JP2022059705A - Prediction value calculation device, control method of prediction value calculation device, and program thereof - Google Patents

Abstract

To provide a prediction value calculation device, control method of the prediction value calculation device, and program thereof that obtain a prediction value of varying object data depending upon an impact of factor data.SOLUTION: In a prediction value calculation system 1000, a prediction value calculation device 100 obtaining a prediction value of varying object data depending upon factor data on a prescribed kind comprises: a relationship expression storage unit that stores a relationship expression indicative of a relationship between factor data and object data; a prediction object period reception unit that receives an input of information designating a prediction object period of calculating a prediction value of the object data; an object data calculation unit that calculates the object data in each period, using the factor data in a plurality of the same past periods corresponding to the prediction object period, and the relationship expression; a prediction value identification information reception unit that receives an input of prediction value identification information identifying the object data to be selected as a prediction value from the object data in each period; and a prediction value output unit that outputs the object data identified by the prediction value identification information as the prediction value of the object data in the prediction object period.SELECTED DRAWING: Figure 1

Description

The present invention relates to a predicted value calculation device, a control method and a program of the predicted value calculation device.

Electric power companies control the amount of power generated by thermal power plants, nuclear power plants, etc. according to the daily demand for electric power. And the electric power company assumes a period of about one month to one year in order to make a supply-demand balance plan that plans the distribution of power generation in power plants that can control the amount of power generation such as thermal power plants and nuclear power plants. We are forecasting the amount of electricity demand.

Various techniques have been proposed for systems and methods for predicting such power demand (see, for example, Patent Document 1).

Japanese Unexamined Patent Publication No. 2013-228891

On the other hand, since the amount of electric power demand changes under the influence of various meteorological conditions such as temperature and amount of solar radiation, accurate weather prediction is important in order to accurately predict the amount of electric power demand.

However, meteorological data that affect electricity demand are diverse, such as precipitation, wind speed, and humidity, in addition to temperature and solar radiation, and these meteorological data change in a complicated manner due to the effects of annual climate change. do.

Therefore, there is a demand for a technology that can predict the amount of electric power demand in consideration of such variations in meteorological data every year.

In addition to electricity demand, data that fluctuates under the influence of meteorological data includes various data such as clothing sales, the number of traffic accidents, the number of spectators at sporting events, and the number of patients with specific illnesses. Therefore, it is preferable that predicted values can be calculated for these target data in consideration of the influence of variations in meteorological data.

In addition to meteorological data, data that affect specific target data includes electricity market prices, the number of workers in specific fields such as agriculture and forestry, and the unemployment rate. It is also preferable to be able to calculate a predicted value in consideration of the influence of variation.

The present invention has been made in view of the above problems, and is a predictive value calculation device and a predictor capable of obtaining a predictive value in consideration of variation in factor data for target data that fluctuates under the influence of a predetermined type of factor data. One purpose is to provide a control method and a program for a value calculation device.

One means for solving the above-mentioned problem is a predictive value calculation device that obtains a predictive value of target data that fluctuates under the influence of a predetermined type of factor data, and determines the relationship between the factor data and the target data. A relational expression storage unit that stores the relational expression to be represented, a prediction target period reception unit that accepts input of information that specifies a prediction target period for calculating the predicted value of the target data, and a plurality of past units corresponding to the prediction target period. The target data calculation unit that calculates the target data for each period using the factor data and the relational expression in the same period, and the target data selected as the predicted value from the target data for each period. Predicted value specific information receiving unit that accepts input of specified predicted value specific information, and predicted value output unit that outputs the target data specified by the predicted value specific information as the predicted value of the target data in the predicted target period. To prepare for.

In addition, the problems disclosed in the present application and the solutions thereof will be clarified by the description in the column of the mode for carrying out the invention, the description in the drawings, and the like.

For the target data that fluctuates under the influence of a predetermined type of factor data, it is possible to obtain a predicted value that takes into account the variation in the factor data.

It is an overall block diagram of the predicted value calculation system. It is a hardware configuration diagram of the predicted value calculation device. It is a hardware configuration diagram of the meteorological data providing device. It is a figure which shows the storage device. It is a figure which shows an example of the meteorological data table. It is a figure which shows how to correct the influence of long-term climate change. It is a figure which shows an example of the temperature correction table. It is a figure which shows the breakdown of the total electric power demand. It is a figure for demonstrating the calculation method of the amount of hydroelectric power generation. It is a figure which shows the functional structure of the predicted value calculation apparatus. It is a figure which shows an example of the input screen of the prediction target period. It is a figure which shows the histogram of the electric power demand. It is a figure which shows the display example of the calculation result of the electric power demand amount. It is a figure which shows an example of the selection screen of a prediction scenario. It is a flowchart which shows an example of the processing flow of the prediction value calculation apparatus.

The description of this specification and the accompanying drawings will clarify at least the following matters. Hereinafter, the present invention will be described according to the embodiment with reference to the accompanying drawings.

== Overall configuration ==
FIG. 1 shows the overall configuration of the predicted value calculation system 1000 according to the embodiment of the present invention. The predicted value calculation system 1000 includes a predicted value calculating device 100 and a meteorological data providing device 200. The predicted value calculation device 100 and the weather data providing device 200 are connected to each other so as to be able to communicate with each other through a network 500 such as the Internet, a LAN (Local Area Network), and a telephone network.

The prediction value calculation device 100 is a computer that predicts target data that fluctuates under the influence of a predetermined type of factor data. In the present embodiment, as an example, the prediction value calculation device 100 predicts the amount of electric power that fluctuates under the influence of meteorological data. Then, in the present embodiment, the predicted value calculation device 100 is used to formulate a supply-demand balance plan of how to allocate the amount of power generation in a power plant whose output can be controlled, such as a nuclear power plant or a thermal power plant. Predict power demand.

Therefore, the electric power demand amount predicted by the predicted value calculation device 100 according to the present embodiment is not the total electric power demand amount (hereinafter, also referred to as gross demand) which is the total electric power consumed by the electric power consumer, but the above-mentioned electric power demand amount. This is the amount of power demand that a power plant that can control such output should bear (hereinafter, also referred to as net demand).

A little more specific explanation will be given with reference to FIG. FIG. 8 schematically shows the breakdown of the total power demand (described as gross demand in FIG. 8) by dividing it into power generation forms. In other words, it is a diagram showing what kind of power generation equipment is used to meet the gross demand. FIG. 8 shows that gross demand is mainly met by electricity from solar power plants, hydropower plants, thermal power plants and nuclear power plants.

