JP2013182448A - Maximum supply amount prediction system, and maximum supply amount prediction method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a maximum supply amount prediction system capable of predicting a maximum supply amount of energy with high accuracy.SOLUTION: A temperature calculating part 22 acquires past supply amount record data, past average temperature record data, scheduled supply amount data for each intended purpose, and the like on the basis of a prediction unit and a prediction period received by an input reception part 21. On the basis of the acquired data, the temperature calculating part 22 calculates a temperature for the prediction unit and the prediction period; a supply amount calculating part 23 calculates a supply amount in the prediction unit and the prediction period for each month, for each day of the week, and for each intended purpose, an in-period maximum supply amount calculating part 24 calculates a supply amount in the prediction unit and the prediction period. A predicted maximum supply amount calculating part 25 calculates a maximum supply amount which can be generated at a constant possibility on the basis of a simulation result of supply amount distribution in the prediction unit and the prediction period.

Description

  The present invention relates to a maximum energy supply amount prediction system and the like.

  Conventionally, an energy supply company such as gas or electricity needs to accurately predict the maximum energy supply amount for an equipment operation plan and an equipment investment plan which are necessary for enhancing energy security.

  For example, Patent Literature 1 proposes a technique for formulating a plurality of demand prediction techniques for skilled operators, giving a result based on a difference from the actual results, and improving the prediction accuracy.

JP 2007-241360 A

  However, in the technique of Patent Document 1, past performance data and weather information are considered. In order to calculate the maximum supply amount of energy with high accuracy, for example, it is necessary to consider “sensitivity to air temperature” and the like that differ for each use of energy.

  Moreover, with the technique of patent document 1, it will remain only in the prediction of the demand in a specific day. Information useful for the facility operation plan and the facility investment plan is the maximum energy supply amount in a certain period (for example, “December to February”, which is the peak period of gas demand).

  The present invention has been made in view of the above-described problems, and the purpose of the present invention is to maximize the maximum supply amount of energy useful for an equipment operation plan and an equipment investment plan. It is to provide a supply amount prediction system and the like.

  In order to achieve the above-described object, the first invention is a maximum supply amount prediction system configured by a computer and predicting the maximum supply amount of energy, and a prediction unit and a prediction period for predicting the maximum supply amount Input receiving means for receiving the input, acquisition means for acquiring past supply amount actual data based on the prediction unit and prediction period received by the input receiving means, and calculating the temperature in the prediction unit and prediction period Based on the first calculation means, the past supply amount actual data acquired by the acquisition means, and the temperature calculated by the first calculation means, the distribution of the supply amount in the prediction unit and the prediction period is calculated. From the second calculation means for calculating, and the distribution of the supply amount in the prediction unit and the prediction period calculated by the second calculation means, A third calculating means for calculating the serial maximum supply amount, a maximum supply amount prediction system comprising: a. According to the first invention, the maximum supply amount in the prediction unit and the prediction period such as the specific period, the specific time, and the specific time can be accurately predicted.

  The second calculation means further calculates the distribution of the supply amount in the prediction unit and the prediction period for each use. The reason for calculating by use is, for example, that for home use, the sensitivity to temperature is high, but for industrial use, it is considered that the sensitivity to temperature is not so high. This is because the difference in sensitivity to temperature can be taken into account.

  The second calculation means stores only a maximum value of the supply amount distribution in the prediction unit and the prediction period in the storage unit, and the third calculation means calculates the supply amount distribution in the prediction unit and the prediction period. The maximum supply amount is calculated from the maximum value. As a result, the memory used can be minimized.

  The first calculation means acquires the temperature in the prediction unit and the prediction period from past performance values. This makes it possible to calculate according to reality.

