JP2019095954A - Airtightness performance prediction device - Google Patents

Abstract

To provide an airtightness performance prediction device capable of predicting airtight performance of a building at a design stage.SOLUTION: An airtightness performance prediction device includes: a storage part 44 in which air tight performance of a building measured in advance is used as an objective variable and a predetermined factor affecting airtight performance at the time of measurement of airtight performance is taken as an explanatory variable, then regression equation calculated by performance of multiple regression analysis by a regression equation calculation part 40 is stored; an input part 42 for inputting the factor in a target building; and a prediction part 46 for predicting the airtight performance of the target building based on the factor inputted by the input part 42 and the regression equation calculated by the storage part 44.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an airtightness performance prediction device that predicts the airtightness performance of a building.

  Patent Document 1 proposes a computer for outputting information indicating the degree of adiabatic effect, including an output unit, a memory for storing data on adiabatic specification of at least one part of a room of a building, and a processor. There is. Specifically, the processor refers to the memory, before and after the renovation, based on the insulation specification of the room before the renovation and the insulation specification of at least one part of the room after the renovation Information indicating the degree of thermal insulation effect of the room of the building is calculated, and information indicating the degree of thermal insulation effect of the room of the building before repair and the insulation specification of at least one part of the room before repair is calculated The information indicating the information and the information indicating the degree of the heat insulation effect of the room of the building after the repair according to the repair menu and the repair menu are associated and output. This makes it possible to efficiently present the effects resulting from the repair or reconstruction.

Patent No. 5965192 gazette

  However, in patent document 1, although the heat insulation effect and energy loss can be predicted based on the airtightness performance of a known building, the airtightness performance itself of a building can not be predicted.

  Further, since the airtightness performance is actually obtained by measurement, it is difficult to predict at the design stage, and even if it can be predicted, only buildings having similar specifications of the design can be predicted.

  The present invention has been made in consideration of the above-mentioned facts, and an object thereof is to provide an airtightness performance prediction device capable of predicting the airtightness performance of a building at the design stage.

  In order to achieve the above object, the airtightness performance prediction device according to the first aspect uses a previously measured airtightness performance of a building as a target variable, and a predetermined factor affecting the airtightness performance at the time of measuring the airtightness performance. A storage unit for storing a regression equation calculated by multiple regression analysis as an explanatory variable, an input unit for inputting the factor in a target building, the factor input by the input unit, and the storage unit And a prediction unit that predicts the airtightness performance of the target building based on the regression equation.

  According to the airtightness performance prediction device of the first aspect, the storage unit uses a previously measured airtightness performance of the building as a target variable, and a predetermined factor that affects the airtightness performance at the time of measuring the airtightness performance. A regression equation calculated by multiple regression analysis as an explanatory variable is stored.

  The input unit inputs predetermined factors that affect the airtightness performance of the target building such as new construction and renovation.

  Then, in the prediction unit, the airtightness performance in the target building is predicted based on the factor input by the input unit and the regression equation stored in the storage unit. That is, the airtightness performance of the building at the design stage can be predicted by inputting the specification of the target building as a factor into the regression equation calculated by the multiple regression analysis.

  The airtightness performance prediction device according to the second aspect is a heavyweight, wherein the airtightness performance of the building measured in advance is used as a target variable, and a predetermined factor affecting the airtightness performance at the time of measurement of the airtightness performance is an explanatory variable. A storage unit for storing a regression equation calculated by regression analysis; an extraction unit for extracting the factor from design specification information of a target building; the factor extracted by the extraction unit; and the storage unit stored in the storage unit And a prediction unit that predicts the airtightness performance of the target building based on the regression equation.

  According to the airtightness performance prediction device according to the second aspect, the storage unit uses a previously measured airtightness performance of the building as a target variable, and a predetermined factor affecting the airtightness performance at the time of measuring the airtightness performance. A regression equation calculated by multiple regression analysis as an explanatory variable is stored.

  The extraction unit extracts, from design specification information of a target building such as new construction and renovation, a predetermined factor that affects the airtightness performance of the target building such as new construction and renovation.