And the power demand that is covered by power plants that can control the output, such as nuclear power plants and thermal power plants, is the net demand, and adding the power demand that is covered by hydropower plants and solar power generation facilities to this net demand. Will result in gross demand.

The electricity demand covered by the photovoltaic power generation equipment can be divided into PV self-consumption, PV surplus, and PV purchase. The electricity demand for PV self-consumption and PV surplus corresponds to the amount of power generated by the solar power generation equipment of the consumer who has a contract for purchasing surplus electricity with the electric power company. In addition, the electric power demand for the total purchase of PV corresponds to the amount of power generated by the solar power generation equipment of the consumer who has a contract for the total purchase of electric power with the electric power company.

The electricity demand for PV self-consumption represents the electricity consumed by the consumer who has a contract for surplus purchase. Therefore, the power demand for PV self-consumption does not appear at the transmission end. Therefore, the power demand obtained by subtracting the power demand for PV self-consumption from the gross demand is also called the transmission end demand.

In addition, the power demand for PV surplus corresponds to the remaining power obtained by subtracting the power for self-consumption from the amount of power generated by the solar power generation equipment of the consumer who has a contract for surplus purchase.

The electric power demand for the total purchase of PV corresponds to the electric power generated by the solar power generation equipment of the consumer who has a contract for the total purchase.

Of course, the power demand amount predicted by the predicted value calculation device 100 may be not only the net demand but also the transmission end demand or the gross demand.

The meteorological data providing device 200 provides the predicted value calculating device 100 with meteorological data used when the predicted value calculating device 100 predicts the amount of electric power demand.

In the present embodiment, the meteorological data providing device 200 provides each meteorological data of temperature, humidity, solar radiation amount, and precipitation amount for the past 50 years.

== Hardware configuration ==
Next, the hardware configurations of the predicted value calculation device 100 and the weather data providing device 200 will be described with reference to FIGS. 2 to 9.

<Predicted value calculation device>
First, the hardware configuration of the predicted value calculation device 100 will be described.

The predicted value calculation device 100 includes a CPU (Central Processing Unit) 110, a memory 120, a communication device 130, a storage device 140, an input device 150, an output device 160, and a recording medium reading device 170.

As shown in FIG. 4, the storage device 140 includes a predictive value calculation device control program 710 executed by the predictive value calculation device 100, a first relational expression 600A, a second relational expression 600B, a third relational expression 600C, and meteorological data. Stores various programs and data such as table 610 and temperature correction table 620.

Various functions of the predictive value calculation device 100 are realized by reading the predictive value calculation device control program 710 stored in the storage device 140 and various data into the memory 120 and executing or processing them by the CPU 110.

Here, the storage device 140 is a non-volatile storage device such as a hard disk, an SSD (Solid State Drive), or a flash memory.

The meteorological data table 610 is a table for storing various meteorological data provided by the meteorological data providing device 200. The weather data table 610 is shown in FIG. The meteorological data table 610 stores observation values of various meteorological data such as temperature, humidity, solar radiation, precipitation, atmospheric pressure, and wind speed for each hour for the past multiple years (50 years in this embodiment). ing. Of course, the meteorological data table 610 stores meteorological data for each target area in which the predicted value calculation device 100 predicts the amount of electric power demand.

The temperature correction table 620 is a table for storing correction coefficients for suppressing the influence of long-term climate change due to global warming and the like. The temperature correction table 620 is shown in FIG. Further, FIG. 6 shows how the temperature is corrected by the correction coefficient stored in the temperature correction table 620.

In this embodiment, the difference in the average temperature between each year from 1970 and the present (latest year) is used as the correction coefficient for each day from January 1st to December 31st. That is, by adding a correction coefficient determined according to the observation time of the temperature to the observation value, it is possible to correct the temperature when the observation time is the present (latest year). Of course, the correction coefficient may be the ratio of the average temperature between each year since 1970 and the present (latest year). In this case, by multiplying the observed value by the correction coefficient according to the observation time of the temperature, it is possible to correct the temperature when the observation time is the present (latest year).

The meteorological data that corrects the effects of long-term climate change due to global warming is not limited to temperature, but other meteorological data such as precipitation and wind speed may be used. In this case, the correction coefficient is determined according to the type of meteorological data and the observation time of the meteorological data.

When the correction coefficient differs depending on the target area where the prediction value calculation device 100 predicts the electric power demand, it is preferable to set the correction coefficient for each area. Further, the meteorological data table 610 may store meteorological data after the effects of long-term climate change have been corrected.

Next, the first relational expression 600A, the second relational expression 600B, and the third relational expression 600C will be described.

The first relational expression 600A, the second relational expression 600B, and the third relational expression 600C are relational expressions expressing the relationship between the meteorological data of a predetermined type and the electric power demand amount. In the following description, when the first relational expression 600A, the second relational expression 600B, and the third relational expression 600C are generically referred to, they are referred to as the relational expression 600.

First, the first relational expression 600A will be described.

The first relational expression 600A expresses the relationship between the temperature data, the humidity data, and the gross demand (total power demand). In the present embodiment, the first relational expression 600A expresses the relationship between the average temperature data of the day, the average humidity data of the day, and the gross demand of the day. For the average temperature data and the average humidity data of the day, the hourly temperature data and the humidity data stored in the meteorological data table 610 may be collected for the day and the average value may be obtained. At this time, the temperature data may be corrected for long-term climate change by using the correction coefficient stored in the temperature correction table 620.

An example of the first relational expression 600A is shown in the equation (1).

lnD (i) = lnD0 + α1 × HMD (i) +
α2 × CDD (i) ＋ α3 × CDD (i) ² ＋
α4 x HDD (i) + α5 x HDD (i) ² +
α6 x Week ... (1)
lnD (i) represents the natural logarithm of the gross demand D on day i. Further, lnD0 is a natural logarithm of the constant D0 determined by regression analysis. HMD is the daily average humidity, CDD is the cooling degree day, HDD is the heating degree day, and Week is the data indicating the day of the week and holidays. Further, α1 to α6 are coefficients determined from regression analysis.

The cooling degree day is a value calculated by "MAX (daily average temperature −A ° C., 0)", and is the difference between the daily average temperature and A ° C. on a day when the daily average temperature is A ° C. or higher. Similarly, the heating degree day is a value calculated by "MAX (B ° C-daily average temperature, 0)", and is the difference between the daily average temperature and B ° C on a day when the daily average temperature is B ° C or lower. ..