  A second invention is a maximum supply amount prediction method executed by a computer and predicting a maximum supply amount of energy, wherein the computer receives an input of a prediction unit for performing prediction of the maximum supply amount and a prediction period. A receiving step, an acquisition step of acquiring past supply amount actual data based on the prediction unit and the prediction period received by the input receiving step, and a first calculation step of calculating the temperature in the prediction unit and the prediction period And calculating a distribution of supply amount in the prediction unit and prediction period based on the past supply amount actual data acquired in the acquisition step and the temperature calculated in the first calculation step. The amount of supply in the prediction unit and the prediction period calculated in the calculation step and the second calculation step. From the fabric, the maximum supply amount prediction method characterized by including: a third calculation step of calculating the maximum supply amount. According to the second invention, the maximum supply amount in the prediction unit and the prediction period such as the specific period, the specific time, and the specific time can be accurately predicted.

  According to the present invention, it is possible to provide a maximum supply amount prediction system and a maximum supply amount prediction method capable of accurately predicting the maximum supply amount of energy useful for facility operation plans and facility investment plans.

It is a figure which shows the outline | summary of the maximum supply amount prediction system. It is a hardware block diagram of the computer which implement | achieves a terminal (server). It is a block diagram which shows the function structural example of the control part in a terminal. It is a flowchart explaining the maximum supply amount calculation process of the maximum supply amount prediction system 1. It is a normal distribution for demonstrating the example which calculates the temperature in a prediction unit and a prediction period. It is a figure which shows the example of the input value of the temperature (average, standard deviation) in a prediction unit and a prediction period. It is a data flow for demonstrating the flow of the process which calculates distribution of the supply amount in the prediction unit according to a month and a day of the week, and a prediction period. It is an example of an output of graph data of distribution of supply amount in t ° C for every use. It is an output example of the graph data of the distribution of the supply amount at t ° C. for all uses. It is a graph for demonstrating the example which calculates the prediction maximum supply amount in a prediction unit and a prediction period.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Hereinafter, gas will be described as an example of target energy for maximum supply amount prediction. However, the application range of the present invention is not limited to gas. The present invention has the same effect when applied to energy having the above-described problems.

FIG. 1 is a diagram showing an outline of the maximum supply amount prediction system 1. As shown in FIG. 1, in the maximum supply amount prediction system 1, for example, a terminal 2 and a server 3 are connected via a network 6. The network 6 is, for example, the Internet or a LAN (Local Area Network). The terminal 2 is, for example, a PC (Personal
Computer, hereinafter referred to as “computer”) or a mobile terminal (cell phone, smartphone, tablet terminal, etc.), etc. Simple equipment may be used. Similarly to the terminal 2, the server 3 may be connected to the network 6 and can perform data communication, but is preferably a high-performance server computer.

  A supply amount prediction program 4 that is an embodiment of the present invention is installed in the terminal 2. In the embodiment of the present invention, the terminal 2 functions as various means according to the supply amount prediction program 4, calculates a necessary gas supply amount for the predicted maximum demand amount of gas, and presents it to the user. The terminal 2 transmits a data request command to the server 3 as necessary.

  In the server 3, a database (hereinafter “DB”) 5 for storing various data used in the embodiment of the present invention is constructed. The database 5 stores, for example, past supply data by usage, past supply actual data, past average temperature actual data, usage-specific planned supply data, and the like. The planned supply amount data is data statistically calculated based on past data. The server 3 searches the DB 5 for the data request and transmits the requested data to the terminal 2.

  The configuration of the maximum supply amount prediction system 1 is not limited to the example shown in FIG. For example, the maximum supply amount prediction system 1 may be configured by only the terminal 2. That is, the terminal 2 may include the DB 5.

  The supply amount prediction program 4 may be installed in the server 3. That is, the server 3 may function as various means according to the supply amount prediction program 4 to calculate a necessary gas supply amount for the predicted maximum gas demand and present it to the user. In this case, the terminal 2 serves as an interface with the user. That is, the terminal 2 transmits data input from the user to the server 3 and outputs (displays, prints, etc.) data received from the server 3.

  Further, instead of DB5, data may be stored as a simple file. The data stored in the DB 5 may be acquired from an external server.

  FIG. 2 is a hardware configuration diagram of a computer that realizes the terminal 2 (server 3). Note that the hardware configuration in FIG. 2 is an example, and various configurations can be adopted depending on the application and purpose.