  Then, in the prediction unit, the airtightness performance of the target building is predicted based on the factor extracted by the extraction unit and the regression equation stored in the storage unit. That is, the airtightness performance of the building at the design stage can be predicted by inputting the specification of the target building as a factor into the regression equation calculated by the multiple regression analysis. Further, since the factor as the explanatory variable is extracted from the design specification, it is possible to predict the airtightness performance of the building without performing the troublesome operation of inputting the factor.

  The factors are: type of distribution board, air conditioning system, heat insulation specification, floor area, type of switch and outlet, floor covering material installation area, and presence or absence of air tightness of post assembly part, unit bus and housing joint. At least one may be applied.

  In addition, the prediction unit may predict at least one report of the equivalent clearance area of the building and the total equivalent clearance area as the airtightness performance.

  As described above, according to the present invention, 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. The airtightness performance prediction apparatus based on this embodiment WHEREIN: It is a figure which shows the functional block diagram showing the function implement | achieved by execution of various programs. (A) is a figure which shows an example of the objective variable input by an input part at the time of multiple regression analysis by a regression formula calculation part, and an explanatory variable, (B) is an example of the regression calculated by the regression formula calculation part FIG. (A) is a perspective view which shows the opening part of a distribution board and a wall, (B) is sectional drawing which shows the ventilation path of a distribution board. (A) is a figure for demonstrating the adiabatic line of floor warming, (B) is a figure for demonstrating the adiabatic line of base thermal insulation. (A) is a figure for demonstrating ventilation with the floor of floor coverings, such as a tatami mat, (B) is a figure for demonstrating ventilation with the floor of flooring. It is a flowchart which shows an example of the flow of the process performed when a multiple regression analysis program is performed with the personal computer of the airtightness performance prediction apparatus based on this embodiment. It is a flowchart which shows an example of the flow of the process performed when an airtightness prediction program is performed with the personal computer of the airtightness performance prediction apparatus based on this embodiment. It is a block diagram which shows schematic structure of the airtight performance prediction apparatus of a modification. It is a flow chart which shows an example of the flow of processing performed when an airtight prediction program is run with a personal computer of an airtight performance prediction device of a modification.

  Hereinafter, an example of an embodiment of the present invention will be described in detail with reference to the drawings. FIG. 1 is a block diagram showing a schematic configuration of a hermetic performance prediction apparatus according to the present embodiment.

  The airtightness performance prediction apparatus 10 according to the embodiment of the present invention functions as a tool for predicting the airtightness performance of a building when building, remodeling, etc. of a building such as a house. For example, by inputting the specification of the building resulting from the airtightness performance of the building, the airtightness performance of the building is predicted, and a process of displaying the prediction result is performed.

  The airtightness performance prediction device 10 is configured to include 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 a bus 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 the airtightness performance, a multiple regression analysis program for calculating a regression equation for predicting the airtightness performance, and the like. The RAM 18 is used as a working memory or the like for performing various calculations and the like performed by the CPU 14.

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

  The airtightness performance prediction apparatus 10 which concerns on this embodiment has the function of the regression expression calculation part 40, the input part 42, the memory | storage part 44, and the prediction part 46, as shown in FIG.

  The regression equation calculation unit 40 performs airtightness performance by performing multiple regression analysis with the airtightness performance of the building measured in advance as a target variable and a predetermined factor affecting the airtightness performance at the time of measurement of the airtightness performance as an explanatory variable. Calculate the regression equation to be used when predicting.

  The input unit 42 inputs a measurement value of the airtightness performance necessary for the regression equation calculation unit 40 to obtain a regression equation, and a predetermined factor which is an explanatory variable at the time of measurement of the airtightness performance. In addition, when predicting the airtightness performance of the target property using the regression equation obtained by the regression equation calculation unit 40, the input unit 42 inputs the design specification information (factor) of the target property, whereby the building is obtained. Indicate the start of the prediction of air tightness performance. The input unit 42 inputs, for example, a target variable, an explanatory variable, and a specification of a property, using the mouse 26, the keyboard 28, 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 airtightness performance of the building. The storage unit 44 may store the regression equation calculated by the regression equation calculation unit 40 in addition to the various disks 34 and the memory.