The predicted value calculation device 100 performs regression analysis using the actual values (temperature, humidity) of the past meteorological data stored in the meteorological data table 610 shown in FIG. 5 and the actual values of the electric power demand (not shown). Then, the first relational expression 600A is obtained by determining the constant D0 and the coefficients α1 to α6 in the equation (1).

When the prediction value calculation device 100 obtains the first relational expression 600A by regression analysis, the actual value and the power demand amount of the past weather data as close as possible to the prediction target period of the power demand amount (that is, the past closer to the present). It is advisable to perform regression analysis using the actual values of. For example, it is advisable to perform a regression analysis using the actual value of the meteorological data and the actual value of the electric power demand in the latest predetermined period (for example, the latest one year).

In such an aspect, the situation such as the penetration rate and performance of various electric power devices such as air-conditioning equipment, which affects the relationship between the temperature and humidity and the total electric power demand (that is, the above constant D0 and the coefficients α1 to α6). (For example, the situation is different between 10 years ago and the present) can be brought closer to the situation assumed in the forecast target period, so that a more accurate forecast value of total power demand can be calculated.

Next, the second relational expression 600B will be described.

The second relational expression 600B represents the relationship between the solar radiation amount data and the amount of photovoltaic power generation generated by the photovoltaic power generation facility. In the present embodiment, the second relational expression 600B represents the relationship between the average daily solar radiation amount and the solar power generation amount on that day. An example of the second relational expression 600B is shown in the equation (2).

PV power generation (i) = (PV interconnection equipment (total) +
(1-Private consumption rate) x PV interconnection equipment amount (surplus)) x solar radiation amount (i) x power generation coefficient ... (2)
Here, the PV power generation amount (i) represents the solar power generation amount on the i-day. The amount of PV power generation (i) includes the amount of power generated for PV self-consumption, PV surplus, and PV total purchase. The PV interconnection equipment amount (total amount) is a value indicating the power generation capacity such as the panel capacity of the solar power generation equipment operating under the purchase contract. The PV interconnection equipment amount (surplus) is a value indicating the power generation capacity such as the panel capacity of the solar power generation equipment operating under the surplus power purchase contract. The self-consumption rate indicates the ratio of the self-consumed power to the amount of power generated by the photovoltaic power generation equipment operating under the surplus power purchase contract. The amount of solar radiation (i) is, for example, the total amount of solar radiation on the horizontal plane on the i-day day. The power generation coefficient is a coefficient for calculating the amount of PV power generation.

As in the case of the first relational expression 600A, the predicted value calculation device 100 is the actual value (solar radiation amount) of the past meteorological data and the actual value of the photovoltaic power generation amount stored in the meteorological data table 610 shown in FIG. The second relational expression 600B is obtained by performing regression analysis using (not shown) and determining the power generation coefficient in the equation (2).

At this time, since the self-consumption rate changes with the times, the past value as close as possible to the forecast target period of the electric power demand (that is, the past closer to the present) is used.

Further, when the prediction value calculation device 100 obtains the second relational expression 600B, the actual value of the meteorological data (solar radiation amount) in the past (that is, the past closer to the present) as close as possible to the prediction target period of the electric power demand and the solar power are used. Regression analysis should be performed using the actual value of power generation. For example, it is advisable to perform a regression analysis using the actual value of the meteorological data and the actual value of the electric power demand in the latest predetermined period (for example, the past one year). In this way, it is possible to bring the situation such as the penetration rate and performance of solar power generation equipment, the self-consumption rate, etc., which affect the relationship between the amount of solar radiation and the amount of solar power generation, closer to the situation expected in the forecast target period. Therefore, it is possible to calculate a more accurate predicted value of the amount of photovoltaic power generation.

Next, the third relational expression 600C will be described.

The third relational expression 600C represents the relationship between the precipitation data and the amount of hydroelectric power generated by the hydroelectric power plant. The amount of daily hydroelectric power generation changes under the influence of the amount of water flowing into the power generation dam from the surrounding rivers on that day, but the amount of water flowing into the dam is not only the amount of precipitation on that day, but also the amount of precipitation in the past and the surroundings. The third relational expression 600C is more complicated than the first relational expression 600A and the second relational expression 600B because it changes in a complicated manner depending on the topography and geology. Examples of the third relational expression 600C are shown in equations (3) to (16). Further, a figure for explaining the third relational expression 600C is shown in FIG.

Hydroelectric power generation (i) = α × Q (i)… (3)
Here, the hydroelectric power generation amount (i) represents the hydroelectric power generation amount on the i-day. Q (i) represents the amount of water flowing into the dam on the i-day. α indicates a coefficient representing the relationship between the amount of water Q (i) and the amount of hydroelectric power generation (i).

Q (i) is expressed by the following equation (4).

Q (i) = Q1 (i) + Q2 (i) + Q3 (i) + Q4 (i) + Q5 (i) ... (4)
Here, Q1 (i) and Q2 (i) represent the amount of water that rains on the forest around the dam flows on the surface of the forest and flows into the dam on the i-day. Q3 (i) represents the amount of water that appears on the surface in a relatively short time after being absorbed by the soil of the forest and then flows into the dam on the i-day. Q4 (i) represents the amount of water that is once absorbed by the soil of the forest, then appears on the surface over a relatively longer period of time than Q3, and flows into the dam on day i. Q5 (i) represents the amount of water that is once absorbed by the soil of the forest, then appears on the surface over a longer period of time than Q4, and flows into the dam on the i-day.

As shown in FIG. 9, Q1 (i) to Q5 (i) can be calculated as the amount of water flowing out from a plurality of tanks (four in the example shown in FIG. 9) arranged in series in the vertical direction.

First, Q1 (i) and Q2 (i) will be described. Q1 (i) and Q2 (i) can be calculated as the amount of water flowing out from the uppermost tank as shown in FIGS. 9, (5) and (6).

Q1 (i) = a1 x I [S1 (i) -Z1] ... (5)
Q2 (i) = a2 × I [S1 (i) -Z2] ... (6)
Here, a1 and a2 are outflow hole coefficients of the outflow holes formed on the side surfaces of the tank. Further, Z1 and Z2 are heights from the bottom of the tank to the outflow hole. Further, S1 (i) is the water depth of the tank on the i-day day. And I [x] is a function in which I [x] = x when x> 0 and I [x] = 0 when x ≦ 0.

