JP2017200363A - Solar radiation amount prediction device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a solar radiation amount prediction device for predicting a solar radiation amount based on gradation information indicating whether moisture vapor in atmospheric air is much or little, without requiring installation of a pyranometer.SOLUTION: The present invention relates to a solar radiation amount prediction device for predicting a solar radiation amount in an area of a user of power in which a photovoltaic power generation facility is installed. The solar radiation amount prediction device comprises: a solar radiation amount prediction expression generation part for generating a prediction expression for a solar radiation amount in the area based on a result value, obtained from transmission information of an artificial satellite, of the gradation information indicating whether moisture vapor in atmospheric air is much or little; a gradation prediction part for calculating a prediction value of the gradation information in a third time zone after a second time zone in the area based on a result value of the gradation information in a first time zone and a result value of the gradation information in the second time zone after the first time zone; and a solar radiation amount prediction part for calculating a prediction value of a solar radiation amount in the third time zone in the area based on the prediction expression for the solar radiation amount and the prediction value of the gradation information.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a solar radiation amount prediction apparatus.

  For example, a method for predicting the amount of power generated by a photovoltaic power generation facility in a predetermined future time period based on the amount of solar radiation and the amount of load power measured by a solar radiation meter installed in a wiring section is known. . (For example, patent document 1).

JP 2012-44740

  In Patent Document 1, in a distribution section where a plurality of solar power generators are arranged, the amount of solar radiation measured by a pyranometer provided in the distribution section and a sensor built-in automatic switch provided in the distribution section are measured. And calculating a power generation amount prediction formula by regression analysis based on the current flow. However, in patent document 1, it is necessary to arrange a pyranometer in all the power distribution sections made into object. Therefore, in order to predict the power generation amount in a wide distribution section, an enormous number of pyranometers are required, which may require enormous costs.

The main present invention that solves the above-mentioned problem is a solar radiation amount predicting device that predicts the solar radiation amount of a region of a power consumer in which a photovoltaic power generation facility is installed, and is obtained from the transmission information of an artificial satellite. Based on the actual value of the gradation information indicating the amount of water vapor, the solar radiation amount prediction formula generating unit for generating the prediction formula of the solar radiation amount in the region, the actual value of the gradation information in the first time zone, Based on the actual value of the gradation information in the second time period after the first time period, the predicted value of the gradation information in the third time period after the second time period in the region A tone prediction unit for calculating
A solar radiation amount predicting unit that calculates a predicted value of the solar radiation amount in the third time zone in the region based on the prediction formula of the solar radiation amount and the predicted value of the gradation information. And

  Other features of the present invention will become apparent from the accompanying drawings and the description of this specification.

  According to the present invention, since the amount of solar radiation is predicted based on gradation information indicating the amount of water vapor in the atmosphere, it is not necessary to install a pyranometer, and therefore it is possible to predict the amount of solar radiation at low cost. Become.

It is a power distribution system system diagram which shows the system system which concerns on this embodiment. It is a figure which shows the structural example of the electric power flow prediction system which concerns on this embodiment. It is a figure which shows an example of the hardware constitutions of the solar radiation amount prediction apparatus which concerns on this embodiment. It is a figure which shows an example of the hardware constitutions of the electric power generation amount prediction apparatus which concerns on this embodiment. It is a figure which shows an example of the hardware constitutions of the demand amount prediction apparatus which concerns on this embodiment. It is a figure which shows an example of the hardware constitutions of the power flow prediction apparatus which concerns on this embodiment. It is a figure which shows the structural example of the gradation performance information table which concerns on this embodiment. It is a figure which shows the structural example of the solar radiation amount results information table which concerns on this embodiment. It is a figure which shows the structural example of the gradation prediction information table which concerns on this embodiment. It is a figure which shows the structural example of the solar radiation amount prediction information table which concerns on this embodiment. It is a figure which shows the structural example of the 1st weather prediction information table which concerns on this embodiment. It is a figure which shows the structural example of the electric power generation amount prediction information table which concerns on this embodiment. It is a figure which shows the structural example of the demand amount results information table which concerns on this embodiment. It is a figure which shows the structural example of the weather performance information table which concerns on this embodiment. It is a figure which shows the structural example of the 2nd weather prediction information table which concerns on this embodiment. It is a figure which shows the structural example of the demand amount prediction information table which concerns on this embodiment. It is a figure which shows the structural example of the electric power flow prediction table which concerns on this embodiment. It is a figure which shows an example of the transition of the gradation information which concerns on this embodiment. It is a flowchart for demonstrating the example of a processing flow of the electric power flow prediction system which concerns on this embodiment. It is a flowchart for demonstrating the example of a processing flow of the solar radiation amount prediction apparatus which concerns on this embodiment. It is a flowchart for demonstrating the example of a processing flow of the electric power generation amount prediction apparatus which concerns on this embodiment. It is a flowchart for demonstrating the example of a processing flow of the demand amount prediction apparatus which concerns on this embodiment. It is a flowchart for demonstrating the example of a processing flow of the power flow prediction apparatus which concerns on this embodiment.

  At least the following matters will become apparent from the description of this specification and the accompanying drawings. In the following description, parts denoted by the same reference numerals represent the same elements, and the basic configuration and operation thereof are the same.

=== System system 10 ===
A power distribution system 200 in which the power flow prediction system 100 according to the present embodiment predicts a power flow will be described with reference to FIG. FIG. 1 is a power distribution system diagram showing a power distribution system system 10 according to the present embodiment.

  A load facility 210 and a power generation facility 220 are connected to the power distribution system 200. The load facility 210 is a facility that operates by receiving power from the power distribution system 200. The power generation facility 220 is, for example, a solar power generation facility that uses renewable energy, and is a facility that transmits power to the power distribution system 200. In the power distribution system 200, fluctuations in the amount of power consumed by the load facility 210 (hereinafter referred to as “demand amount”) and the amount of power generated by the power generation facility 220 (hereinafter referred to as “power generation amount”). Along with this, the power flow changes. Due to fluctuations in the power flow, the voltage of the distribution system 200 may increase, thereby deviating from the supply voltage under the Electricity Business Law, and the fluctuation of the frequency of the distribution system 200 may be increased. Therefore, the power supply company adjusts the operation of the power plant (not shown), adjusts the voltage of the power distribution system 200, and charges and discharges the power storage equipment (not shown) connected to the power distribution system 200. To rationally adjust the power flow.