  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, multiple regression analysis by the regression equation calculation unit 40 will be described in detail. FIG. 3A is a diagram showing an example of the objective variable and the explanatory variable input by the input unit 42 in the multiple regression analysis by the regression equation calculation unit 40. Moreover, FIG. 3 (B) is a figure which shows an example of the regression calculated by the regression calculation part 40. FIG.

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

  FIG. 3 (B) shows a regression equation based on the factors of the explanatory variables.

  Hereinafter, among the factors of the above-mentioned explanatory variables, the main factors will be described for reasons related to the airtightness performance.

  In addition, the distribution board includes a plurality of types such as a distribution board with a door, a distribution board for solar power generation, a sensor distribution board, an information distribution board for HEMS (Home Energy Management System), etc. There is a power distribution board. As shown in FIGS. 4A and 4B, the distribution board 52 is wired through the opening 54 of the wall, and communicates outside the wall, through the ceiling, through the vent 56, and the like. In addition, since the size of the opening also varies depending on the type of distribution board 52, the type of distribution board 52 greatly affects the airtightness performance of the building.

  In the air conditioning system, the area passing through the duct body differs between the room air conditioner and the whole room air conditioning, so the air tightness performance of the building is greatly affected by the difference in the air conditioning system.

  The insulation specification includes, for example, floor insulation and base insulation. As shown in FIG. 5 (A), the floor insulation makes the underfloor space outside the insulation line and insulates at the floor surface. On the other hand, in base insulation, as shown in FIG. 5B, the underfloor space is inside the insulation line and is insulated at the base portion, has double airtight lines, and the airtight performance greatly affects the airtightness performance.

  Also, since there are types of switch outlets that are provided with an opening in the wall and having an airtight structure on the back side, the type of switch outlet greatly affects the airtight performance of the building.

  Further, as shown in FIG. 6 (A), floor coverings such as tatami mats ventilate under the floor and around the baseboard 60 and ventilate from under the floor and around the under floor joint portion. On the other hand, as shown in FIG. 6 (B), in the case of flooring, the surface is smooth, a gap with the base wood 60 is unlikely to occur, and ventilation from the underfloor joint portion is also maintained. That is, the area of tatami and textile greatly affects the airtightness performance of the building.

  In addition, the air tightness of the building is also greatly affected by the presence or absence of air tightness of the frame by the air tight tape.

  In the present embodiment, as described above, by inputting the objective variable and the explanatory variable described above by the input unit 42, the regression equation calculation unit 40 calculates a regression equation by performing multiple regression analysis. An example of the calculated regression equation is shown in FIG. 3 (B). In the example of FIG. 3 (B), the example which made the object variable the total equivalent clearance area is shown. As an explanatory variable, a factor with a high contribution rate was determined by repeating multiple regression analysis. In the example of FIG. 3 (B), the post-assembly 50, distribution board 52, air conditioning system, heat insulation specification, floor area, switch socket, tatami / textile, UB airtightness, and the presence or absence of airtightness tape are explanatory variables Shows the regression equation of In addition, X1-X17 of FIG. 3 (B) show the constant derived | led-out when calculating regression.

  Subsequently, 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 multiple regression analysis program is executed by the personal computer 12 of the airtightness performance prediction device 10 according to the present embodiment.

  In step 100, the CPU 14 displays on the display 24 a predetermined target variable input screen for inputting a target variable of the multiple regression analysis, and shifts to step 102. That is, a screen for inputting the equivalent clearance area and the total equivalent clearance area measured for each property as an explanatory variable is displayed on the display 24.

  In step 102, the CPU 14 determines whether or not the input unit 42 has input an objective variable. In the present embodiment, it is determined whether or not the measured equivalent clearance area and the total equivalent clearance area of the measured property have been input, and the process waits until the determination is affirmed, and then proceeds to step 104.