Then, in FIG. 9 and the formula (7), the amount of water absorbed from the forest into the soil is represented by g1 (i).

g1 (i) = b1 × S1 (i)… (7)
Here, b1 is a penetration hole coefficient of the outflow hole formed at the bottom of the uppermost tank.

The water depth S1 (i) of the tanks described in the formulas (5) to (7) is expressed as the following formula (8).

S1 (i) = S1 (i-1)-{Q1 (i-1) + Q2 (i-1) + g1 (i-1)}
+ P (i) -E (i) ... (8)
That is, the water depth S1 (i) of the tank on the i-day is the amount of water that flows out within the previous day with respect to the water depth S1 (i-1) of the tank of the previous day {Q1 (i-1) + Q2 (i-1). It can be calculated by subtracting + g1 (i-1)} and adding the newly increased amount of water P (i) -E (i).

Here, P (i) represents the amount of precipitation on the i-day, and E (i) represents the amount of water evaporated into the atmosphere.

Next, Q3 (i) will be described. Q3 (i) can be calculated as the amount of water flowing out from the tank in the second stage from the top, as shown in FIG. 9 and equation (9).

Q3 (i) = a3 × I [S2 (i) -Z3] ... (9)
Here, a3 is the outflow hole coefficient of the outflow hole formed on the side surface of the second stage tank. Further, Z3 is the height from the bottom of the second stage tank to the outflow hole. Further, S2 (i) is the water depth of this tank on the i-day.

As shown in FIG. 9 and the formula (10), the amount of water g1 (i) once absorbed into the soil from the forest does not flow out and flows through the soil as it is is represented by g2 (i).

g2 (i) = b2 × S2 (i)… (10)
Here, b2 is the penetration hole coefficient of the outflow hole formed at the bottom of the second-stage tank.

Further, the water depth S2 (i) of the tanks described in the formulas (9) to (10) is expressed as the following formula (11).

S2 (i) = S2 (i-1)-{Q3 (i-1) + g2 (i-1)} + g1 (i) ... (11)
That is, the water depth S2 (i) on the i-day of the second-stage tank is the amount of water that has flowed out within the previous day with respect to the water depth S2 (i-1) of the tank on the previous day {Q3 (i-1) + g2 (i). It can be calculated by subtracting -1)} and adding the newly increased amount of water g2 (i).

Next, Q4 (i) will be described. Q4 (i) can be calculated as the amount of water flowing out from the tank in the third stage from the top, as shown in FIG. 9 and equation (12).

Q4 (i) = a4 × I [S3 (i) -Z4] ... (12)
Here, a4 is the outflow hole coefficient of the outflow hole formed on the side surface of the third-stage tank. Further, Z4 is the height from the bottom of the third stage tank to the outflow hole. Further, S3 (i) is the water depth of this tank on the i-day.

As shown in FIG. 9 and the formula (13), the amount of water flowing in the soil without flowing out of the water g2 (i) flowing in the forest is represented by g3 (i).

g3 (i) = b3 × S3 (i)… (13)
Here, b3 is a penetration hole coefficient of the outflow hole formed at the bottom of the third-stage tank.

Further, the water depth S3 (i) of the tanks described in the formulas (12) to (13) is expressed as the following formula (14).

S3 (i) = S3 (i-1)-{Q4 (i-1) + g3 (i-1)} + g2 (i) ... (14)
That is, the water depth S3 (i) on the i-day of the third-stage tank is the amount of water that flows out within the previous day with respect to the water depth S3 (i-1) of the tank on the previous day {Q4 (i-1) + g3 (i). It can be calculated by subtracting -1)} and adding the newly increased amount of water g2 (i).

Next, Q5 (i) will be described. Q5 (i) can be calculated as the amount of water flowing out from the tank in the fourth stage from the top, as shown in FIG. 9 and equation (15).

Q5 (i) = a5 x S4 (i) ... (15)
Here, a5 is the outflow hole coefficient of the outflow hole formed on the side surface of the fourth stage tank. Further, S4 (i) is the water depth of this tank on the i-day.

Further, the water depth S4 (i) of the tank described in the formula (15) is expressed as the following formula (16).

S4 (i) = S4 (i-1) -Q5 (i-1) + g3 (i) ... (16)
That is, the water depth S4 (i) on the i-day of the fourth-stage tank reduces the amount of water Q5 (i-1) that flowed out the day before with respect to the water depth S4 (i-1) of the tank on the previous day, and at the same time, It can be calculated by adding the newly increased amount of water g3 (i).

As in the case of the first relational expression 600A and the second relational expression 600B, the predicted value calculation device 100 has the actual value (precipitation amount) and the hydraulic power of the past meteorological data stored in the meteorological data table 610 shown in FIG. Regression analysis is performed using the actual value of the amount of power generation (not shown), and various coefficients in the equations (3) to (16) are determined to obtain the third relational expression 600C.

When the prediction value calculation device 100 obtains the third relational expression 600C, the actual value of the past meteorological data (precipitation) as close as possible to the prediction target period of the power demand (that is, the past closer to the present) and the hydroelectric power generation. Regression analysis should be performed using the actual value of the quantity. For example, it is advisable to perform a regression analysis using the actual value of the meteorological data and the actual value of the electric power demand in the latest predetermined period (for example, the past one year). In such an aspect, the conditions such as the condition of forests and rivers and the performance of the hydroelectric power plant, which affect the relationship between the amount of precipitation and the amount of hydroelectric power generation, can be brought closer to the conditions expected in the forecast target period. It is possible to calculate a more accurate predicted value of hydroelectric power generation.

Returning to FIG. 2, the predicted value calculation device control program 710 is a general term for programs for realizing the functions of the predicted value calculation device 100, and for example, an application program or an OS operating on the predicted value calculation device 100. (Operating System), various libraries, etc. are included.

The recording medium reading device 170 reads the predicted value calculation device control program 710 and various data recorded on the recording medium 800 such as an SD card, and stores the data in the storage device 140.

The communication device 130 exchanges the predicted value calculation device control program 710 and various data with the weather data providing device 200 and other computers (not shown) via the network 500.

For example, the predicted value calculation device control program 710 and the relational expression 600 described above are stored in another computer, and the prediction value calculation device 100 downloads the prediction value calculation device control program 710 and the relational expression 600 from this computer. be able to.

The input device 150 includes various buttons and switches that receive commands and data input by the user, a keyboard, a touch sensor that detects the touch position on the touch panel display, an input interface such as a microphone, an acceleration sensor, a temperature sensor, a GPS receiver, and the like. Position detection sensors such as compasses, cameras, etc.

