JP7114956B2 - Power demand calculation device and program - Google Patents

Description

The present invention relates to a power demand calculation device for calculating power demand and a program for calculating power demand.

When formulating plans for the development of power plants and the maintenance of transmission networks, the main prerequisite is the creation of medium- to long-term forecasts of power demand. For example, in Japan, electricity demand forecasts are created using time series or regression equations with economic indicators, as seen in examples of supply plans based on the Electricity Business Act (see Non-Patent Document 1). ).

In addition, as conventional technologies related to power demand prediction, technology for predicting wide-range energy demand based on past actual demand (see, for example, Patent Document 1) and technology for predicting daily maximum power demand (for example, , see Patent Document 2) and the like.

JP-A-5-88714 JP-A-10-117437

Organization for Cross-regional Coordination of Transmission Operators, “Demand Estimation Guidelines”, October 18, 2016

In recent years, the center of world population and economic growth is shifting from developed countries to emerging countries. Along with this, the center of power demand growth linked to economic growth is also shifting from developed countries to emerging countries. Due to the rapid increase in power demand in emerging countries in recent years, the tightness of power supply and demand has become more serious in some countries and regions, necessitating infrastructure development such as the development of power plants and the construction of power transmission networks.

Therefore, the inventors of the present application have studied the assumption of future medium- to long-term power demand in emerging countries for infrastructure development in emerging countries. As a result of the examination, it became clear that there were the following problems.

In general, the power demand information necessary for planning infrastructure development includes the amount of power (kWh) used in a certain period of time (one day, one month, one year, etc.) and the temporal change in that power amount. and a maximum power (kW) representing the power demand during the most frequently used time (for example, hourly average) within a certain period.

However, conventionally, when assuming the power demand of target countries (emerging countries), the annual use of emerging countries as seen in the World Energy Outlook (WEO) by the International Energy Agency (IEA) It is common to assume only the amount of electric power, and the hourly load curve in emerging countries is not assumed, and it cannot be said that sufficient information is obtained for planning infrastructure development.

In addition, according to the inventors' study, in countries such as emerging countries where the economy is developing rapidly, as the economic level typified by GDP (Gross Domestic Product) per capita rises, The industrial structure of some industries tends to change.

However, in the conventional methods of estimating power demand disclosed in Non-Patent Document 1 and Patent Document 2 described above, the future power demand is calculated using only the past performance of the target country, so the calculated power demand , reflects the past trend of electricity demand in the target country, but does not reflect the changes in the industrial structure accompanying the rise in the economic level described above. Therefore, it is difficult to say that the conventional method of estimating power demand can predict power demand in emerging countries with sufficient accuracy.

The present invention has been made in view of the problems described above, and an object of the present invention is to more precisely calculate future power demand in emerging countries based on changes in industrial structures.

A power demand calculation device according to a representative embodiment of the present invention includes an economic indicator forecasting unit that calculates predicted values of economic indicators in an assumed fiscal year of a target country for which power demand is calculated; Based on the calculated predicted values of the economic indicators of the target country and the input-output table of the target country, the predicted value of input costs to the energy industry for each industry that constitutes the economy of the target country in the assumed year. and an input cost prediction unit that calculates the power demand in the assumed year based on the predicted value of the input cost to the energy industry for each industry in the assumed year calculated by the input cost prediction unit. and a prediction unit.

According to the power demand calculation device of the present invention, it is possible to more precisely calculate future power demand in emerging countries.

1 is a diagram showing a functional block configuration of a power demand calculation device according to one embodiment of the present invention; FIG. It is a figure which shows the hardware constitutions of a power demand calculation apparatus. FIG. 4 is a flow diagram showing a flow of processing for calculating power demand by the power demand calculating device; It is a figure which shows the structure of an economic-index forecasting part. FIG. 4 is a flowchart showing a detailed flow of processing in step S1 (economic index calculation step). 4 is a diagram for explaining data generated in step S1; FIG. It is a figure for demonstrating the calculation method of the predicted value of labor productivity for every industry. It is a figure which shows an example of the input-output table of a target country. It is a figure which shows the structure of an input expense prediction part. FIG. 4 is a diagram for explaining a method of calculating the composition ratio of each demand item in real GDP; It is a figure for demonstrating the calculation method of the composition ratio of each industry to each demand item. FIG. 8 is a diagram showing input coefficients corresponding to the input-output table of FIG. 7; It is a figure for demonstrating the calculation method of the predicted value of an input coefficient. FIG. 11 is a flowchart showing a detailed flow of processing in step S2 (input cost prediction step); It is a figure for demonstrating the data produced|generated by step S2. It is a flowchart which shows the flow of a detailed process of step S4 (electric power consumption prediction step). FIG. 4 is a diagram showing the configuration of a load curve generator; FIG. 10 is a flowchart showing a detailed processing flow of step S5 (load curve generation step); FIG. 4 is a diagram showing an example of an electric energy-temperature model; FIG. 4 is a diagram showing a configuration example of data of an electric energy-temperature model; It is a figure which shows the structural example of hourly temperature normal year value data. It is a figure which shows the structural example of hourly electric energy theoretical value data. It is a figure which shows the structural example of the data of annual electric power consumption. It is a figure which shows an example of a load curve.

1. Outline of Embodiment First, an outline of a representative embodiment of the invention disclosed in the present application will be described. In the following description, as an example, reference numerals on the drawings corresponding to constituent elements of the invention are described with parentheses.

[1] An electricity demand calculation device (100) according to a representative embodiment of the present invention includes an economic index prediction unit (1 ), the predicted values of the economic indicators of the target country calculated by the economic indicator forecasting unit, and the input-output table of the target country, for each industry that makes up the economy of the target country in the assumed year An input cost prediction unit (2) that calculates a predicted value of the input cost to the energy industry, and based on the predicted value of the input cost to the energy industry for each industry in the assumed year calculated by the input cost prediction unit and a power demand prediction unit (3) for predicting the power demand in the assumed year for each application and each industry.

[2] In the power demand calculation device, the power demand prediction unit calculates a predicted value of annual power consumption for each application and for each industry based on the predicted value of input cost to the energy industry for each industry. an annual power consumption prediction unit (4) to be used, an hourly allocation ratio indicating the ratio of the hourly power consumption of the target country to the annual power consumption of the target country, and the assumed year calculated by the power consumption prediction unit and a load curve generation unit (5) that generates an hourly load curve for each application and each industry in the assumed year based on the predicted value of the annual power consumption for each application and each industry. good too.

[3] In the electricity demand calculation device, the economic index is GDP (final demand), and the input cost prediction unit predicts the GDP of the target country in the assumed year calculated by the economic index prediction unit. A GDP calculation unit for each demand item (22) for calculating GDP for each demand item of the input-output table based on the value, and based on the GDP for each demand item calculated by the GDP calculation unit for each demand item, An industry-specific GDP calculation unit (24) that calculates the industry-specific GDP of each demand item of the input-output table, and an input coefficient calculation unit (25) that calculates the input coefficient of the input-output table in the assumed year of the target country ), the industry-specific GDP of each demand item calculated by the industry-specific GDP calculation unit, the input coefficient calculated by the input coefficient calculation unit, and the model formula of the input-output table, A domestic production value calculation unit (26) that calculates the domestic production value for each industry in the target country in the assumed year, the domestic production value calculated by the domestic production value calculation unit, and the input coefficient calculation unit and an input cost calculation unit (27) for calculating a predicted value of input cost to the energy industry for each industry in the assumed year of the target country based on the calculated input coefficient. .

[4] In the power demand calculation device, the input cost prediction unit calculates the actual value of GDP per population (US dollar/population) in the target country and the composition of each industry in the demand items in the target country. ratio, actual values of GDP per capita (hereinafter the same) not only in the target country but also in other countries (multiple countries) with different economic levels from the target country (same as above), and the industry in each of the demand items in the other country Calculate a model showing the medium- to long-term relationship between GDP per population in the target country and the composition ratio of each industry in each demand item, based on the model further comprising a demand item-by-industry composition ratio calculation unit (23) for calculating the composition ratio of each industry in each of the demand items in the target country in the assumed year, wherein the industry-by-industry GDP calculation unit Based on the predicted value of GDP for each demand item calculated by the GDP calculation unit for each demand item, and the composition ratio for each industry in each demand item calculated by the industry composition ratio calculation unit, each of the above The industry-specific GDP of demand items may be calculated.

[5] In the above power demand calculation device, the input coefficient calculation unit calculates the actual value of GDP per population in the target country, the actual value of the input coefficient in the target country, the target country as well as the target Based on the actual value of GDP per population in other countries (multiple countries) with different economic levels from the country and the actual value of the input coefficient in the target country, GDP per population in the target country and the input coefficient A model showing the medium- to long-term relationship may be calculated, and the input coefficient for the target country in the assumed year may be calculated based on the model.

[6] In the above power demand calculation device, the input cost prediction unit calculates the actual value of GDP per population in the target country, the actual value of the composition ratio of each demand item in the GDP of the target country, and the Based on the actual value of GDP per population not only in the target country but also in other countries (multiple countries) whose economic level is different from that of the target country, and the actual value of the composition ratio of each of the above-mentioned demand items to the GDP in the above-mentioned other country, Calculate a model showing the medium- to long-term relationship between the GDP per capita of the target country and the composition ratio of each of the demand items in the GDP, and based on the model, each of the demand items in the GDP of the target country in the assumed fiscal year based on the model a demand item composition ratio calculation unit (21) for calculating a predicted value of the composition ratio of , and the predicted value of the composition ratio of each demand item calculated by the demand item composition ratio calculation unit, the GDP for each of the demand items in the assumed year of the target country may be calculated.

[7] In the above power demand calculation device, the economic index forecasting unit predicts the total number of workers in the target country in the assumed year, and the composition ratio of each industry to the total number of workers in the target country. Based on this, the number of workers by industry calculation unit (12) that calculates the number of workers in each industry in the assumed year of the target country, and the labor productivity of each industry in the assumed year in the target country , the number of workers in each industry calculated by the number of workers by industry calculation unit, and the labor production in each industry calculated by the labor productivity calculation unit a GDP forecasting unit (13) that calculates a predicted value of GDP of the target country in the assumed year based on the labor productivity calculation unit, and the labor productivity calculation unit is a specific developed country different from the target country Based on a model showing changes in labor productivity over time in (benchmark countries) and the actual values of labor productivity in the target countries, Calculate a model that shows the time change of labor productivity in the target country so that the difference with productivity will gradually decrease, and based on the model, labor production for each industry in the assumed year in the target country Gender may be calculated.

[8] In the power demand calculation device, the load curve generating unit generates hourly temperature average year value data including hourly average year temperature values for one year, based on actual hourly temperature values for a plurality of years. based on an hourly temperature normal value data generation unit (52), a model showing the relationship between hourly power consumption and temperature, and the hourly temperature normal value data generated by the hourly temperature normal value data generation unit , an hourly electric energy theoretical value calculation unit (53) for calculating the theoretical value of the hourly electric energy consumption of the target country for a year; and the hourly electric energy consumption calculated by the hourly electric energy theoretical value calculating unit an hourly allocation ratio calculation unit (54) for calculating the hourly allocation ratio of the target country for each application and each industry based on theoretical values; and for each application and each industry calculated by the hourly allocation ratio calculation unit. The hourly distribution ratio is multiplied by the predicted value of the annual power consumption for each application and for each industry calculated by the annual power consumption prediction unit, and for each application and industry in the assumed year of the target country and an hourly power consumption prediction unit (55) for calculating the hourly power consumption for each hour, wherein the hourly temperature average annual value data generation unit calculates the maximum temperature for each day of each month for each past year. They may be ranked in descending order, and the average value of hourly temperatures on days of the same rank in the same month in each year may be used as the average value of hourly temperatures on days of that rank in the month.

[9] A program (1021) according to a representative embodiment of the present invention provides a computer with an economic index prediction step (S1 ), and based on the predicted values of the economic indicators of the target country calculated by the economic indicator prediction step and the input-output table of the target country, for each industry that makes up the economy of the target country in the assumed year An input cost prediction step (S2) for calculating a predicted value of the input cost to the energy industry, and based on the predicted value of the input cost to the energy industry for each industry in the assumed year calculated by the input cost prediction step , and a power demand prediction step (S3) for predicting the power demand in the assumed year.

[10] In the above program, the power demand forecasting step calculates a predicted value of annual power consumption for each application and for each industry based on the predicted value of input cost to the energy industry for each industry. an electric energy prediction step (S4); an hourly allocation ratio indicating the ratio of the hourly electric energy of the target country to the annual electric energy consumption of the target country; and a predicted value of annual power consumption for each industry, a load curve generating step (S5) for generating an hourly load curve for each application and each industry in the assumed year.

[11] In the above program, the economic indicator is GDP (final demand), and the input cost prediction step is based on the predicted value of GDP in the assumed year of the target country calculated by the economic indicator prediction step a demand item-specific GDP calculation step (S22) for calculating the GDP for each demand item of the input-output table; and based on the GDP for each demand item calculated by the demand item-specific GDP calculation step, an industry-specific GDP calculation step (S24) for calculating the industry-specific GDP of each demand item in the table; an input coefficient calculation step (S25) for calculating the input coefficient of the input-output table in the assumed year of the target country; Based on the industry-specific GDP of each demand item calculated by the industry-specific GDP calculation step, the input coefficient calculated by the input coefficient calculation step, and the model formula of the input-output table, the assumed fiscal year Calculated by the domestic production value calculation step (S26) for calculating the domestic production value for each industry in the target country, the domestic production value calculated by the domestic production value calculation step, and the input coefficient calculation step and an input cost calculation step (S27) of calculating a predicted value of input cost to the energy industry for each industry in the assumed year of the target country based on the input coefficient.