  In step 104, the CPU 14 displays on the display 24 a predetermined explanatory variable input screen for inputting an explanatory variable of the multiple regression analysis, and shifts to step 102.

  In step 106, the CPU 14 determines whether the input unit 42 has input an explanatory variable. In the present embodiment, it is determined whether or not a predetermined factor related to the airtightness performance has been input among the specifications of the measured property, and the process waits until the determination is affirmed and shifts to step 108.

  In step 108, the CPU 14 determines whether there is a measurement value of another property. That is, it is determined whether or not all objective variables and explanatory variables of the measured property have been input. Specifically, it is determined whether or not an instruction to input another property has been made by the input unit 42, and if the determination is affirmed, the process returns to step 100 and the target variable and the explanatory variable of the other property Make an input. On the other hand, if the determination is negative, the process proceeds to step 110.

  In step 110, the CPU 14 performs multiple regression analysis by the regression equation calculation unit 40 using the input objective variable and explanatory variable, calculates a regression equation, and shifts to step 112.

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

  Next, a specific process of predicting the airtightness 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 apparatus 10 according to the present embodiment.

  In step 200, the CPU 14 displays a predetermined target variable selection screen on the display 24 and shifts to step 202.

  In step 202, the CPU 14 determines whether or not the target variable has been selected by the input unit 42, and waits for the determination to be affirmative, and then proceeds to step 204. In the present embodiment, either the equivalent clearance area or the total equivalent clearance area is selected.

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

  In step 206, the CPU 14 determines whether or not the specification of the target property has been input by the input unit 42. In this determination, it is determined whether or not a specification corresponding to a predetermined factor relating to the airtightness performance has been input, and the process waits until the determination is affirmed and shifts to step 208.

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

  In step 210, the CPU 14 causes the prediction unit 46 to predict airtightness performance based on the input property specification and the read regression equation, and proceeds to step 212.

  In step 212, the CPU 14 displays the prediction result of the airtightness performance on the display 24 and shifts to step 214.

  In step 214, the CPU 14 determines whether to end the prediction. This determination determines whether the end of the prediction has been instructed by the keyboard 28 or the like, and if the determination is denied, the process returns to step 200 and prediction of airtightness performance is performed again, and a series of places where the determination is affirmed I finish my life.

  As described above, in the present embodiment, the regression equation is calculated by performing multiple regression analysis with the airtightness performance of the building as a target variable and a predetermined factor affecting the airtightness performance at the time of measurement of the airtightness performance as an explanatory variable. The airtightness performance of the building is predicted using the calculated regression equation. As a result, it is possible to indicate the degree of influence on the air tightness performance of the part related to the structure that can not be determined by the actual measurement value.

  Moreover, it also becomes possible to detect a construction failure etc. from the difference of the measured value and prediction value of the airtightness performance of a building.

  Furthermore, by performing the prediction of the airtightness performance at the time of renovation, the improved airtightness performance can be predicted and presented.

  When the total equivalent clearance area is predicted as the airtightness performance of the building, the total equivalent clearance area may be predicted by dividing the floor area by predicting the equivalent clearance area, but similar to the equivalent clearance area, A regression equation for predicting the total equivalent clearance area by multiple regression analysis may be calculated and predicted.

  Then, the modification of the airtight performance prediction device concerning this embodiment is explained. FIG. 9 is a block diagram showing a schematic configuration of the airtightness performance prediction device of the modification.

  The airtightness performance prediction apparatus 11 of the modification is different from the above embodiment in that the network 36 is further connected to the input / output port 20.

  A database (DB) 38 and another personal computer (PC) 39 are connected to the network 36, and can exchange information with the DB 38 and PC 39 connected to the network 36.

  In a modification, the DB 38 stores design specification information of a building such as CAD (Computer Aided Design System) information of a building created by the personal computer 12, 39 or the like in the DB 38. Further, the DB 38 stores, for example, information such as the above-described regression equation, measurement values used when calculating the regression equation, and design specification information of another building.