The output device 160 is an output user interface such as a display device such as a display, a speaker, a vibrator, and lighting.

<Weather data provider>
Next, the weather data providing device 200 will be described.

As shown in FIG. 3, the weather data providing device 200 includes a CPU 210, a memory 220, a communication device 230, a storage device 240, an input device 250, an output device 260, and a recording medium reading device 270. As described above, the hardware configuration of these meteorological data providing devices 200 is basically the same as the hardware configuration of the predicted value calculating device 100. Therefore, duplicate description of these hardware configurations will be omitted.

The storage device 240 stores the weather data providing device control program 720 executed by the weather data providing device 200 and various programs and data.

Various functions of the weather data providing device 200 are realized by reading the weather data providing device control program 720 and various data stored in the storage device 240 into the memory 220 and executing or processing them by the CPU 210.

== Functional configuration ==
FIG. 10 shows a functional block diagram of the predicted value calculation device 100 according to the present embodiment. The predicted value calculation device 100 includes functions of a relational expression storage unit 101, a prediction target period reception unit 102, a target data calculation unit 103, a prediction value specific information reception unit 104, and a prediction value output unit 105. These functions are realized by executing or processing the predicted value calculation device control program 710 and various data according to the present embodiment by the hardware shown in FIG.

<Relational expression memory>
The relational expression storage unit 101 stores a relational expression representing the relationship between a predetermined type of meteorological data and the amount of electric power demand. In the present embodiment, the relational expression storage unit 101 is embodied as a storage device 140. Further, the relational expressions expressing the relationship between the predetermined type of meteorological data and the electric power demand amount are embodied as the first relational expression 600A, the second relational expression 600B, and the third relational expression 600C.

<Forecast period reception department>
The forecast target period reception unit 102 accepts input of information for designating the forecast target period of the electric power demand. The operator can directly specify and input a specific prediction target period, such as "January-December 2021" or "February-March 2021", and is shown in FIG. It is also possible to select from the selection screen of such a scenario assumption period (prediction target period). In this case, for example, when the scenario assumption period code "101" is selected, one year from the next April to March of the following year is specified.

When selecting the scenario assumption period, in this embodiment, there are three types of options, "annual", "2 months", and "monthly", but other periods such as "3 months" and "6 months". May be.

<Target data calculation unit>
The target data calculation unit 103 calculates the power demand amount for each period by using the predetermined type of meteorological data for the same period of the past plurality of years corresponding to the prediction target period and the relational expression 600.

For example, if the forecast target period is "April", the target data calculation unit 103 will use the meteorological data (temperature, humidity, solar radiation, precipitation) for 51 years from 1970 to the latest 2020. Amount) and the first relational expression 600A, the second relational expression 600B, and the third relational expression 600C are used to obtain the total daily power demand, solar power generation amount, and hydroelectric power generation amount for one month in April every year. Accumulate and calculate the total total power demand, solar power generation, and hydroelectric power generation for the month of April every year. Then, the target data calculation unit 103 calculates the electric power demand (net demand) for 51 years in April every year by subtracting the solar power generation amount and the hydroelectric power generation amount from the total electric power demand amount for each year. ..

The plurality of electric power demands (net demand) calculated in this way have variations according to the variations of the meteorological data of each year.

<Predicted value specific information reception department>
The predicted value specifying information receiving unit 104 receives input of predicted value specifying information that specifies the power demand amount to be selected as the predicted value from the power demand amount (in April of each year) in each of the above periods.

The predicted value specific information can be, for example, information indicating a percentile value (for example, a 30th percentile value) in the electric power demand amount in each of the above periods.

In this case, for example, as shown in FIG. 13, the electric power demand for 51 years from April 1970 to April 2020 (for each period) is arranged in order of size (for example, in ascending order) (for example, in ascending order). Up to the sixth is shown in FIG. 13), the power demand such that the position in the whole when counting from the smallest is the percentile value (for example, the 30th percentile value) specified by the predicted value specific information. The quantity (or the power demand with the closest percentile value) is specified as the predicted value.

Alternatively, the predicted value specific information may be information that the power demand amount closest to the average value of the power demand amount in each of the above periods is set as the predicted value.

In this case, the average value of the electric power demand for 51 years from April 1970 to April 2020 and the electric power demand for each April every year are compared, and the average value is the highest. A close power demand is specified as a predicted value. Note that FIG. 13 shows that the power demand amount closest to the average value is displayed with information (for example, a black circle) indicating that fact in the “average value” column.

Alternatively, the predicted value specific information may be information to the effect that the mode value of the electric power demand for each of the above periods is used as the predicted value.

In this case, the electric power demand for 51 years from April 1970 to April 2020 is classified into multiple groups according to the value of the electric power demand, and the largest electric power demand is classified. The average value of the electric power demand in the group and each electric power demand in this group are compared, and the electric power demand closest to this average value is specified as a predicted value.

FIG. 12 shows how the power demand is classified into a plurality of groups. And in FIG. 12, the group in which the most power demand is classified is described as "mode".

Then, from the electric power demands classified into the group described as the mode, the electric power demand closest to the average value of the electric power demands in this group is specified as a predicted value (mode).

Further, FIG. 13 also has a column of "mode", and it is shown that information (for example, a black circle) indicating to that effect is displayed in the power demand amount specified as the mode.

The operator can also directly specify the electric power demand for a specific year (for example, 2008) as a predicted value from the electric power demand for the above 51 years in April every year.

Further, the predicted value specifying information receiving unit 104 may display a menu as shown in FIG. 14 so that the operator can select the method for selecting the power demand amount as the predicted value.

<Predicted value output unit>
The predicted value output unit 105 outputs the power demand amount specified by the predicted value specifying information as a predicted value of the power demand amount in the prediction target period.

With such an aspect, it is possible to predict the amount of electric power demand in consideration of the variation of the meteorological data every year. In other words, first, since the power demand for each year is calculated using the weather data for the past multiple years that actually vary, the value of those power demand reflects the variation in the weather data every year. It has been done. Since the relational expression 600 used for calculating the electric power demand is common, the calculated electric power demand is the electric power demand in consideration of the variation of the meteorological data.

By creating the relational expression 600 using past meteorological data and power demand as close as possible to the prediction target period, the power demand calculated using this relational expression 600 is expected in the prediction target period. It is possible to approach the amount of electricity demand. In this case, the electric power demand amount calculated by the target data calculation unit 103 can be regarded as the electric power demand amount in consideration of the variation of the meteorological data in the prediction target period.

