Method, processing apparatus, and storage medium

Abstract

To provide a method for preparing internal heat generation density data used for dynamic heat load calculation.SOLUTION: The data generation method to be executed by the processing device 1 is a data generation method for generating, for first data corresponding to an internal heat generation density for one hour in a room of a first building, second data in which a coefficient of 1 or less is assigned to each section obtained by dividing a time of one day by one hour, and an internal heat generation density for one day in a room of a second building measured every hour, and third data obtained by collecting data in which a measured date and a measured day of the week are associated with each other for 365 days, second processing of calculating fourth data corresponding to an internal heat generation density of the room of the first building for each hour, third processing of calculating fifth data which is data of a correction value for correcting the fourth data, and fourth processing of calculating data in which a date and a day of the week are associated with the internal heat generation density of the room of the first building for each hour for one day for 365 days to generate sixth data.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a method, a processing device or a program. [Background technology]

[0002] Devices, programs, etc. that perform dynamic heat load calculations are known (Patent Document 1). One program known for performing dynamic heat load calculations is HASP (Heating, Air-conditioning and Sanitary Engineering Program) (Non-Patent Document 1). HASP calculates the annual heat load for a room in a building based on the internal heat generation of the room. Internal heat generation refers to heat generated by all heat sources in the room, such as lighting fixtures and machinery installed in the room. When calculating the annual heat load using HASP, internal heat generation density data is input, which is the internal heat generation divided by the floor area of ​​the room. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-101880 [Non-patent literature]

[0004] [Non-Patent Document 1] "HASP (Building Services and Building Engineering Society)"<URL: https: / / www.jabmee.or.jp / hasp / > (Retrieved June 19, 2024) Summary of the Invention [Problem to be solved by the invention]

[0005] Incidentally, internal heat generation is thought to vary from day to day. Therefore, in order to perform more accurate dynamic heat load calculations, internal heat generation density data that varies from day to day is required. However, it is difficult to obtain such internal heat generation density data.

[0006] The present invention aims to provide a method for preparing internal heat generation density data for use in dynamic heat load calculations. [Means for solving the problem]

[0007] In order to solve the above problems, a data generation method to be executed by a processing device includes a first process for inputting first data corresponding to an internal heat generation density obtained by dividing the internal heat generation in a room of a first building at a predetermined time by the floor area of ​​the room of the first building, second data being table data in which a coefficient of 1 or less is assigned to each section obtained by dividing a day into the predetermined time periods, with at least one of the sections being assigned a coefficient of 1, and third data being a collection of data for a predetermined number of days in which the internal heat generation density for one day obtained by dividing the internal heat generation in a room of a second building measured for each of the divided sections by the floor area of ​​the room of the second building is associated with the date of measurement and the day of the week; The method includes a second process of calculating, based on the second data, fourth data corresponding to the internal heat generation density of one room of the first building for each of the divided sections; a third process of calculating, from the third data, fifth data, which is correction value data for correcting the internal heat generation density of one room of the first building for each of the divided sections, wherein the correction value of the divided section is smaller than the internal heat generation density of the divided section corresponding to the fourth data; and a fourth process of calculating, from the third data, the fourth data, and the fifth data, data corresponding to the date and the day of the week for one day for a predetermined number of days to generate sixth data. [Effects of the Invention]

[0008] According to the present invention, it is possible to prepare internal heat generation density data to be used in dynamic heat load calculations. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is an outline of the hardware configuration of a processing device 1. [Figure 2] 1 is an outline of the functional configuration (software configuration) of the processing device 1. [Figure 3] 1 is a flowchart showing the flow of processing executed by the processing device 1. [Figure 4] 10 is a flowchart showing details of a first process S1. [Figure 5] FIG. 10 is a diagram illustrating an example of first data. [Figure 6] FIG. 10 is a diagram illustrating an example of second data. [Figure 7] FIG. 10 is a diagram illustrating an example of third data. [Figure 8] FIG. 10 is a diagram showing an example of fourth data (for weekdays). [Figure 9] FIG. 10 is a diagram showing an example of fourth data (for holidays). [Figure 10] 10 is a flowchart showing details of a third process S3. [Figure 11] FIG. 10 is a diagram showing a state in which third data has been selected. [Figure 12] FIG. 11 is a diagram showing an example of seventh data (for weekdays). [Figure 13] FIG. 11 is a diagram showing an example of seventh data (for holidays). [Figure 14] FIG. 10 is a diagram illustrating an example of fifth data. [Figure 15] 10 is a flowchart showing details of a fourth process S4. [Figure 16] FIG. 10 is a diagram illustrating an example of subtraction data. [Figure 17] FIG. 13 is a diagram showing an example of eighth data. [Figure 18] FIG. 10 is a schematic diagram showing a method for calculating the total internal heat generation density value Σwi on weekdays. [Figure 19] FIG. 10 is a schematic diagram showing a method for calculating the total internal heat generation density value Σhi on holidays. [Figure 20] FIG. 13 is a diagram showing an example of the ninth data. [Figure 21] FIG. 1 is a graph showing a probability density function. [Figure 22] FIG. 10 is a diagram showing an example of the values ​​a and b used when calculating the area of ​​each region in the graph. [Figure 23] FIG. 10 is a diagram showing the correspondence between the area of ​​each region in the graph and the number of days data. [Figure 24] FIG. 13 is a diagram showing an example of tenth data (weekday data). [Figure 25] FIG. 13 is a diagram showing an example of tenth data (for holidays). [Figure 26] FIG. 10 is a diagram illustrating an example of descending order data. [Figure 27] 10 is a diagram showing an example of date and day of the week data in descending order; [Figure 28] FIG. 11 is a diagram showing an example of eleventh data (for weekdays). [Figure 29] FIG. 11 is a diagram showing an example of eleventh data (for holidays). [Figure 30] FIG. 10 is a diagram showing an example of sixth data. [Figure 31] 10 is a flowchart showing details of a third process S3 in a modified example. [Figure 32] FIG. 10 is a schematic diagram showing the total internal heat generation density value Σwi and its standard deviation σdw on weekdays. [Figure 33] FIG. 10 is a schematic diagram showing the total internal heat generation density value Σhi and its standard deviation σdh on a holiday. [Figure 34] FIG. 10 is a diagram illustrating an example of ratio data. [Figure 35] FIG. 11 is a diagram showing an example of seventh data in a modified example. [Figure 36] FIG. 10 is a diagram showing an example of fifth data (for weekdays) in a modified example. [Figure 37] FIG. 10 is a diagram showing an example of fifth data (for holidays) in a modified example. [Figure 38] 10 is a flowchart showing details of a third process S3 in a modified example. [Figure 39] FIG. 10 is a diagram showing an example of standard deviation data (for weekdays). [Figure 40] FIG. 10 is a diagram showing an example of standard deviation data (for holidays). [Figure 41]FIG. 10 is a diagram illustrating an example of normalized standard deviation data. [Figure 42] FIG. 11 is a diagram showing an example of seventh data in a modified example. [Figure 43] FIG. 10 is a diagram showing an example of fifth data in a modified example. [Figure 44] 10 is a flowchart showing details of a third process S3 in a modified example. [Figure 45] FIG. 11 is a diagram showing an example of seventh data in a modified example. [Figure 46] FIG. 10 is a diagram showing an example of fifth data (for weekdays) in a modified example. [Figure 47] FIG. 10 is a diagram showing an example of fifth data (for holidays) in a modified example. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, although the embodiments described below are subject to various limitations that are technically preferable for implementing the present invention, the scope of the present invention is not limited to the following embodiments and illustrated examples.

[0011] A processing device 1 according to an embodiment of the present invention will be described below. The processing device 1 is capable of generating internal heat generation density data used for dynamic heat load calculation.