  A computer that realizes the terminal 2 (server 3) includes a control unit 11, a storage unit 12, a media input / output unit 13, a communication control unit 14, an input unit 15, a display unit 16, a peripheral device I / F unit 17, and the like. 18 is connected.

  The control unit 11 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The CPU calls a program stored in the storage unit 12, ROM, recording medium, etc. to a work memory area on the RAM and executes it, drives and controls each device connected via the bus 18, and will be described later. Realize processing. The ROM is a non-volatile memory and permanently holds a computer boot program, a program such as BIOS, data, and the like. The RAM is a volatile memory, and temporarily stores programs, data, and the like loaded from the storage unit 12, ROM, recording medium, and the like, and includes a work area used by the control unit 11 for performing various processes.

  The storage unit 12 is, for example, an HDD (Hard Disk Drive), and stores a program executed by the control unit 11, data necessary for program execution, an OS (Operating System), and the like. As for the program, a control program corresponding to the OS and an application program (supply amount prediction program 4) for causing a computer to execute processing to be described later are stored. Each of these program codes is read by the control unit 11 as necessary, transferred to the RAM, read by the CPU, and executed as various means.

  The media input / output unit 13 (drive device) inputs / outputs data, for example, media such as a CD drive (-ROM, -R, -RW, etc.), DVD drive (-ROM, -R, -RW, etc.) Has input / output devices. The communication control unit 14 includes a communication control device, a communication port, and the like, and is a communication interface that mediates communication between the computer and the network 6, and controls communication with other computers via the network 6. The network 6 may be wired or wireless.

  The input unit 15 inputs data and includes, for example, a keyboard, a pointing device such as a mouse, and an input device such as a numeric keypad. An operation instruction, an operation instruction, data input, and the like can be performed on the computer via the input unit 15. The display unit 16 includes a liquid crystal panel, a display device such as an organic EL, and a logic circuit or the like (video adapter or the like) for realizing a video function of a computer in cooperation with the display device. The input unit 15 and the display unit 16 may be integrated like a touch panel display.

  The peripheral device I / F (interface) unit 17 is a port for connecting a peripheral device to the computer, and the computer transmits and receives data to and from the peripheral device via the peripheral device I / F unit 17. The peripheral device I / F unit 17 is configured by a USB or the like, and usually includes a plurality of peripheral devices I / F. The connection form with the peripheral device may be wired or wireless. The bus 18 is a path that mediates transmission / reception of control signals, data signals, and the like between the devices.

  FIG. 3 is a block diagram illustrating a functional configuration example of the control unit 11 in the terminal 2. At least a part of the functional units shown in FIG. 3 is realized by the supply amount prediction program 4 being executed by the control unit 11 of FIG.

  The input receiving unit 21 inputs a unit (day, time, etc.) for which a maximum supply amount is to be obtained by a user operating the input unit 15 on an input screen (not shown) displayed on the display unit 16. And an input of a period for predicting the maximum supply amount. Hereinafter, a unit for which the maximum supply amount is desired is defined as “prediction unit”, and a period during which the maximum supply amount is predicted is defined as “prediction period”.

  The input reception unit 21 supplies the input information (prediction unit and prediction period) received from the user to the temperature calculation unit 22. The input receiving unit 21 receives the prediction unit and the prediction period independently. For example, when the prediction period is “December to February” and the prediction unit is “day”, the maximum daily supply amount in December to February is obtained. When the prediction period is “December” and the prediction unit is “8 to 9 o'clock”, the maximum supply amount at 8 to 9 o'clock in December is obtained. For past actual supply amount actual data and past supply amount actual data used for processing to be described later, the unit of input data is changed in accordance with the prediction unit.

  The air temperature calculation unit 22 accesses the DB 5 of the server 3 via the network 6, and based on the input information (prediction unit and prediction period) supplied from the input reception unit 21, the past supply amount actual data by use, the past Acquire actual supply amount data, past average temperature actual result data, usage-specific planned supply amount data, and the like. The temperature calculation unit 22 calculates the temperature in the prediction unit and the prediction period from the past average temperature actual data in the prediction unit and the prediction period, and the calculation result is used as the past supply amount actual data for each usage in the prediction unit and the prediction period. The supply amount is supplied to the supply amount calculation unit 23 together with past supply amount actual data and usage-specific planned supply amount data.