[12] In the above program, the input cost prediction step includes the actual value of GDP per population in the target country, the actual value of the composition ratio of each industry to the demand items in the target country, and the target Based on the actual value of GDP per population not only in the country but also in other countries (multiple countries) with different economic levels from the target country, and the actual value of the composition ratio of each industry to each of the demand items in the other country, Calculate a model showing the medium- to long-term relationship between the GDP per population of the target country and the composition ratio of each industry in each of the demand items, and based on the model, each of the above in the assumed year of the target country Further comprising a demand item-by-industry composition ratio calculation step (S23) for calculating the composition ratio of each industry in demand items, wherein the industry-by-industry GDP calculation step includes the demand item calculated by the demand item-by-demand GDP calculation step. calculating the GDP by industry for each demand item based on the predicted value of GDP for each demand item and the composition ratio for each industry in each demand item calculated by the industrial composition ratio by demand item calculation step; may include steps.

[13] In the above program, the input coefficient calculation step includes the actual value of GDP per population in the target country, the actual value of the input coefficient in the target country, and not only the target country but also the target country and the economy A medium- to long-term relationship between the GDP per capita in the target country and the input coefficient based on the actual values of GDP per population in other countries (multiple countries) with different levels and the actual values of the input coefficients in the other countries. and calculating the input coefficient for the target country in the assumed year based on the model.

[14] In the above program, the input cost prediction step includes the actual value of GDP per population in the target country, the actual value of the composition ratio of each of the demand items in the GDP of the target country, and only the target country Based on the actual value of GDP per population in other countries (multiple countries) with different economic levels from the target country, and the actual value of the composition ratio of each of the above-mentioned demand items in the GDP in the above-mentioned other country, Calculating a model showing the medium- to long-term relationship between GDP per population and the composition ratio of each demand item in GDP, and based on the model, the composition of each demand item in GDP of the target country in the assumed fiscal year Further includes a demand item composition ratio calculation step (S21) for calculating a predicted value of the ratio, wherein the GDP forecast by demand item step includes the predicted value of GDP in the assumed year calculated by the economic indicator forecast step, and the demand item composition ratio calculation step (S21). A step of calculating the GDP of each of the demand items in the assumed fiscal year of the target country based on the predicted value of the composition ratio of each of the demand items calculated by the item composition ratio calculation step.

[15] In the above program, the economic indicator prediction step is based on the predicted value of the total number of workers in the target country in the assumed year and the composition ratio of each industry to the total number of workers in the target country. a step (S13) for calculating the number of workers by industry for calculating the number of workers in each industry in the target country in the assumed year; and calculating the labor productivity for each industry in the target country in the assumed year The labor productivity calculation step (S14), the number of workers for each industry calculated by the step for calculating the number of workers by industry, and the labor productivity for each industry calculated by the labor productivity calculation step. a GDP forecasting step (S15, S16) of calculating a forecasted value of GDP of the target country in the assumed year based on the target country, and the labor productivity calculating step includes a specific developed country (benchmark Based on a model showing changes in labor productivity over time in a country) and the actual labor productivity of the target country, the labor productivity of the other country and the target country will change over time. Calculate a model that shows the time change of labor productivity in the target country so that the difference between may include the step of

[16] In the above program, the load curve generating step generates hourly temperature average data including a yearly average hourly temperature based on actual hourly temperature values for a plurality of years. Based on the average year value data generation step (S52), a model showing the relationship between hourly power consumption and temperature, and the hourly temperature average year value data generated by the hourly temperature average year value data generation step, the target An hourly theoretical value calculation step (S53) for calculating the theoretical value of the national hourly power consumption for a year; an hourly allocation ratio calculation step (S55) for calculating the hourly allocation ratio of the target country for each use and each industry, and the hourly allocation ratio for each use and industry calculated by the hourly allocation ratio calculation step The allocation ratio is multiplied by the predicted value of the annual power consumption for each application and industry calculated by the annual power consumption prediction step, and each hour for each application and industry in the assumed year of the target country an hourly power usage prediction step (S56) for calculating the power usage, and in the hourly temperature average year value data generation step, for each past year, the days of each month are sorted in descending order of the maximum temperature of the day. ranking and taking the average hourly temperature for the same ranked day in the same month in each year as the average hourly temperature for that ranked day in the month.

2. Specific Examples of Embodiments Specific examples of embodiments of the present invention will be described below with reference to the drawings. In the following description, constituent elements common to each embodiment are denoted by the same reference numerals, and repeated descriptions are omitted.

(1) Configuration of Power Demand Calculation Device FIG. 1 is a diagram showing a functional block configuration of a power demand calculation device according to an embodiment of the present invention.
A power demand calculation device 100 shown in the figure is a device for calculating power demand in a desired year in a desired country. The power demand calculation device 100 calculates the power demand of the desired country in the desired year based on the predicted values of the economic indexes of the desired country in the desired year. According to the power demand calculation device 100, it is possible to calculate the future power demand of emerging countries, for example, more precisely than conventional methods based on changes in industrial structures.

Specifically, the power demand calculation device 100 includes an economic index prediction unit 1, an input cost prediction unit 2, and a power demand prediction unit 3 as functional units for calculating power demand. These functional units are implemented by a computer such as a personal computer or a server executing a program.

FIG. 2 is a diagram showing the hardware configuration of the power demand calculation device 100. As shown in FIG.
As shown in FIG. 2, the power demand calculation device 100 includes an arithmetic device 101, a storage device 102, an input device 103, an I/F (Interface) device 104, a display device 105, and a bus 106 as main hardware components. I have.

The arithmetic unit 101 is configured by a program processing device such as a CPU (Central Processing Unit) or a DSP (Digital Signal Processor). The storage device 102 has a storage area for storing a program 1021 for causing the arithmetic device 101 to execute various data processing, and data 1022 such as parameters and calculation results used in the data processing by the arithmetic device 101. For example, it includes ROM (Read Only Memory), RAM (Random Access Memory), HDD, and flash memory.

The input device 103 is a functional unit that detects input of information from the outside, and includes, for example, a keyboard, a mouse, a pointing device, buttons, a touch panel, or the like. The I/F device 104 is a functional unit that transmits and receives information to and from the outside, and includes a communication control circuit, an input/output port, and the like for performing wired or wireless communication.

The display device 105 is a functional unit that displays information and the like obtained by data processing by the arithmetic device 101, and includes, for example, an LCD (Liquid Crystal Display), an organic EL (Electro Luminescence), and the like. The bus 106 has a function of interconnecting the display device 105, the arithmetic device 101, the storage device 102, the input device 103, the I/F device 104, and the display device 105 and enabling data exchange between these devices. Department.

In the power demand calculation device 100, the arithmetic device 101 executes calculation according to the program 1021 stored in the storage device 102, and controls the storage device 102, the input device 103, the I/F device 104, the display device 105, and the bus 106. By doing so, the economic index forecasting unit 1, the input cost forecasting unit 2, and the power demand forecasting unit 3 shown in FIG. 1 are realized.

Note that the power demand calculation device 100 may be realized by a single computer as shown in FIG. may be implemented, and the power demand calculation device 100 is not limited to the embodiment shown in FIG.

Each functional unit constituting the power demand calculation device 100 will be described below.
The economic index forecasting unit 1 is a functional unit that calculates predicted values of economic indexes in an assumed year of a target country for which electricity demand is calculated.

Here, the economic index is, for example, GDP, more preferably real GDP. In this embodiment, as an example, the economic index is real GDP.

The input cost prediction unit 2 predicts the target country's economy in the assumed year based on the target country's economic index forecast values calculated by the economic index prediction unit 1 and the target country's input output table. It is a functional unit that calculates the predicted value of the input cost to the energy industry for each of the constituent industries.

Here, "industry" refers to the types of goods and services provided by energy users (eg, foodstuffs, transportation machinery, shops/department stores, etc.).

Also, an input-output table is a statistical table that expresses, in a matrix form, the transaction values of transactions between a plurality of industries that make up the economy of a specific country during a certain period of time. A detailed calculation method using the input-output table will be described later.

The energy industry refers to industries that supply energy such as electricity, gas, and petroleum/coal products.

The power demand forecasting unit 3 is a functional unit that forecasts the power demand in the assumed year based on the estimated input cost to the energy industry for each industry in the assumed year calculated by the input cost forecasting unit 2 . Specifically, the power demand prediction unit 3 has an annual power consumption prediction unit 4 and a load curve generation unit 5 .

The annual power consumption prediction unit 4 predicts the annual power consumption for each application and for each industry based on the predicted value of the input cost to the energy industry for each industry in the assumed year calculated by the input cost prediction unit 2. is a functional unit that calculates The load curve generation unit 5 generates an hourly distribution ratio that indicates the ratio of the hourly power consumption of the target country to the annual power consumption of the target country, and It is a functional unit that generates the hourly power consumption (load curve) for each application and industry in the assumed year based on the predicted annual power consumption.

In the present application, the term "use" refers to the types of energy usage (eg, household, commercial, industrial, air conditioning, lighting, etc.).

In addition, in the present application, "business use" basically refers to a place of business that is supplied with a voltage exceeding 600V and that mainly uses a large number of electric lights and small appliances. In addition, "industrial use" basically refers to a place of business that is supplied with electricity at a voltage exceeding 600V and mainly uses three-phase power equipment. "Home use" excludes "commercial use" and "industrial use" among electricity users. However, if the classification of electricity users in the target country does not follow the above, the classification of the target country shall be followed.

(2) Outline of Processing for Calculating Electricity Demand by Electricity Demand Calculating Apparatus Next, an outline of processing for calculating electric power demand by the electric power demand calculating apparatus 100 will be described with reference to FIG.

As shown in FIG. 3, first, the economic index forecasting unit 1 calculates the predicted value of the economic index (real GDP) in the assumed year of the target country (step S1: economic index forecasting step). Next, the input cost prediction unit 2 predicts the target country based on the predicted value of the economic index (real GDP) in the target country's assumed year calculated in step S1 and the input-output table in the target country's assumed year Calculate the predicted value of the input cost of each industry to the energy industry in the fiscal year (step S2: input cost prediction step).

Next, the power demand forecasting unit 3 forecasts the power demand in the assumed year of the target country based on the predicted value of the input cost to the energy industry of each industry calculated in step S2 (step S3: power demand forecast step). Specifically, in step S3, first, the annual power consumption prediction unit 4 calculates the annual power consumption of each industry in the assumed year based on the predicted value of the input cost to the energy industry of each industry calculated in step S2. A predicted value of the amount is calculated (step S4: annual power consumption prediction step). Next, the load curve generation unit 5 calculates the power consumption for each application and industry in the target country in the assumed fiscal year based on the predicted value of the power consumption for each application and industry in the target country in the assumed fiscal year calculated in step S4. A load curve is generated (step S5: load curve generation step).

By executing the processing by the power demand calculation device 100 in such a procedure, the load curve in the assumed year of the target country and the maximum power (kW) can be obtained.

Each functional unit constituting the power demand calculation device 100 and steps S1 to S5 executed by each functional unit will be described in detail below.

(3) Economic Indicator Forecasting Division 1
FIG. 4 is a diagram showing the configuration of the economic index prediction unit 1. As shown in FIG.
The economic index forecasting unit 1 is a functional unit that executes step S1 (economic index forecasting step) in FIG. 3, and as shown in FIG. It includes a GDP prediction unit 13 , an employee industry composition ratio calculation unit 14 , and a labor productivity calculation unit 15 .

The total number of employed person calculation unit 11 is a functional unit that calculates the predicted value of the total number of employed persons in the assumed year of the target country. The worker industry composition ratio calculation unit 14 is a functional unit that calculates the predicted value of the composition ratio of each industry in the target country to the number of workers in the target country in the assumed year.

The number of workers by industry calculation unit 12 calculates the predicted number of all workers in the target country calculated by the number of workers calculation unit 11, and the composition ratio of each industry calculated by the worker industry composition ratio calculation unit 14. It is a functional part that calculates the predicted value of the number of workers in each industry based on the predicted value of .

The labor productivity calculation unit 15 is a functional unit that calculates the predicted value of labor productivity of each industry in the target country. The GDP forecasting unit 13 calculates the predicted number of employees for each industry calculated by the number of employees calculating unit 12 for each industry, and the labor productivity forecast for each industry in the target country calculated by the labor productivity calculating unit 15. It is a functional part that calculates the predicted value of the GDP of the target country in the assumed year based on the value.

FIG. 5A is a flowchart showing the detailed processing flow of step S1 (economic indicator calculation step). FIG. 5B is a diagram for explaining data generated in step S1.

In the following description of step S1, the population and labor productivity of the target country are classified into four categories: "working age" males, "elderly" males, "working age" females, and "elderly" females. Then, the case where the target country's industry is classified into "primary industry", "secondary industry" and "tertiary industry" will be explained as an example.

In step S1, first, the total number of employed persons calculation unit 11 calculates the predicted value of the total number of employed persons in the assumed year of the target country (step S11). Specifically, the total number of employed persons calculation unit 11 calculates the predicted values of the target country's population by gender and age group in the assumed year, and the target country's gender-specific and age group-specific labor force participation rates. Based on the above, we calculated the predicted values of the labor force population by age group and gender in the assumed year of the target country, and based on the calculated labor force population, the number of employed people by age group and by gender in the assumed year calculate.