[0012] <Processing equipment> 1 is a diagram illustrating an example of hardware of a processing device 1. The processing device 1 includes a processor 101, a main memory device 102, an auxiliary memory device 103, an input device 104, an output device 105, and a communication device 106. These components included in the processing device 1 are connected to each other so as to be able to communicate with each other via communication means such as a bus (not shown).

[0013] It should be noted that the entire configuration of the processing device 1 does not necessarily need to be realized by hardware. For example, all or part of the configuration of the processing device 1 may be realized by virtual resources such as a cloud server of a cloud system. Furthermore, the processing device 1 does not necessarily need to be configured by a single device.

[0014] The processor 101 is configured using a CPU (Central Processing Unit), an MPU (Micro Processing Unit), etc. The processor 101 reads and executes a program stored in a main memory device 102, thereby realizing the functions of the processing device 1.

[0015] The main memory device 102 is a device that stores programs and data, and is a read-only memory (ROM), a random access memory (RAM), a non-volatile semiconductor memory (NVRAM (Non-Volatile RAM)), or the like.

[0016] The auxiliary storage device 103 is various non-volatile memories (NVRAM (Non Volatile RAM)) such as SSD (Solid State Drive) and SD memory cards, hard disk drives, optical storage devices (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), storage areas of cloud servers, etc.

[0017] The input device 104 is an interface that accepts input of information, and is, for example, a keyboard, a mouse, a touch panel, a card reader, a voice input device (such as a microphone), a voice recognition device, etc. The input device 104 may be configured so that the processing device 1 accepts input of information between itself and another device via the communication device 106.

[0018] The output device 105 is an interface that outputs various types of information, and is, for example, a screen display device (such as a liquid crystal monitor, LCD (Liquid Crystal Display), or graphic card), a printer, an audio output device (such as a speaker), or an audio synthesizer. The output device 105 may be configured so that the processing device 1 outputs information to and from other devices via the communication device 106.

[0019] The communication device 106 is a wired or wireless communication interface that enables communication with other devices via a network, and the communication device 106 is, for example, a NIC (Network Interface Card), a wireless communication module, a USB (Universal Serial Bus) module, a serial communication module, etc.

[0020] <Functional configuration> 2 shows the main functional configuration of the processing device 1. The processing device 1 includes a storage area 110 and a management unit 120.

[0021] The storage area 110 is formed in the main storage device 102 or the auxiliary storage device 103. The storage area 110 stores programs for performing each process described below and data calculated and generated in each process.

[0022] The functions of the management unit 120 are realized by the processor 101 reading and executing a program stored in the main storage device 102 or the auxiliary storage device 103. The management unit 120 performs each process described below.

[0023] <Processing details> Next, a description will be given of the processing executed by the processing device 1. The processing executed by the processing device 1 is realized by the management unit 120 executing each process shown in FIG.

[0024] The management unit 120 sequentially performs four processes, from a first process S1 to a fourth process S4, shown in Fig. 3. Each process will be described in detail below.

[0025] (First process S1) The first process S1 is a process for acquiring the first to third data. As shown in Fig. 4, the first process S1 is made up of three processes from S11 to S13.

[0026] [S11] First, the management unit 120 acquires first data. The management unit 120 acquires the first data input by a user of the processing device 1 (hereinafter referred to as the user). Note that if the first data is stored in advance in the main memory device 102, the management unit 120 may acquire the first data by reading the first data from the main memory device 102.

[0027] Here, the first data refers to internal heat generation density data obtained by dividing the internal heat generation in one room of the building (first building) for which the dynamic heat load calculation is performed during a specific time period by the floor area of ​​the room. Here, the specific time period is any time period, such as 30 minutes or 1 hour.

[0028] In this embodiment, the first data is internal heat generation density data Ew for one hour on a weekday and internal heat generation density data Eh for one hour on a holiday, as shown in Fig. 5. Here, weekdays refer to Monday through Friday. Holidays refer to Saturdays, Sundays, national holidays, and any other day as a "holiday."

[0029] [S12] Next, the management unit 120 acquires the second data. The management unit 120 acquires the second data input by the user. Note that if the second data is stored in advance in the main memory device 102, the management unit 120 may acquire the second data by reading the second data from the main memory device 102.

[0030] Here, the second data is table data in which a coefficient of 1 or less is assigned to each interval obtained by dividing a day into specific time periods, and at least one interval is assigned a coefficient of 1. Here, the specific time period is an arbitrary period such as 30 minutes or 1 hour, and is the same as the time period set in the first data.

[0031] In this embodiment, the second data is data in which a coefficient is assigned to each time slot assuming one weekday and a coefficient is assigned to each time slot assuming one holiday, as shown in FIG. 6. Each time slot in the second data is obtained by dividing 24 hours into specific time slots in the first data, that is, into one hour. In FIG. 6, for example, each value of the time slot is "1" indicating one hour from midnight to 1:00, and "24" indicating one hour from 11:00 pm to midnight. The definitions of weekdays and holidays are the same as those in the first data. The coefficients for each time slot assuming a weekday are Rw1 to Rw2 for time slots 1 to 24, respectively. 24 are assigned, and at least one coefficient is 1. Similarly, the coefficients for each time period assuming holidays are Rh1 to Rh2 for time periods 1 to 24, respectively. 24 is assigned, with at least one coefficient being 1.

[0032] [S13] Next, the management unit 120 acquires the third data. The management unit 120 acquires the third data input by the user. Note that if the third data is stored in advance in the main memory device 102, the management unit 120 may acquire the third data by reading the third data from the main memory device 102.

[0033] Here, the third data refers to data collected for a predetermined number of days in which data is associated with the date of measurement and the day of the week for one day's internal heat density data, which is obtained by actually measuring the internal heat generation in one room of an existing building (second building) for each section of the day divided by a specific time period, and dividing the internal heat generation by the floor area of ​​the room. The predetermined number of days may be any number of days, but 365 days is preferable. In addition to Monday through Friday, Saturday, Sunday, and public holidays, any day may be designated as a "holiday."

[0034] In this embodiment, the third data is data on internal heat generation density in rooms of an existing building for 365 days from January 1 to December 31, as shown in Fig. 7. The third data includes 246 data items for weekdays and 119 data items for holidays. Like the second data, each time period in the third data is obtained by dividing 24 hours into specific time periods in the first data, i.e., into one-hour periods.

[0035] (Second process S2) The second process S2 is a process for calculating fourth data based on the first data and the second data.

[0036] Here, the fourth data is internal heat generation density data for a room in a building for which dynamic heat load calculations are performed for each section obtained by dividing a day into specific time periods. Each time period in the fourth data is obtained by dividing 24 hours into specific time periods in the first data, i.e., one hour.

[0037] In this embodiment, the management unit 120 calculates the weekday portion of the fourth data using equation (1) shown in Fig. 8. For example, the internal heat generation density data Dw1 for time period "1" is a value obtained by multiplying Ew, a value assumed to be for a weekday in the first data, by a coefficient Rw1 assigned to time period "1" on a weekday in the second data.

[0038] Next, the management unit 120 calculates the holiday portion of the fourth data using equation (2) shown in Fig. 9. For example, the internal heat generation density data Dh2 for time period "2" is a value obtained by multiplying Eh, a value in the first data that assumes a holiday, by a coefficient Rh2 assigned to time period "2" on the holiday in the second data.

[0039] (Third process S3) The third process S3 is a process for calculating fifth data based on the third data. As shown in Fig. 10, the third process S3 is made up of three processes from S311 to S313.

[0040] [S311] The management unit 120 refers to the days of the week in the third data and sorts the third data into weekday data and holiday data. Here, weekdays are Monday to Friday, and holidays are Saturday, Sunday, public holidays, and holidays. In this embodiment, the management unit 120 sorts the third data shown in FIG. 7 into 246 weekday data and 119 holiday data, as shown in FIG. 11.