  The supply amount calculation unit 23 calculates the monthly temperature from the prediction unit and the temperature in the prediction period supplied from the temperature calculation unit 22, the past supply amount actual data by use, the past supply amount actual data, and the planned supply amount data by use, Calculate the distribution of supply in forecast units by day of the week and usage. Then, the supply amount calculation unit 23 adds the data for all uses in consideration of the correlation between uses, and calculates a single supply amount that is added up.

  The temperature calculation unit 22 and the supply amount calculation unit 23 can obtain supply amounts for all uses in the prediction unit and the prediction period by repeating the calculation process of the supply amount in the prediction unit and the prediction period, Output to the maximum supply amount calculation unit 24 during the period.

  The maximum supply amount calculation unit 24 during the period repeats the prediction unit supplied from the supply amount calculation unit 23 and the supply amount of all uses in the prediction period by a predetermined number of times, and calculates the maximum value. . The maximum supply amount calculation unit 24 during the period outputs a simulation result of the prediction unit and the distribution of the supply amount in the prediction period to the predicted maximum supply amount calculation unit 25.

  The predicted maximum supply amount calculation unit 25 calculates a number of maximum supply amounts from the simulation result of the maximum supply amount in the prediction unit and the prediction period supplied from the maximum supply amount calculation unit 24 during the period, and further distributes the distribution. calculate. The predicted maximum supply amount calculation unit 25 calculates the maximum supply amount that can occur with a certain probability from the distribution of a large number of maximum supply amounts.

  Next, the maximum supply amount calculation process of the maximum supply amount prediction system 1 will be described with reference to the flowchart of FIG.

  In step S <b> 1, the input receiving unit 21 receives an input of a prediction unit and a prediction period from a user on an input screen (not shown) displayed on the display unit 16.

  For example, the prediction period is “December to February”, and the prediction unit is “day (for example, December 1, December 2,...)”.

  In addition to the prediction unit and the prediction period, a parameter called “prediction region” can also be used. The prediction area is specified by the user when the prediction target area is limited. Thereby, the predicted maximum supply amount in the prediction target area can be calculated, and the soundness of the gas conduit facility can be verified.

  In step S2, the air temperature calculation unit 22 accesses the DB 5 of the server 3 via the network 6, and the past usage amount actual data and past supply in the prediction unit and the prediction period received in the process of step S1. Acquire actual volume data, past average temperature actual data, usage-specific planned supply volume data, etc. And the temperature calculation part 22 calculates the temperature in a prediction unit and a prediction period from the acquired past average temperature actual result data.

  FIG. 5 is a normal distribution for explaining an example of calculating the temperature in the prediction unit and the prediction period. In FIG. 5, the horizontal axis is the temperature, and the vertical axis is the occurrence probability. In this example, a case where the temperature is calculated assuming that the prediction unit is “day” and the prediction period is “December to February” will be described.

  The temperature calculation unit 22 calculates the temperature in the prediction unit and the prediction period by simulation in accordance with the normal distribution in each month from December to February. For example, the average of the normal distribution is the expected average temperature of the month, and the standard deviation is calculated using data for the past five years of the standard deviation of the actual temperature of the month.

  FIG. 6 is a diagram illustrating an example of the input of the temperature in the prediction unit and the prediction period.

  In the example of FIG. 6, the planned temperature “8.9 ° C.” and the standard deviation “2.6 ° C.” are input in December, and the planned temperature “6.5 ° C.” and the standard deviation “1.9” are input in January. "Centigrade" was entered, and in February, the planned temperature "7.2 C" and the standard deviation "2.4 C" were entered.

  Returning to the description of FIG. In step S3, the supply amount calculation unit 23 calculates the temperature in the prediction unit and the prediction period calculated in the process of step S2, past supply amount actual data by use, past supply amount actual data, and planned supply amount data by use. Then, the distribution of the supply amount in the prediction unit and prediction period for each month, each day of the week, and each use is calculated.

