-12-31 View, Inc. Controlling transitions in optically switchable devices
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US10539854B2 (en) 2013-02-21 2020-01-21 View, Inc. Control method for tintable windows
US10712627B2 (en) 2011-03-16 2020-07-14 View, Inc. Controlling transitions in optically switchable devices
US10802372B2 (en) 2013-02-21 2020-10-13 View, Inc. Control method for tintable windows
CN112292620A (zh) * 2018-06-19 2021-01-29 古野电气株式会社 云观测装置、云观测系统、云观测方法及程序
US10908470B2 (en) 2011-03-16 2021-02-02 View, Inc. Multipurpose controller for multistate windows
US11255722B2 (en) 2015-10-06 2022-02-22 View, Inc. Infrared cloud detector systems and methods
US11261654B2 (en) 2015-07-07 2022-03-01 View, Inc. Control method for tintable windows
CN114462723A (zh) * 2022-04-12 2022-05-10 南方电网数字电网研究院有限公司 基于高空风资源影响的云层迁移分钟级光伏功率预测方法
US11566938B2 (en) 2014-09-29 2023-01-31 View, Inc. Methods and systems for controlling tintable windows with cloud detection
US11635666B2 (en) 2012-03-13 2023-04-25 View, Inc Methods of controlling multi-zone tintable windows
US11674843B2 (en) 2015-10-06 2023-06-13 View, Inc. Infrared cloud detector systems and methods
CN116452066A (zh) * 2023-05-16 2023-07-18 中交第二公路勘察设计研究院有限公司 一种考虑炫光影响的路侧光伏选址方法
US11719990B2 (en) 2013-02-21 2023-08-08 View, Inc. Control method for tintable windows
US11781903B2 (en) 2014-09-29 2023-10-10 View, Inc. Methods and systems for controlling tintable windows with cloud detection
US11950340B2 (en) 2012-03-13 2024-04-02 View, Inc. Adjusting interior lighting based on dynamic glass tinting
US11960190B2 (en) 2013-02-21 2024-04-16 View, Inc. Control methods and systems using external 3D modeling and schedule-based computing
US11966142B2 (en) 2013-02-21 2024-04-23 View, Inc. Control methods and systems using outside temperature as a driver for changing window tint states

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005312163A (ja) * 2004-04-20 2005-11-04 Canon Inc 発電制御装置及び発電システム
JP2007184354A (ja) * 2006-01-05 2007-07-19 Mitsubishi Electric Corp 太陽光発電システム
JP2009252940A (ja) * 2008-04-04 2009-10-29 Mitsubishi Electric Corp 太陽光発電システムの出力予測装置およびそれを用いた需給制御システム

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005312163A (ja) * 2004-04-20 2005-11-04 Canon Inc 発電制御装置及び発電システム
JP2007184354A (ja) * 2006-01-05 2007-07-19 Mitsubishi Electric Corp 太陽光発電システム
JP2009252940A (ja) * 2008-04-04 2009-10-29 Mitsubishi Electric Corp 太陽光発電システムの出力予測装置およびそれを用いた需給制御システム