[0041] [S312] The management unit 120 calculates seventh data from the third data. The management unit 120 calculates the standard deviation σw of the internal heat generation density data for each time period of the weekday data among the third data using the formula (3) shown in FIG. i In equation (3), n is the number of weekday data, Xi is the internal heat generation density data value for a certain time period on the i-th day, and X ave is the average value of n internal heat generation density data values ​​in a certain time period.

[0042] In this embodiment, the management unit 120 calculates the σw1 to σw2 values ​​shown in FIG. 12 from the data of the 246 weekdays shown in FIG. 24 Calculate the standard deviation of the 24 internal heat generation density data (standard deviation for weekdays). In equation (3), n is 246, Xi is the value of the internal heat generation density data for a certain time period on the i-th day, and X aveis the average value of 246 internal heat generation density data values ​​in a certain time period. For example, the standard deviation σw1 of the internal heat generation density data in time period “1” is calculated from the internal heat generation density data values ​​in time period “1” for 246 days.

[0043] Next, the management unit 120 calculates the standard deviation σh of the internal heat generation density in each time period of the holiday data among the third data using the formula (4) shown in FIG. i In equation (4), n is the number of data points on holidays, Xi is the internal heat density data value for a certain time period on the i-th day, and X ave is the average value of n internal heat generation density data values ​​in a certain time period.

[0044] In this embodiment, the management unit 120 calculates the holiday data of 119 days from σh1 to σh shown in FIG. 24 Calculate the standard deviation (standard deviation on holidays) of the 24 internal heat generation density data. In equation (4), n is 119, Xi is the value of the internal heat generation density data in a certain time period on the i-th day, and X ave is the average value of 119 internal heat generation density data values ​​in a certain time period. For example, the standard deviation σh2 of the internal heat generation density data in time period “2” is calculated from the internal heat generation density data values ​​in time period “2” for 119 days.

[0045] The management unit 120 calculates σw1 to σw 24 The weekday standard deviation of σh1 to σh 24 The seventh data shown in FIGS. 12 and 13 is generated by combining the standard deviation of the holiday.

[0046] [S313] Next, the management unit 120 calculates the fifth data.

[0047] Here, the fifth data refers to correction value data for correcting the internal heat generation density data of one room in a building for which dynamic heat load calculations are performed for each time period obtained by dividing a day into specific time periods. Each time period in the fifth data is obtained by dividing 24 hours into specific time periods, i.e., one hour, in the first data. Furthermore, the correction value for each time period in the fifth data is smaller than the internal heat generation density data value for the corresponding time period in the fourth data.

[0048] The management unit 120 calculates the fifth data by correcting the seventh data by an arbitrary positive integer. Note that any number of positive integers may be used. The value by which each value of the seventh data is corrected is not limited to an integer and may be any number including decimals and fractions, but is limited to a value that makes each calculated value of the fifth data smaller than the value of the internal heat density data for the corresponding time period of the fourth data.

[0049] In this embodiment, the management unit 120 sets an arbitrary positive integer to 4. The management unit 120 calculates each value of the seventh data shown in FIGS. 12 and 13, that is, σw1 to σw 24 and σh1 to σh 24 The fifth data shown in FIG. 14 is calculated by multiplying each of the values ​​by integers from 1 to 4 as correction values.

[0050] (Fourth process S4) The fourth process S4 is a process for generating sixth data. As shown in Fig. 15, the fourth process S4 is made up of 13 processes from S401 to S413.

[0051] Here, the sixth data is data calculated for 365 days in which the internal heat generation density data for one room in a building is associated with the date and day of the week and used for dynamic heat load calculations for each section obtained by dividing a day into specific time periods. Each time period in the sixth data is obtained by dividing 24 hours into specific time periods in the first data, i.e., one hour. The sixth data is internal heat generation density data used in dynamic heat load calculations.

[0052] [S401] The management unit 120 calculates subtraction data by subtracting the value of the fifth data from the value of the fourth data for each corresponding time period of the fourth data and the fifth data. In this embodiment, the management unit 120 calculates the subtraction data shown in Fig. 16 by subtracting the value of the fifth data shown in Fig. 14 from the value of the fourth data shown in Fig. 8 and Fig. 9 for each corresponding time period.

[0053] [S402] The management unit 120 generates eighth data by adding the subtraction data to the fourth data. In this embodiment, the management unit 120 adds the subtraction data shown in Fig. 16 to the fourth data shown in Fig. 8 and Fig. 9 to generate eighth data shown in Fig. 17.

[0054] [S403] The management unit 120 refers to the day of the week of the third data and sorts the third data into weekday data and holiday data. In this embodiment, the management unit 120 refers to the day of the week of the third data shown in Fig. 7 and sorts the third data into 246 weekday data and 119 holiday data as shown in Fig. 11.

[0055] [S404] The management unit 120 calculates the total internal heat generation density value from the third data sorted into weekday data and holiday data. Here, the total internal heat generation density value is the sum of the internal heat generation density data values ​​for each time period of a certain day for 24 hours.

[0056] In this embodiment, the management unit 120 calculates the internal heat generation density sums Σw1 to Σw2 shown in FIG. 18 from the weekday data among the third data shown in FIG. 11. 246 Next, the management unit 120 calculates the internal heat generation density totals Σh1 to Σh shown in FIG. 19 from the holiday data among the third data shown in FIG. 119 Calculate.

[0057] [S405] The management unit 120 generates the ninth data by linking the date and day of the week of the third data with the internal heat generation density total value. In this embodiment, the management unit 120 links the date and day of the week of the third data shown in FIG. 11 with the internal heat generation density total values ​​Σw1 to Σw2 shown in FIGS. 246 and Σh1 to Σh 119 Link these to generate the ninth data.

[0058] [S406] The management unit 120 calculates a probability density function from the ninth data. The management unit 120 calculates the probability density function for weekdays using equation (5) in Fig. 21. Here, μ is the ratio of the total internal heat generation density values ​​Σw1 to Σw n σ is the average value of the internal heat generation density sum Σw1 to Σw n is the standard deviation of

[0059] In this embodiment, the management unit 120 calculates the internal heat generation density sums Σw1 to Σw2 for weekdays from the ninth data shown in FIG. 246 where μ is the total internal heat generation density value Σw1 to Σw 246 σ is the average value of the internal heat generation density sum Σw1 to Σw 246 is the standard deviation of

[0060] Next, the management unit 120 calculates the probability density function for the holidays using equation (5) in Fig. 21. Here, μ is the total internal heat generation density value Σh1 to Σh n σ is the average value of the internal heat generation density Σh1 to Σh n is the standard deviation of

[0061] In this embodiment, the management unit 120 calculates the total internal heat generation densities Σh1 to Σh2 for the holidays from the ninth data shown in FIG. 119 where μ is the total internal heat generation density value Σh1 to Σh 119 σ is the average value of the internal heat generation density Σh1 to Σh 119 The shape of the graph of the probability density function for weekdays and the probability density function for holidays calculated by the management unit 120 is as shown in FIG.

[0062] [S407] The management unit 120 calculates the graph area from the graphs of the probability density function for weekdays and the probability density function for holidays. The management unit 120 divides the graph area for each of the graphs of the probability density function for weekdays and the probability density function for holidays. Here, the number by which the graph area is divided is a number obtained by adding 1 to the number of any positive integer set in S313. The management unit 120 divides the region of x so that μ is the median. The management unit 120 calculates the area of ​​each divided graph using equation (6) in FIG. 21. In equation (6), f(x) is the probability density function, and a and b are the maximum and minimum values ​​of x in the divided graph region.