More specifically, first, the total number of employed persons calculation unit 11 calculates the predicted values of the population by gender and age group in the target country in the assumed year, and the predicted value of the labor force participation rate in the target country in the assumed year. By multiplying, the predicted values of the labor force population by gender and by age group in the target country in the assumed year are calculated (step S111).

As a method of calculating the predicted value of the labor force participation rate of the target country for the target year, for example, a method of using the actual value of the labor force participation rate in the previous year of the target country as it is, or a method of using the past several years of the target country A method using the average value of the labor force participation rate in the target country, and regression analysis based on changes in the labor force participation rate over the past several years in the target country. A method of calculating the predicted value of the participation rate can be exemplified.

Here, as the predicted values of the population of the target country by sex and age group, for example, the predicted values of the population in the assumed year of the target country announced by the United Nations may be used. As for the actual value of the labor force participation rate of the target country, it is possible to use information on the labor force participation rate in the assumed year of the target country announced by the World Bank, for example. Data based on these pieces of information are stored in advance in, for example, the storage device 102 that constitutes the power demand calculation device 100, and the total number of workers calculation unit 11 calculates the above-mentioned data based on the data stored in the storage device 102. do the math.

For example, when calculating the labor force population of “productive-age men”, the total number of employed persons calculation unit 11 calculates the predicted value of the “productive-age” male population in the assumed year of the target country and Multiplying by the predicted labor force participation rate of "working-age" men, we calculate the predicted value of the "working-age male" labor force population in the target country in the assumed year. The total number-of-employed person calculation unit 11 similarly calculates predicted values of the labor force population of "elderly men", "working-age women", and "elderly women".

Next, the total number of employed persons calculation unit 11 calculates the total number of employed persons in the target country in the assumed year based on the calculated predicted values of the labor force population by sex and by age group (step S112).
Specifically, by multiplying the forecasted labor force population by gender and age group by (100% - forecasted unemployment rate [%]), the number of employed persons by gender and age group are calculated, and as shown in Fig. 5B, the total number of workers in the target country in the assumed year is calculated by adding up the calculated numbers of workers by gender and age group.

Here, the predicted value of the unemployment rate can be calculated using, for example, the past actual values of the unemployment rate of the target country announced by the World Bank. For example, the average value of the unemployment rate for the past several years (for example, the past three years) announced by the World Bank may be used as the forecast value for the unemployment rate of the target country in the assumed year. Note that data related to the actual value of the unemployment rate is stored in advance in the storage device 102 that constitutes the power demand calculation device 100, and the total number of employed persons calculation unit 11 calculates the perform the above calculations.

In step S112, for example, when calculating the number of "working-age males" employed, the total employed-employees calculation unit 11 adds { 100%-(predicted value of unemployment rate [%])} to calculate the number of employed men of working age. The total number-of-employed person calculation unit 11 also calculates predicted values of the number of employees of "old men", "working-age women", and "old women" by the same method. Then, as shown in Figure 5B, by totaling the number of workers in each of the following categories: “working-age men,” “elderly men,” “working-age women,” and “elderly women,” Calculate the forecast value for the total number of workers in the target country.

Next, in the flowchart shown in FIG. 5A, the worker industry composition ratio calculation unit 14 calculates the predicted value of the composition ratio of each industry in the target country to the number of workers in the target country in the assumed year (step S12). . Specifically, the employee industry composition ratio calculation unit 14 calculates the composition ratio of the primary industry to the number of employees in the assumed year, the composition ratio of the secondary industry to the number of employees in the assumed year, and the number of employees in the assumed year. Calculate the predicted value of each composition ratio of the tertiary industry.

More specifically, the worker industry composition ratio calculation unit 14 first performs a regression analysis based on the actual value of the composition ratio of the primary industry to the past number of employees in the target country, and obtains the Using this model, we calculate the composition ratio of the primary industry to the number of workers in the assumed fiscal year. In addition, the worker industry composition ratio calculation unit 14 performs regression analysis based on the actual value of the composition ratio of the secondary industry to the past number of employees in the target country, and uses the model obtained by the regression analysis. , Calculate the composition ratio of the secondary industry to the number of workers in the assumed year. Next, the worker industry composition ratio calculation unit 14 calculates the composition ratio of the primary industry to the number of workers and the composition ratio of the secondary industry to the number of workers in the assumed fiscal year calculated using the model based on the above regression analysis. add up. Then, the worker industry composition ratio calculation unit 14 takes the value obtained by subtracting the total value from the total (100%) as the composition ratio of the tertiary industry to the number of workers in the assumed year.

Here, the actual composition ratio of each industry (primary industry, secondary industry, and tertiary industry) to the past number of employees in the target country is, for example, the past few years announced by the World Bank (for example, For the past 10 years), the ratio of workers in each industry to the number of workers in the target country should be used. Data related to the actual values are stored in advance in the storage device 102 constituting the power demand calculation device 100, and the worker industry composition ratio calculation unit 14 calculates the above-mentioned data based on the data stored in the storage device 102. perform the calculation of

Next, in the flow chart shown in FIG. 5A, the calculation unit 12 for the number of workers by industry calculates the predicted number of workers in the target country calculated by the calculation unit 11 for the total number of workers, Based on the predicted value of the composition ratio of each industry to the number of workers in the target country calculated by the part 14, calculate the predicted value of the number of workers for each industry (Step S13: Calculate the number of workers by industry ).

Specifically, as shown in FIG. 5B, the number-of-employed-persons-by-industry calculation unit 12 adds the predicted value of the total number of workers in the assumed fiscal year of the target country calculated in step S11 to the "assumed fiscal year" calculated in step S12. The number of workers engaged in the primary industry is calculated by multiplying by the industry composition ratio of the primary industry to the number of workers in the country. In addition, the number-of-employed-persons-by-industry calculation unit 12 adds the predicted value of the total number of employees in the target country in the assumed fiscal year calculated in step S11 to the "secondary industry accounting for the number of employees in the assumed fiscal year" calculated in step S12. The number of workers engaged in the secondary industry is calculated by multiplying by the industry composition ratio. Furthermore, the number-of-employed-persons-by-industry calculation unit 12 adds the predicted value of the total number of employees in the assumed fiscal year of the target country calculated in step S11 to the "industry of the tertiary industry accounting for the number of employees in the assumed fiscal year" calculated in step S12. Calculate the number of workers engaged in the tertiary industry by multiplying the composition ratio.

Next, the labor productivity calculation unit 15 calculates the predicted value of labor productivity of each industry in the assumed year of the target country (step S14: labor productivity calculation step).

Labor productivity means the value added per worker, and is known to be obtained by dividing the value added by the number of workers.
The power demand calculation device 100 according to the present embodiment calculates a predicted value of labor productivity in an assumed fiscal year of the target country for each industry. That is, in the case of the above example, the labor productivity calculation unit 15 calculates the labor productivity of the primary industry, the labor productivity of the secondary industry, and the labor productivity of the tertiary industry in the assumed year of the target country.

FIG. 6 is a diagram for explaining a method of calculating a predicted value of labor productivity for each industry.
In FIG. 6, the horizontal axis represents fiscal year, and the vertical axis represents labor productivity (US dollar/number of workers). In FIG. 6, model 300A represents the temporal transition of labor productivity in the secondary industry (mining and manufacturing, etc.) of country A, which is an advanced country, and model 300B represents the secondary industry (mining and manufacturing, etc.) of country B, an emerging country. etc.) represents the temporal transition of labor productivity.

As shown in FIG. 6, generally, the difference between the labor productivity of country A, which is an advanced country, and the labor productivity of country B, which is an emerging country, tends to decrease with the passage of time. In other words, the labor productivity of country B, which is an emerging country, tends to move closer to the labor productivity of country A, which is an advanced country, over time. This can be attributed to the fact that emerging countries develop their economies by introducing existing technologies developed by developed countries, rather than by developing their own technologies from scratch.

Therefore, in the power demand calculation device 100 according to the present embodiment, the convergence speed (coefficient ) is set for each target country, and a model is calculated in which the labor productivity of the target country approaches the labor productivity of the benchmark country according to the convergence speed.

Specifically, the labor productivity calculation unit 15 is based on a model 300A that shows temporal changes in labor productivity in a specific developed country (benchmark country) different from the target country, and the actual value of labor productivity in the target country. , Calculate Model 300B that shows the changes in the labor productivity of the target country over time so that the difference between the labor productivity of the developed country and the target country narrows over time, and based on the model 300B Calculate the predicted value of labor productivity in the target country in the assumed year by industry.

Here, the passage of time is affected by the size of the productivity gap with developed countries and the speed of productivity improvement in the target country.

Here, the target country is country B (emerging country), and a specific developed country (benchmark country) is country A above. Step S14 in FIG. 5A will be described in detail, taking as an example the case of calculating the predicted value of the labor productivity of the secondary industry in 2030 in country B using the actual value of .

In step S14, the labor productivity calculation unit 15 first performs a regression analysis based on the actual values of labor productivity in the secondary industry from 1998 to 2016 in country A. A model 300A showing the time change of is calculated (step S141).

Next, the labor productivity calculation unit 15, based on a model showing changes in labor productivity over time in country A and actual values of labor productivity in the secondary industry from 1998 to 2016 in country B, A model showing temporal changes in labor productivity in the secondary industry of country B is calculated (step S142). For example, the labor productivity calculation unit 15 performs a regression analysis based on the actual values of labor productivity in the secondary industry of country B from 1998 to 2016, and calculates the labor productivity of the secondary industry in country B. A model 301A showing changes in labor productivity over time is calculated, and a convergence coefficient is calculated based on the model 301A and a model 300A showing changes in labor productivity over time in country A. The labor productivity calculation unit 15 uses the convergence coefficient to correct the model 301A calculated above, thereby calculating a model 301B that indicates the temporal change in the labor productivity of the secondary industry in country B.

Next, the labor productivity calculation unit 15 calculates the predicted value of labor productivity in the assumed year (2030) based on the corrected model 301B of country B (step S143).
By the above process, the predicted value of labor productivity of each industry in the target country is obtained in step S23.

Next, in the flow chart shown in FIG. 5A, the GDP prediction unit 13 calculates the real GDP by industry of the target country in the assumed year (step S15: GDP prediction step). Specifically, as shown in FIG. 5B, the GDP prediction unit 13 multiplies the "number of workers engaged in the primary industry" calculated in step S13 by the "labor productivity of the primary industry" calculated in step S14. Calculate the “real GDP of the primary industry” of the target country for the assumed fiscal year. Similarly, the GDP forecasting unit 13 multiplies the "number of workers engaged in the secondary industry" by the "labor productivity of the secondary industry" to calculate the "real GDP of the secondary industry" of the target country in the assumed fiscal year. ” and multiplying the “number of workers engaged in tertiary industry” and “labor productivity of tertiary industry” to calculate the “real GDP of tertiary industry” of the target country for the assumed fiscal year.

Next, in the flow chart shown in FIG. 5A, the GDP prediction unit 13 calculates the real GDP of the entire target country in the assumed year based on the calculated real GDP for each industry (step S16: GDP prediction step). Specifically, as shown in FIG. 5B, the GDP prediction unit 13 sums up the “real GDP of the primary industry”, the “real GDP of the secondary industry”, and the “real GDP of the tertiary industry” calculated in step S16. Calculate the real GDP of the target country as a whole in the assumed fiscal year.

As described above, the economic index forecasting unit 1 executes the above-described steps S11 to S16 to calculate the forecasted value of the real GDP of the target country in the assumed fiscal year.

(4) Input cost prediction unit 2
The input cost prediction unit 2 is a functional unit that executes step S2 (input cost prediction step) in FIG. Based on the input-output table of the country, the estimated value of the input cost to the energy industry for each industry that makes up the economy of the target country in the assumed year is calculated.

As described above, an input-output table is a statistical table that expresses, in matrix form, the transaction values of transactions between multiple industries that make up the economy of a specific country during a certain period of time.
FIG. 7 is a diagram showing an example of an input-output table of the target country.
In Figure 7, as a simplified example, the target country consists of three industries, industry 1, industry 2, and industry 3 (i = 1, 2, 3, j = 1, 2, 3). 1 is the steel industry, industry 2 is the energy industry, and industry 3 is other industries other than the steel industry and the energy industry.

As shown in FIG. 7, the input-output table shows, as items in the column (vertical) direction, the intermediate input for each industry in the target country (xij: intermediate input from industry i to industry j) and gross added value ( V) and domestic production value (X), and the items in the row (horizontal) direction are intermediate demand, final demand, imports (M), and domestic production value (X) for each industry of the target country. and The final demand includes final consumption expenditure (C), gross capital formation (I), and exports (E).

Generally, in an input-output table, it is known that the column sum of each column and the row sum of each row are equal to the domestic production value of each industry. For example, in the input-output table shown in FIG. 7, the domestic production value X1 of industry 1 is represented by the following formula (1).

The input cost forecasting unit 2 uses such an input-output table to calculate the energy industry for each industry in the target country in the assumed year from the forecast value of the target country's real GDP in the assumed year calculated by the economic indicator forecasting unit 1. Calculate the projected cost of inputs to

FIG. 8 is a diagram showing the configuration of the input cost prediction unit 2. As shown in FIG.
As shown in FIG. 8, the input cost prediction unit 2 includes a demand item composition ratio calculation unit 21, a demand item-specific GDP calculation unit 22, a demand item-specific industry composition ratio calculation unit 23, an industry-specific GDP calculation unit 24, and an input coefficient calculation unit. It includes a unit 25 , a domestic production value calculation unit 26 and an input cost calculation unit 27 .

