JP7115702B2 - Airtight performance prediction device - Google Patents

Description

The present invention relates to an airtight performance prediction device for predicting the airtight performance of a building.

Patent Literature 1 proposes a computer for outputting information indicating the degree of thermal insulation effect, which includes an output unit, a memory for storing data relating to thermal insulation specifications of at least one part of a room in a building, and a processor. there is Specifically, by referring to the memory, the processor, based on the insulation specifications of the room of the building before renovation and the insulation specification of at least one part of the room of the building after renovation, information indicating the degree of insulation effect of the building room, and the information indicating the degree of insulation effect of the building room before renovation and the insulation specification of at least one part of the room of the building before renovation to the output unit. The information to be displayed is associated with the repair menu and the information to be output indicating the degree of thermal insulation effect of the room of the building after the repair according to the repair menu. This makes it possible to efficiently present the effects of repair or reconstruction.

Japanese Patent No. 5965192

However, in Patent Literature 1, although the thermal insulation effect and energy loss can be predicted based on the known airtightness of the building, the airtightness of the building itself cannot be predicted.

In addition, since the airtightness performance is determined by actual measurements, it is difficult to predict it at the design stage.

SUMMARY OF THE INVENTION It is an object of the present invention to provide an airtightness performance prediction apparatus capable of predicting the airtightness performance of a building at the design stage.

The airtight performance prediction device uses the airtightness performance of the building measured in advance as an objective variable, and a regression formula calculated by multiple regression analysis using a predetermined factor affecting the airtightness performance at the time of measuring the airtightness performance as an explanatory variable. , an input unit for inputting the factor in the target building, the factor input by the input unit, and the airtightness in the target building based on the regression equation stored in the storage unit a predictor for predicting performance.

According to the airtightness performance prediction device according to the first aspect, the storage unit stores the airtightness performance of the building, which is measured in advance, as an objective variable, and the predetermined factors that affect the airtightness performance when measuring the airtightness performance. Regression formulas calculated by multiple regression analysis as explanatory variables are stored.

The input unit inputs a predetermined factor that affects the airtightness of the target building, such as new construction or renovation.

Then, the prediction unit predicts the airtight performance of the target building based on the factor input by the input unit and the regression equation stored in the storage unit. That is, by inputting the specification of the target building as a factor into the regression equation calculated by the multiple regression analysis, it is possible to predict the airtightness performance of the building at the design stage.

Further, in order to achieve the above object, the airtightness performance prediction device according to the first aspect uses the airtightness performance of a building measured in advance as an objective variable, and determines the airtightness performance when measuring the airtightness performance. A storage unit that stores a regression formula calculated by multiple regression analysis using a factor as an explanatory variable for each type of the airtight performance as the objective variable; an extraction unit for extracting the factor from information ; an input unit for inputting the selection result of the objective variable; and the storage corresponding to the factor extracted by the extraction unit and the selection result input by the input unit. a prediction unit that predicts the airtightness performance of the target building based on the regression equation stored in the unit.

According to the airtightness performance prediction device according to the second aspect, the storage unit stores the airtightness performance of the building measured in advance as an objective variable, and the predetermined factor affecting the airtightness performance at the time of measuring the airtightness performance. A regression formula calculated by multiple regression analysis as an explanatory variable is stored for each type of airtight performance as an objective variable .

The extraction unit acquires design specification information of a target building, such as new construction or renovation, and extracts predetermined factors that affect the airtight performance of the target building, such as new construction or renovation, from the acquired design specification information .

Then, the prediction unit predicts the airtightness performance of the target building based on the factors extracted by the extraction unit and the regression equation stored in the storage unit corresponding to the selection result of the objective variable input by the input unit. be. That is, by inputting the specifications of the target building as a factor into the regression equation calculated by the multiple regression analysis, it is possible to predict the airtightness performance of the building at the design stage. In addition, since the factors as explanatory variables are extracted from the design specifications, it is possible to predict the airtight performance of the building without the troublesome task of inputting the factors.

The factors include the type of distribution board, air conditioning system, insulation specifications, floor area, type of switches and outlets, installation area of floor finishing materials, and presence or absence of airtightness of post assembly, unit bath, and building joints. At least one may apply.

Further, the prediction unit may predict at least one of the equivalent gap area and the total equivalent gap area of the building as the airtight performance.

As described above, according to the present invention, there is an effect that it is possible to provide an airtightness performance prediction device capable of predicting the airtightness performance of a building at the design stage.