  However, for example, in a thermal power plant (not shown), a power output of a certain level or more must be maintained due to the characteristics of the power generation equipment. Therefore, in the power distribution system 200 connected to the thermal power plant, there is a possibility that the power generation amount of the power generation facility 220 that depends on the weather or the like cannot be complemented by the operation adjustment in the thermal power plant.

  Therefore, as shown in FIG. 1, in the power distribution system system 10 according to the present embodiment, the power flow prediction system 100 has various information in the load facility 210 and the power generation facility 220 connected to the smart meter 230 via the network 300. And a function of predicting the power flow of the distribution system 200 based on various information acquired from the reception system 410 in which information received from the artificial satellite 400 is stored. Thereby, the electric power supply provider can rationally adjust the power flow of the distribution system 200 using the pumped-storage power generation or the power storage facility based on the prediction of the power flow.

  In the system system 10 according to the present embodiment, the artificial satellite 400 is, for example, Himawari Nos. 8 and 9, and the receiving system 410 is a system owned by another person who receives information from the artificial satellite 400. is there. The weather information server 500 is, for example, a weather information disclosure server owned by the Japan Meteorological Agency. The smart meter 230 is a device owned by the power supply company, and is a device that transmits and receives information about the demand amount of the load facility 210 and the power generation amount of the power generation facility 220 in the consumer via the network 300. .

=== Power Flow Prediction System 100 ===
The power flow prediction system 100 will be described with reference to FIG. FIG. 2 is a diagram illustrating a configuration example of the power flow prediction system 100 according to the present embodiment.

  The power flow prediction system 100 stores a reception system 410 (for example, a system owned by the Japan Meteorological Agency) that stores information transmitted from the artificial satellite 400 via the network 300, and stores a demand amount and a power generation amount at a consumer. The smart meter 230 is connected to a weather information server 500 (for example, a server owned by the Japan Meteorological Agency) that stores past and future weather information. In addition, the power flow prediction system 100, for example, the power flow in the distribution system 200 based on information observed by the artificial satellite 400, information measured by the smart meter 230, and weather information released from the Japan Meteorological Agency. It is a system for predicting. The power flow system includes, for example, a solar radiation amount prediction device 110 that predicts a future solar radiation amount, a power generation amount prediction device 120 that predicts a future power generation amount based on the future solar radiation amount, and past and future weather information. A demand forecast device 130 that predicts a future demand based on the current demand and a past demand, a power flow forecast device 140 that predicts a future power flow based on the future power generation and the future demand, It is comprised including.

=== Insolation amount prediction device 110 ===
The solar radiation amount prediction apparatus 110 will be described with reference to FIG. FIG. 3 is a diagram illustrating an example of a hardware configuration of the solar radiation amount prediction apparatus 110 according to the present embodiment.

  The solar radiation amount predicting device 110 is gradation information indicating the gradation in the past predetermined time zone acquired from the receiving system 410 of the artificial satellite 400 in a predetermined area (hereinafter referred to as “gradation result information”). And solar radiation amount information indicating the amount of solar radiation acquired from the receiving system 410 in the past predetermined time period in which the gradation performance information was acquired (hereinafter referred to as “solar radiation amount performance information”), and gradation performance information. A function for generating a solar radiation amount prediction formula for calculating a solar radiation amount in a predetermined time period in the future based on the information on the solar radiation amount outside the atmosphere in the past predetermined time period acquired in A quantity prediction formula generation unit 111A). Then, the solar radiation amount prediction device 110 calculates gradation information (hereinafter referred to as “gradation prediction information”) in a future predetermined time zone based on the gradation result information (gradation prediction unit 111B), Calculation of solar radiation amount information (hereinafter referred to as “sunlight amount prediction information”) indicating the amount of solar radiation in a predetermined future time period calculated by substituting the gradation prediction information into the solar radiation amount prediction formula (irradiation amount prediction unit) 111C). The predetermined area refers to an area including a consumer where the load facility 210 and the power generation facility 220 are arranged. In addition, gradation refers to a numerical value indicating the amount of water vapor in the atmosphere among meteorological information acquired from an artificial satellite. In other words, the gradation is a numerical value indicating the amount of water vapor contained in the cloud. Therefore, the movement of the cloud can be predicted by observing the transition of gradation in a predetermined area. In addition, radiation information observed by the artificial satellite 400 (for example, Himawari Nos. 8 and 9) is divided into 16 frequency bands (hereinafter referred to as “band numbers 1 to 16”) that are divided by the central wavelength of the radiation. Further, each frequency band is expressed by 1024 gradations. That is, in each frequency band, the amount of water vapor in the atmosphere is expressed by 1024 levels of gradation information. In addition, the solar radiation amount outside the atmosphere is the solar radiation energy received per unit time in a unit area in a plane perpendicular to the sun rays at the upper end of the earth's atmosphere (above 8 km) when the earth is at an average distance from the sun. Say quantity. The amount of solar radiation outside the atmosphere is calculated based on the latitude, longitude, and date and time (predetermined date in the past or future).

  The solar radiation amount prediction device 110 includes, for example, a first arithmetic processing unit 111, a first storage unit 112, an input unit 113, an output unit 114, a network interface unit 115, and a memory 116. Yes.

  The first arithmetic processing unit 111 has a function of reading out and executing a program held in the first storage unit 112 to the memory 116 and performing overall control of the solar radiation amount prediction apparatus 110. The first calculation processing unit 111 is realized by executing a program, a solar radiation amount prediction formula generation unit 111A that calculates a solar radiation amount prediction formula, a gradation prediction unit 111B that calculates gradation prediction information, and a solar radiation amount prediction It is configured to include a function of a solar radiation amount prediction unit 111C that calculates solar radiation amount prediction information from the formula and the gradation prediction information. Details of each function will be described later.