[0063] In this embodiment, the management unit 120 divides the area of ​​the graph of the probability density function for weekdays and the probability density function for holidays into five areas. Specifically, as shown in FIG. 21 , the management unit 120 divides the area of ​​x into five areas E to A, ranging from -infinity to μ-1.5σ, μ-1.5σ to μ-0.5σ, μ-0.5σ to μ+0.5σ, μ+0.5σ to μ+1.5σ, and μ+1.5σ to +infinity. At this time, the areas formed by each area of ​​x and the probability density function for weekdays are designated Ew to Aw, respectively, and the areas formed by each area of ​​x and the probability density function for holidays are designated Eh to Ah, respectively. The area of ​​the area Ew to Aw is designated Sw5 to Sw1, and the area of ​​the area Eh to Ah is designated Sh5 to Sh1.

[0064] Next, the management unit 120 calculates the areas Sw5 to Sw1 of the regions Ew to Aw. The management unit 120 calculates the areas Sw5 to Sw1 using equation (6) in FIG. 21. Here, f(x) in equation (6) is the probability density function for weekdays calculated in S407. At this time, the management unit 120 calculates Sw5 to Sw1 using the values ​​of a and b shown in FIG. 22. Note that the total value of Sw5 to Sw1 is 246, as shown in equation (7) in FIG. 22.

[0065] Next, the management unit 120 calculates the areas Sh5 to Sh1 of the regions Eh to Ah. The management unit 120 calculates the areas Sh5 to Sh1 using equation (6) in FIG. 21. Here, f(x) in equation (6) is the probability density function for the holiday calculated in S407. At this time, the management unit 120 calculates Sh5 to Sh1 using the values ​​of a and b shown in FIG. 22. Note that the total value of Sh5 to Sh1 is 119, as shown in equation (8) in FIG. 22.

[0066] [S408] The management unit 120 corrects the graph area to calculate the number of days data. The management unit 120 corrects the graph area to an integer value. Note that possible methods for correcting the graph area include, for example, rounding the graph area to the first decimal place, or rounding down or rounding up the decimal point of the graph area. The management unit 120 corrects the corrected graph area value so that the total corrected graph area becomes the total number of weekdays or the total number of holidays, and calculates the number of days data. One possible method for correcting the corrected graph area value is to add or subtract the shortfall or excess of the corrected total graph area value relative to the total number of weekdays or the total number of holidays to either of the corrected graph area values.

[0067] In this embodiment, the management unit 120 rounds the graph area values ​​Sw5 to Sw1 to the nearest integer. As shown in FIG. 23, the rounded integer values ​​Sw5 to Sw1 are set as corrected graph area values ​​Sw5' to Sw1'. Next, as shown in equation (9) in FIG. 23, the management unit 120 checks whether the total value obtained by adding up all of the corrected graph area values ​​Sw5' to Sw1' is 246, which is the total number of weekdays. If the total value is not 246, the management unit 120 subtracts the total value from 246 and adds the difference to Sw3'. In this way, the management unit 120 calculates the number of days data (for weekdays).

[0068] Next, the management unit 120 rounds the graph area values ​​Sh5 to Sh1 to the nearest integer. As shown in FIG. 23, the rounded integer values ​​of Sh5 to Sh1 are set as corrected graph area values ​​Sh5' to Sh1'. Next, as shown in equation (10) in FIG. 23, the management unit 120 checks whether the total value obtained by adding up all of the corrected graph area values ​​Sh5' to Sh1' is 119, which is the total number of holidays. If the total value is not 119, the management unit 120 subtracts the total value from 119 and adds the difference to Sh3'. In this way, the management unit 120 calculates the number of days data (for holidays).

[0069] [S409] The management unit 120 generates the eighth data for each of the number of days data to generate the tenth data.

[0070] In this embodiment, as shown in FIG. 24, the management unit 120 calculates the values ​​of the fourth data for one day (i.e., the time slots 1 to 24 and the corresponding Dw1 to Dw2) in the eighth data for weekdays shown in FIG. 17. 24 Similarly, for the eighth data for weekdays, the management unit 120 generates Sw2' pieces of data that are one time the subtraction data for one day, Sw3' pieces of data that are twice the value of the subtraction data for one day, Sw4' pieces of data that are three times the value of the subtraction data for one day, and Sw5' pieces of data that are four times the value of the subtraction data for one day.

[0071] Next, as shown in FIG. 24, the management unit 120 calculates the values ​​of the fourth data for one day (i.e., the time periods 1 to 24 and the corresponding Dh1 to Dh2) in the eighth data for the holiday shown in FIG. 17. 2424 and 25. Similarly, for the eighth data for holidays, the management unit 120 generates Sh2' data that is one time the subtraction data for one day, Sh3' data that is twice the subtraction data for one day, Sh4' data that is three times the subtraction data for one day, and Sh5' data that is four times the subtraction data for one day. Here, the tenth data is a combination of the data shown in FIGS. 24 and 25.

[0072] [S410] The management unit 120 rearranges the internal heat generation density sum values ​​of the ninth data in descending order, and generates descending order data together with the ranking data. In this embodiment, the management unit 120 rearranges the internal heat generation density sum values ​​of the ninth data shown in FIG. 20 in descending order, adds the ranking data, and generates descending order data as shown in FIG.

[0073] [S411] The management unit 120 extracts the rank and the date / day of the week from the descending order data to generate descending order date / day of the week data. In this embodiment, the management unit 120 extracts the rank and the date / day of the week from the descending order data shown in Fig. 26 to generate descending order date / day of the week data as shown in Fig. 27.

[0074] [S412] The management unit 120 associates the tenth data with the descending date and day of the week data to generate the eleventh data.

[0075] In this embodiment, the management unit 120 associates the value of the fourth data value for one day of the tenth data for weekdays with the first to Sw1'th places of the descending date and day of the week data for weekdays shown in Figure 27, as shown in Figure 28. Next, the management unit 120 associates data with the Sw1'+1st place to the Sw1'+Sw2'th place of the descending date and day of the week data for weekdays, which is one time the value of the subtraction data value for one day of the tenth data for weekdays. Next, the management unit 120 associates data with the Sw1'+Sw2'+1st place to the Sw1'+Sw2'+Sw3'th place of the descending date and day of the week data for weekdays, which is twice the value of the subtraction data value for one day of the tenth data for weekdays. Next, the management unit 120 links the data from the Sw1'+Sw2'+Sw3'+1st place to the Sw1'+Sw2'+Sw3'+Sw4' place in the descending date and day data for weekdays with data that is three times the value of the subtraction data for one day of the tenth data for weekdays.Next, the management unit 120 links the data from the Sw1'+Sw2'+Sw3'+Sw4'+1st place to the Sw1'+Sw2'+Sw3'+Sw4'+Sw5' (i.e., 246th place) place in the descending date and day data for weekdays with data that is four times the value of the subtraction data for one day of the tenth data for weekdays.

[0076] Next, the management unit 120 links the value of the fourth data for one day of the tenth data for the holiday to the first to Sh1'th places of the descending date and day of the week data for the holiday shown in Figure 27, as shown in Figure 29. Next, the management unit 120 links the value of one time the subtraction data for one day of the tenth data for the holiday to the values ​​of the Sh1'+1st to Sh1'+Sh2'th places of the descending date and day of the week data for the holiday. Next, the management unit 120 links the value of twice the value of one day the subtraction data for one day of the tenth data for the holiday to the values ​​of the Sh1'+Sh2'+1st to Sh1'+Sh2'+Sh3' places of the descending date and day of the week data for the holiday. Next, the management unit 120 associates data from the Sh1'+Sh2'+Sh3'+1st place to the Sh1'+Sh2'+Sh3'+Sh4' place in the descending order date and day of the holiday data with data that is three times the value of the subtraction data for one day of the tenth data for the holiday. Next, the management unit 120 associates data from the Sh1'+Sh2'+Sh3'+Sh4'+1st place to the Sh1'+Sh2'+Sh3'+Sh4'+Sh5' (i.e., 119th place) place in the descending order date and day of the holiday data with data that is four times the value of the subtraction data for one day of the tenth data for the holiday. Here, the 11th data is a combination of the data shown in Figures 28 and 29.