The demand item composition ratio calculation unit 21 is a functional unit that calculates, for each demand item in the input-output table of the target country, the predicted value of the composition ratio of each demand item in the real GDP in the assumed year.

The above demand items include imports (M) in addition to final consumption expenditure (C), gross capital formation (I), and exports (E) as final demand in the input-output table.

Specifically, the demand item composition ratio calculation unit 21 calculates the composition ratio of final consumption expenditure (C) in the real GDP of the target country in the assumed year, and the total capital formation (I) in the real GDP of the target country in the assumed year. Calculate the composition ratio and the composition ratio of exports (E) in the real GDP of the target country in the assumed fiscal year.

The demand item composition ratio calculation unit 21 calculates the predicted value of the composition ratio of each demand item in the real GDP of the target country in the assumed fiscal year. ) in the composition ratio of each demand item in real GDP. This point will be described in detail below.

FIG. 9 is a diagram for explaining a method of calculating the composition ratio of each demand item in real GDP.
In FIG. 9, the horizontal axis represents GDP per population (US dollar/population), and the vertical axis represents the composition ratio of final consumption expenditure in real GDP. In addition, reference numeral 310 indicates the actual value of the composition ratio of final consumption expenditure to real GDP in country B, which is an emerging country, and reference numeral 310A is obtained by regression analysis based only on the actual value 310. , shows a model showing the relationship between GDP per capita and the composition ratio of final consumption expenditure to real GDP in country B. Reference numeral 311 indicates the actual value of the composition ratio of final consumption expenditure to real GDP in multiple countries (including developed countries and other emerging countries) other than country B, and reference numeral 311A indicates the actual value 311 and the actual value. A model showing the relationship between GDP per capita and the composition ratio of final consumption expenditure to real GDP in multiple countries including country B, obtained by regression analysis based on the value 310. FIG.

As shown in Figure 9, according to the model 311A derived from the actual values of multiple countries (actual values 311 and 310), the composition ratio of final consumption expenditure to the real GDP of multiple countries including developed countries , there is a tendency for the direction and speed of change to fluctuate depending on the level of GDP per capita. On the other hand, according to the model 310A derived from the actual value 310 of country B, which is an emerging country, the composition ratio of final consumption expenditure to the real GDP of country B rises rapidly as the GDP per capita increases. become a trend.

Looking at these trends, it can be seen that during the high-growth period when the economic level was rising rapidly, the composition ratio of final consumption expenditure to real GDP increased rapidly along with the increase in disposable income per capita (≒ GDP). However, once the economic level reaches a certain level, the rate of increase in the composition ratio of final consumption expenditure to GDP per capita will slow down and eventually reverse.

Therefore, the power demand calculation device 100 according to the present embodiment takes into account the trend change in the composition ratio of each demand item in real GDP not only in the target country but also in other countries (multiple countries) with economic levels different from those of the target country. Then, calculate the predicted value of the composition ratio of each demand item in the real GDP of the target country in the assumed fiscal year.

Specifically, the demand item composition ratio calculation unit 21 calculates the actual value of GDP per population in the target country, the actual value of the composition ratio of each demand item in the GDP of the target country, and the target country as well as the target country. GDP and GDP per population in the target country based on the actual values of GDP per population in other countries (multiple countries) with different economic levels and the actual values of the composition ratio of each demand item to GDP in the other countries. Then, based on the model, calculate the predicted value of the composition ratio of each demand item in the GDP of the target country in the assumed fiscal year.

For example, in the case of Figure 9 above, the composition ratio of final consumption expenditure to the real GDP of country B is not model 310A calculated from the past actual values of country B only, but multiple countries other than country B (developed countries ) and model 311A calculated from the past actual values of country B, corrected to the level of the actual values of country B, and using 310B, the composition ratio of final consumption expenditure to the real GDP of country B in the assumed fiscal year is calculated. calculate.

The demand item-specific GDP calculation unit 22 is a functional unit that calculates the GDP for each demand item of the input-output table based on the predicted value of the GDP of the target country in the assumed fiscal year calculated by the economic index prediction unit 1 . Specifically, the demand item-specific GDP calculation unit 22 predicts the GDP forecast value in the assumed year calculated by the economic index forecasting unit 1 and the composition ratio of each demand item calculated by the demand item composition ratio calculation unit 21. Calculate the GDP for each demand item in the assumed year of the target country based on the value and

More specifically, the demand item-specific GDP calculation unit 22 adds the predicted value of the real GDP calculated by the economic index forecasting unit 1 to the demand item composition ratio calculation unit 21 that accounts for each demand item in the real GDP. Calculate real GDP by final consumption expenditure (C), total capital formation (I), and exports (E) by multiplying the composition ratios respectively.

The demand item-by-industry composition ratio calculation unit 23 is a functional unit that calculates the predicted value of the composition ratio of each industry in each demand item in the assumed year for each demand item in the input-output table of the target country. For example, in the case of the input-output table shown in FIG. 7, the industry composition ratio calculation unit 23 by demand item calculates the composition ratio of each industry 1 to 3 in final consumption expenditure and the composition ratio of each industry 1 to 3 in total capital formation. , the composition ratio of each industry 1 to 3 in exports, and the forecast value for the import ratio of each industry 1 to 3 in the assumed fiscal year.
For example, the composition ratio of each industry 1 to 3 in the final consumption expenditure is expressed as C1/(C1+C2+C3), C2/(C1+C2+C3), C3/(C1+C2+C3), respectively.

In addition, the import ratio of each industry 1 to 3 means the ratio of the portion covered by imports in the total value of intermediate input, final consumption expenditure and gross capital formation. M1/(x11+x12+x13+C1+I1), M2/(x21+x22+x23+C2+I2), M3/(x31+x32+x33+C3+I3).

When calculating the predicted value of the composition ratio of each industry in each demand item in the assumed fiscal year of the target country, the demand item composition ratio calculation unit 23 performs the same as the demand item composition ratio calculation unit 21 described above. Consider changes in the composition ratio of each industry in each demand item not only in the country but also in other countries (multiple countries) with different economic levels from the target country. This point will be described in detail below.

FIG. 10 is a diagram for explaining a method of calculating the composition ratio of each industry in each demand item.
In FIG. 10, the horizontal axis represents GDP per population (US dollar/population), and the vertical axis represents the composition ratio of other service industries in final consumption expenditure. In addition, reference numeral 320 indicates the past performance value of the composition ratio of other service industries to the final consumption expenditure in country B, which is an emerging country. This is the obtained model showing the relationship between GDP per capita in country B and the composition ratio of other service industries in final consumption expenditure. Furthermore, in FIG. 10, reference numeral 321 indicates past performance values of the composition ratio of other service industries in final consumption expenditure in multiple countries (including developed countries and other emerging countries) other than country B, see Symbol 321A is the relationship between GDP per population and the composition ratio of other service industries in final consumption expenditure in multiple countries, including country B, obtained by regression analysis based on actual values 321 and 320. shows a model that shows

As shown in Figure 10, according to the model 321A derived from the results of multiple countries (actual values 321 and 320), the composition ratio of the other service industry in the final consumption expenditure of multiple countries including developed countries will tend to rise with a gradual slowdown in line with the increase in GDP per capita. On the other hand, according to the model 320A derived from the actual value 320 of country B, which is an emerging country, the composition ratio of other service industries in the final consumption expenditure of country B rapidly increases as the GDP per capita increases. tend to

Looking at these trends, it can be seen that in emerging countries, during the period of rapid economic growth when economic levels were rising rapidly, other service industries accounted for final consumption expenditure as disposable income (≒ GDP per capita) increased. However, once the economic level reaches a certain level, the rate of rise in the composition ratio of other services in final consumption expenditure is expected to slow down.

Therefore, in the power demand calculation device 100 according to the present embodiment, considering the trend change in the composition ratio of each industry in each demand item not only in the target country but also in other countries with different economic levels from the target country, the target country Calculate the predicted value of the composition ratio of each industry in each demand item in the assumed fiscal year.

Specifically, the demand item-specific industry composition ratio calculation unit 23 calculates the GDP per population in the target country, the actual value of the composition ratio for each industry in the demand items in the target country, and the target country as well as the target country. Based on the GDP per capita of other countries with different economic levels and the actual composition ratio of each industry to each demand item in the other country, the GDP of the target country and the ratio of each industry to each demand item A model showing the relationship is calculated, and based on the model, the forecast value of the composition ratio of each industry to each demand item in the target country in the assumed fiscal year is calculated.

For example, in the case of FIG. 10, instead of the model 320A calculated from the past performance values of only country B, the model 321A calculated from the past performance values of multiple countries other than country B (including developed countries) and country B Using 320B corrected according to the country's actual level, calculate the composition ratio of other service industries in the final consumption expenditure of country B in the assumed fiscal year.

The GDP calculation unit by industry 24 calculates the predicted value of GDP for each demand item in the assumed year of the target country calculated by the GDP calculation unit by demand item 22, and the target country calculated by the industry composition ratio calculation unit by demand item 23. It is a functional part that calculates the predicted value of GDP by industry for each demand item in the target country in the assumed year based on the predicted value of the composition ratio of each industry in each demand item in the assumed year.

Specifically, the industry-specific GDP calculation unit 24 multiplies the predicted value of the real GDP for each demand item in the assumed fiscal year by the predicted value of the composition ratio of each industry in each demand item to obtain the target country's Calculate the predicted value of GDP by industry for each demand item in the assumed fiscal year. For example, in the input-output table of FIG. 7, C1-C3, I1-I3, and E1-E3 are calculated respectively.

The input coefficient calculation unit 25 is a functional unit for calculating the input coefficient of the input-output table.
Here, the input coefficient is obtained by dividing the intermediate input of each industry in the input-output table by the domestic production value of that industry.

FIG. 11 shows input coefficients corresponding to the input-output table of FIG.
In the input-output table of FIG. 7, the input coefficient aij is represented by the following equation (2), where xij is the intermediate input from industry i to industry j, and Xi is the domestic production value of industry i. That is, the input coefficient aij indicates the magnitude of the input cost of industry i used to produce one unit in industry j.

When calculating the predicted value of the input coefficient aij in the assumed fiscal year of the target country, the input coefficient calculation unit 25, like the demand item composition ratio calculation unit 21 and the demand item-specific industry composition ratio calculation unit 23, In addition, the actual values of the input coefficients aij in other countries (multiple countries) with different economic levels from the target country are considered. This point will be described in detail below.

FIG. 12 is a diagram for explaining a method of calculating the predicted value of the input coefficient.
In Figure 12, the horizontal axis represents GDP per capita (US dollar/population), and the vertical axis represents the input cost of industry 2 (energy industry) used to produce one unit in industry 1. a21. In addition, reference numeral 330 indicates the past performance value of the input coefficient a21 in country B, which is an emerging country, and reference numeral 330A indicates the per capita It is a model showing the relationship between GDP and input coefficient a21.

Further, in FIG. 12, reference numeral 331 indicates past actual values of the input coefficient a21 in a plurality of countries other than country B (including developed countries and other emerging countries), and reference numeral 331A indicates actual values 331 and A model showing the relationship between GDP per capita and input coefficient a21 in multiple countries including country B, obtained by regression analysis based on actual values 330, is shown.

In the example of FIG. 12, according to the model 331A derived from the results of multiple countries (actual values 331 and 330), the input coefficient a21 of one country changes in the direction of change according to the level of GDP per capita. A tendency for the speed to fluctuate can be observed. On the other hand, according to the model 330A derived from the actual value 330 of country B, which is an emerging country, the input coefficient a21 of country B tends to rise rapidly as the GDP per population increases.

In other words, during the high-growth period when the economic level rose rapidly, the input coefficient also changed rapidly with the increase in GDP per capita. The speed is expected to slow down gradually. In general, it is thought that the input coefficient of the energy industry will rise due to the progress of mechanization during the high growth period, and that the input coefficient will decrease due to the impact of energy conservation in many industries in the maturity period.

Therefore, in the power demand calculation device 100 according to the present embodiment, the input coefficients model is calculated, and using that model, the forecast value of the input coefficient in the assumed year of the target country is calculated.

Specifically, the input coefficient calculation unit 25 calculates the actual value of GDP per population in the target country, the actual value of input costs in the target country, and other countries (multiple countries ) and the actual value of the input coefficient in the other country, a model showing the relationship between the GDP per population and the input coefficient in the target country is calculated, and based on the model, the target country Calculate the forecast value of the input coefficient in the assumed year.

For example, in the case of Figure 12, instead of the model 330A calculated from the past input coefficient actual values of only country B, the actual input coefficient values of multiple countries other than country B and the past input coefficient actual values of country B are used. Using 330B, which is obtained by correcting the calculated model 331A according to the level of actual values in country B, the input coefficient for country B in the assumed fiscal year is calculated. For example, in the input coefficient table of FIG. 11 corresponding to the input-output table of FIG. 7, a11-a13, a21-a23, and a31-a33 are calculated respectively.