  The first storage unit 112 includes a nonvolatile storage element such as an SSD (Solid State Drive) or a hard drive. The first storage unit 112 has a gradation performance information table 112A that stores gradation performance information, a solar radiation performance information table 112B that stores solar radiation performance information, and a gradation that stores gradation prediction information. A prediction information table 112C and a solar radiation amount prediction information table 112D in which solar radiation amount prediction information is stored are stored. Details of each table will be described later.

  The input unit 113 has a function of accepting key input or voice input from a worker or the like. The output unit 114 has a function of displaying the result processed by the first arithmetic processing unit 111 on a display or the like. The network interface unit 115 is connected to the network 300 and has a function of performing communication processing with other devices connected to the network 300. Specifically, the network interface unit 115 is connected to the reception system 410, the weather information server 500, and the smart meter 230 via the network 300 so as to be communicable. The memory 116 is a main storage device including, for example, a processor, a RAM, a ROM, and the like.

<< Solar Radiation Prediction Formula Generation Unit 111A >>
The function of the solar radiation amount prediction formula generation unit 111A will be described in detail as follows.

  In a predetermined first area, the solar radiation amount prediction formula generation unit 111 </ b> A is in the past time zone that is the same as the past time zone indicated by the grayscale result information acquired from the reception system 410 of the artificial satellite 400 and the grayscale result information. This is a function for generating a solar radiation amount prediction formula based on the solar radiation amount actual information and the outdoor solar radiation amount information in the same past time zone as indicated by the gradation actual information. More specifically, in the solar radiation amount prediction model represented by the equation (1), the gradation past record information Cn in the past predetermined time zone in the predetermined first region and the past predetermined predetermined same as the gradation past record information Cn. The solar radiation amount result information P in the time zone and the outdoor atmospheric radiation amount information D in the past predetermined time zone same as the gradation result information Cn are substituted.

（但し、Ｐは目的変数（日射量実績情報）、Ａは回帰定数、Ｂｎは偏回帰係数、Ｃｎは説明変数（階調実績情報）、Ｄは大気外日射量情報を表す。）

(However, P is an objective variable (actual solar radiation amount information), A is a regression constant, Bn is a partial regression coefficient, Cn is an explanatory variable (gradation actual information), and D is outdoor solar radiation amount information.)

  Similarly, in a predetermined past time zone that is different from the past predetermined time zone described above, the gradation actual result information Cn, the solar radiation amount actual information P, and the outdoor solar radiation amount information D are converted into a solar radiation amount prediction model. Assign to. By repeating this, a plurality of (for example, 16) solar radiation amount prediction models in a predetermined first region are generated. And the solar radiation amount prediction formula production | generation part 111A performs a regression analysis with respect to several solar radiation amount prediction model. Thereby, the solar radiation amount prediction formula shown by Formula (2) is produced | generated by calculating the regression constant A and the partial regression coefficients B1-B16 in a predetermined 1st area. Further, in the area different from the predetermined first area, the solar radiation amount prediction formula is generated in the same manner as described above. That is, the solar radiation amount prediction formula generation unit 111A generates respective solar radiation amount prediction formulas in a plurality of regions.

（但し、Ｐは目的変数（日射量予測情報）、Ａは回帰定数、Ｂ１〜Ｂ１６は偏回帰係数、Ｃ１〜Ｃ１６は説明変数（階調予測情報）、Ｄは大気外日射量情報を表す。）

(However, P is an objective variable (irradiation amount prediction information), A is a regression constant, B1 to B16 are partial regression coefficients, C1 to C16 are explanatory variables (gradation prediction information), and D is outdoor atmospheric radiation amount information. )

  Note that the regression analysis described above includes single regression analysis and multiple regression analysis, and a least square method or a Bayesian estimation method may be used instead of the regression analysis. In addition, although n is calculated as 1 to 16 in the above, n may be limited to 3, 7, 8, 13, 15, etc. In this case, band numbers 3, 7, 8, 13, 15 The solar radiation amount prediction formula is generated only for the above.

<< Gradation Prediction Unit 111B >>
The function of the gradation prediction unit 111B will be described in detail as follows.

  The gradation prediction unit 111B is a function that generates gradation prediction information in a predetermined first area based on gradation result information in a predetermined area including the predetermined first area. More specifically, as illustrated in FIG. 18, for example, the gradation predicting unit 111 </ b> B has, for example, the third gradation performance information of the third region in the past first time period after the first time period. Based on the time difference between the first time zone and the second time zone and the distance between the third region and the second region when the second gradation performance information is shown in the second region in the second time zone, This is a function for calculating tone prediction information for the future third time zone in one area. More specifically, the gradation prediction unit 111B calculates the transition (distance traveled) of the cloud (gradation record information) in the elapsed time in the past time zone, and predicts the position of the cloud in the future time zone. This is a function for calculating tone prediction information.

  Note that the gradation prediction information has been described as being calculated based on the distance between the third region and the second region, but instead of the distance between the third region and the second region, the clouds in the respective time zones are flattened. Alternatively, it may be calculated based on the moving distance of the center of gravity of the cloud as viewed.

<< Insolation amount prediction unit 111C >>
The function of the solar radiation amount prediction unit 111C will be described in detail as follows.

  The solar radiation amount prediction unit 111C calculates the solar radiation amount prediction information based on the solar radiation amount prediction formula calculated by the solar radiation amount prediction formula generation unit 111A and the gradation prediction information calculated by the gradation prediction unit 111B. It is a function. More specifically, in the solar radiation amount prediction formula (Formula 2) in a predetermined first area, gradation prediction information in the predetermined first area is input to C1 to C16, and D in the predetermined first area. The solar radiation amount information is input and the solar radiation amount prediction information P is calculated. Furthermore, the solar radiation amount prediction information P in each predetermined area is calculated by performing the same process as described above in an area different from the predetermined first area. That is, the solar radiation amount prediction unit 111C is a function that calculates solar radiation amount prediction information in a predetermined first area and a plurality of predetermined areas.