[0077] [S413] The management unit 120 rearranges the 11th data in chronological order to generate the sixth data. The management unit 120 rearranges the 11th data for weekdays and the 11th data for holidays shown in Figures 28 and 29 in chronological order. The management unit 120 then deletes the ranking from the data rearranged in chronological order to generate the sixth data as shown in Figure 30.

[0078] Finally, the management unit 120 stores the generated sixth data in the main storage device 102 or the auxiliary storage device 103.

[0079] Here, the sixth data is internal heat generation density data calculated for 365 days from January 1 to December 31 in one room of a building for which hourly dynamic heat load calculations are performed. Therefore, by performing the above processing, the management unit 120 can prepare internal heat generation density data to be used in dynamic heat load calculations.

[0080] <Modification> The processing performed by the processing device 1 may be applied by combining the modifications described below.

[0081] (1) Variation 1 As shown in FIG. 31, the third process S3 may be made up of seven processes from S321 to S327.

[0082] [S321] The management unit 120 refers to the day of the week of the third data and sorts the third data into weekday data and holiday data. In this modification, the management unit 120 refers to the day of the week of the third data shown in Fig. 7 and sorts the third data into 246 weekday data and 119 holiday data as shown in Fig. 11.

[0083] [S322] The management unit 120 calculates the internal heat generation density total value from the third data sorted into the weekday data and the holiday data. In this modification, the management unit 120 calculates the internal heat generation density total values ​​Σw1 to Σw2 shown in FIG. 32 from the weekday data of the third data shown in FIG. 246 Next, the management unit 120 calculates the internal heat generation density totals Σh1 to Σh shown in FIG. 33 from the holiday data among the third data shown in FIG. 119 Calculate.

[0084] [S323] The management unit 120 calculates the standard deviation from the internal heat generation density total value. The management unit 120 calculates the internal heat generation density total values ​​Σw1 to Σw2 for the weekday data using equation (11) shown in FIG. nCalculate the standard deviation σdw (standard deviation of the internal heat generation density total value on weekdays). In equation (11), n ​​is the number of weekday data, and Xi is the internal heat generation density total value Σw on the i-th day. i , X ave is the average value of n total internal heat generation densities.

[0085] In this modification, the management unit 120 calculates the internal heat generation density sums Σw1 to Σw2 in the weekday data shown in FIG. 246 In equation (11), n ​​is 246, Xi is the total internal heat generation density value Σw on the i-th day. i , X ave is the average value of the total internal heat generation density of 246 samples.

[0086] Next, the management unit 120 calculates the total internal heat generation density values ​​Σh1 to Σhh in the holiday data using the formula (12) shown in FIG. n Calculate the standard deviation σdh (standard deviation of the total internal heat generation density on a holiday). In equation (12), n is the number of holiday data, and Xi is the total internal heat generation density value Σh on the i-th day. i , X ave is the average value of n total internal heat generation densities.

[0087] In this modification, the management unit 120 calculates the internal heat generation density sums Σh1 to Σh in the holiday data shown in FIG. 119 In equation (12), n is 119, Xi is the total internal heat generation density value Σh on the i-th day. i , X ave is the average value of the total internal heat generation density of 119 samples.

[0088] [S324] The management unit 120 calculates the total value of the coefficients by adding up the coefficients for each time period of the second data. In this modification, the management unit 120 calculates the total value of the coefficients Rw1 to Rw2 for each time period on weekdays in the second data shown in FIG. 24 Similarly, the management unit 120 calculates the total coefficient value ΣRw for weekdays by adding the coefficients Rh1 to Rh2 for each time period on holidays in the second data.24 The total coefficient for holidays is calculated by adding up to ΣRh.

[0089] [S325] The management unit 120 calculates the ratio data from the sum of the second data and the coefficient. The management unit 120 calculates the ratio data for weekdays using the formula (13) shown in FIG. 34. Here, Rw i is the coefficient for each time period in the second data for weekdays, and ΣRw is the sum of the coefficients for weekdays calculated in S324.

[0090] In this modification, the management unit 120 calculates the coefficients Rw1 to Rw2 for each time period in the weekday data of the second data shown in FIG. 24 Divide each by the sum of the coefficients for weekdays, ΣRw, to obtain the ratio data Pw1 to Pw2 as shown in Figure 34. 24 Calculate.

[0091] Next, the management unit 120 calculates the ratio data for the holiday using the formula (14) shown in FIG. i is the coefficient of each time period in the second data for the holiday, and ΣRh is the total value of the coefficients for the holiday calculated in S324.

[0092] In this modification, the management unit 120 calculates the coefficients Rh1 to Rh2 for each time period in the holiday data of the second data shown in FIG. 24 Divide each by the total value of the holiday coefficients ΣRh to obtain the ratio data Ph1 to Ph2 as shown in Figure 34. 24 Calculate.

[0093] [S326] The management unit 120 calculates seventh data by multiplying the ratio data by the standard deviation of the internal heat generation density total value. In this modification, the management unit 120 calculates the seventh data by multiplying the ratio data shown in FIG. 34 by the standard deviation of the internal heat generation density total value. 24 34. Next, the management unit 120 multiplies the values ​​Ph1 to Ph2 of the ratio data shown in FIG. 34 for each time period on a holiday by the standard deviation σdw of the total internal heat generation density value on a weekday to calculate the seventh data for the weekday as shown in FIG. 35.24 is multiplied by the standard deviation σdh of the total internal heat generation density on the holiday to calculate seventh data for the holiday as shown in FIG.

[0094] [S327] The management unit 120 calculates the fifth data by correcting the seventh data by an arbitrary positive integer. Note that any number of positive integers may be used. The value by which each value of the seventh data is corrected is not limited to an integer and may be any number including decimals and fractions, but is limited to a value that makes each calculated value of the fifth data smaller than the value of the internal heat generation density for the corresponding time period of the fourth data.

[0095] In this modification, the management unit 120 sets an arbitrary positive integer to 4. The management unit 120 calculates each value of the seventh data shown in FIG. 35, that is, (Pw1×σdw) to (Pw 24 ×σdw) and (Ph1×σdh) to (Ph 24 ×σdh) is multiplied by integers from 1 to 4 as correction values ​​to calculate fifth data as shown in FIGS.

[0096] (2) Variation 2 As shown in FIG. 38, the third process S3 may be composed of six processes from S331 to S336.

[0097] [S331] The management unit 120 refers to the day of the week of the third data and sorts the third data into weekday data and holiday data. In this modification, the management unit 120 refers to the day of the week of the third data shown in Fig. 7 and sorts the third data into 246 weekday data and 119 holiday data as shown in Fig. 11.

[0098] [S332] The management unit 120 calculates the standard deviation data from the third data. The management unit 120 calculates the standard deviation σw of the internal heat generation density data for each time period of the weekday data among the third data using the formula (3) shown in FIG. iIn equation (3), n is the number of weekday data, Xi is the internal heat generation density data value for a certain time period on the i-th day, and X ave is the average value of n internal heat generation density data values ​​in a certain time period.

[0099] In this modification, the management unit 120 calculates the σw1 to σw2 values ​​shown in FIG. 39 from the data of 246 weekdays shown in FIG. 24 Calculate the standard deviation of the 24 internal heat generation density data (standard deviation for weekdays). In equation (3), n is 246, Xi is the value of the internal heat generation density data for a certain time period on the i-th day, and X ave is the average value of 246 internal heat generation density data values ​​in a certain time period.