It is a block diagram showing a schematic structure of an airtightness performance prediction device concerning this embodiment. FIG. 2 is a functional block diagram representing functions realized by executing various programs in the airtightness performance prediction device according to the present embodiment; (A) is a diagram showing an example of objective variables and explanatory variables input by an input unit during multiple regression analysis by a regression formula calculation unit, and (B) is an example of a regression formula calculated by a regression formula calculation unit. It is a figure which shows. (A) is a perspective view showing a distribution board and an opening in a wall, and (B) is a cross-sectional view showing a ventilation path of the distribution board. (A) is a diagram for explaining an insulation line for floor heating, and (B) is a diagram for explaining an insulation line for basic insulation. (A) is a diagram for explaining ventilation between a floor finishing material such as a tatami mat, and (B) is a diagram for explaining ventilation between a flooring and the like. It is a flow chart which shows an example of the flow of processing performed when a multiple regression analysis program is run with a personal computer of an airtightness performance prediction device concerning this embodiment. 5 is a flow chart showing an example of the flow of processing performed when an airtightness prediction program is executed by the personal computer of the airtightness performance prediction device according to the present embodiment. It is a block diagram which shows schematic structure of the airtight performance prediction apparatus of a modification. It is a flowchart which shows an example of the flow of a process performed when an airtightness prediction program is run by the personal computer of the airtightness performance prediction apparatus of a modification.

An example of an embodiment of the present invention will be described in detail below with reference to the drawings. FIG. 1 is a block diagram showing a schematic configuration of an airtightness performance prediction device according to this embodiment.

The airtightness performance prediction device 10 according to the embodiment of the present invention functions as a tool for predicting the airtightness performance of a building when constructing or renovating a building such as a house. For example, by inputting the specifications of the building resulting from the airtightness of the building, processing for predicting the airtightness of the building and displaying the prediction result is performed.

The airtightness performance prediction device 10 includes a personal computer 12 . As shown in FIG. 1, the personal computer 12 includes a CPU 14, a ROM 16, a RAM 18, and an input/output port 20, which are connected via buses 22 such as an address bus, a data bus, and a control bus.

A display 24, a mouse 26, a keyboard 28, a hard disk (HDD) 30, and a disk drive 32 for reading information from various disks 34 are connected to the input/output port 20 as various input/output devices.

The ROM 16 stores an airtightness prediction program for predicting airtightness performance, a multiple regression analysis program for calculating a regression equation for predicting airtightness performance, and the like. The RAM 18 is used as a work memory or the like for performing various calculations and the like performed by the CPU 14 .

FIG. 2 is a functional block diagram showing functions realized by executing various programs in the airtightness performance prediction device 10 according to this embodiment.

The airtightness performance prediction device 10 according to the present embodiment has functions of a regression equation calculation unit 40, an input unit 42, a storage unit 44, and a prediction unit 46, as shown in FIG.

The regression formula calculation unit 40 performs multiple regression analysis using the previously measured airtightness performance of the building as an objective variable and using a predetermined factor that affects the airtightness performance at the time of measuring the airtightness performance as an explanatory variable, thereby calculating the airtightness performance. Calculate the regression formula used for prediction.

The input unit 42 inputs a measured value of the airtight performance necessary for the regression equation calculation unit 40 to obtain a regression equation, and a predetermined factor to be an explanatory variable when measuring the airtight performance. Further, the input unit 42 inputs design specification information (factors) of the target property when predicting the airtightness performance of the target property using the regression formula obtained by the regression formula calculation unit 40. command to start predicting the airtightness performance of The input unit 42 uses, for example, the mouse 26 and the keyboard 28 to input objective variables, explanatory variables, property specifications, and the like.

The storage unit 44 stores the regression equation calculated by the multiple regression analysis by the regression equation calculation unit 40 in the HDD 30, and reads it when predicting the airtight performance of the building. Note that the storage unit 44 may store the regression equations calculated by the regression equation calculation unit 40 in addition to various disks 34 and memories.

The prediction unit 46 reads the regression equation stored in the storage unit 44, and predicts the airtightness performance of the target building based on the read regression equation and the specification information of the target property input by the input unit 42. . The prediction unit 46 also displays the prediction result of the airtightness performance of the target building on the display 24 .

Next, the multiple regression analysis by the regression formula calculation unit 40 will be described in detail. FIG. 3A is a diagram showing an example of objective variables and explanatory variables input by the input unit 42 when the regression equation calculation unit 40 performs multiple regression analysis. FIG. 3B is a diagram showing an example of the regression equation calculated by the regression equation calculation unit 40. As shown in FIG.