  For example, “by day of the week” means to classify by day of the week such as “Monday, Friday” or “Tue, Wed, Thu”, etc., and “by use” means “A use, B use, C use, "..." and so on. In addition, the reason for dividing into “Monday, Friday” and “Tue, Wed, Thu” by day of the week is that the supply amount generally tends to decrease on Mondays, Fridays, and the like.

  FIG. 7 is a data flow for explaining the flow of processing for calculating the distribution of the supply amount in the prediction unit by month and day of the week.

  The supply amount calculation unit 23 includes a day of the week 31, an air temperature t ° C. 32, a planned supply amount 33 according to usage, a past supply amount actual data 34 according to past usage, a past supply amount actual data (all usage combined data) 35, and a past average temperature. Using the record data 36, graph data 37 indicating the relationship between the temperature and the supply amount is created and output for each month, each day of the week, and each use.

Here, a method for obtaining the past supply amount data 34 by usage will be described with a specific example. For example, consider a case where the past supply amount actual data (total usage total data) 35 is included, but the past supply amount actual data 34 is not included. In this case, a sampling survey is performed with respect to the consumer for each usage, and the past supply volume actual data 34 for each usage is estimated from the result of the sampling survey and the past supply volume actual data (total usage total data) 35. For example, it is assumed that the total value of sampling survey data on December 1 is obtained as follows.
Application A (sampling value): 10
Application B (sampling value): 20
Application C (sampling value): 30
Total use total value X (sampling value): 60

On the other hand, each total value is expanded so that the total value of the sampling survey data becomes the data of the entire population. For example, when the number of samples: the number of populations = 1: 10, it is as follows.
Application A (simple amplification value of sampling value): 100
Application B (simple amplified value of sampling value): 200
Use C (simple amplified value of sampling value): 300
Total use total value X (simple amplification value of sampling value): 600

Furthermore, application A (simple amplification value of sampling value), application B (simple amplification value of sampling value) so that total use value X (simple amplification value of sampling value) becomes equal to past supply amount actual data (total application total data) 35. The estimated value is obtained by correcting the simple amplification value of the sampling value) and the application C (simple amplification value of the sampling value). For example, when the past supply amount actual data (total usage total data) 35 is 660, it is as follows.
Application A (estimated value): 110
Application B (estimated value): 220
Application C (estimated value): 330
Total use value X (actual value): 660

  As described above, even when a part of the data is based on the sampling survey data, the actual survey can be predicted by correcting the sampling survey data based on the actual value.

  The supply amount calculation unit 23 creates graph data 37 indicating the relationship between the distribution of the temperature and the supply amount by month, by day of the week, and by use by summarizing the above calculation results by month, day of the week, and temperature. Can be output.

  The reason for calculating by use is, for example, that for home use, the sensitivity to temperature is high, but for industrial use, it is considered that the sensitivity to temperature is not so high. This is because the difference in sensitivity to temperature can be taken into account.

  For example, when the supply amount is the daily supply amount, the daily supply amount may be divided by the monthly actual supply amount. The reason is to use data for a plurality of years in order to increase the number of samples, and to normalize the variation for each year. In this case, when calculating the graph data 38, it is necessary to multiply the monthly planned supply amount 33 by usage. In addition, this makes it possible to take into account the difference in the base amount of the entire supply amount from the past due to economic fluctuations in the forecast period.

  For example, if a graph (graph data 37) showing the relationship between the temperature and the supply amount distribution is created for each application, the supply amount increases as the temperature decreases (the temperature is sensitive), and the supply amount does not depend on the temperature. It can be seen that there are applications that are uniformly distributed (no temperature sensitivity).

  FIG. 8 is an output example of the graph data 39 of the supply amount distribution 38 at t ° C. for each application. In FIG. 8, the horizontal axis is the supply amount, and the vertical axis is the frequency.

  FIG. 8A is a graph showing the distribution of the supply amount at application A and at an air temperature of 5 ° C. FIG. 8B is a graph showing the distribution of the supply amount at application B and at an air temperature of 5 ° C.