[0100] Next, the management unit 120 calculates the standard deviation σh of the internal heat density data for each time period of the holiday data among the third data using the formula (4) shown in FIG. i In equation (4), n is the number of data points on holidays, Xi is the internal heat density data value for a certain time period on the i-th day, and X ave is the average value of n internal heat generation density data values ​​in a certain time period.

[0101] In this modification, the management unit 120 calculates the holiday periods σh1 to σh1 shown in FIG. 40 from the data of the 119 holidays shown in FIG. 24 Calculate the standard deviation (standard deviation on holidays) of the 24 internal heat generation density data. In equation (4), n is 119, Xi is the value of the internal heat generation density data in a certain time period on the i-th day, and X ave is the average value of 119 internal heat generation density data values ​​in a certain time period.

[0102] The management unit 120 calculates σw1 to σw 24 The standard deviation of weekdays and σh1 to σh 24 The standard deviation data shown in FIGS. 39 and 40 is generated by combining the standard deviations for the days and months during the holidays.

[0103] [S333] The management unit 120 extracts the maximum value of the internal heat generation density data from the third data. In this modification, the management unit 120 extracts the largest internal heat generation density data value Dwmax from the third data for weekdays shown in Fig. 11 as the maximum value of the internal heat generation density data for the weekday. Dwmax is the value of the internal heat generation density data for a certain time period on a certain weekday, such as the value for time period 13 on Tuesday, July 29th.

[0104] Next, the management unit 120 extracts the largest internal heat generation density data value Dhmax as the maximum internal heat generation density value on the holiday from the third data for the holiday shown in Fig. 11. Dhmax is the internal heat generation density data value for a certain time period on a day that is a holiday, such as the value for time period 14 on Sunday, July 27th.

[0105] [S334] The management unit 120 calculates the normalized standard deviation data from the standard deviation data and the maximum value of the internal heat generation density data. The management unit 120 calculates the normalized standard deviation σcw for weekdays using the formula (15) shown in FIG. i In equation (15), σw i is the standard deviation data for each time period in the standard deviation data for weekdays, and Dwmax is the maximum value of the internal heat generation density data on weekdays.

[0106] In this modification, the management unit 120 calculates the standard deviations σw1 to σw for each time period in the weekday standard deviation data shown in FIG. 24 is divided by the maximum value Dwmax of the internal heat generation density data on weekdays extracted in S333 to obtain the normalized standard deviations σcw1 to σcw on weekdays as shown in Figure 41. 24 Calculate.

[0107] Next, the management unit 120 calculates the normalized standard deviation σch for holidays using equation (16) shown in FIG. i In equation (16), σh i is the standard deviation data for each time period in the standard deviation data for the holiday, and Dhmax is the maximum value of the internal heat generation density data for the holiday.

[0108] In this modification, the management unit 120 calculates the standard deviations σh1 to σh for each time period in the standard deviation data for holidays shown in FIG. 24 is divided by the maximum value Dhmax of the internal heat generation density data on holidays extracted in S333 to obtain the normalized standard deviations σch1 to σch on holidays as shown in Figure 41. 24 Calculate.

[0109] The management unit 120 calculates σcw1 to σcw 24 The normalized standard deviation of weekdays and σch1 to σch 24 The normalized standard deviation data shown in FIG. 41 is generated by combining this with the normalized standard deviation of the holidays.

[0110] [S335] The management unit 120 calculates the seventh data from the normalized standard data and the first data. The management unit 120 calculates the seventh data Dcwi for weekdays using equation (17) shown in FIG. 42. Here, σcw i is the normalized standard deviation on weekdays, and Ew is the internal heat generation density data assuming a specific time on a weekday.

[0111] In this modification, the management unit 120 calculates the normalized standard deviations σcw1 to σcw2 for weekdays shown in FIG. 24 and the internal heat generation density data Ew assumed for one hour on a weekday are multiplied to obtain the seventh data Dcw1 to Dcw2 for the weekday period shown in Figure 42. 24 Calculate.

[0112] Next, the management unit 120 calculates the seventh data Dchi for the holiday using the formula (18) shown in FIG. i is the normalized standard deviation on the holiday, and Eh is the internal heat generation density data assuming a specific time on the holiday.

[0113] In this modification, the management unit 120 calculates the normalized standard deviations σch1 to σch2 for holidays shown in FIG. 24 and the internal heat generation density data Eh for one hour on a holiday are multiplied to obtain the seventh data Dch1 to Dch2 for the holiday as shown in Figure 42. 24 Calculate.

[0114] The management unit 120 manages the Dcw1 to Dcw 24 The 7th data of the weekday and Dch1 to Dch 24 The seventh data shown in FIG. 42 is generated by combining the seventh data of the 24 holidays.

[0115] [S336] The management unit 120 calculates the fifth data by correcting the seventh data by an arbitrary positive integer. Note that any number of positive integers may be used. The value by which each value of the seventh data is corrected is not limited to an integer and may be any number including decimals and fractions, but is limited to a value that makes each calculated value of the fifth data smaller than the value of the internal heat density data for the corresponding time period of the fourth data.

[0116] In this modification, the management unit 120 sets an arbitrary positive integer to 4. The management unit 120 calculates the values ​​of the seventh data shown in FIG. 42, that is, Dcw1 to Dcw 24 and Dch1 to Dch 24 The fifth data shown in FIG. 43 is calculated by multiplying each of the values ​​by integers from 1 to 4 as correction values.

[0117] (3) Variation 3 As shown in FIG. 44, the third process S3 may be made up of ten processes from S341 to S350.

[0118] [S341] The management unit 120 refers to the day of the week of the third data and sorts the third data into weekday data and holiday data. In this modification, the management unit 120 refers to the day of the week of the third data shown in Fig. 7 and sorts the third data into 246 weekday data and 119 holiday data as shown in Fig. 11.

[0119] [S342] The management unit 120 calculates the internal heat generation density total value from the third data sorted into weekday data and holiday data. In this modification, as shown in FIG. 32, the management unit 120 calculates the internal heat generation density total values ​​Σw1 to Σw for the weekday data of the third data. 246 Next, as shown in FIG. 33, the management unit 120 calculates the internal heat generation density totals Σh1 to Σh for the holiday data among the third data. 119 Calculate.

[0120] [S343] The management unit 120 calculates the standard deviation from the internal heat generation density total value. The management unit 120 calculates the internal heat generation density total values ​​Σw1 to Σw2 for the weekday data using equation (11) shown in FIG. n Calculate the standard deviation σdw (standard deviation of the internal heat generation density total value on weekdays). In equation (11), n ​​is the number of weekday data, and Xi is the internal heat generation density total value Σw on the i-th day. i , X ave is the average value of n total internal heat generation densities.

[0121] In this modification, the management unit 120 calculates the internal heat generation density sums Σw1 to Σw2 in the weekday data shown in FIG. 246 In equation (11), n ​​is 246, Xi is the total internal heat generation density value Σw on the i-th day. i , X ave is the average value of the total internal heat generation density of 246 samples.

[0122] Next, the management unit 120 calculates the total internal heat generation density values ​​Σh1 to Σhh in the holiday data using the formula (12) shown in FIG. n Calculate the standard deviation σdh (standard deviation of the total internal heat generation density on a holiday). In equation (12), n is the number of holiday data, and Xi is the total internal heat generation density value Σh on the i-th day. i , X ave is the average value of n total internal heat generation densities.

[0123] In this modification, the management unit 120 calculates the internal heat generation density sums Σh1 to Σh in the holiday data shown in FIG. 119 In equation (12), n is 119, Xi is the total internal heat generation density value Σh on the i-th day. i , X ave is the average value of the total internal heat generation density of 119 samples.