Then, from the multiple power demands calculated from the weather data of the past multiple years in this way, select the power demand of the year in which the tendency of the variation of the weather data is expected to be close to the forecast target period. Therefore, it is possible to obtain the predicted value of the electric power demand in line with the forecast of the variation of the meteorological data in the forecast target period.

For example, when the temperature in the forecast target period is assumed to be higher than usual, the power demand calculated from the meteorological data of the past years with the same tendency can be obtained as the predicted value. Alternatively, if it is difficult to estimate the degree of variation in the meteorological data during the forecast period, the forecast target can be obtained by obtaining multiple power demands calculated from the meteorological data of various past years as predicted values. It is also possible to know how much the electricity demand during the period may vary.

Further, when the predicted value specific information is information indicating a percentile value, the predicted value output unit 105 compares the percentile value of the power demand for each period with the percentile value specified in the predicted value specific information. Predicted value Outputs the power demand with the percentile value closest to the percentile value specified in the specific information as the predicted value.

According to such an aspect, it is possible to obtain a predicted value of the power demand when it is assumed that the variation of the power demand in the prediction target period is a predetermined position (percentile value) in the whole. When the percentile value is 50%, the predicted value is the median value.

Further, when the predicted value specific information is information that the power demand amount closest to the average value of the power demand amount in each period is set as the predicted value, the predicted value output unit 105 averages the power demand amount in each period. The value is compared with the power demand for each period, and the power demand closest to the average value is output as the predicted value.

According to such an aspect, it is possible to obtain a predicted value of the electric power demand when it is assumed that the variation of the electric power demand in the forecast target period is average.

Further, when the predicted value specifying information is information that the mode value of the power demand amount in each period is the predicted value, the predicted value output unit 105 determines the power demand amount in each period as the power demand amount. It is classified into multiple groups according to the value, and the average value of the power demand in the group in which the most power demand is classified is compared with each power demand in the group, and this average value is used. The closest power demand is output as a predicted value.

According to such an embodiment, it is possible to obtain a predicted value of the power demand when it is assumed that the power demand in the prediction target period is the mode within the distribution of the variation.

Further, when the predicted value output unit 105 outputs the predicted value, the power demand amount in each period may be combined and output.

With such an aspect, it is possible to determine the predicted value while observing the overall variation in the amount of electric power demand.

At this time, the predicted value output unit 105 may output the electric power demand for each of the above periods in chronological order together with the information representing the period (for example, "March to April 2010"). ..

With such an aspect, it is possible to determine the predicted value while looking at the past power demand amount in chronological order.

Further, the predicted value output unit 105 may output the electric power demand amount of each of the above periods in order of the magnitude of the electric power demand amount together with the information representing the period.

According to such an aspect, it is possible to judge the predicted value while looking at the past electric power demand in order of magnitude.

Further, when the predicted value output unit 105 outputs the predicted value, the power demand amount classified into each group is classified according to the value of the power demand amount in each of the above periods. A histogram showing the value indicating the range and the frequency of the power demand classified into each group may be output. FIG. 12 shows how the predicted value output unit 105 outputs a histogram.

With such an aspect, it is possible to determine the predicted value while observing the overall distribution of the past electric power demand.

The predicted value of the electric power demand obtained as described above can be used for studying the power generation plan of the thermal power plant or the nuclear power plant during the forecast target period. In this case, for example, a power generation plan that minimizes the power generation cost in the prediction target period is obtained by simulation using information such as the fuel cost expected in the prediction target period and the repair plan of the power plant.

== Processing flow ==
Next, a control method of the predicted value calculation device 100 according to the present embodiment will be described with reference to FIG. FIG. 15 is a flowchart showing the procedure, and these steps are realized by the CPU 110 executing the predictive value calculation device control program 710 stored in the storage device 140 of the predictive value calculation device 100.

First, the predicted value calculation device 100 creates the relational expression 600 in advance using the past meteorological data and the electric power demand amount, and stores it in the storage device 140. Then, the prediction value calculation device 100 accepts the input of the prediction target period (S1000). At this time, the predicted value calculation device 100 displays a screen for selecting a scenario assumption period (prediction target period) as shown in FIG. 11, for example, and prompts the operator to input the prediction target period. At this time, for example, when the scenario assumption period code "101" is selected, one year from the next April to March of the following year is designated as the prediction target period.

Next, the predicted value calculation device 100 calculates the power demand for each period by using the predetermined type of meteorological data for the same period of the past plurality of years corresponding to the predicted period and the relational expression 600 (S1010). .. As described above, the predicted value calculation device 100 calculates the gross demand (total power demand), the photovoltaic power generation amount, and the hydroelectric power generation amount for each period, and from the gross demand for each period. By subtracting the amount of solar power generation and the amount of hydroelectric power generation, the net demand (electricity demand) is calculated for each period.

Then, the predicted value calculation device 100 receives input of predicted value specifying information, that is, a predicted scenario, which specifies the amount of electric power demand selected as the predicted value among the net demand calculated for each period (S1020).

Then, the predicted value calculation device 100 outputs the power demand amount corresponding to the prediction scenario as the predicted value of the power demand amount in the prediction target period (S1030).

After that, when the operator inputs another prediction scenario, the processing of S1020 and S1030 is repeated again (S1040). With such an aspect, it becomes possible to examine the predicted value of the electric power demand in the forecast target period while assuming various variations of the meteorological data.

When the operator selects the end of the process in S1040, the predicted value calculation device 100 ends the process.

The control method and program of the predicted value calculation device 100 and the predicted value calculation device 100 according to the present embodiment have been described above. Prediction is possible. In other words, first, since the power demand for each year is calculated using the weather data for the past multiple years that actually vary, the value of those power demand reflects the variation in the weather data every year. It has been done. Since the relational expression 600 used for calculating the electric power demand is common, the calculated electric power demand is the electric power demand in consideration of the variation of the meteorological data.

By creating the relational expression 600 using past meteorological data and power demand as close as possible to the prediction target period, the power demand calculated using this relational expression 600 is expected in the prediction target period. It is possible to approach the amount of electricity demand. In this case, the electric power demand amount calculated by the target data calculation unit 103 can be regarded as the electric power demand amount in consideration of the variation of the meteorological data in the prediction target period.