  The supply amount calculation unit 23 calculates the distribution 40 of the combined supply amount from the calculated supply amount distribution 38 at t ° C. for each application, assuming a normal distribution and considering the correlation for each application. Data 41 is output.

When considering the correlation for each application, the monthly planned supply amount S _{i, m} is calculated according to the following equation (1). S ′ _{i, m} is the monthly planned supply amount before correction, and n ′ _m is the number of days in m month.

As shown in the above equation (1), the monthly planned supply amount S _{i, m} is the monthly planned supply amount (m ³ ) of the use i for m months corrected by the number of days of the month.

At this time, the distribution Y _{m, n} of the total daily supply amount (the distribution of the total daily supply amount for all uses in m month and day of week classification n) is the normal distribution N (μ _{Y, t, m} , σ _{Y, t, m} ). The average μ _{Y, t, m} is defined as shown in the following equation (2), and the standard deviation σ _{Y, t, m} is defined as shown in the following equation (3). Here, I is a set of uses with temperature sensitivity, J is a set of uses without temperature sensitivity, and t _m is a distribution of daily average temperatures in m months. However, the coefficient a _{i, n} , the coefficient b _{i, n} , and the covariance σ _{esn, etn} are calculated in advance from past performance data.

  The supply amount calculation unit 23 assumes the normal distribution by using the above formula, calculates the distribution 40 of the combined use amount while considering the correlation for each use, and outputs the graph data 41. can do. Then, one supply amount is randomly calculated from the distribution.

  FIG. 9 is an output example of the graph data 41 of the distribution of the supply amount at t ° C. for all uses. In FIG. 9, the horizontal axis is the supply amount, and the vertical axis is the frequency. In particular, it is a graph showing the distribution of the supply amount at t = 5 ° C.

  Returning to the description of FIG. In step S4, the supply amount calculation unit 23 determines whether or not the distribution of the supply amount in the prediction unit and the prediction period has been calculated. If it is determined that the distribution has not yet been calculated, each month from December to February is determined. In Steps S2 and S3, the processes are repeatedly executed. In step S4, when the supply amount calculation unit 23 determines that the distribution of supply amounts for all uses in the prediction unit and the prediction period is calculated, one supply amount is randomly calculated from the distribution, and the process proceeds to step S5. .

  In step S5, the maximum supply amount calculation unit 24 during the period randomly calculates one supply amount from the supply amount distribution 40 at t ° C. of all uses calculated in the process of step S3, and the simulation result during the period. The maximum supply amount during the period is calculated by simply taking the maximum value. For example, in the previous phase, the calculation result of the supply amount on December 1 is “10”, the supply amount on December 2 is “15”, the supply amount on December 3 is “12”,. If obtained, the maximum supply amount is calculated by MAX (10, 15, 12,...).

  In step S6, the maximum supply amount calculation unit 24 during the period determines whether or not the process of calculating the maximum supply amount during the period has been performed a predetermined number of times (for example, 1 million times). When it determines, it returns to step S2 and performs the process mentioned above repeatedly. That is, the maximum supply amount calculation unit 24 during the period calculates the distribution of the maximum supply amount in the prediction unit and the prediction period by repeatedly executing (simulating) the processes of steps S2 to S6 a predetermined number of times.

  In step S6, if the maximum supply amount calculation unit 24 during the period determines that the process of calculating the maximum supply amount during the period has been performed a predetermined number of times, the process proceeds to step S7.

  In step S <b> 7, the predicted maximum supply amount calculation unit 25 repeatedly calculates (simulates) the processes in steps S <b> 2 to S <b> 6 a predetermined number of times, thereby calculating a distribution of a large number of maximum supply amounts in the prediction unit and the prediction period. Then, the predicted maximum supply amount calculation unit 25 calculates the maximum supply amount that can occur with a certain probability from the distribution of a large number of maximum supply amounts.

  FIG. 10 is a graph for explaining an example of calculating the predicted maximum supply amount in the prediction unit and the prediction period. In FIG. 10, the horizontal axis represents the predicted maximum supply amount, and the vertical axis represents the occurrence probability.