The domestic production value calculation unit 26 calculates the predicted value of the input coefficient for the target country in the assumed fiscal year calculated by the input coefficient calculation unit 25, and the estimated value of each demand item for the target country in the assumed fiscal year calculated by the industry-specific GDP calculation unit 24. It is a functional unit that calculates the predicted value of domestic production for each industry in the assumed year of the target country based on the predicted value of GDP by industry and the model formula of the input-output table.
Here, it is known that the model formula of the input-output table is represented by the following formula (3).

In equation (3), X is the production value vector, I is the identity matrix, Mh (the hat of M) is the import dependency matrix, A is the input coefficient matrix, Y is the final demand vector (Y=C+I+E), and E is the export vector. are represented respectively.

The domestic production value calculation unit 26 calculates the predicted value of the GDP by industry (C1 to C3, I1 to I3, E1 to E3) of each demand item in the assumed year of the target country calculated by the GDP calculation unit 24, and the demand The predicted value of the import ratio by industry in the assumed year calculated by the item-by-item industry composition ratio calculation unit 23 and the predicted value of the input coefficient in the assumed year calculated by the input coefficient calculation unit 25 (a11 to a13, a21 to a23 , a31-a33) into the model formula of the input-output table represented by the above formula (3), calculate the domestic production value X1-X3 of each industry 1-3 in the assumed year of the target country, respectively. do.

The input cost calculation unit 27 calculates the predicted value (X1 to X3) of the domestic production value for each industry in the assumed fiscal year of the target country calculated by the domestic production value calculation unit 26, and the assumed fiscal year calculated by the input coefficient calculation unit 25 It is a functional unit that calculates the predicted value of the input cost to the energy industry for each industry in the assumed year based on the predicted value (a21 to a23) of the input coefficient of the energy industry in .

Next, a detailed processing flow of step S2 (input cost prediction step) executed by the input cost prediction unit 2 will be described. Here, the case of using the input-output table shown in FIG. 7 will be described as an example.

FIG. 13A is a flowchart showing the detailed processing flow of step S2 (input cost prediction step). FIG. 13B is a diagram for explaining data generated in step S2.

First, the demand item composition ratio calculation unit 21 calculates the predicted value of the composition ratio of each demand item in the real GDP in the assumed year for each demand item (excluding imports) in the input-output table of the target country (step S21 : demand item composition ratio calculation step).
Here, in FIG. 9 described above, the case of calculating the predicted value of the composition ratio of final consumption expenditure in the real GDP of country B in 2030 will be taken as an example, and the processing contents of step S21 will be specifically described.

In step S21, first, the demand item composition ratio calculation unit 21 performs a regression analysis based on the composition ratio of final consumption expenditure to the real GDP in multiple countries (actual value 311 and actual value 310). A model 311A showing the relationship between the GDP per capita of a country and the composition ratio of final consumption expenditure to the real GDP is calculated (step S211). The regression analysis methods include a method of selecting a highly explanatory approximation formula such as linear regression, quadratic curve regression, cubic curve regression, etc. according to the relationship between the explanatory variable and the dependent variable, and a neural network and error backpropagation. There is a method of approximating a nonlinear regression equation using the method.

Next, the demand item composition ratio calculation unit 21 corrects the model 311A using the actual value 310 of the composition ratio of final consumption expenditure to the real GDP in country B according to the method described above, thereby obtaining A model 310B showing the relationship between GDP and the composition ratio of final consumption expenditure in real GDP is calculated (step S212).

After that, the demand item composition ratio calculation unit 21 predicts the real GDP in the assumed year (2030) of country B calculated by the economic index forecasting unit 1 and the population forecast in the assumed year (2030) of country B and the model 310B, the predicted value of the composition ratio of final consumption expenditure to the real GDP in 2030 in country B is calculated (step S213).

In this way, the demand item composition ratio calculation unit 21 calculates the composition ratio of final consumption expenditure (C) in the real GDP of the target country (Country B) in the assumed year, the total capital in the real GDP of the target country in the assumed year, Calculate the forecasted values for the composition ratio of growth (I) and the composition ratio of exports (E) in the real GDP of the target country in the assumed fiscal year.

Next, the demand item-specific GDP calculation unit 22 calculates a predicted value of GDP for each demand item in the input-output table of the target country (step S22: demand item-specific GDP calculation step). Specifically, the demand item-specific GDP calculation unit 22 adds the composition ratio of each demand item to the real GDP of the target country calculated in step S21 to the predicted value of the target country's real GDP calculated in step S1. Multiplication yields real GDP by final consumption expenditure (C), gross capital formation (I), and exports (E).

As a result, as shown in Figure 13B, (C1 + C2 + C3) is obtained as the real GDP of final consumption expenditure in the assumed fiscal year, (I1 + I2 + I3) is obtained as the real GDP of total capital formation in the assumed fiscal year, and (E1+E2+E3) is obtained as GDP.

Next, the demand item-by-industry composition ratio calculation unit 23 calculates, for each demand item in the input-output table of the target country, the predicted value of the composition ratio of each industry in each demand item in the assumed year (step S23: demand item-by-item industry composition ratio calculation step).
Here, in FIG. 10 described above, the case of calculating the predicted value of the composition ratio of other service industries in the final consumption expenditure of country B in 2030 will be taken as an example, and the processing contents of step S213 will be specifically described.

In step S23, first, the demand-item-specific industry composition ratio calculation unit 23 performs a regression analysis based on the actual composition ratios of other service industries in final consumption expenditure in multiple countries (actual values 321 and 320). Then, a model 321A showing the relationship between GDP per population and the composition ratio of other service industries in final consumption expenditure in multiple countries is calculated (step S231). The regression analysis methods include a method of selecting a highly explanatory approximation formula such as linear regression, quadratic curve regression, cubic curve regression, etc. according to the relationship between the explanatory variable and the dependent variable, and a neural network and error backpropagation. There is a method of approximating a nonlinear regression equation using the method.

Next, the demand item-specific industry composition ratio calculation unit 23 corrects the model 321A using the actual value 320 of the composition ratio of the other service industry in the final consumption expenditure in Country B, using the above-described method. A model 320B showing the relationship between GDP per capita and the composition ratio of other service industries in final consumption expenditure is calculated (step S232).

After that, the demand item-specific industry composition ratio calculation unit 23 calculates the predicted value of the real GDP of country B in the assumed year (2030) calculated in step S1, and the predicted value of the population in the assumed year (2030) of country B. , and the model 320B, the predicted value of the composition ratio of other service industries in the final consumption expenditure in 2030 in country B is calculated (step S233).

In this way, the industry composition ratio calculation unit by demand item 23 calculates the composition ratio of each industry 1 to 3 in final consumption expenditure, the composition ratio of each industry 1 to 3 in total capital formation, and the composition ratio of each industry in exports. A predicted value for each of the composition ratios 1 to 3 is calculated.

Next, the industry-specific GDP calculation unit 24 calculates the predicted value of the industry-specific GDP of each demand item in the assumed year of the target country (step S24: industry-specific GDP calculation step). Specifically, the industry-specific GDP calculation unit 24 adds the predicted value of the real GDP for each demand item in the assumed year calculated in step S22 to the composition ratio of each industry in each demand item calculated in step S23. By multiplying each forecast value, the forecast value of GDP by industry for each demand item in the assumed year of the target country is calculated.

For example, the GDP calculation unit 24 calculates the value obtained by multiplying the real GDP (C1+C2+C3) of final consumption expenditure by the composition ratio of industry 1 in final consumption expenditure (C1/(C1+C2+C3)). This is obtained by multiplying the real GDP of final consumption expenditure (C1 + C2 + C3) by the composition ratio of industry 2 in final consumption expenditure (C2 / (C1 + C2 + C3)). This value is used as the predicted value of the real GDP of industry 2 in final consumption expenditure (C2), and the real GDP of final consumption expenditure (C1 + C2 + C3) is multiplied by the composition ratio of industry 3 in final consumption expenditure (C3/(C1 + C2 + C3)). The value thus obtained is the predicted value (C3) of the real GDP of industry 3 of final consumption expenditure.

In this way, as shown in FIG. 13B, the industry-by-industry GDP calculation unit 24 calculates the real GDP (C1, C2, C3) of final consumption expenditure for each industry 1 to 3, and the total capital formation for each industry 1 to 3. real GDP (I1, I2, I3) of export (E) and real GDP (E1, E2, E3) of each industry 1 to 3 of export (E).

In addition, the demand item-specific industry composition ratio calculation unit 23 calculates the import ratio. Specifically, as in the case of the composition ratio of each industry in each demand item above, the actual value of the import ratio in the target country and the target country as well as other countries with different economic levels (multiple countries) ), calculate a model showing the relationship between the GDP per capita and the import ratio of the target country, and calculate the model of the target country based on the model Calculate the forecast value of the import ratio in the assumed year. For industries with large disparities in the import ratio of each country due to preconditions such as weather conditions and endowments (agriculture, forestry, fisheries, mining, etc.), trends in other countries (multiple countries) with different economic levels from the target country The forecast value may be calculated based on the actual trend of the target country only, without considering the change.

Next, the input coefficient calculation part 25 calculates the input coefficient of the input-output table (step S25: input coefficient calculation step).
Here, the processing contents of step S25 will be specifically described by taking as an example the case of calculating the input coefficient a21 in 2030 for country B in FIGS. 11 and 12 described above.

In step S25, first, the input coefficient calculation unit 25 performs regression analysis based on the actual values (actual values 331 and 330) of the input coefficients a21 in multiple countries to obtain GDP per capita and input in multiple countries. A model 331A representing the relationship with the coefficient a21 is calculated (step S251). The regression analysis methods include a method of selecting a highly explanatory approximation formula such as linear regression, quadratic curve regression, cubic curve regression, etc. according to the relationship between the explanatory variable and the dependent variable, and a neural network and error backpropagation. There is a method of approximating a nonlinear regression equation using the method.

Next, the input coefficient calculation unit 25 corrects the model 331A using the actual value 330 of the past input coefficient a21 of the country B by the above-described method, and calculates the GDP per population and the input coefficient a21 in the country B. A model 330B representing the relationship between is calculated (step S252).

After that, the input coefficient calculation unit 25 calculates the predicted value of the real GDP in the assumed year (2030) of country B calculated in step S1, the predicted value of the population in the assumed year (2030) of country B, and the model 330B Based on and, the predicted value of the input coefficient a21 in 2030 in Country B is calculated (step S253).

In this manner, the input coefficient calculator 25 calculates the input coefficients a11 to a13, a21 to a23, and a31 to a33 of the input coefficient table (FIG. 11) corresponding to the input-output table of FIG.

Next, the domestic production value calculation unit 26 calculates the predicted value of the domestic production value for each industry in the target country in the assumed year (step S26: domestic production value calculation step). Specifically, as described above, the domestic production value calculation unit 26 calculates the predicted values of GDP by industry for each demand item (C1 to C3, I1 to I3, E1 to E3) calculated in step S24, and The predicted value of the import ratio calculated in S24 and the predicted value of the input coefficient (a11-a13, a21-a23, a31-a33) calculated in step S25 are used for the industry represented by the above formula (3). By substituting into the model formula of the linkage table, domestic production values X1 to X3 of industries 1 to 3 in the target country in the assumed year are calculated, respectively, as shown in FIG. 13B.

Next, the input cost calculation unit 27 calculates the estimated domestic production value for each industry in the target country in the assumed year calculated in step S26 and the predicted value of the input coefficient calculated in step S25. Calculate the predicted value of the input cost to the energy industry for each industry in the fiscal year (step S27: input cost calculation step).

For example, as shown in FIG. 13B, the input cost calculation unit 27 multiplies the value obtained by multiplying the domestic production value X1 of Industry 1 by an input coefficient a21, and calculates the input cost x21 from Industry 1 to Industry 2 (energy industry). The value obtained by multiplying the domestic production value X2 of industry 2 by the input coefficient a22 is the input cost x22 from industry 2 to industry 2 (energy industry), and the domestic production value X3 of industry 3 is multiplied by the input coefficient a23 The value obtained by multiplying is set as input cost x23 from industry 3 to industry 2 (energy industry).

By executing the processing by the input cost prediction unit 2 according to the procedure of steps S21 to S27 described above, the input cost to the energy industry for each industry in the assumed year of the target country is obtained.
It should be noted that the final consumption expenditure (C2) of industry 2 (energy industry) in the input-output table of FIG.

(5) Annual power consumption prediction unit 4
The annual power consumption prediction unit 4 is a functional unit that executes step S4 (power consumption prediction step) in FIG. Based on this, it is a functional unit that calculates a predicted value of electric energy for each application and for each industry.

FIG. 14 is a flowchart showing the detailed processing flow of step S4 (power consumption prediction step).
As shown in FIG. 14, in step S4, first, the annual power consumption prediction unit 4 calculates the predicted input cost for the electricity sector included in the energy industry from the predicted input cost for the energy industry for each industry. is calculated (step S41). For example, if the energy industry includes the electric field, the gas field, and the petroleum/coal product field, the predicted value of the input cost to the energy industry calculated in step S2 based on the composition ratio of the electric field in the energy industry. Calculate the predicted value of the input cost (electricity rate) to the electricity sector included in .

As a method of calculating the predicted value of the composition ratio (by industry) of the electricity sector in the energy industry of the target country in the assumed fiscal year, for example, the latest actual composition ratio of the target country can be used as a forecast value, A method using the average composition ratio of the target country over the past several years, and a regression analysis with time series and electricity prices (relative prices to other energy goods) based on changes in the composition ratio over the past several years in the target country. , using the model obtained by the regression analysis, it is possible to exemplify the method of calculating the forecast value of the composition ratio of the target country in the assumed year. In practice, the most appropriate method will be selected according to the quality and quantity of available data on the target country.