Then, from the multiple power demands calculated from the weather data of the past multiple years in this way, select the power demand of the year in which the tendency of the variation of the weather data is expected to be close to the forecast target period. Therefore, it is possible to obtain the predicted value of the electric power demand in line with the forecast of the variation of the meteorological data in the forecast target period.

For example, when the temperature in the forecast target period is assumed to be higher than usual, the power demand calculated from the meteorological data of the past years with the same tendency can be obtained as the predicted value. Alternatively, if it is difficult to estimate the degree of variation in the meteorological data during the forecast period, the forecast target can be obtained by obtaining multiple power demands calculated from the meteorological data of various past years as predicted values. It is also possible to know how much the electricity demand during the period may vary.

It should be noted that the above-described embodiment is for facilitating the understanding of the present invention, and is not for limiting the interpretation of the present invention. The present invention can be modified and improved without departing from the spirit thereof, and the present invention also includes an equivalent thereof.

For example, in the above embodiment, the net demand is calculated by subtracting the photovoltaic power generation amount and the hydroelectric power generation amount from the gross demand, but this is an example. For example, the photovoltaic power generation amount and the hydropower amount are calculated from the gross demand. The net demand may be calculated by subtracting the amount of power generation and the amount of wind power generation. In this case, a relational expression expressing the relationship between the weather data such as the wind speed that affects the amount of wind power generation and the amount of wind power generation is generated, and predetermined weather data such as the wind speed is input to this relational expression. Then, calculate the amount of wind power generation.

Further, in the above embodiment, the predicted value calculation device 100 acquires the weather data from the weather data providing device 200 in advance and stores it in the weather data table 610, but each time the predicted value is calculated, the weather required for the calculation is performed. The data may be acquired from the meteorological data providing device 200.

In addition to the power demand, the predicted value calculation device 100 fluctuates due to the influence of meteorological data such as sales of clothing, the number of traffic accidents, the number of spectators of sporting events, and the number of patients with a specific illness. It is possible to calculate the predicted values of various target data.

Further, the predicted value calculation device 100 can obtain a predicted value of the target data that fluctuates under the influence of these factor data by using various factor data that affect the target data in addition to the meteorological data.

As a combination of such factor data and target data, for example, it is affected by the amount of profit for a predetermined period (for example, one week) that fluctuates under the influence of the electric power market price, and the number of workers in a specific field such as agriculture and forestry. There are various things such as the sales amount of specific products that fluctuate, the number of lightning strikes and the number of power outages.

In addition, when calculating the predicted value of the profit amount in consideration of the variation in the electric power market price, the predicted value calculation device 100 uses the meteorological data (temperature data, etc.) in addition to the above-mentioned first relational expression 600A to third relational expression 600C. ) And the market price, the fourth relational expression 600D is used. In this case, the target data calculation unit 103 calculates the market price for each of the above periods by using the meteorological data during the past plurality of synchronizations corresponding to the prediction target period and the fourth relational expression 600D. Then, the target data calculation unit 103 calculates the power demand amount (net demand) for each period separately calculated using the first relational expression 600A to the third relational expression 600C, and the target data calculation unit 103 for each period calculated using the fourth relational expression 600D. By inputting the market price into the power generation plan creating device 300 (not shown), the profit amount for each period is calculated. The power generation plan creating device 300 is a computer communicably connected to the predicted value calculation device 100, and executes a program for calculating the profit amount for each period from the net demand for each period and the market price for each period. By doing so, the profit amount for each period is calculated. With such an aspect, it is possible to calculate a predicted value of the profit amount in consideration of the variation of the electric power market price that changes under the influence of the fluctuation of the meteorological data.

Further, in the above embodiment, the target data calculation unit 103 calculates the power demand for each period by using the predetermined type of meteorological data for the same period of the past plurality of years corresponding to the prediction target period and the relational expression 600. As an example, the past period corresponding to the forecast target period is not limited to the same period of each year. For example, monthly synchronization (for example, the period from 1st to 10th of every month), daily synchronization (for example, the period from 9:00 to 12:00 every day), and weekly synchronization (for example, every Wednesday from 10:00 to 12:00). Period) and so on.

That is, during the past synchronization corresponding to the prediction target period, at what cycle the target data calculation unit 103 uses the past factor data when calculating the target data using the past factor data and the relational expression 600. Can be determined by specifying.

For example, if the factor data is the daily hourly temperature data and the target data is the daily hourly power demand, the period from 10:00 to 14:00 the next day is specified as the prediction target period. Consider the case where it was done.

At this time, for example, when the information to calculate the electric power demand amount by using the temperature data of the same time zone every day for the past one month and the relational expression 600 is input to the predicted value calculation device 100, The target data calculation unit 103 uses the temperature data in the same time zone (10:00 to 14:00) every day for the past month corresponding to the forecast target period and the relational expression 600, and power demand for each period. Calculate the amount. Alternatively, when the information to calculate the electric power demand amount is input using the temperature data of the same time zone of the same day of the past 10 years and the relational expression 600, the target data calculation unit 103 may perform the target data calculation unit 103. The power demand for each period is calculated using the temperature data for the same time zone (10:00 to 14:00) on the same day for the past 10 years corresponding to the forecast target period and the relational expression 600.

In addition, the predicted value calculation device 100 may be provided with such a function of accepting the designation of which cycle the past factor data is used to calculate the target data, or may be provided with a predetermined cycle (for example, one year) in advance. ) May be a mode in which it is stipulated that past factor data is used.

In addition, factor data obtained by calculation by the method described below may be used. As a calculation method, it is possible to create a set of factor data larger than the past results by an experimental simulation that creates factor data based on past data or a simulation using a model estimated by regression analysis or the like.