  FIG. 10A is a graph showing a simulation result of the distribution of the predicted maximum supply amount in the prediction unit and the prediction period (result of the process in step S7).

  The result of repeating such a simulation for calculating the predicted maximum supply amount in the prediction unit and the prediction period a predetermined number of times (infinite times) is as shown in the graph of FIG.

  The predicted maximum supply amount calculation unit 25 calculates the maximum supply amount that can occur with a certain probability (for example, a 90% confidence interval) from the graph shown in FIG. The predicted maximum supply amount calculation unit 25 displays the calculation result (prediction result) on the display unit 16 and also stores it in the storage unit 12.

  Thus, it becomes possible to predict the maximum value of the supply amount in a certain period.

  Further, in the repeated calculation of 1 million times by simulation, not all the prediction results of each day (for example, December 1, December 2,...) Are stored in the storage unit 12 but from December to By storing only the maximum value of the prediction result for February in the storage unit 12, the memory usage can be reduced.

  In the above description, the simulation is performed assuming that the temperature of the day follows a normal distribution, but the present invention is not limited to this example. For example, a distribution other than the normal distribution may be used. Further, for example, when the prediction unit is “8 to 9 o'clock”, the temperature in the time zone having a strong correlation between the prediction unit and the prediction period may be used, such as using the temperature of “8 to 9 o'clock”. In addition, for example, the temperature of the corresponding day is randomly obtained from the past actual values and simulated, so that it is not necessary to take into consideration a temperature that cannot actually occur (for example, −50 ° etc.). Can be calculated.

[Effects of the embodiment of the invention]
1. It is possible to accurately calculate (predict) the maximum supply amount in a prediction unit such as a specific period, a specific time, and a specific time designated by the user.
2. By calculating the distribution of maximum supply for each application, the difference in sensitivity to temperature can be taken into account.
3. Since only the maximum value is stored in the storage unit as the calculation result of the distribution of the maximum supply amount in the prediction unit and the prediction period, the memory used can be minimized.
4). By using the past actual values, simulations can be performed according to reality.

[Modification]
In the above description, the maximum supply amount is calculated by simulation. However, the present invention is not limited to this, and the maximum supply amount can also be calculated by analytical calculation.

Here, a method of calculating the maximum supply amount by analytical calculation will be described. First, the variables of the calculation formula used for the calculation process of the maximum supply amount are defined.
y _{i, m, n} : Distribution of daily supply for use i in m month and day of week division n (m ³ )
Y _{m, n} : Distribution of the total daily supply for all uses in m month, day of week classification n (distribution of combined daily supply) (m ³ )
F _{y, m, n} (x): cumulative probability density function Y _{m, n} distribution of total daily supply for m month, day of week division n Si, _m : m month usage i corrected by the number of days in the month Monthly planned supply (m ³ )
S ′ _{i, m} : Monthly planned supply amount for use i in m month before correcting the number of days in the month (m ³ )
I: Set of uses with temperature sensitivity J: Set of uses without temperature sensitivity t _m : Distribution of daily average temperature of m month (with a distribution according to normal distribution N (μ _{t, m} , σ _{t, m} ))
Y ^* : Distribution of maximum daily supply (m ³ )
F _{Y *} (x): Cumulative probability density function of daily maximum supply distribution Y ^* M: Set of months in which maximum supply occurs N: Set of days of week division n _{m, n} : Weekdays of m month and day of week division n Days n ' _m : days in m month

First, the distribution Y _m, n of the total day supply amount for m month and day of week classification n is defined as the following equation (4), and the distribution (average, standard deviation) is calculated.
However, in applications with temperature sensitivity,
y _{i, m, n} = S _{i, m} × (a _{i, n} × t + b _{i, n} + e _{i, n} ); e _{i, n} follows a normal distribution N (0, σ _{ei, m} ).
On the other hand, in applications where there is no temperature sensitivity,
y _{j, m, n} = S _{j, m} × (b _{j, n} + e _{j, n} ); e _{j, n} follows a normal distribution N (0, σ _{ej, n} ).
Note that the covariance between e _{s, n} and e _{t, n} is σ _esn , _etn (∀sεI, J, ∀tεI, J).