Cited By (45)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11520207B2 (en) 2011-03-16 2022-12-06 View, Inc. Controlling transitions in optically switchable devices
US10908470B2 (en) 2011-03-16 2021-02-02 View, Inc. Multipurpose controller for multistate windows
US10712627B2 (en) 2011-03-16 2020-07-14 View, Inc. Controlling transitions in optically switchable devices
US11950340B2 (en) 2012-03-13 2024-04-02 View, Inc. Adjusting interior lighting based on dynamic glass tinting
US11635666B2 (en) 2012-03-13 2023-04-25 View, Inc Methods of controlling multi-zone tintable windows
US10520784B2 (en) 2012-04-17 2019-12-31 View, Inc. Controlling transitions in optically switchable devices
US11126057B2 (en) 2013-02-21 2021-09-21 View, Inc. Control method for tintable windows
US10802372B2 (en) 2013-02-21 2020-10-13 View, Inc. Control method for tintable windows
US11966142B2 (en) 2013-02-21 2024-04-23 View, Inc. Control methods and systems using outside temperature as a driver for changing window tint states
US11960190B2 (en) 2013-02-21 2024-04-16 View, Inc. Control methods and systems using external 3D modeling and schedule-based computing
US11940705B2 (en) 2013-02-21 2024-03-26 View, Inc. Control method for tintable windows
US11899331B2 (en) 2013-02-21 2024-02-13 View, Inc. Control method for tintable windows
US10539854B2 (en) 2013-02-21 2020-01-21 View, Inc. Control method for tintable windows
US11719990B2 (en) 2013-02-21 2023-08-08 View, Inc. Control method for tintable windows
JP2015171247A (ja) * 2014-03-07 2015-09-28 株式会社 日立産業制御ソリューションズ 発電電力急減予兆検知装置、太陽光発電システム、発電電力急減予兆検知方法、及びプログラム
CN104181612A (zh) * 2014-08-13 2014-12-03 中国人民解放军理工大学 一种红外与激光联合的地基测云方法
US11221434B2 (en) 2014-09-29 2022-01-11 View, Inc. Sunlight intensity or cloud detection with variable distance sensing
WO2016054112A1 (fr) 2014-09-29 2016-04-07 View, Inc. Detection de l'intensite de lumiere solaire ou de nuages avec capteur de distance variable
US10732028B2 (en) 2014-09-29 2020-08-04 View, Inc. Combi-sensor systems
US10895498B2 (en) 2014-09-29 2021-01-19 View, Inc. Combi-sensor systems
EP3201613A4 (fr) * 2014-09-29 2018-05-30 View, Inc. Detection de l'intensite de lumiere solaire ou de nuages avec capteur de distance variable
CN106796305A (zh) * 2014-09-29 2017-05-31 唯景公司 具有可变距离感测的日光强度或云检测
US10234596B2 (en) 2014-09-29 2019-03-19 View, Inc. Sunlight intensity or cloud detection with variable distance sensing
US10539456B2 (en) 2014-09-29 2020-01-21 View, Inc. Combi-sensor systems
CN106796305B (zh) * 2014-09-29 2021-11-23 唯景公司 具有可变距离感测的日光强度或云检测
US11781903B2 (en) 2014-09-29 2023-10-10 View, Inc. Methods and systems for controlling tintable windows with cloud detection
US11566938B2 (en) 2014-09-29 2023-01-31 View, Inc. Methods and systems for controlling tintable windows with cloud detection
US11346710B2 (en) 2014-09-29 2022-05-31 View, Inc. Combi-sensor systems
US11261654B2 (en) 2015-07-07 2022-03-01 View, Inc. Control method for tintable windows
US11175178B2 (en) 2015-10-06 2021-11-16 View, Inc. Adjusting window tint based at least in part on sensed sun radiation
US11674843B2 (en) 2015-10-06 2023-06-13 View, Inc. Infrared cloud detector systems and methods
US10533892B2 (en) 2015-10-06 2020-01-14 View, Inc. Multi-sensor device and system with a light diffusing element around a periphery of a ring of photosensors and an infrared sensor
US11255722B2 (en) 2015-10-06 2022-02-22 View, Inc. Infrared cloud detector systems and methods
US10690540B2 (en) 2015-10-06 2020-06-23 View, Inc. Multi-sensor having a light diffusing element around a periphery of a ring of photosensors
US11280671B2 (en) 2015-10-06 2022-03-22 View, Inc. Sensing sun radiation using a plurality of photosensors and a pyrometer for controlling tinting of windows
USD816518S1 (en) 2015-10-06 2018-05-01 View, Inc. Multi-sensor
CN105469391A (zh) * 2015-11-17 2016-04-06 中国科学院遥感与数字地球研究所 一种云阴影检测方法及系统
WO2018067996A1 (fr) * 2016-10-06 2018-04-12 View, Inc. Systèmes et procédés de détection de nuage infrarouge
CN107742171B (zh) * 2017-10-31 2020-08-21 浙江工业大学 基于移动阴影图像识别的光伏电站发电功率预测方法
CN107742171A (zh) * 2017-10-31 2018-02-27 浙江工业大学 基于移动阴影图像识别的光伏电站发电功率预测方法
CN112292620B (zh) * 2018-06-19 2023-06-06 古野电气株式会社 云观测装置、云观测系统、云观测方法及存储介质
CN112292620A (zh) * 2018-06-19 2021-01-29 古野电气株式会社 云观测装置、云观测系统、云观测方法及程序
CN114462723A (zh) * 2022-04-12 2022-05-10 南方电网数字电网研究院有限公司 基于高空风资源影响的云层迁移分钟级光伏功率预测方法
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