<< First Storage Unit 112 >>
The first storage unit 112 will be described in detail as follows.

  The first storage unit 112 stores, for example, a gradation result information table 112A, a solar radiation amount information table 112B, a gradation prediction information table 112C, and a solar radiation amount prediction information table 112D.

  As shown in FIG. 7, in the gradation performance information table 112A, for example, a date indicating a past predetermined time zone, an hour and minute indicating a past predetermined time zone, and a radiation frequency band are shown. The band number and the gradation performance information in each frequency band are stored in association with each other. That is, the gradation performance information corresponding to the respective band numbers 1 to 16 is stored in the past predetermined time zone. The past predetermined time zone preferably includes, for example, the past several years.

  As shown in FIG. 8, the solar radiation amount record information table 112B includes, for example, a predetermined area, a date indicating a past predetermined time zone, an hour and minute indicating a past predetermined time zone, and a solar radiation amount. The quantity result information is stored in association with each other. That is, the solar radiation amount record information in the past predetermined time zone in a plurality of predetermined areas is stored. The past predetermined time zone preferably includes, for example, the past several years.

  As shown in FIG. 9, the gradation prediction information table 112C includes, for example, a predetermined region, a date indicating a predetermined time zone in the future, an hour and minute indicating a predetermined time zone in the future, and radiation. Are stored in association with the band number indicating the frequency band of the image and the gradation prediction information in each frequency band. That is, tone prediction information corresponding to each band number 1 to 16 is stored in a predetermined future time zone in a plurality of predetermined regions.

  As shown in FIG. 10, the solar radiation amount prediction information table 112D includes, for example, a predetermined area, a date indicating a predetermined time zone in the future, an hour and minute indicating a predetermined time zone in the future, and a solar radiation. The amount prediction information is stored in association with each other. That is, the solar radiation amount prediction information in a future predetermined time zone is stored in a plurality of predetermined areas.

=== Power Generation Prediction Device 120 ===
The power generation amount prediction device 120 will be described with reference to FIG. FIG. 4 is a diagram illustrating an example of a hardware configuration of the power generation amount prediction apparatus 120 according to the present embodiment.

  The power generation amount prediction device 120 is based on the solar radiation amount prediction information acquired from the solar radiation amount prediction device 110 and the first weather prediction information in a predetermined future time zone acquired from the weather information server 500. It is an apparatus for calculating the amount of power generation in a predetermined time zone.

  The power generation amount prediction apparatus 120 includes a second arithmetic processing unit 121, a second storage unit 122, an input unit 123, an output unit 124, a network interface unit 125, and a memory 126. For convenience of explanation, the input unit 123, the output unit 124, the network interface unit 125, and the memory 126 in the power generation amount prediction device 120 are the input unit 113, the output unit 114, the network interface unit 115, and the memory 116 in the solar radiation amount prediction device 110. Since this is the same, the description thereof is omitted.

  The second arithmetic processing unit 121 has a function of reading out and executing a program held in the second storage unit 122 to the memory 126 and performing overall control of the power generation amount prediction apparatus 120. The second arithmetic processing unit 121 executes the program to obtain the power generation amount prediction information from the first weather prediction unit 121A that acquires the first weather prediction information, the solar radiation amount prediction information, and the first weather prediction information. The power generation amount prediction unit 121B to be calculated is configured to include the function. Details of each function will be described later.

  The second storage unit 122 includes a nonvolatile storage element such as an SSD (Solid State Drive) or a hard drive. The second storage unit 122 stores a first weather prediction information table 122A in which first weather prediction information is stored, and a power generation amount prediction information table 122B in which power generation amount prediction information is stored. Details of each table will be described later.

<< First Weather Forecasting Unit 121A >>
The function of the first weather prediction unit 121A will be described in detail as follows.

  The first weather prediction unit 121A has a function of acquiring first weather prediction information indicating a weather condition in a predetermined time zone in the future from, for example, a weather forecast server owned by the Japan Meteorological Agency. In order to use the calculation formula prescribed | regulated to JIS8907 mentioned later, it is preferable that 1st weather prediction information is comprised including at least temperature information and wind speed information.

<< Power generation amount prediction unit 121B >>
The function of the power generation amount prediction unit 121B will be described in detail as follows.

  The power generation amount prediction unit 121B includes the solar radiation amount prediction information calculated by the solar radiation amount prediction device 110, the first weather prediction information acquired by the first weather prediction unit 121A, and the power generation amount prediction formula defined in IS8907. The power generation amount prediction information is calculated based on the above. More specifically, the power generation amount prediction information is calculated by inputting the temperature information and the wind speed information included in the first weather prediction information and the solar radiation amount prediction information in the predetermined first region into the power generation amount prediction formula. . Further, the same processing as described above is performed in an area different from the predetermined first area, and the power generation amount prediction information in each predetermined area is calculated. That is, the power generation amount prediction unit 121B is a function that calculates power generation amount prediction information in a predetermined first region and a plurality of predetermined regions.

<< Second Storage Unit 122 >>
The second storage unit 122 will be described in detail as follows.

  In the second storage unit 122, for example, a first weather prediction information table 122A and a power generation amount prediction information table 122B are stored.

  As shown in FIG. 11, the first weather forecast information table 122A includes, for example, a predetermined region, a date indicating a predetermined time zone in the future, an hour and minute indicating a predetermined time zone in the future, Of the first weather forecast information, the temperature information indicating the temperature, the wind speed information indicating the wind speed, and the precipitation information indicating the precipitation are stored in association with each other. That is, the first weather prediction information in a predetermined future time zone in a plurality of areas is stored.

  As shown in FIG. 12, the power generation amount prediction information table 122B includes, for example, a predetermined region, a date indicating a predetermined time zone in the future, an hour and minute indicating a predetermined time zone in the future, The amount prediction information is stored in association with each other. That is, the power generation amount prediction information indicating the power generation amount flowing backward from the power generation facility 220 to the power distribution system 200 in a predetermined future time zone in a plurality of predetermined regions is stored.