At this time, monthly plan supply amount S _i applications i of m months corrected in days of the _{month, m,} as shown in the above formula (1), before the days correction monthly plan supply amount _S'i, the _m It is calculated by correcting. The coefficient a _{i, n} , coefficient b _{i, n} , and covariance σ _{esn, etn} are calculated using past performance data.

Then, the average μ _{t, m} , standard deviation σ _{t, m} of the daily average temperature distribution t in m month, and the monthly planned supply amount S _{i, m} (∀i∈I, J ) To calculate the distribution Y _{m, n} of the combined supply amount.

The distribution calculated above is the distribution of daily supply per day during peak weekdays. From this distribution, the distribution of the maximum supply amount in the peak period (a set M of months in which the maximum supply amount occurs) is calculated. Peak when the cumulative probability density function F _{y, m, n} (x) of the distribution of the total daily supply of m month and day of week division n and the number of weekdays n _{m, n} of m month and day of week division _n are given The distribution of the maximum supply amount in the period is defined as the following equation (5).

  As described above, in the analytical calculation, for example, assuming that the distribution of daily average temperatures in December, January, and February follows a normal distribution, the distribution of the maximum supply is calculated. It is possible to shorten the processing time.

  The preferred embodiments of the image output receiving terminal and the like according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes or modifications can be conceived within the scope of the technical idea disclosed in the present application, and these naturally belong to the technical scope of the present invention. Understood.

1 ......... Maximum supply amount prediction system 2 ......... Terminal 4 ......... Supply amount prediction program 11 ......... Control unit 12 ......... Storage unit 15 ......... Input unit 16 ......... Display unit 21 ......... Input accepting unit 22 ......... Temperature calculation unit for the day 23 ......... Daily supply amount calculation unit for the day 24 ......... Maximum supply amount calculation unit during the period 25 ......... Predicted maximum supply amount calculation unit

Claims (5)

A maximum supply amount prediction system configured by a computer and predicting the maximum supply amount of energy,
Input receiving means for receiving an input of a prediction unit and a prediction period for predicting the maximum supply amount;
An acquisition means for acquiring past supply amount actual data based on the prediction unit and the prediction period received by the input reception means;
First calculating means for calculating the temperature in the prediction unit and the prediction period;
Second calculation means for calculating a distribution of the supply amount in the prediction unit and the prediction period based on the past supply amount actual data acquired by the acquisition means and the temperature calculated by the first calculation means. When,
Third calculation means for calculating the maximum supply amount from the prediction unit calculated by the second calculation means and the distribution of supply amount in the prediction period;
A maximum supply amount prediction system comprising: 2. The maximum supply amount prediction system according to claim 1, wherein the second calculation unit further calculates a supply amount distribution in the prediction unit and the prediction period for each application. The second calculation unit stores only the maximum value of the distribution of the supply amount in the prediction unit and the prediction period in the storage unit,
3. The maximum supply amount prediction system according to claim 1, wherein the third calculation unit calculates the maximum supply amount from the maximum value of the distribution of the supply amount in the prediction unit and the prediction period. . The said 1st calculation means acquires the temperature in the said prediction unit and prediction period from the past performance value, The maximum supply amount prediction system in any one of the Claims 1 thru | or 3 characterized by the above-mentioned. A maximum supply amount prediction method executed by a computer and predicting the maximum supply amount of energy,
The computer is
An input receiving step for receiving an input of a prediction unit for performing prediction of the maximum supply amount and a prediction period;
An acquisition step of acquiring past supply amount actual data based on the prediction unit and the prediction period received by the input reception step;
A first calculation step of calculating the temperature in the prediction unit and the prediction period;
Second calculation step for calculating distribution of supply amount in the prediction unit and prediction period based on the past supply amount actual data acquired in the acquisition step and the air temperature calculated in the first calculation step. When,
A third calculation step for calculating the maximum supply amount from the distribution of the supply amount in the prediction unit and the prediction period calculated in the second calculation step;
The maximum supply amount prediction method characterized by including.

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