Next, the annual power consumption prediction unit 4 calculates the predicted value of the power consumption in the assumed year of the target country based on the input cost (electricity rate) to the electricity sector (step S42). For example, in the case of the input-output table of FIG. 7, the annual power consumption prediction unit 4 calculates the input cost (electricity rate) to the electricity field included in the input cost (x21) of industry 1 to industry 2 calculated in step S2 , the input cost to the electricity sector (electricity rate) included in the input cost for industry 2 of industry 2 (x22), and the input cost to the electricity sector included in the input cost for industry 2 of industry 3 (x23) ( electricity rate), the amount of power used by industry 1, the amount of power used by industry 2, and the amount of power used by industry 3 are calculated.

As a method of calculating the predicted value of power consumption in the target country in the assumed fiscal year, for example, the actual value of power consumption for each industry in the target country over the past several years and the electricity sector for each industry in the target country over the past several years A method of performing a regression analysis based on changes in the input cost (electricity rate) to the target country and using the model obtained from the regression analysis to calculate the predicted value of the electricity consumption for each industry in the target country for the assumed fiscal year. Alternatively, a method of using the rate of increase in electricity charges for each industry in the target country from the previous year as it is as the rate of increase from the previous year to the predicted value of the annual power consumption for each industry can be exemplified.

The premise of the above calculation method is that changes in production value and input costs (nominal values) are generally caused by a combination of changes in quantity and changes in prices. The calculated production values and input costs are considered to be real values that are affected only by changes in quantity because the effects of price fluctuations have been removed in the realization process.

In addition, in the power consumption of the target countries above, we have mainly illustrated the power consumption of each industry in the three industrial fields (industries 1 to 3) in the input-output table in Figure 7, but the power consumption of the target countries In addition to the amount of electric power used by each industry, there is also the amount of electric power used by households other than industries 1-3. For this reason, in the following description, a case will be described as an example in which the power consumption of the target country is classified into three categories of "household use", "commercial use", and "industrial use" for each application. At this time, "commercial use" is further subdivided into the industrial fields in the input-output table, such as wholesale and retail trade, food and beverage/accommodation, transportation/warehousing, communication/postal, education, medical/welfare, and Other services, etc. may be included in the classification. In addition, as with “commercial use”, “industrial use” is further subdivided into sub-categories, which are the industrial fields in the input-output table, such as agriculture, forestry and fisheries, mining, food, textile, lumber and wood products, paper pulp and The printing industry, the coal/oil industry, the chemical industry, the ceramics industry, the metal industry, the general machinery/electrical industry, the transportation machinery industry, the construction industry, and other manufacturing industries may be included in the classification.

In addition, when calculating the predicted value of household power consumption in the target country in the assumed fiscal year, as in the case of industries 1 to 3 above, based on the composition ratio of the electricity sector in the energy sector, the inter-industry Calculate the input cost for the electricity sector included in the final consumption expenditure of the energy industry in the table, and calculate the predicted value of household power consumption based on the input cost (electricity charge) for the electricity sector. .

Here, the actual values of the above-mentioned electric power consumption by application and industry (electric power consumption by application and industry) are, for example, A method of converting actual consumption values into electricity consumption (1J=(1/3.6)×10 ^-6 kW・h) and using results based on recorded data from smart meters, etc. introduced in target countries. The method used can be exemplified. In practice, the most appropriate method will be selected according to the quality and quantity of available data on the target country.

In this way, the predicted value of the power consumption for each application and each industry (power consumption by application/industry) in the assumed fiscal year of the target country is obtained.

(6) Load curve generator 5
FIG. 15 is a diagram showing the configuration of the load curve generator 5. As shown in FIG.
The load curve generation unit 5 is a functional unit that executes step S5 (load curve generation step) in FIG. 3, and as shown in FIG. 52 , an hourly electric energy theoretical value calculating unit 53 , an hourly allocation ratio calculating unit 54 , and an hourly electric energy consumption predicting unit 55 .

The power consumption vs. temperature model generation unit 51 creates a model (hereinafter referred to as “power consumption (also referred to as "temperature model"). Specifically, the power consumption vs. temperature model generation unit 51 generates a power consumption vs. temperature model for each type of day in the target country. In addition to hourly temperature, hourly solar radiation (or hourly sunshine duration), hourly rainfall, hourly humidity, etc. can be considered as weather conditions that affect hourly power consumption. Based on the data available for the target country, a power consumption-weather model may be generated in which weather conditions other than the temperature are added as explanatory variables.

In this embodiment, as an example, days are classified into weekdays, Saturdays, and holidays. Here, holidays include Sundays and public holidays.
In this case, the power usage vs. temperature model generation unit 51 generates a power usage vs. temperature model for each of weekdays, Saturdays, and holidays.

The hourly temperature average year value data generating unit 52 is a functional unit that calculates the hourly average yearly temperature value for a year based on the actual hourly temperature values for a plurality of years. The hourly temperature normal year value data generation unit 52 calculates the hourly temperature normal year value for one year (for example, for 365 days) based on, for example, the actual value of the hourly temperature for 10 years in the target country. 19 hourly temperature average data 500).
When meteorological data other than the hourly temperature are added as explanatory variables, a normal year value may be similarly calculated for each of these meteorological data.

The hourly electric energy theoretical value calculation unit 53 combines the electric energy-temperature model calculated by the electric energy usage vs. temperature model generation unit 51 with the hourly temperature normal value data 500 generated by the hourly temperature normal value data generation unit 52. Based on this, it is a functional part that calculates the theoretical value of hourly power consumption in the target country for a year. The theoretical value of the annual power consumption in the target country is calculated by totaling all the theoretical values of the power consumption per hour in the target country.

Here, the theoretical value of the hourly power consumption is an estimated value of the power consumption per hour in a specific time period on a specific day. The specific time period on the specific day referred to here indicates, for example, 1:00 to 2:00 on January 1st.

The hourly distribution ratio calculation unit 54 calculates the theoretical value of the hourly power consumption of the target country for a year calculated by the hourly power theoretical value calculation unit 53 and the annual power consumption (application It is a functional part that calculates the hourly allocation ratio for each application and industry based on the theoretical value of the amount of electricity used by each industry.

Here, the hourly allocation ratio is the ratio of the amount of power consumed in a specific time period on a specific day to the annual power consumption of the target country.

The hourly electric power consumption prediction unit 55 calculates the hourly distribution ratio for each industrial field and household use in the target country calculated by the hourly distribution ratio calculation unit 54, and the assumed year of the target country calculated by the annual electric power consumption prediction unit 4. Based on the predicted value of electric power consumption for each industrial field and household (electric power consumption by application and industry) in It is a functional unit that calculates the predicted value of the amount of power consumption (other power consumption).

Next, a detailed processing flow of step S5 (road curve generation step) executed by the load curve generation unit 5 will be described.

FIG. 16 is a flowchart showing the detailed processing flow of step S5 (load curve generation step).
First, the power consumption vs. temperature model generation unit 51 generates a power consumption vs. temperature model for the target country (step S51). Specifically, the electricity usage vs. temperature model generation unit 51 generates actual values of hourly electricity usage on weekdays, Saturdays, and holidays in the target country, and hourly temperatures on weekdays, Saturdays, and holidays in the target country. Based on the actual values, power amount-temperature models for weekdays, Saturdays, and holidays are generated.

Here, the actual value of power consumption for each hour on weekdays, Saturdays, and holidays in the target country and the actual value for temperature for each hour on weekdays, Saturdays, and holidays in the target country are stored in storage device 102 as data 1022. It is assumed that

FIG. 17 is a diagram showing an example of the electric energy-temperature model.
In the figure, the vertical axis represents hourly power consumption, and the horizontal axis represents hourly temperature.
As shown in FIG. 17 , the electricity usage vs. temperature model generation unit 51 uses the hourly electricity usage actual values and the hourly actual temperature values in the target country to generate specific values for weekdays, Saturdays, and holidays. A scatter diagram is created by plotting sample values 401 that show the correspondence relationship between temperature and power consumption at time, and by performing regression analysis based on the scatter diagram, power that shows the relationship between power consumption and temperature. Calculating the volume-temperature model (also referred to as the “power usage vs. temperature model”) 401A.

As a method of the regression analysis, based on the relationship between the hourly temperature and the hourly power consumption of the target country, a method of selecting the approximate expression with the highest explanatory power such as linear regression, quadratic curve regression, cubic curve regression, and neural There are methods for approximating nonlinear regression equations using networks and backpropagation.

The power consumption vs. temperature model generation unit 51 calculates the power consumption vs. temperature model 401A for every hour (24 hours) on weekdays, Saturdays, and holidays in the target country. That is, the power consumption vs. temperature model generation unit 51 generates the power consumption-temperature models 401A (24 models) every hour from 1:00 am to 24:00 pm on weekdays, and every hour from 1:00 am to 24:00 pm on Saturdays. and hourly electric energy-temperature models 401A (24) from 1:00 am to 24:00 pm on holidays.

The power consumption vs. temperature model generation unit 51 generates the power consumption-temperature model 401A by the method described above.
In the following description, it is assumed that the power consumption is divided into three categories: home use, business use, and industrial use. In this case, as shown in FIG. 18, using the hourly power consumption and temperature actual values for household, business, and industrial use in the target country, the actual values for household use on weekdays, Saturdays, and a data group 402_1 including an hourly (24-hour) electric energy-temperature model 401A for each holiday, and an hourly (24-hour) electric energy-temperature model 401A for business use on weekdays, Saturdays, and holidays. A data group 402_2 and a data group 402_3 including a power amount-temperature model 401A every hour (for 24 hours) for industrial weekdays, Saturdays, and holidays are generated respectively.

Further, as shown in FIG. 18, the usage of the power consumption is further classified into small categories, and an hourly (24-hour) power consumption-temperature model 401A for each weekday, Saturday, and holiday is generated for each of the small categories. may For example, household power consumption may be categorized by household appliance such as "air conditioner", "refrigerator", and "other equipment", and industrial power consumption may be categorized into "materials". , “Machinery”, and “Other Industrial”.

Here, "materials" include major product industries such as primary metals and chemicals, and "machinery" includes general machinery, electrical machinery, and transportation machinery. "Other industries" includes industries not included in "Materials" and "Machinery."
It should be noted that the above-mentioned small classifications for industrial use and business use, and the classifications for industries 1 to 3 in the input-output table used in the above step S2 (input cost prediction step) are only examples, and the industrial composition of the target country. Alternatively, any classification according to the quality and quantity of available data may be used (see FIG. 7).

Next, in the flow chart shown in FIG. 16, the hourly temperature average year value data generating unit 52 calculates the hourly average temperature value for one year based on the actual hourly temperature values for a plurality of years (step S52: Hourly temperature average year value data generation step).

Specifically, the hourly temperature average year value data generator 52 ranks the days of each month in descending order of maximum temperature for each past year, and averages the hourly temperatures of the days of the same order in each year. A value (eg, arithmetic mean) is calculated, and the mean is taken as the average hourly temperature for that rank day in the month.

By adopting the average value for the same time period on the same day instead of the average value for the same time period on the same day, it is possible to create a load curve that takes into account the standard temperature fluctuations in January, and it is possible to create a power facility formation. This leads to avoidance of underestimation of the maximum power prediction value, which is a prerequisite.

For example, when using actual hourly temperature values for the past three years from 2014 to 2016, the temperature at 7 am on the day when the maximum temperature in August 2014 was the highest is T1, and in 2015 If the temperature at 7:00 am on the day with the highest temperature in August of 2016 is T2, and the temperature at 7:00 am on the day with the highest temperature in August in 2016 is T3, The average temperature at 7:00 am on the day with the highest maximum temperature in August (ranked first) is (T1+T2+T3)/3.

Similarly, for example, the temperature at 9 am on the 31st highest temperature in August 2014 (the day with the lowest maximum temperature in August) is T5, and the highest temperature in August 2015 is If the temperature at 9:00 am on the 31st hottest day is T6, and if the temperature at 9:00 am on the 31st hottest day in August in 2016 is T7, then the maximum temperature in August is 31. The average temperature at 9:00 am on the hottest day (ranked 31st) is (T5+T6+T7)/3.

The hourly temperature average year value data generation unit 52 ranks the highest temperature day to the lowest day for each month in each fiscal year in the target country. Calculate the average hourly temperature on the day of the same rank in the month, and use that average as the average hourly temperature on the day of that rank in the month. The average hourly temperature values of the target country calculated in this way for one year are stored in the storage device 102 as the hourly average temperature value data 500 as shown in FIG. 19 .

Next, in the flow chart shown in FIG. 16, the hourly theoretical electric energy value calculation unit 53 calculates based on the electric energy-temperature model 401A calculated in step S51 and the hourly temperature average value data 500 generated in step S52. Then, the theoretical value of the hourly power consumption in the target country is calculated (step S53: hourly power theoretical value calculation step).

Specifically, the hourly electric energy theoretical value calculation unit 53 determines a specific base year, and determines a specific By reading the temperature value at a specific hour of the day from the hourly average temperature data 500 and substituting the read temperature value into the electric energy-temperature relational expression corresponding to the specific time period of the specific day. , the theoretical value of the power consumption in that particular time period on that particular day is calculated.