=== Demand Prediction Device 130 ===
The demand amount prediction apparatus 130 will be described with reference to FIG. FIG. 5 is a diagram illustrating an example of a hardware configuration of the demand amount prediction apparatus 130 according to the present embodiment.

  The demand amount prediction device 130 is a demand amount information (hereinafter referred to as “demand amount actual information”) indicating a demand amount in a predetermined time zone acquired from a smart meter 230 installed in a consumer in a predetermined area. And meteorological information (hereinafter referred to as “weather performance information”) indicating the weather situation acquired from the weather server 500 in a predetermined past time zone in which the demand amount actual information has been acquired. And a function for generating a demand amount prediction formula for calculating a demand amount in a predetermined time zone in the future. Then, the demand amount prediction device 130 substitutes second weather forecast information, which will be described later, into the demand amount prediction formula and calculates demand amount information (hereinafter referred to as “demand amount prediction information”) in the future predetermined time zone. It is a device for calculating.

  The demand amount prediction device 130 includes a third arithmetic processing unit 131, a third storage unit 132, an input unit 133, an output unit 134, a network interface unit 135, and a memory 136. For convenience of explanation, the input unit 133, the output unit 134, the network interface unit 135, and the memory 136 in the demand amount prediction device 130 are the input unit 113, the output unit 114, the network interface unit 115, and the memory 116 in the solar radiation amount prediction device 110. Since this is the same, the description thereof is omitted.

  The third arithmetic processing unit 131 has a function of reading out and executing a program held in the third storage unit 132 to the memory 136 and performing overall control of the demand amount prediction apparatus 130. The third arithmetic processing unit 131 is realized by executing a program, a second weather prediction unit 131A that acquires second weather prediction information, a demand amount prediction formula calculation unit 131B that calculates a demand amount prediction formula, (2) The demand amount prediction unit 131C that calculates the demand amount prediction information from the weather forecast information and the demand amount prediction formula is configured to include a function. Details of each function will be described later.

  The third storage unit 132 includes a nonvolatile storage element such as an SSD (Solid State Drive) or a hard drive. The third storage unit 132 stores a demand amount result information table 132A in which demand amount result information is stored, a weather result information table 132B in which weather result information is stored, and a second weather in which second weather prediction information is stored. A prediction information table 132C and a demand amount prediction information table 132D in which demand amount prediction information is stored are stored. Details of each table will be described later.

<< Second Weather Prediction Unit 131A >>
The function of the second weather prediction unit 131A will be described in detail as follows.

  The second weather prediction unit 131A has a function of acquiring second weather prediction information indicating a weather situation in a predetermined time zone in the future from, for example, a weather forecast server owned by the Japan Meteorological Agency. The second weather forecast information is preferably configured to include at least temperature information and precipitation information in order to be used in a demand forecast formula described later.

<< Demand Quantity Prediction Formula Calculation Unit 131B >>
The function of the demand amount prediction formula calculation unit 131B will be described in detail as follows.

  The demand amount prediction formula calculation unit 131B calculates the solar radiation amount prediction formula based on the demand amount result information acquired from the smart meter 230 and the weather result information acquired from the weather information server 500 in the predetermined first area. It is a function to generate. More specifically, in the demand amount prediction model represented by the equation (3), the demand amount actual information Q in the past predetermined time zone and the past predetermined amount same as the demand amount actual information Q in the predetermined first region. The weather performance information Gn in the time zone is substituted.

（但し、Ｑは目的変数（需要量実績情報）、Ｅは回帰定数、Ｆｎは偏回帰係数、Ｇｎは説明変数（気象実績情報）を表す。）

(However, Q represents an objective variable (demand amount actual information), E represents a regression constant, Fn represents a partial regression coefficient, and Gn represents an explanatory variable (weather actual information).)

  Similarly, the actual demand information Q and the actual weather information Gn are substituted into the demand prediction model in a past predetermined time zone different from the past predetermined time zone described above. By repeating this, a plurality of demand amount prediction models in a predetermined first region are generated. Then, the demand amount prediction formula calculation unit 131B performs regression analysis on the plurality of demand amount prediction models. As a result, the regression constant E and the partial regression coefficient Fn in the predetermined first area are calculated, thereby generating the demand amount prediction formula represented by the formula (4). Further, in a region different from the predetermined first region, a demand amount prediction formula is generated in the same manner as described above. That is, the demand amount prediction formula calculation unit 131B generates solar radiation amount prediction formulas in a plurality of regions. Further, in a region different from the predetermined first region, a demand amount prediction formula is generated in the same manner as described above. That is, the demand amount prediction formula calculation unit 131B generates respective demand amount prediction formulas in a plurality of regions.

（但し、Ｑは目的変数（需要量予測情報）、Ｅは回帰定数、Ｆ１〜Ｆｍは偏回帰係数、Ｇ１〜Ｇｍは説明変数（第２気象予測情報）を表す。）

(However, Q is an objective variable (demand amount prediction information), E is a regression constant, F1 to Fm are partial regression coefficients, and G1 to Gm are explanatory variables (second weather prediction information).)

  Note that the regression analysis described above includes single regression analysis and multiple regression analysis, and instead of regression analysis, a least square method, a Bayesian estimation method, or the like may be used. Further, n in the above is m = 2 when the second weather prediction information is temperature information and precipitation information, for example. That is, the demand amount prediction formula is generated with the number of items included in the second weather prediction information as the value of m.

<< Demand Quantity Prediction Unit 131C >>
The function of the demand amount prediction unit 131C will be described in detail as follows.

The demand amount prediction unit 131C is a function that calculates the demand amount prediction information based on the second weather prediction information and the demand amount prediction formula calculated by the demand amount prediction formula calculation unit 131B. More specifically, in the demand forecast formula (Formula 4) in a predetermined first area, when the second weather forecast information in the first area is input to G1 to Gm, the demand forecast information Q is calculated. . Furthermore, the demand amount prediction information Q in each predetermined area is calculated by performing the same process as described above even in an area different from the predetermined first area. That is, the demand amount prediction unit 131C is a function that calculates demand amount prediction information in a predetermined first region and a plurality of predetermined regions.