[0124] [S344] The management unit 120 extracts the maximum value from the internal heat generation density total value. In this modification, the management unit 120 extracts the maximum value from the internal heat generation density total values ​​Σw1 to Σw2 in the weekday data of the third data shown in FIG. 246 The largest total internal heat generation density value is extracted as the maximum value Σwmax for weekdays. The maximum value Σwmax for weekdays is, for example, Σw 145 This is the total internal heat generation density value for one weekday.

[0125] Next, the management unit 120 calculates the internal heat generation density sums Σh1 to Σh2 for the holiday data in the third data shown in FIG. 119 The largest total internal heat generation density value is extracted as the maximum value Σhmax on the holiday. The maximum value Σhmax on the holiday is, for example, 72 This is the total internal heat generation density value for a day with a holiday.

[0126] [S345] The management unit 120 calculates the normalized standard deviation by dividing the standard deviation by the maximum value. In this modification, the management unit 120 calculates the normalized standard deviation σcdw for weekdays by dividing the standard deviation σdw of the internal heat generation density sum values ​​for weekdays calculated in S343 by the maximum value Σwmax for weekdays.

[0127] Next, the management unit 120 divides the standard deviation σdh of the total internal heat generation density value on a holiday by the maximum value Σhmax on a holiday to calculate the normalized standard deviation σcdh on a holiday.

[0128] [S346] The management unit 120 multiplies the normalized standard deviation by the first data to calculate the daily corrected internal heat density value. In this modification, the management unit 120 multiplies the normalized standard deviation σcdw for weekdays by the first data Ew for weekdays to calculate the daily corrected internal heat density value Ddw for weekdays.

[0129] Next, the management unit 120 multiplies the normalized standard deviation σcdh for the holiday by the first data Eh for the holiday to calculate the daily corrected internal heat density value Ddh for the holiday.

[0130] [S347] The management unit 120 calculates the total value of the coefficients by adding up the coefficients for each time period of the second data. In this modification, the management unit 120 calculates the total value of the coefficients Rw1 to Rw2 for each time period on weekdays in the second data shown in FIG. 24 Similarly, the management unit 120 calculates the total coefficient value ΣRw for weekdays by adding the coefficients Rh1 to Rh2 for each time period on holidays in the second data. 24 The total coefficient for holidays is calculated by adding up to ΣRh.

[0131] [S348] The management unit 120 calculates the ratio data from the sum of the second data and the coefficient. The management unit 120 calculates the ratio data for weekdays using the formula (13) shown in FIG. 34. Here, Rw i is the coefficient for each time period in the second data for weekdays, and ΣRw is the sum of the coefficients for weekdays calculated in S347.

[0132] In this modification, the management unit 120 calculates the coefficients Rw1 to Rw2 for each time period in the weekday data of the second data shown in FIG. 24 Divide each by ΣRw to get the ratio data Pw1 to Pw as shown in Figure 34. 24 Calculate.

[0133] Next, the management unit 120 calculates the ratio data for the holiday using the formula (14) shown in FIG. iis the coefficient of each time period in the second data for the holiday, and ΣRh is the total value of the coefficients for the holiday calculated in S324.

[0134] In this modification, the management unit 120 calculates the coefficients Rh1 to Rh2 for each time period in the holiday data of the second data shown in FIG. 24 Divide each by ΣRh to obtain the ratio data Ph1 to Ph2 as shown in Figure 34. 24 Calculate.

[0135] [S349] The management unit 120 multiplies the ratio data by the daily internal heat density correction value to calculate seventh data. In this modification, the management unit 120 calculates the seventh data by multiplying the ratio data by the daily internal heat density correction value. 24 is multiplied by the daily internal heat generation density correction value Ddw on weekdays to calculate seventh data for weekdays as shown in FIG.

[0136] Next, the management unit 120 calculates the values ​​Ph1 to Ph2 of the ratio data shown in FIG. 24 is multiplied by the daily correction value Ddh of the internal heat density on the holiday to calculate seventh data for the holiday as shown in FIG.

[0137] [S350] The management unit 120 calculates the fifth data by correcting the seventh data by an arbitrary positive integer. Note that any number of positive integers may be used. The value by which each value of the seventh data is corrected is not limited to integers from 1 to 4, and may be any number including decimals and fractions, but is limited to a value that makes each calculated value of the fifth data smaller than the value of the internal heat generation density for the corresponding time period of the fourth data.

[0138] In this modification, the management unit 120 sets an arbitrary positive integer to 4. The management unit 120 calculates each value of the seventh data shown in FIG. 45, that is, (Pw1×Ddw) to (Pw 24 ×Ddw) and (Ph1×Ddh) to (Ph 24×Ddh) is multiplied by integers from 1 to 4 as correction values ​​to calculate the fifth data as shown in FIGS.

[0139] (4) Variation 4 In the above embodiment, the heat generated inside the room is assumed to be generated from one heat source, but it may be generated from multiple heat sources, such as lighting, heat-generating equipment, etc. In this case, the above method may be performed for each heat source.

[0140] (5) Variation 5 The third data may include interpolated data. For example, when generating 365 days' worth of sixth data, assume that there is only 360 days' worth of measurement data for internal heat generation in rooms of an existing building. In this case, measurement data from nearby dates may be interpolated for the five days for which there is no measurement data.

[0141] (6) Variation 6 After calculating the sixth data, the processing device 1 may perform a dynamic heat load calculation using the sixth data. In this case, a dynamic heat load calculation program is stored in advance in the main storage device 102 or the auxiliary storage device 103. Then, the management unit 120 executes the dynamic heat load calculation program, causing the processing device 1 to perform a dynamic heat load calculation.

[0142] <Effects> (Aspect 1) In this embodiment, the data generation method executed by the processing device 1 includes a first process of inputting first data corresponding to an internal heat generation density obtained by dividing the internal heat generation in a room of a building for which a dynamic heat load calculation is performed by the floor area of ​​the room of the building for which the dynamic heat load calculation is performed, second data being table data in which a coefficient of 1 or less is assigned to each section obtained by dividing a day into hours, with a coefficient of 1 assigned to at least one section, and third data being a collection of data for a predetermined number of days in which the measurement date and day of the week are associated with the internal heat generation density for one day obtained by dividing the internal heat generation in a room of an existing building measured for each divided section by the floor area of ​​the room of the existing building. The method includes a second process for calculating, based on the first data and the second data, fourth data corresponding to the internal heat generation density of one room of the building for which dynamic heat load calculations are performed for each divided section; a third process for calculating, from the third data, fifth data, which is correction value data for correcting the internal heat generation density of one room of the building for which dynamic heat load calculations are performed for each divided section, wherein the correction values ​​of the divided sections are each smaller than the internal heat generation density of the corresponding divided section in the fourth data; and a fourth process for calculating, for a predetermined number of days, data that associates the internal heat generation density of one room of the building for which dynamic heat load calculations are performed for each divided section with a date and a day of the week, from the third data, fourth data, and fifth data, to generate sixth data.

[0143] (Aspect 2) In aspect 1, the data generation method executed by the processing device 1 includes a third process that calculates the standard deviation of the internal heat generation density of rooms in an existing building in a divided section from the third data, collects the standard deviation for one day to generate seventh data, and multiplies the value for each divided section of the seventh data by a coefficient greater than 0 using multiple different coefficients to calculate fifth data.

[0144] (Aspect 3) In aspect 1, the data generation method executed by the processing device 1 includes the following third process: a process of calculating a total internal heat generation density value for 365 days by adding the internal heat generation density of rooms in an existing building in a divided section for one day from the third data; a process of calculating a standard deviation from the total internal heat generation density value for 365 days; a process of calculating a total value of coefficients by adding the coefficients for each divided section of the second data for one day; a process of calculating ratio data by dividing the coefficients for each divided section of the second data by the total value of the coefficients; a process of multiplying the value for each divided section of the ratio data by the standard deviation to calculate seventh data; and a process of multiplying the value for each divided section of the seventh data by a coefficient greater than 0 using multiple different coefficients to calculate fifth data.