In this embodiment, as the objective variable of the multiple regression analysis, at least one of the measured value of the equivalent clearance area (C value) and the total equivalent clearance area (αA) for the buildings of the same construction method is applied. For example, the result of measuring the equivalent clearance area of the unit building or the total equivalent clearance area is input as the objective variable. The equivalent gap area=total equivalent gap area/floor area.

FIG. 3B shows a regression equation based on these explanatory variables.

Hereinafter, reasons related to the airtight performance will be described for major factors among the factors of the explanatory variables described above.

In addition, there are multiple types of distribution boards, such as distribution boards with doors, distribution boards that support solar power generation, sensor distribution boards, and information distribution boards that support HEMS (Home Energy Management System). There is a distribution board of As shown in FIGS. 4A and 4B, the distribution board 52 is wired through an opening 54 in the wall and communicates with the inside of the wall, the ceiling, and the outside through a vent 56 or the like. In addition, since the size of the opening differs depending on the type of distribution board 52, the type of distribution board 52 greatly affects the airtight performance of the building.

In addition, since the area through which the duct frame is penetrated differs between room air conditioners and whole building air conditioners, the difference in air conditioning system also greatly affects the airtight performance of the building.

Insulation specifications include, for example, floor insulation and basic insulation. In floor insulation, as shown in FIG. On the other hand, in basic insulation, as shown in FIG. 5(B), the underfloor space is inside the insulation line, the foundation is insulated, and the airtightness performance is greatly affected by the insulation specifications.

In addition, since some types of switches/outlets are attached to the wall by providing an opening in the wall and have an airtight structure on the back side, the type of switch/outlet greatly affects the airtightness of the building.

As shown in FIG. 6A, floor finishing materials such as tatami mats are ventilated under the floor and around baseboards 60, as well as through the underfloor and underfloor joints. On the other hand, as shown in FIG. 6B, in the case of flooring, the surface is smooth and gaps with the baseboard 60 are less likely to occur, and ventilation from the underfloor joints is maintained. In other words, the area of tatami mats and textiles greatly affects the airtightness of the building.

The airtightness of the building is greatly affected by the presence or absence of the airtightness of the frame with the airtight tape.

In this embodiment, as described above, the input unit 42 inputs the objective variable and the explanatory variable, and the regression equation calculation unit 40 performs multiple regression analysis to calculate the regression equation. An example of the calculated regression equation is shown in FIG. 3(B). The example of FIG. 3B shows an example in which the target variable is the total equivalent gap area. As explanatory variables, factors with a high contribution rate were determined by repeating multiple regression analysis. In the example of FIG. 3B, the explanatory variables are the post assembly section 50, the distribution board 52, the air conditioning system, the heat insulation specifications, the floor area, the switches/outlets, the tatami mats/textiles, the UB airtightness, and the presence or absence of the airtight tape. shows the regression equation of Note that X1 to X17 in FIG. 3B indicate constants derived when calculating the regression equation.

Next, specific processing contents performed by the airtightness performance prediction device 10 according to the present embodiment configured as described above will be described. FIG. 7 is a flow chart showing an example of the flow of processing performed when the personal computer 12 of the airtightness performance prediction device 10 according to this embodiment executes the multiple regression analysis program.

At step 100 , the CPU 14 displays a predetermined objective variable input screen for inputting objective variables for multiple regression analysis on the display 24 , and proceeds to step 102 . That is, the display 24 displays a screen for inputting the equivalent gap area measured for each property and the total equivalent gap area as explanatory variables.

At step 102 , the CPU 14 determines whether or not the input unit 42 has input an objective variable. In this embodiment, it is determined whether or not the measured equivalent clearance area and the total equivalent clearance area of the object have been input, and the process proceeds to step 104 after waiting until the determination is affirmative.

At step 104 , the CPU 14 displays a predetermined explanatory variable input screen for inputting explanatory variables for multiple regression analysis on the display 24 , and proceeds to step 102 .

At step 106 , the CPU 14 determines whether or not explanatory variables have been input through the input unit 42 . In the present embodiment, it is determined whether or not a predetermined factor related to the airtight performance among the measured specifications of the property has been input, and the process waits until the determination is affirmative before proceeding to step 108 .