100 Predicted value calculation device 101 Relational expression storage unit 102 Prediction target period reception unit 103 Target data calculation unit 104 Predicted value specific information reception unit 105 Predicted value output unit 110 CPU
120 Memory 130 Communication device 140 Storage device 150 Input device 160 Output device 170 Recording medium reader 200 Meteorological data provider 300 Power generation plan creation device 500 Network 600 Relational expression 600A First relational expression 600B Second relational expression 600C Third relational expression 600D Fourth relational expression 610 meteorological data table 620 temperature correction table 710 predicted value calculation device control program 720 meteorological data providing device control program 800 recording medium 1000 predicted value calculation system

Claims (17)

It is a predicted value calculation device that obtains the predicted value of the target data that fluctuates under the influence of a predetermined type of factor data.
A relational expression storage unit that stores a relational expression representing the relationship between the factor data and the target data,
A prediction target period reception unit that accepts input of information that specifies a prediction target period for calculating the prediction value of the target data, and a prediction target period reception unit.
A target data calculation unit that calculates target data for each period using the factor data and the relational expression during a plurality of past synchronizations corresponding to the prediction target period.
Predicted value specific information receiving unit that accepts input of predicted value specific information that specifies target data to be selected as the predicted value from the target data of each period.
A predicted value output unit that outputs the target data specified by the predicted value specific information as a predicted value of the target data in the predicted target period.
Predicted value calculation device. The predicted value calculation device according to claim 1.
The predicted value specific information is information indicating the percentile value of the target data in each period.
The predicted value output unit compares the percentile value of the target data in each period with the percentile value specified in the predicted value specific information, and the percentile closest to the percentile value specified in the predicted value specific information. A predicted value calculation device that outputs target data having a value as the predicted value. The predicted value calculation device according to claim 1.
The predicted value specific information is information to the effect that the target data closest to the average value of the target data in each period is set as the predicted value.
The predicted value output unit is a predicted value calculation device that compares the average value of the target data with the target data of each period and outputs the target data closest to the average value as the predicted value. The predicted value calculation device according to claim 1.
The predicted value specific information is information to the effect that the mode value of the target data in each period is the predicted value.
The predicted value output unit classifies the target data for each period into a plurality of groups according to the value of the target data, and the average value of the target data in the group in which the most target data is classified and the said. A predictive value calculation device that compares each target data in the group with the target data closest to the average value and outputs the target data as the predicted value. The predicted value calculation device according to any one of claims 1 to 4.
The predicted value output unit is a predicted value calculation device that outputs target data for each period when outputting the predicted value. The predicted value calculation device according to claim 5.
The predicted value output unit is a predicted value calculation device that outputs the target data of each period in chronological order together with information representing the period. The predicted value calculation device according to claim 5.
The predicted value output unit is a predicted value calculation device that outputs the target data of each period together with information representing the period in order of the size of the target data. The predicted value calculation device according to any one of claims 1 to 4.
When outputting the predicted value, the predicted value output unit sets a range of target data classified into each group for each group in which the target data of each period is classified according to the value of the target data. A predictive value calculation device that outputs a histogram that displays the values shown and the frequency of the target data classified into each group. The predicted value calculation device according to any one of claims 1 to 8.
The predicted value calculation device, in which the predetermined type of factor data is meteorological data. The predicted value calculation device according to claim 9.
The meteorological data of at least a part of the predetermined types of meteorological data is
In order to suppress the effects of long-term climate change, the observed values of the meteorological data were corrected by performing calculations using correction coefficients determined according to the type of the meteorological data and the observation time of the meteorological data. Predicted value calculation device that is data. The predicted value calculation device according to any one of claims 1 to 10.
The target data is a predicted value calculation device, which is a power demand amount. The predicted value calculation device according to any one of claims 1 to 10.
The target data is a profit amount, a predicted value calculation device. The predicted value calculation device according to any one of claims 1 to 8.
The predetermined type of factor data is meteorological data.
The target data is the amount of power demand.
The predetermined type of meteorological data includes temperature data, humidity data, solar radiation amount data, and precipitation amount data, as well as.
The relational expression
The first relational expression expressing the relationship between the temperature data, the humidity data, and the total power demand,
A second relational expression expressing the relationship between the solar radiation amount data and the amount of solar power generated by the photovoltaic power generation facility, and
A third relational expression expressing the relationship between the precipitation data and the amount of hydroelectric power generated by the hydroelectric power plant, and
Is included,
The target data calculation unit is
From the temperature data, the humidity data, and the total power demand calculated using the first relational expression, the solar radiation amount data, the solar power generation amount calculated using the second relational expression, and the precipitation. A predicted value calculation device that calculates the power demand amount by subtracting the amount data and the hydroelectric power generation amount calculated by using the third relational expression. The predicted value calculation device according to any one of claims 1 to 8.
The predetermined type of factor data is meteorological data and market price.
The target data is the amount of profit,
The predetermined type of meteorological data includes temperature data, humidity data, solar radiation amount data, and precipitation amount data, as well as.
The relational expression
The first relational expression expressing the relationship between the temperature data, the humidity data, and the total power demand,
A second relational expression expressing the relationship between the solar radiation amount data and the amount of solar power generated by the photovoltaic power generation facility, and
A third relational expression expressing the relationship between the precipitation data and the amount of hydroelectric power generated by the hydroelectric power plant, and
The fourth relational expression expressing the relationship between the meteorological data and the market price, and
Is included,
The target data calculation unit is
From the temperature data, the humidity data, and the total power demand calculated by using the first relational expression, the solar radiation amount data, the solar power generation amount calculated by using the second relational expression, and the precipitation. By subtracting the quantity data and the hydroelectric power generation amount calculated by using the third relational expression, the electric power demand amount is calculated, and the market price is calculated by using the meteorological data and the fourth relational expression. Calculate and
A predictive value calculation device that calculates the profit amount by inputting the calculated power demand amount and the market price into the power generation plan creation device. The predicted value calculation device according to claim 13.
The temperature data is data corrected by performing an operation on the observed value of the temperature data using a correction coefficient determined according to the observation time of the observed value in order to suppress the influence of long-term climate change. There is a predicted value calculation device. It is a control method of a predicted value calculation device that obtains a predicted value of target data that fluctuates under the influence of a predetermined type of factor data.
The predicted value calculation device
A relational expression expressing the relationship between the factor data and the target data is stored.
Accepts the input of information that specifies the prediction target period for calculating the prediction value of the target data,
Using the factor data and the relational expression in the past plurality of synchronizations corresponding to the prediction target period, the target data for each period is calculated.
Accepts the input of the predicted value specific information that specifies the target data to be selected as the predicted value from the target data of each period.
A control method of a predicted value calculation device that outputs target data specified by the predicted value specific information as predicted values of target data in the predicted target period. On the computer
A function to store a relational expression expressing the relationship between a predetermined type of factor data and the target data that fluctuates under the influence of the factor data, and
A function that accepts input of information that specifies the prediction target period for calculating the prediction value of the target data, and
A function to calculate the target data for each period by using the factor data and the relational expression in the past plurality of synchronizations corresponding to the prediction target period.
A function that accepts input of predicted value specific information that specifies target data to be selected as the predicted value from the target data of each period.
A function to output the target data specified by the predicted value specific information as a predicted value of the target data in the predicted target period, and
A program to realize.

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