For example, consider the case of calculating the theoretical value of power consumption in the target country at 8:00 am on August 10th. In this case, the hourly electric energy theoretical value calculation unit 53 first determines whether August 10 in the reference year is a weekday, a Saturday, or a holiday. Select model 401A. For example, if August 10 is a weekday, the power usage versus temperature model 401A for 8:00 am on a weekday is selected from the (24×3) kinds of power usage versus temperature models 401A calculated in step S51. .

Next, the hourly electric energy theoretical value calculation unit 53 determines where August 10 in the reference year ranks in the maximum temperature ranking for August of the target country, and calculates the temperature value corresponding to the determination result. Read out from the hourly average temperature data 500 . For example, if August 10th is the third highest temperature ranking for August in the target country, from the hourly average temperature data 500, the third highest temperature ranking for August will be Reads the hourly temperature value.

Next, the hourly electric energy theoretical value calculation unit 53 obtains by substituting the temperature value read from the hourly average temperature data 500 into the previously selected electric energy usage vs. temperature model 401A for 8:00 am on weekdays. The obtained value is assumed to be the theoretical value of power consumption at 8:00 am on August 10th in the target country.

The hourly electric energy theoretical value calculation unit 53 calculates the theoretical value of the hourly electric energy consumption for one year (365 days) for each purpose of the electric energy consumption by the method described above (step S54). For example, as in the example above, the power usage is categorized into household, commercial, and industrial; When the amount of electric power used for industrial use is classified into "materials," "machinery," and "other industrial uses," as shown in Fig. 20, the amount of electric power used every hour for one year (365 days) for each of these categories is calculated and stored in the storage device 102 as the hourly electric energy theoretical value data 501 .

Next, in the flow chart shown in FIG. 16, the hourly allocation ratio calculation unit 54 calculates the theoretical value of the hourly electric energy for the target country for one year calculated by the theoretical hourly electric energy value calculation unit 53 and the annual power consumption of the target country. Based on the theoretical value of the amount, the hourly allocation ratio of the target country is calculated (step S55: hourly allocation ratio calculation step).

Specifically, the hourly distribution ratio calculation unit 54 calculates the theoretical value of the power amount in a specific time zone on a specific day, which is calculated by the hourly power theoretical value calculation unit 53, by the hourly power amount theoretical value calculation unit 53. The hourly allocation ratio is calculated by dividing by the calculated theoretical value of the annual power consumption.

For example, when calculating the hourly allocation ratio at 8:00 am on August 10 in the industrial field of “materials”, the hourly allocation ratio calculation unit 54 calculates The value obtained by dividing the theoretical value of the amount of power used by the "material" at 8:00 am by the theoretical value of the annual amount of power used by the "material" calculated by the hourly theoretical value calculation unit 53 is The hourly allocation rate at 8:00 am on August 10th in the "Materials" industry sector.
In this way, the hourly allocation ratio calculator 54 calculates the hourly allocation ratio for each application and industry of the power consumption for (365 days×24 hours).

In addition, if it is possible to obtain the actual values of the load curve over multiple years, and if there is a time-series change in the shape of the load curve for a specific application or industry, the hourly data for the specific application or industry In the calculation of the distribution ratio, it is also possible to change the future predicted value by reflecting the time-series change.

Next, in the flow diagram of FIG. and the predicted value of the power consumption in the target country in the assumed fiscal year (step S56: hourly power consumption prediction step).

Specifically, the hourly power consumption prediction unit 55 calculates the hourly power consumption in the assumed year by multiplying the hourly allocation ratio by the annual power consumption in the assumed year. For example, when calculating the power consumption of “materials” at 8:00 am on August 10th in the assumed fiscal year, the hourly allocation ratio of “materials” at 8:00 am on August 10th The value obtained by multiplying the annual power consumption is set as the power consumption of the "material" at 8:00 am on August 10 of the assumed year.

In this way, the hourly power consumption prediction unit 55 calculates the hourly power consumption for 365 days×24 hours in the assumed year for each application and each industry.

Next, the hourly power consumption prediction unit 55 calculates the predicted value of the hourly power consumption for 365 days x 24 hours in the assumed year for a certain period (for example, every day or every month) in the assumed year. A load curve for each application and each industry is generated (step S57). In addition, the hourly power consumption prediction unit 55 selects the predicted value of the hourly power consumption for 365 days×24 hours in the calculated assumed year, and selects the maximum power (for example, hourly average) for the most frequently used time (e.g., hourly average). kW) is calculated (step S58).

FIG. 22 is a diagram showing an example of a load curve.
The figure shows a typical one-day load curve in summer. In FIG. 22, reference numeral 600 indicates a load curve of electric energy for household use, reference numeral 601 indicates a load curve of electric energy for industrial use (materials), and reference numeral 602 indicates a load curve of electric energy for industrial use (machinery). A power load curve is shown, and reference numeral 603 indicates a commercial power load curve.
As shown in FIG. 22, the hourly power usage prediction unit 55 can generate a load curve for each desired application and industry.

As described above, the power demand calculation device 100 can generate load curve data and maximum power (kW) data as the power demand in the assumed year of the target country.

(7) Effect of Power Demand Calculating Device 100 As described above, the power demand calculating device 100 according to the present embodiment can calculate the predicted value of the economic index (for example, GDP) in the assumed fiscal year of the target country and the input-output table of the target country. Based on this, the input cost for each industry in the target country is calculated, and based on the input cost, the forecast value of the power demand for the target country in the assumed fiscal year is calculated.

That is, the power demand calculation device 100 uses the input-output table to calculate the input cost to the energy industry for each industry that is correlated with the amount of power consumption for each application and industry in the target country. It is possible to precisely predict the amount of electricity used for each application and industry in a country. This makes it possible to precisely calculate future power demand even if the target country is an emerging country.

In addition, the power demand calculation device 100 calculates the predicted value of the amount of power for each application and each industry based on the investment cost for each industry in the energy industry in the assumed fiscal year of the target country. Generate an hourly load curve for each application and industry in the assumed fiscal year of the target country based on the amount of electric power and the hourly allocation ratio. According to this, even if the target country is an emerging country, it is possible to generate an hourly load curve for each application and for each industry, so that the future power demand of the emerging country can be calculated more precisely. becomes possible.

In addition, as described above, in the power demand calculation device 100, each demand item of the input-output table, which is one of the parameters necessary for calculating the input cost to the energy industry for each industry in the assumed year of the target country The composition ratio of each industry (for example, C1/(C1+C2+C3), C2/(C1+C2+C3), C3/(C1+C2+C3), etc.) in the target country, and the trend change in the actual value of the composition ratio in other countries with different economic levels from the target country Calculate with consideration.

That is, in the power demand calculation device 100, the actual value of the composition ratio of each industry in each demand item in the target country, the GDP per population in other countries with different economic levels from the target country, and the composition of each industry in each demand item A model showing the relationship between economic development in the target country and the composition ratio of each industry in each demand item is calculated based on the model showing the relationship between Calculate the predicted value of the composition ratio of each industry in each demand item.

According to this, as mentioned above, even if the target country is an emerging country, the composition ratio of each industry in the demand item (consumption) in the assumed fiscal year is calculated, and each industry Since household power consumption is calculated based on the composition ratio of industries, changes in the consumption structure due to rising income levels in emerging countries are taken into account. Compared to calculating the above-mentioned composition ratio for the assumed fiscal year using only the actual composition ratio of It becomes possible to

In addition, the composition ratio of each industry in each demand item (consumption, investment, export) in the assumed fiscal year is calculated, and based on the composition ratio of each industry in each demand item (consumption, investment, export), Since the energy input cost is calculated and the amount of electricity used for each industry is calculated based on the energy input cost for each industry, changes in the industrial structure accompanying the rise in the economic level of the emerging country are taken into account, and the business of the target country It will be possible to make more precise forecasts of electric power consumption for commercial and industrial use, reflecting medium- to long-term changes in the industrial structure.

Further, in the power demand calculation device 100, as described above, the target country's industry The input coefficients (a11 to a33) for each energy industry are calculated in consideration of trends in actual values of input coefficients in other countries whose economic levels are different from those of the target country.

That is, in the power demand calculation device 100, the actual value of the input coefficient to the energy industry for each industry in the target country, the GDP per population in other countries with different economic levels from the target country, and the input coefficient to the energy industry for each industry A model showing the relationship between GDP per capita in the target country and its input coefficient is calculated based on the model showing the relationship between Calculate the predicted value of the input coefficient.

According to this, as mentioned above, even if the target country is an emerging country, the input coefficient for the assumed fiscal year is calculated in consideration of the changes in the production structure accompanying the rise in the economic level of the emerging country. , compared to the calculation of input coefficients using only the past actual values of input coefficients in emerging countries, It is possible to make a more precise prediction.

Further, in the power demand calculation device 100, as described above, the GDP The composition ratio of each demand item to the GDP of the target country is calculated by taking into consideration the trend change in the actual value of the composition ratio of each demand item to GDP in other countries whose economic levels are different from that of the target country.

That is, the power demand calculation device 100 calculates the actual value of the composition ratio of each demand item in the GDP of the target country in the assumed fiscal year, and the GDP per population and each demand item in the GDP in other countries with different economic levels from the target country. Calculate a model that shows the relationship between GDP per population and the composition ratio of each demand item in GDP in the target country, based on the model that shows the relationship with the composition ratio, and based on the model, estimate the target country Calculate the predicted value of the composition ratio of each demand item in GDP in the fiscal year.

According to this, even if the target country is an emerging country, as mentioned above, each demand item in the GDP of the assumed fiscal year will be Therefore, it is possible to more precisely predict the amount of electricity used by application and industry in the target country compared to the calculation using only past performance values of the relevant composition ratio of emerging countries. This makes it possible to more precisely calculate future power demand in emerging countries.

Further, in the power demand calculation device 100, as described above, the labor productivity in the assumed year of the target country, which is one of the parameters necessary for calculating the economic index (GDP) in the assumed year of the target country, Calculated by taking into account the labor productivity of a specific developed country as a benchmark.

That is, in the power demand calculation device 100, based on a model indicating temporal changes in labor productivity of a specific developed country serving as a benchmark and the actual value of labor productivity of the target country, the advanced Calculate a model that shows changes in the target country's labor productivity over time so that the difference between the country's labor productivity and the target country's labor productivity is reduced, and based on the model, the target country's labor production in the assumed fiscal year Calculate the predictive value of sex for each industry.

According to this, as mentioned above, it is possible to reasonably predict the labor productivity of the target country in the assumed fiscal year using the past performance of developed countries as a reference index, so the economic index (GDP) of the target country in the assumed fiscal year can be reasonably calculated. This makes it possible to more rationally calculate future power demand in emerging countries.

In addition, as described above, in the power demand calculation device 100, when calculating the average value of the hourly temperature of the target country, which is one of the parameters necessary for calculating the load curve in the assumed year of the target country, The days of each month in the past years are ranked in descending order of maximum daily temperature. is the average yearly value of

According to this, it is possible to create a normal year value that reflects the standard temperature fluctuations in January according to the degree of temperature variation on the same day and time period in the target country over the past several years. In addition, it is possible to predict the hourly load curve in the assumed year of the target country with the fluctuation range that can normally occur according to temperature fluctuations, and to prevent underestimation of the annual maximum power demand, which is a prerequisite for future power facility formation. can be avoided.

<<Expansion of Embodiment>>
Although the invention made by the inventors of the present invention has been specifically described above based on the embodiment, it should be noted that the invention is not limited to it, and various changes can be made without departing from the gist of the invention. Not even.

For example, in the load curve generation unit 5, the actual values of hourly power consumption and temperature used to calculate the power consumption vs. temperature model of the target country are not the actual values of the target country, but are different from the target country. It may be the actual value of In this case, the electric energy-temperature model calculated using the actual values of countries other than the target country may be appropriately corrected according to the climate of the target country, and the corrected model may be used as the electric power-temperature model of the target country.

In addition, in the above embodiment, the case of dividing the industrial fields in the input-output table into three industries 1 to 3 was exemplified, but it is not limited to this, and each application and industry that requires information such as load curve and maximum power You can separate each. For example, when power demand is calculated by classifying it into three categories, household, commercial, and industrial, as described above, the uses included in household are classified into air conditioners, refrigerators, and other equipment, and commercial The industries included in are classified into wholesale and retail, food and beverage, accommodation, transportation and warehousing, communication and postal, education, medical and welfare, and other service industries. Agriculture, forestry and fisheries, mining, food industry, textile industry, lumber and wood products industry, paper pulp and printing industry, coal and petroleum industry, chemical industry, ceramics industry, metal industry, general machinery and electrical industry, transportation machinery industry, It may be classified into the construction industry and other manufacturing industries.

1 Economic index prediction unit 2 Input cost prediction unit 3 Power demand prediction unit 4 Annual power consumption prediction unit 5 Road curve generation unit 11 Total number of workers calculation unit 12 By industry Employed person number calculation part 13... GDP forecast part 14... Employed person industry composition ratio calculation part 15... Labor productivity calculation part 21... Demand item composition ratio calculation part 22... GDP calculation part by demand item 23... Industrial composition ratio calculation unit by demand item 24 GDP calculation unit by industry 25 Input coefficient calculation unit 26 Domestic production amount calculation unit 27 Input cost calculation unit 51 Power consumption vs. temperature model generation unit 52 Hourly temperature average year value data generation unit 53 Hourly theoretical value calculation unit 54 Hourly distribution ratio calculation unit 55 Hourly power consumption prediction unit 100 Power demand calculation device 101 Arithmetic device 102 Memory device 103 Input device 104 I/F device 105 Display device 106 Bus 1021 Program 1022 Data.