<< Third Storage Unit 132 >>
The third storage unit 132 will be described in detail as follows.

  The third storage unit 132 stores, for example, a demand amount result information table 132A, a weather result information table 132B, a second weather prediction information table 132C, and a demand amount prediction information table 132D.

  As shown in FIG. 13, the demand amount record information table 132A includes, for example, a predetermined region, a date indicating a past predetermined time zone, an hour and minute indicating a past predetermined time zone, a demand The quantity result information is stored in association with each other. That is, the demand amount actual information in the past predetermined time zone in a plurality of predetermined areas is stored. The past predetermined time zone preferably includes, for example, the past several years.

  As shown in FIG. 14, the weather record information table 132B includes, for example, a predetermined region, a date indicating a past predetermined time zone, an hour and minute indicating a past predetermined time zone, and a weather record. Information is stored in association with each other. In other words, weather record information in a predetermined past time zone in a plurality of predetermined regions is stored. The past predetermined time zone preferably includes, for example, the past several years.

  As shown in FIG. 15, the second weather forecast information table 132C includes, for example, a predetermined region, a date indicating a predetermined time zone in the future, an hour and minute indicating a predetermined time zone in the future, Of the first weather forecast information, the temperature information indicating the temperature and the precipitation information indicating the precipitation are stored in association with each other. That is, the second weather prediction information in a predetermined future time zone in a plurality of predetermined regions is stored.

  As shown in FIG. 16, the demand amount prediction information table 132D includes, for example, a predetermined region, a date indicating a predetermined time zone in the future, an hour and minute indicating a predetermined time zone in the future, a demand The amount prediction information is stored in association with each other. That is, the demand amount prediction information in a predetermined future time zone in a plurality of predetermined regions is stored.

=== Power Flow Prediction Device 140 ===
The power flow prediction device 140 will be described with reference to FIG. FIG. 6 is a diagram illustrating an example of a hardware configuration of the power flow prediction apparatus 140 according to the present embodiment.

  The power flow prediction device 140 has a function of consolidating power generation amount prediction information and demand amount prediction information in a predetermined region into power generation amount prediction information and demand amount prediction information in a predetermined region in which a plurality of predetermined regions are collected. Furthermore, the apparatus that aggregates the power generation amount prediction information and the demand amount prediction information in a predetermined section into the power generation amount prediction information and the demand amount prediction information in a predetermined transformer section to which the power distribution system 200 of a plurality of predetermined sections is connected. It is.

  The power flow prediction device 140 includes a fourth arithmetic processing unit 141, a fourth storage unit 142, an input unit 143, an output unit 144, a network interface unit 145, and a memory 146. For convenience of explanation, the input unit 143, the output unit 144, the network interface unit 145, and the memory 146 in the power flow prediction device 140 are the input unit 113, the output unit 114, the network interface unit 115, and the memory 116 in the solar radiation amount prediction device 110. Since this is the same, the description thereof is omitted.

  The fourth arithmetic processing unit 141 has a function of reading out and executing a program held in the fourth storage unit 142 to the memory 146 and performing overall control of the power flow prediction device 140. The fourth arithmetic processing unit 141 is implemented by executing a program, and an area aggregation unit 141A that aggregates a plurality of predetermined areas into a predetermined area, and a transformer that consolidates the plurality of predetermined areas into a predetermined transformer section. It is configured to include the functions of a condenser aggregation unit 141B and a power flow prediction unit 141C that predicts a power flow in the transformer section. Details of each function will be described later.

  The fourth storage unit 142 includes a nonvolatile storage element such as an SSD (Solid State Drive) or a hard drive. The fourth storage unit 142 stores a power flow prediction table 142A in which power flow prediction information is stored. Details of the power flow prediction table 142A will be described later.

<< Area Aggregation Unit 141A >>
The function of the area aggregation unit 141A will be described in detail as follows.

  The area aggregation unit 141A aggregates each predetermined area into a predetermined area based on the power generation amount prediction information in the predetermined area and the demand amount prediction information in the predetermined area, and generates the power generation amount in the predetermined area. This is a function for calculating prediction information and power generation amount prediction information. More specifically, the area aggregation unit 141A aggregates, for example, power generation amount prediction information and demand amount prediction information in a predetermined region A and power generation amount prediction information and demand amount prediction information in a predetermined region B. This is a function for calculating power generation amount prediction information and demand amount prediction information in a predetermined area AA.

  In addition, the function of the area aggregation unit 141A may be integrated into the function of the transformer aggregation unit 141B described later. In this case, the transformer aggregating unit 141B aggregates a predetermined area and calculates the power generation amount prediction information and the demand amount prediction information in the transformer section.

<< Transformer consolidating unit 141B >>
The function of the transformer consolidating unit 141B will be described in detail as follows.

  The transformer aggregating unit 141B aggregates the power generation amount prediction information and the demand amount prediction information in the predetermined area, which are aggregated by the area aggregating unit 141A, into a predetermined transformer section for each predetermined area, It is a function which calculates as power generation amount prediction information and power generation amount prediction information in the unit classification. More specifically, the transformer aggregating unit 141B aggregates, for example, power generation amount prediction information and demand amount prediction information in a predetermined area AA, and power generation amount prediction information and demand amount prediction information in a predetermined area BB. This is a function for calculating power generation amount prediction information and demand amount prediction information in a predetermined transformer section AAA.

<< Power Flow Prediction Unit 141C >>
The function of the power flow prediction unit 141C will be described in detail as follows.

  The power flow prediction unit 141C includes power generation amount prediction information and demand amount prediction information in a predetermined region, power generation amount prediction information and demand amount prediction information in a predetermined region, and power generation amount prediction information and demand amount prediction information in a transformer section. And the function of calculating the power flow status in the power distribution system 200 based on the above. More specifically, the power flow prediction unit 141C calculates, for example, a region shortage power amount indicating a power shortage in a predetermined region from the power generation amount prediction information and the demand amount prediction information in the predetermined region, An area shortage electric power indicating a shortage of electric power in a predetermined area is calculated from the power generation amount prediction information and the demand amount prediction information in the predetermined area, and the predetermined transformer section is calculated from the power generation amount prediction information and the demand amount prediction information in the predetermined transformer section. It is a function which calculates the transformer shortage electric energy which shows the electric power shortage in.