At step 108, the CPU 14 determines whether or not there are other property measurements. That is, it is determined whether or not all objective variables and explanatory variables of the measured object have been input. Specifically, it is determined whether or not the input unit 42 has given an instruction to input another property. input. On the other hand, if the determination is negative, the process proceeds to step 110 .

At step 110 , the CPU 14 performs multiple regression analysis by the regression equation calculator 40 using the input objective variable and explanatory variable to calculate a regression equation, and proceeds to step 112 .

At step 112, the CPU 14 stores the regression equation in the HDD 30 by the storage unit 44, and ends the series of processes.

Next, specific processing for predicting the airtight performance using the regression equation calculated and stored as described above will be described. FIG. 8 is a flow chart showing an example of the flow of processing performed when the airtightness prediction program is executed by the personal computer 12 of the airtightness performance prediction device 10 according to this embodiment.

At step 200 , the CPU 14 displays a predetermined objective variable selection screen on the display 24 and proceeds to step 202 .

At step 202 , the CPU 14 determines whether or not an objective variable has been selected by the input unit 42 , waits until the determination is affirmative, and proceeds to step 204 . In this embodiment, either the equivalent clearance area or the total equivalent clearance area is selected.

At step 204 , the CPU 14 displays a predetermined property specification input screen for inputting the specifications of the target property on the display 24 , and proceeds to step 102 .

At step 206 , the CPU 14 determines whether or not the specifications of the target property have been input through the input unit 42 . This determination is to determine whether or not specifications corresponding to predetermined factors related to airtight performance have been input, wait until the determination is affirmative, and proceed to step 208 .

At step 208 , the CPU 14 reads the regression equation corresponding to the selected target variable from among the regression equations stored in the storage unit 44 , and proceeds to step 210 .

In step 210 , the prediction unit 46 of the CPU 14 predicts the airtightness performance based on the input property specifications and the read regression equation, and the process proceeds to step 212 .

At step 212 , the CPU 14 displays the airtight performance prediction result on the display 24 and proceeds to step 214 .

At step 214, the CPU 14 determines whether or not to end the prediction. In this determination, it is determined whether or not an instruction to end the prediction has been given by the keyboard 28 or the like. End the noshori.

As described above, in the present embodiment, multiple regression analysis is performed using the airtightness performance of the building as the objective variable and the predetermined factor affecting the airtightness performance at the time of measuring the airtightness performance as the explanatory variable to calculate the regression equation, The calculated regression equation is used to predict the airtightness of the building. This makes it possible to show the degree of influence on the airtight performance of the portion related to the structure, which was not known from the actual measurement.

In addition, it is possible to detect construction defects and the like from the difference between the measured value and the predicted value of the airtightness of the building.

Furthermore, by predicting the airtightness performance at the time of renovation, it is possible to predict and present the improved airtightness performance.

When predicting the total equivalent gap area as the airtight performance of the building, the equivalent gap area may be predicted and divided by the floor area to predict the total equivalent gap area. A regression formula for predicting the total equivalent gap area may be calculated and predicted by multiple regression analysis.

Next, a modification of the airtightness performance prediction device according to this embodiment will be described. FIG. 9 is a block diagram showing a schematic configuration of an airtightness performance prediction device according to a modification.

The modified airtightness performance prediction device 11 differs from the above-described embodiment in that the input/output port 20 is further connected to the network 36 .

A database (DB) 38 and another personal computer (PC) 39 are connected to the network 36 so that information can be exchanged with the DB 38 and PC 39 connected to the network 36 .

In a modification, the DB 38 stores building design specification information such as building CAD (Computer Aided Design system) information created by the personal computers 12, 39 and the like. The DB 38 also stores information such as the above-described regression formula, measured values used when calculating the regression formula, and design specification information of other buildings, for example.

In the modified example, when predicting the airtightness of the building using a regression equation, the prediction unit 46 extracts factors that serve as explanatory variables from the CAD information stored in the DB 38, and extracts the airtightness of the building (equivalent gap area or total equivalent gap area). That is, the prediction unit 46 of the modified example further has an extraction function of extracting factors serving as explanatory variables from the CAD information in contrast to the above-described embodiment. In the modified example, the specification information is automatically extracted from the CAD information, so it is possible to save the trouble of inputting the specification. For example, predetermined factors that affect the airtightness of the building are extracted from the CAD information, and input to a regression equation that is calculated and stored to predict the airtightness of the building.