Claims (10)

An electric power functioning as an economic index forecasting unit, an input cost forecasting unit, and an electric power demand forecasting unit, including an arithmetic unit and a storage device, wherein the arithmetic unit performs arithmetic operations according to the programs and data stored in the storage device. A demand calculation device,
The economic indicator forecasting unit calculates forecast values of economic indicators in the assumed fiscal year of the target country for which electricity demand is calculated ,
The input cost forecasting unit configures the economy of the target country in the assumed year based on the forecasted values of the economic indicators of the target country calculated by the economic index forecasting unit and the input-output table of the target country. , Calculate the predicted value of the input cost to the energy industry for each industry listed in the input-output table ,
The power demand forecasting unit predicts the power demand in the assumed year for each application and industry based on the estimated input cost to the energy industry for each industry in the assumed year calculated by the input cost forecasting unit. predict ,
The economic indicator is GDP,
The economic indicator forecasting unit
Multiplying the forecast value of the total number of workers in the target country in the assumed fiscal year by the composition ratio of each industry in the total number of workers in the target country, a calculation unit for the number of workers by industry that calculates the number of workers;
By performing regression analysis based on the actual values of labor productivity in a specific developed country different from the target country, a first model showing the time change in labor productivity in a specific developed country different from the target country is calculated. , Calculate a second model that shows the time change of labor productivity of the target country by regression analysis of the actual value of labor productivity of the target country, and calculate based on the first model and the second model. Using the calculated convergence coefficient, the second model is corrected so that the difference between the labor productivity of the developed country and the labor productivity of the target country decreases over time, and the corrected second model a labor productivity calculation unit that calculates the labor productivity for each industry in the assumed year of the target country using
By multiplying the number of workers for each industry calculated by the number of workers by industry calculation unit and the labor productivity for each industry calculated by the labor productivity calculation unit, a GDP forecasting unit that calculates a forecast value of GDP in an assumed year;
The input cost prediction unit
a GDP calculation unit for each demand item that calculates the GDP for each demand item of the input-output table based on the predicted value of the GDP of the target country in the assumed year calculated by the economic indicator prediction unit;
By multiplying the GDP for each demand item calculated by the GDP calculation unit for each demand item by the predicted value of the composition ratio of each industry in each demand item, an industry-by-industry GDP calculation unit that calculates GDP;
An input coefficient calculation unit that calculates the input coefficient of the input-output table in the assumed year of the target country;
Based on the industry-specific GDP of each demand item calculated by the industry-specific GDP calculation unit, the input coefficient calculated by the input coefficient calculation unit, and the model formula of the input-output table, the assumed fiscal year a domestic production value calculation unit that calculates the domestic production value for each industry in the target country;
Based on the domestic production value calculated by the domestic production value calculation unit and the input coefficient to the energy industry calculated by the input coefficient calculation unit, the energy for each industry in the assumed year of the target country an input cost calculation unit that calculates a predicted value of the input cost to the industry,
The power demand forecasting unit
Based on the predicted value of the input cost to the energy industry for each industry and the composition ratio of the electricity field in the energy industry, calculate the electricity fee that is the input cost to the electricity field, and based on the electricity fee an annual power consumption prediction unit that calculates a predicted value of annual power consumption for each application and for each industry;
Hourly allocation ratio indicating the ratio of the hourly power consumption of the target country to the annual power consumption of the target country, and the annual power consumption for each application and industry in the assumed year calculated by the annual power consumption prediction unit and a load curve generation unit that generates an hourly load curve for each application and each industry in the assumed year based on the predicted value of electric energy.
Power demand calculator. In the power demand calculation device according to claim 1 ,
The input cost prediction unit
The actual value of GDP per population in the target country, the actual value of the composition ratio of each industry in each of the demand items in the target country, and the actual GDP per population in other countries whose economic levels are different from those of the target country. relationship between GDP per population in the target country and the composition ratio of each industry to each demand item, based on the value and the actual value of the composition ratio of each industry to each demand item in the other country. further comprising a demand item-specific industry composition ratio calculation unit that calculates a model showing the model and calculates, based on the model, the composition ratio of each industry to the demand item in the target country in the assumed year;
The industry-by-industry GDP calculation unit calculates the predicted value of GDP for each of the demand items calculated by the demand-item-specific GDP calculation unit, and the Calculate the GDP by industry for each demand item based on the composition ratio for each industry,
A power demand calculation device characterized by: In the power demand calculation device according to claim 1 or 2 ,
The input coefficient calculation unit calculates the actual value of GDP per population in the target country, the actual value of the input coefficient in the target country, and the actual value of GDP per population in another country whose economic level is different from that of the target country. , based on the actual value of the input coefficient in the other country, calculate a model showing the relationship between the GDP per capita in the target country and the input coefficient, and based on the model, the assumed year of the target country A power demand calculation device, characterized in that it calculates the input coefficient in the above. In the power demand calculation device according to any one of claims 1 to 3 ,
The input cost prediction unit
The actual value of GDP per population in the target country, the actual value of the composition ratio of each demand item in the GDP of the target country, and the actual value of GDP per population in another country whose economic level is different from that of the target country. and calculating a model showing the relationship between the GDP per population of the target country and the composition ratio of each of the demand items in GDP of the target country, based on the actual value of the composition ratio of each of the demand items in the GDP of the other country. , further comprising a demand item composition ratio calculation unit that calculates a predicted value of the composition ratio of each of the demand items in the GDP of the target country in the assumed year based on the model,
The demand item-specific GDP calculation unit calculates the predicted value of GDP in the assumed fiscal year calculated by the economic index forecasting unit, and the predicted value of the composition ratio of each demand item calculated by the demand item composition ratio calculation unit. a GDP for each of the demand items in the assumed year of the target country, based on the power demand calculation device. In the power demand calculation device according to any one of claims 1 to 4 ,
The load curve generation unit
an hourly temperature normal value data generation unit that generates hourly temperature normal value data including the hourly temperature normal value for one year based on the actual hourly temperature values for a plurality of years;
Annual hourly power consumption of the target country based on a model showing the relationship between hourly power consumption and temperature, and the hourly temperature normal year value data generated by the hourly temperature normal year value data generation unit An hourly electric energy theoretical value calculation unit that calculates the theoretical value of
an hourly allocation ratio calculation unit that calculates the hourly allocation ratio of the target country for each application and each industry based on the theoretical value of the hourly electric energy consumption calculated by the hourly theoretical electric energy amount calculation unit; ,
The predicted value of the annual power consumption for each application and each industry calculated by the annual power consumption prediction unit is added to the hourly distribution ratio for each application and each industry calculated by the hourly distribution ratio calculation unit. and an hourly power consumption prediction unit that calculates the hourly power consumption for each application and each industry in the assumed year of the target country,
The hourly temperature average year value data generation unit ranks the days of each month in order of the highest daily maximum temperature for each past year, and averages the hourly temperature of the days of the same order in the same month in each year. is the average value of the hourly temperature on the day of the month in that order. to the computer,
an economic index prediction step of calculating the predicted value of the economic index in the assumed fiscal year of the target country for which electricity demand is to be calculated;
Based on the predicted value of the economic index of the target country calculated by the economic index prediction step and the input-output table of the target country, the input-output table that constitutes the economy of the target country in the assumed year An input cost prediction step of calculating a predicted value of the input cost to the energy industry for each listed industry;
a power demand prediction step of predicting the power demand in the assumed year for each application and industry based on the predicted value of the input cost to the energy industry for each industry in the assumed year calculated by the input cost prediction step; is a program for executing
The economic indicator is GDP,
The economic indicator forecasting step includes:
Multiplying the forecast value of the total number of workers in the target country in the assumed fiscal year by the composition ratio of each industry in the total number of workers in the target country, a step for calculating the number of workers by industry;
By performing regression analysis based on the actual values of labor productivity in a specific developed country different from the target country, a first model showing the time change in labor productivity in a specific developed country different from the target country is calculated. , Calculate a second model that shows the time change of labor productivity of the target country by regression analysis of the actual value of labor productivity of the target country, and calculate based on the first model and the second model. Using the calculated convergence coefficient, the second model is corrected so that the difference between the labor productivity of the developed country and the labor productivity of the target country decreases over time, and the corrected second model a labor productivity calculation step of calculating the labor productivity for each industry in the assumed year of the target country using
By multiplying the number of workers for each industry calculated by the number of workers by industry calculation step and the labor productivity for each industry calculated by the labor productivity calculation step, the number of workers in the target country a GDP forecasting step of calculating a forecasted value of GDP in the assumed year,
The input cost prediction step includes:
a GDP by demand item calculation step of calculating GDP for each demand item of the input-output table based on the predicted value of GDP in the assumed year of the target country calculated by the economic indicator prediction step;
By multiplying the GDP for each demand item calculated by the GDP calculation step for each demand item by the predicted value of the composition ratio of each industry in each demand item, an industry-specific GDP calculation step for calculating GDP;
an input coefficient calculation step of calculating the input coefficient of the input-output table in the assumed year of the target country;
Based on the industry-specific GDP of each demand item calculated by the industry-specific GDP calculation step, the input coefficient calculated by the input coefficient calculation step, and the model formula of the input-output table, the assumed fiscal year a domestic production value calculation step of calculating the domestic production value for each industry in the target country;
Based on the domestic production value calculated by the domestic production value calculation step and the input coefficient to the energy industry calculated by the input coefficient calculation step, energy for each industry in the assumed year of the target country an input cost calculation step of calculating a predicted value of the input cost to the business,
The power demand prediction step includes:
Based on the predicted value of the input cost to the energy industry for each industry and the composition ratio of the electricity field in the energy industry, calculate the electricity fee that is the input cost to the electricity field, and based on the electricity fee an annual power consumption prediction step of calculating a predicted value of annual power consumption for each application and for each industry;
Hourly allocation ratio indicating the ratio of the hourly power consumption of the target country to the annual power consumption of the target country, and the annual power consumption for each application and industry in the assumed year calculated by the annual power consumption prediction step and a load curve generating step of generating an hourly load curve for each application and each industry in the assumed year based on the predicted value of electric energy.
program. In the program according to claim 6 ,
The input cost prediction step includes:
The actual value of GDP per population in the target country, the actual value of the composition ratio of each industry in each of the demand items in the target country, and the actual GDP per population in other countries whose economic levels are different from those of the target country. relationship between GDP per population in the target country and the composition ratio of each industry to each demand item, based on the value and the actual value of the composition ratio of each industry to each demand item in the other country. calculating a model showing and based on the model, calculating the composition ratio of each industry in each of the demand items in the assumed year of the target country;
The step of calculating the GDP by industry includes: the predicted value of the GDP for each of the demand items calculated by the step of calculating the GDP by demand item; calculating the GDP by industry for each of the demand items based on the composition ratio for each industry;
A program characterized by In the program according to claim 6 or 7 ,
The input coefficient calculation step includes the actual value of GDP per population in the target country, the actual value of the input coefficient in the target country, and the actual value of GDP per population in another country with an economic level different from that of the target country. , based on the actual value of the input coefficient in the other country, calculate a model showing the relationship between the GDP per capita in the target country and the input coefficient, and based on the model, the assumed year of the target country calculating the input factor in
A program characterized by In the program according to any one of claims 6 to 8 ,
The input cost prediction step includes:
The actual value of GDP per population in the target country, the actual value of the composition ratio of each demand item in the GDP of the target country, and the actual value of GDP per population of another country whose economic level is different from that of the target country. Calculating a model showing the relationship between the GDP per population of the target country and the composition ratio of each of the demand items in the GDP of the target country, based on the actual values of the composition ratio of each of the demand items in the GDP of the other country. and further comprising a demand item composition ratio calculation step of calculating a predicted value of the composition ratio of each of the demand items in the GDP of the target country in the assumed year based on the model,
The step of calculating GDP by demand item includes the predicted value of GDP in the assumed fiscal year calculated by the step of predicting economic indicators, and the predicted value of the composition ratio of each demand item calculated by the step of calculating composition ratio of demand items. calculating the GDP for each of the demand items in the assumed year of the target country based on
A program characterized by In the program according to any one of claims 6 to 9 ,
The load curve generation step includes:
an hourly temperature normal value data generating step of generating hourly temperature normal value data including the hourly temperature normal value for one year based on the actual hourly temperature values for a plurality of years;
Based on a model showing the relationship between hourly power consumption and temperature, and the hourly temperature normal year value data generated by the hourly temperature normal year value data generation step, the annual hourly power consumption of the target country An hourly electric energy theoretical value calculation step for calculating the theoretical value of
an hourly allocation ratio calculating step of calculating the hourly allocation ratio of the target country for each application and each industry based on the theoretical value of the hourly electric energy consumption calculated by the hourly theoretical electric energy value calculating step; ,
The predicted value of the annual power consumption for each application and each industry calculated by the annual power consumption prediction step is added to the hourly distribution ratio for each application and each industry calculated by the hourly distribution ratio calculation step. an hourly power consumption prediction step of calculating the hourly power consumption for each application and each industry in the assumed year of the target country by multiplying by
including
The average hourly temperature data generation step ranks the days of each month in order of the highest daily maximum temperature for each past year, and averages the hourly temperatures of the days of the same rank in the same month in each year. is the average hourly temperature for that order day of the month.

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