[0145] (Aspect 4) In aspect 1, the data generation method executed by the processing device 1 includes the following third processing: calculating the standard deviation of the internal heat generation density of rooms in existing buildings in the divided section from the third data, and collecting the standard deviations for one day to obtain standard deviation data; extracting the maximum value of the internal heat generation density of rooms in existing buildings in the divided section from the third data; dividing the standard deviation value of the divided section of the standard deviation data by the maximum value to calculate a normalized standard deviation, and collecting the normalized standard deviations for one day to obtain normalized standard deviation data; multiplying the normalized standard deviation for each divided section of the normalized standard deviation data by the internal heat generation density of the first data to calculate a time-specific corrected internal heat generation density value, and collecting the time-specific corrected internal heat generation density value for one day to obtain seventh data; and multiplying the value for each divided section of the seventh data by a coefficient greater than 0 using multiple different coefficients to calculate fifth data.

[0146] (Embodiment 5) In embodiment 1, the data generation method executed by the processing device 1 includes the steps of: calculating a total internal heat generation density value for 365 days by adding the internal heat generation densities of the rooms of the two existing buildings in the divided section for one day from the third data; calculating a standard deviation from the total internal heat generation density value for a predetermined number of days; extracting a maximum value from the total internal heat generation density value for the predetermined number of days; dividing the standard deviation by the extracted maximum value to calculate a normalized standard deviation; and multiplying the normalized standard deviation by the internal heat generation density of the first data. a process of calculating a daily corrected value of the internal heat generation density by multiplying the value of each divided section of the ratio data by the daily corrected value of the internal heat generation density; a process of calculating a total value of coefficients obtained by adding up the coefficients for each divided section of the second data for one day; a process of multiplying the value of each divided section of the ratio data by the daily corrected value of the internal heat generation density to calculate seventh data; and a process of multiplying the value of each divided section of the seventh data by a coefficient greater than 0 using a plurality of different coefficients to calculate fifth data.

[0147] According to the above method, it is possible to generate sixth data by calculating data for a predetermined number of days in which the internal heat generation density of one room in a building for which a dynamic heat load calculation is performed is associated with the date and day of the week. Therefore, it is possible to prepare the internal heat generation density data to be used in the dynamic heat load calculation.

[0148] (Embodiment 6) In embodiments 1 to 5, the data generation method executed by the processing device 1 is such that the predetermined number of days is 365 days.

[0149] According to the above method, the sixth data is generated for 365 days, and therefore, it is possible to prepare internal heat generation density data that is particularly easy to use for annual dynamic heat load calculations.

[0150] (Aspect 7, Aspect 8) This embodiment provides a processing device 1 that executes the method according to any one of aspects 1 to 6, and a program that causes the processing device to execute the method.

[0151] According to the above-described processing device 1 and program, sixth data can be generated by calculating data for a predetermined number of days in which the internal heat generation density of one room in a building for which a dynamic heat load calculation is performed is associated with the date and day of the week. [Explanation of symbols]

[0152] 1...Processing equipment

Claims

1. A data generation method to be executed by a processing device, comprising: First data corresponding to an internal heat generation density obtained by dividing the internal heat generation at a specific time in one room of a preset first building by the floor area of ​​the one room of the first building; second data, which is table data in which a coefficient of 1 or less is assigned to each interval obtained by dividing a day by the specific time, and in which a coefficient of 1 is assigned to at least one of the intervals; a third data set obtained by collecting data for a predetermined number of days in which the internal heat generation density for one day is calculated by dividing the internal heat generation in one room of the second building measured for each of the divided sections by the floor area of ​​the one room of the second building, and the data set is associated with the date of measurement and the day of the week; A first process of inputting a second process of calculating fourth data corresponding to the internal heat generation density of one room of the first building for each of the divided sections based on the first data and the second data; a third process for calculating, from the third data, fifth data, which is data of a correction value for correcting the internal heat generation density of one room of the first building for each of the divided sections, and the correction value for each of the divided sections is a value smaller than the internal heat generation density of the corresponding divided section in the fourth data; a fourth process of calculating data for a predetermined number of days in which the internal heat generation density of one room of the first building for each of the divided sections for one day is associated with the date and the day of the week from the third data, the fourth data, and the fifth data, thereby generating sixth data; A method comprising:

2. The third process is a process of calculating a standard deviation of the internal heat generation density of the rooms of the second building in the divided section from the third data, and collecting the standard deviations for one day to set as seventh data; a process of multiplying a value of each of the divided sections of the seventh data by a coefficient greater than 0 using a plurality of different coefficients to calculate the fifth data; The method of claim 1 , comprising:

3. The third process is a process of calculating a total internal heat generation density value for the predetermined number of days by adding up the internal heat generation densities of the rooms of the second building in the divided section for one day from the third data; A process of calculating a standard deviation from the total internal heat generation density value for the predetermined number of days; A process of calculating a total value of coefficients obtained by adding coefficients for each of the divided sections of the second data for one day; a process of calculating ratio data by dividing a coefficient for each of the divided sections of the second data by a total value of the coefficients; a process of multiplying the value of each of the divided sections of the ratio data by the standard deviation to calculate seventh data; a process of multiplying a value of each of the divided sections of the seventh data by a coefficient greater than 0 using a plurality of different coefficients to calculate the fifth data; The method of claim 1 , comprising:

4. The third process is a process of calculating a standard deviation of the internal heat generation density of the rooms of the second building in the divided section from the third data, and collecting the standard deviation for one day to obtain standard deviation data; extracting a maximum value of the internal heat generation density of the room of the second building in the divided section from the third data; a process of dividing the standard deviation value of the divided section of the standard deviation data by the maximum value to calculate a normalized standard deviation, and collecting the normalized standard deviations for one day to obtain normalized standard deviation data; a process of multiplying the normalized standard deviation for each of the divided sections of the normalized standard deviation data by the internal heat generation density of the first data to calculate an internal heat generation density time-based corrected value, and collecting the internal heat generation density time-based corrected values ​​for one day to create seventh data; a process of multiplying a value of each of the divided sections of the seventh data by a coefficient greater than 0 using a plurality of different coefficients to calculate the fifth data; The method of claim 1 , comprising:

5. The third process is a process of calculating a total internal heat generation density value for the predetermined number of days by adding up the internal heat generation densities of the rooms of the second building in the divided section for one day from the third data; A process of calculating a standard deviation from the total internal heat generation density value for the predetermined number of days; extracting a maximum value from the total internal heat generation density values ​​for the predetermined number of days; a process of dividing the standard deviation by the extracted maximum value to calculate a normalized standard deviation; a process of multiplying the normalized standard deviation by the internal heat generation density of the first data to calculate a daily corrected internal heat generation density value; A process of calculating a total value of coefficients obtained by adding coefficients for each of the divided sections of the second data for one day; a process of calculating ratio data by dividing a coefficient for each of the divided sections of the second data by a total value of the coefficients; a process of multiplying the value of each of the divided sections of the ratio data by the daily internal heat generation density correction value to calculate seventh data; a process of multiplying a value of each of the divided sections of the seventh data by a coefficient greater than 0 using a plurality of different coefficients to calculate the fifth data; The method of claim 1 , comprising:

6. The method of claim 1 , wherein the predetermined number of days is 365 days.

7. A processing device for carrying out the method according to any one of claims 1 to 6.

8. A program for causing a processing device to execute the method according to any one of claims 1 to 6.

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