Next, specific processing contents performed by the airtightness performance prediction device 11 of the modified example will be described. Since the processing performed when the multiple regression analysis program is executed is the same as that of the above-described embodiment, the description is omitted, and the processing performed when the airtightness prediction program is executed will be described below. FIG. 10 is a flow chart showing an example of the flow of processing performed when the airtightness prediction program is executed by the personal computer 12 of the airtightness performance prediction device 11 of the modified example. Note that the same processing as in the above embodiment will be described with the same reference numerals.

At step 200 , the CPU 14 displays a predetermined objective variable selection screen on the display 24 and proceeds to step 202 .

At step 202 , the CPU 14 determines whether or not an objective variable has been selected by the input unit 42 , waits until the determination is affirmative, and proceeds to step 203 . In a modified example, either the equivalent clearance area or the total equivalent clearance area is selected as in the above embodiment.

At step 203 , the CPU 14 determines whether or not the input unit 42 has instructed a predicted object. The determination is whether or not information representing the property to be predicted has been input, or whether or not an instruction has been given to select a prediction target from the CAD information stored in advance in the DB 38 or the like. judge. It waits until the determination is affirmative and proceeds to step 205 .

At step 205 , the CPU 14 requests the DB 38 via the network 36 for the design specification information of the designated property, and the process proceeds to step 207 .

At step 207 , the CPU 14 determines whether or not the requested property design specification information has been received.

At step 208 , the CPU 14 reads the regression equation corresponding to the selected target variable from among the regression equations stored in the storage unit 44 , and proceeds to step 209 .

At step 209 , the CPU 14 extracts factors necessary for prediction from the property design specification information, and proceeds to step 211 . For example, the prediction unit 46 extracts predetermined factors from the property design specification information received from the DB 38 in order to predict the airtightness of the building.

In step 211 , the CPU 14 causes the prediction unit 46 to predict airtight performance based on the extracted factor and the read regression equation, and proceeds to step 212 .

At step 212 , the CPU 14 displays the airtight performance prediction result on the display 24 and proceeds to step 214 .

At step 214, the CPU 14 determines whether or not to end the prediction. In this determination, it is determined whether or not an instruction to end the prediction has been given by the keyboard 28 or the like. End the noshori.

In this way, in the modified example, the factors to be input into the regression equation are extracted from the CAD information, so there is no need to perform complicated work such as inputting the factors, and prediction of the airtightness performance of the building is easier than in the above embodiment. It becomes possible to go to

In addition, in the modified example, a configuration has been described in which building design specification information is received from the DB 38 connected to the network 36 and the factors required for prediction are extracted, but the present invention is not limited to this. For example, as in the above embodiment, the information to be stored in the DB 38 may be stored in the HDD 30 or the like of the personal computer 12, and the factors required for prediction may be extracted from the HDD 30 to predict the airtight performance of the building.

In addition, in the above embodiment and modification, an example was described in which at least one of the equivalent gap area and the total equivalent gap area was predicted as the airtightness of the building, but the present invention is not limited to this, and the airtightness of other buildings You may predict a value for

Further, although the processing performed by the personal computer 12 in the above embodiment has been described as software processing, it may be processing performed by hardware, or may be performed by a combination of both. In the case of software, it may be distributed as a program stored in a storage medium or the like.

In addition, the present invention is not limited to the above, and it goes without saying that various modifications can be made without departing from the spirit of the present invention.

10, 11 airtight performance prediction device 12 personal computer 14 CPU
30 HDDs
40 Regression calculation unit 42 Input unit 44 Storage unit 46 Prediction unit

Claims (3)

A regression formula calculated by multiple regression analysis using the pre-measured airtightness performance of the building as the objective variable and a predetermined factor affecting the airtightness performance at the time of measuring the airtightness performance as the explanatory variable was used as the objective variable. a storage unit that stores each type of airtight performance;
an extraction unit that acquires design specification information of a target building and extracts the factor from the acquired design specification information;
an input unit for inputting a selection result of the objective variable;
a prediction unit that predicts the airtightness performance of a target building based on the factors extracted by the extraction unit and the regression equation stored in the storage unit corresponding to the selection result input by the input unit; ,
Airtightness performance prediction device with. The above factors include the type of distribution board, air conditioning system, type of switches and outlets, heat insulation specifications, floor area, installation area of floor finishing materials, and presence or absence of airtightness of post assembly parts, unit baths, and building joints. The airtightness performance prediction device according to claim 1, comprising at least one of The airtightness performance prediction device according to claim 1 or 2, wherein the prediction unit predicts at least one of a building equivalent clearance area and a total equivalent clearance area as the airtightness performance.

Images