<< Fourth Storage Unit 142 >>
The fourth storage unit 142 will be described in detail as follows.

  The fourth storage unit 142 stores, for example, a power flow prediction table 142A.

  As shown in FIG. 17, the power flow prediction table 142A includes, for example, a date indicating a future predetermined time zone, an hour and minute indicating a future predetermined time zone, a predetermined transformer category, The predetermined area, the predetermined area, the power generation amount prediction information, the demand amount prediction information, the region shortage electric energy (indicated as “region shortage” in FIG. 17), and the shortage electric energy (in FIG. And a transformer shortage electric energy (shown as “transformer shortage” in FIG. 17) in association with each other. That is, information indicating the amount of electric power predicted to be insufficient in the future in the predetermined region, area, and transformer section is stored in the predetermined transformer section, area, and region.

=== Processing procedure ===
<< Power flow forecasting system 100 >>
The processing procedure of the power flow prediction system 100 will be described with reference to FIG. FIG. 19 is a flowchart for explaining a processing flow example of the power flow prediction system 100 according to the present embodiment.

  In S100, the solar radiation amount prediction information calculated by the solar radiation amount prediction device 110 is acquired. The acquired solar radiation amount prediction information is input to the power flow prediction table 142A. In S200, the power generation amount prediction information calculated by the power generation amount prediction device 120 is acquired. The acquired power generation amount prediction information is input to the power flow prediction table 142A. In S300, the demand amount prediction information calculated by the demand amount prediction apparatus 130 is acquired. The acquired demand amount prediction information is input to the power flow prediction table 142A. In S400, the area shortage power amount, the area shortage power amount, and the transformer shortage power amount in the future time zone calculated by the power flow prediction device 140 are displayed on, for example, a display terminal.

<< Insolation Forecast Device 110 >>
The processing procedure of the solar radiation amount prediction apparatus 110 will be described in detail with reference to FIG. FIG. 20 is a flowchart for explaining a processing flow example of the solar radiation amount prediction apparatus 110 according to the present embodiment.

  S101 to S108 are processing procedures relating to the function of the solar radiation amount prediction formula generation unit 111A. In S101 to S106, the solar radiation amount represented by the formula (1) based on the gradation performance information, the solar radiation amount actual information, and the outdoor solar radiation amount information in the past predetermined time zone in the predetermined region. Calculate the prediction model. The solar radiation amount prediction formula generation unit 111A calculates a plurality of solar radiation amount prediction models (formula (1)) by repeating the steps S101 to S106 as many times as a predetermined time period (LOOP1). In S107 and S108, the solar radiation amount prediction formula (formula (2)) in a predetermined area is calculated from a plurality of solar radiation amount prediction models using regression analysis. The number of minutes in a predetermined time zone refers to the number of solar radiation amount prediction models for calculating a solar radiation amount prediction formula by performing regression analysis on a plurality of solar radiation amount prediction models.

  S109 to S112 are processing procedures relating to the function of the gradation prediction unit 111B. In S109 to S112, tone prediction information in the future third time zone is calculated based on the tone performance information in the first time zone and the tone performance information in the second time zone in a predetermined area. The future third time zone refers to a time zone obtained by adding the time difference between the first time zone and the second time zone to the current time zone. For example, 10 minutes after the current time (stored in the receiving system) Storage interval of the actual gradation result information).

  S113 to S115 are processing procedures related to the function of the solar radiation amount predicting unit 111C. In S113 to S115, the floor prediction information is substituted into the solar radiation amount prediction formula (formula (2)) in a predetermined area to calculate solar radiation prediction information in the predetermined area.

  The solar radiation amount prediction device 110 calculates a plurality of solar radiation amount prediction information in a plurality of regions by repeating the procedure of S101 to S115 as many times as a predetermined region (LOOP2). The number of minutes for a predetermined area refers to the number of predetermined areas included in a predetermined transformer category.

<< Power Generation Prediction Device 120 >>
A processing procedure of the power generation amount prediction apparatus 120 will be described in detail with reference to FIG. FIG. 21 is a flowchart for explaining a processing flow example of the power generation amount prediction apparatus 120 according to the present embodiment.

  S201 to S206 are processing procedures related to the function of the power generation amount prediction unit 121B. In S201 to 206, the power generation amount prediction information is calculated by substituting weather prediction information and solar radiation amount prediction information in a predetermined future time zone into a power generation amount prediction formula defined in JIS 8907 in a predetermined region.

  The power generation amount prediction apparatus 120 calculates a plurality of pieces of power generation amount prediction information in a plurality of regions by repeating the steps S201 to S206 for the number of times of a predetermined region (LOOP3). The number of minutes for a predetermined area refers to the number of predetermined areas included in a predetermined transformer category.

<< Demand amount prediction device 130 >>
The processing procedure of the demand amount prediction apparatus 130 will be described in detail with reference to FIG. FIG. 22 is a flowchart for explaining a processing flow example of the demand amount prediction apparatus 130 according to the present embodiment.

  S301 to S307 are processing procedures relating to the function of the demand amount prediction formula calculation unit 131B. In S301 to S305, the demand amount prediction model represented by the equation (3) is calculated based on the demand amount actual information and the weather actual result information in the past predetermined time zone in the predetermined region. The demand amount prediction formula calculation unit 131B calculates a plurality of demand amount prediction models (formula (3)) by repeating the steps S301 to S305 as many times as the predetermined time period (LOOP4). In S306 and S307, a demand amount prediction formula (formula (4)) in a predetermined region is calculated from a plurality of demand amount prediction models using regression analysis. Note that the number of minutes in a predetermined time zone refers to the number of a plurality of demand amount prediction models for calculating a demand amount prediction formula by performing regression analysis on a plurality of demand amount prediction models.

  S308 to S312 are processing procedures related to the function of the demand amount prediction unit 131C. In S308 to S312, the second weather forecast information is substituted into the solar radiation amount prediction formula (formula (4)) in a predetermined area to calculate demand amount prediction information in the predetermined area.

  The demand amount prediction apparatus 130 calculates a plurality of pieces of demand amount prediction information in a plurality of regions by repeating the steps of S301 to S311 as many times as a predetermined region (LOOP5). The number of minutes for a predetermined area refers to the number of predetermined areas included in a predetermined transformer category.

<< Power flow prediction device 140 >>
The processing procedure of the power flow prediction device 140 will be described in detail with reference to FIG. FIG. 23 is a flowchart for explaining a processing flow example of the power flow prediction apparatus 140 according to the present embodiment.

  S401 to S405 are processing procedures related to the function of the area aggregation unit 141A. In S401 to S405, power generation amount prediction information in a plurality of predetermined regions is read from the power generation amount prediction device 120, demand amount prediction information in a plurality of predetermined regions is read from the demand amount prediction device 130, and a plurality of predetermined regions are aggregated. It is summarized as power generation amount prediction information and demand amount prediction information in the area to be operated. In addition, about an area, since one area is defined for every several predetermined area of a fixed range, it is comprised by the several area depending on the number of several predetermined areas.

  In S406, the power generation amount prediction information and the demand amount prediction information in a predetermined transformer section to which a plurality of areas are connected are collected.

  In S407, the power amount deficient in the predetermined area, the predetermined area, and the predetermined transformer section is calculated from the power generation amount prediction information and the demand amount prediction information in the predetermined transformer section.

=== Summary ===
As described above, the solar radiation amount predicting apparatus 110 according to the present embodiment is a solar radiation amount predicting apparatus 110 that predicts the solar radiation amount of a predetermined first area of a power consumer in which the power generation facility 220 is installed. A solar radiation amount prediction formula generation unit 111A that generates a solar radiation amount prediction formula in the first region based on gradation result information indicating the amount of water vapor in the atmosphere obtained from the transmission information of the artificial satellite 400; Based on the gradation performance information of the third time zone and the gradation performance information of the second time zone after the third time zone, the first time zone after the second time zone in the first region The solar radiation amount prediction for calculating the solar radiation amount prediction information in the first time zone in the first region based on the gradation prediction unit 111B that calculates the grayscale prediction information, the solar radiation amount prediction formula, and the gradation prediction information 111C. According to this embodiment, since the predicted value of the amount of solar radiation in the future time zone can be calculated based on information obtained from the artificial satellite 400 without increasing the amount of solar radiation meter, the cost is reduced.

  In the solar radiation amount prediction apparatus 110 according to the present embodiment, the gradation result information is gradation result information acquired in each of a plurality of frequency bands of electromagnetic waves radiated from the ground to the artificial satellite 400, and the amount of solar radiation is The prediction formula generation unit 111A generates a solar radiation amount prediction formula based on the gradation performance information. According to the present embodiment, since the gradation performance information (1024 gradations) acquired in each of a plurality of frequency bands (16 bands) is used for calculating the amount of solar radiation, the accuracy when calculating the amount of solar radiation is improved. .

  In addition, in the solar radiation amount predicting apparatus 110 according to the present embodiment, the solar radiation amount prediction formula generating unit 111A has gradation performance information, outdoor solar radiation information in the first region, solar radiation performance information in the first region, Based on the above, a solar radiation amount prediction formula is generated. According to the present embodiment, since not only the gradation result information but also the atmospheric solar radiation amount information and the solar radiation amount actual information are used for calculating the solar radiation amount, the accuracy is improved when the solar radiation amount is calculated.

  In addition, in the solar radiation amount predicting apparatus 110 according to the present embodiment, the solar radiation amount prediction formula generation unit 111A performs multiple regression analysis to generate a solar radiation amount prediction formula. According to this embodiment, system construction becomes easy by using a specific regression analysis technique.

  In addition, said embodiment is for making an understanding of this invention easy, and is not for limiting and interpreting this invention. The present invention can be changed and improved without departing from the gist thereof, and the present invention includes equivalents thereof.

110 solar radiation amount prediction device 111A solar radiation amount prediction formula generation unit 111B gradation prediction unit 111C solar radiation amount prediction unit 220 power generation equipment 400 artificial satellite

Claims (4)

A solar radiation amount predicting device for predicting the amount of solar radiation in a region of a power consumer where a photovoltaic power generation facility is installed,
Based on the actual value of gradation information indicating the amount of water vapor in the atmosphere obtained from the transmission information of the artificial satellite, a solar radiation amount prediction formula generating unit that generates a prediction formula of the solar radiation amount in the region;
Based on the actual value of the gradation information in the first time zone and the actual value of the gradation information in the second time zone after the first time zone, from the second time zone in the region A gradation prediction unit for calculating a prediction value of the gradation information in the later third time zone;
A solar radiation amount predicting unit that calculates a predicted value of the solar radiation amount in the third time zone in the region based on the prediction formula of the solar radiation amount and the predicted value of the gradation information;
The solar radiation amount prediction apparatus characterized by comprising. The gradation information is gradation information acquired in each of a plurality of frequency bands of electromagnetic waves radiated from the ground to the artificial satellite,
The solar radiation amount prediction apparatus according to claim 1, wherein the solar radiation amount prediction formula generation unit generates the solar radiation amount prediction formula based on the gradation information. The said solar radiation amount prediction formula production | generation part produces | generates the said solar radiation amount prediction formula based on the said gradation information, the actual value of the atmospheric solar radiation amount in the said area, and the actual value of the solar radiation amount in the said area The solar radiation amount prediction apparatus of Claim 2 characterized by the above-mentioned. The said solar radiation amount prediction formula production | generation part performs multiple regression analysis, and produces | generates the said solar radiation amount prediction formula. The solar radiation amount prediction apparatus of Claim 3 characterized by the above-mentioned.

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