  Then, in the modification, when the prediction unit 46 predicts the airtightness performance of a building using a regression equation, it extracts a factor serving as an explanatory variable from the CAD information stored in the DB 38, Predict the gap area or total equivalent gap area). That is, the prediction unit 46 of the modification further has an extraction function of extracting a factor serving as an explanatory variable from the CAD information in the above embodiment. In the modification, the specification information is automatically extracted from the CAD information, so that it is possible to save the trouble of inputting the specification. For example, a predetermined factor that affects the airtightness performance of a building is extracted from CAD information, and the calculated airtightness performance of the building is predicted by inputting into a regression equation which has been calculated and stored.

  Next, the specific processing content performed by the airtightness performance prediction apparatus 11 of a modification is demonstrated. The process performed when the multiple regression analysis program is executed is the same as that of the above-described embodiment, and thus the description thereof is omitted. The process 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 apparatus 11 of the modification. The same processes as those of the above embodiment will be described with the same reference numerals.

  In step 200, the CPU 14 displays a predetermined target variable selection screen on the display 24 and shifts to step 202.

  In step 202, the CPU 14 determines whether or not the selection of the objective variable has been performed by the input unit 42, and waits until the determination is affirmed and shifts to step 203. In the modification, as in the above embodiment, either the equivalent clearance area or the total equivalent clearance area is selected.

  In step 203, the CPU 14 determines whether or not the instruction of the predicted property has been given by the input unit 42. This determination is whether or not input of information representing the object to be predicted has been performed, or whether or not an instruction to select a target to be predicted has been performed among CAD information stored in advance in the DB 38 etc. Determine The process waits until the determination is affirmed and proceeds to step 205.

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

  In step 207, the CPU 14 determines whether or not the reception of the design specification information of the requested property has been completed, and waits until the determination is affirmed and shifts to step 208.

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

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

  In step 211, the CPU 14 predicts the airtightness performance by the prediction unit 46 based on the extracted factor and the read regression equation, and proceeds to step 212.

  In step 212, the CPU 14 displays the prediction result of the airtightness performance on the display 24 and shifts to step 214.

  In step 214, the CPU 14 determines whether to end the prediction. This determination determines whether the end of the prediction has been instructed by the keyboard 28 or the like, and if the determination is denied, the process returns to step 200 and prediction of airtightness performance is performed again, and a series of places where the determination is affirmed I finish my life.

  As described above, in the modification example, since the factor to be input in the regression equation is extracted from the CAD information, it is not necessary to perform complicated work such as input of the factor, and prediction of the airtightness performance of the building is easier than the above embodiment. It is possible to do

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

  Moreover, although said example and the modification demonstrated the example which predicts at least one of an equivalent clearance area and a total equivalent clearance area as airtightness performance of a building, it does not restrict to this, and airtightness performance of another building You may predict the value for.

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

  Further, the present invention is not limited to the above, and it goes without saying that the present invention can be variously modified and implemented without departing from the scope of the invention other than the above.

10, 11 airtight performance prediction device 12 personal computer 14 CPU
30 HDD
40 regression equation calculation unit 42 input unit 44 storage unit 46 prediction unit

Claims (4)

A storage unit for storing a regression equation calculated by multiple regression analysis with an airtightness performance of a building measured in advance as a target variable and 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 a target building;
A prediction unit that predicts an airtightness performance of a target building based on the factor input by the input unit and the regression equation stored in the storage unit;
Airtight performance prediction device equipped with. A storage unit for storing a regression equation calculated by multiple regression analysis with an airtightness performance of a building measured in advance as a target variable and a predetermined factor affecting the airtightness performance at the time of measuring the airtightness performance as an explanatory variable;
An extraction unit that extracts the factor from design specification information of a target building;
A prediction unit that predicts an airtightness performance of a target building based on the factor extracted by the extraction unit and the regression equation stored in the storage unit;
Airtight performance prediction device equipped with.   The factors are: type of distribution board, air conditioning system, heat insulation specification, floor area, type of switch and outlet, floor covering material installation area, and presence or absence of airtightness of post assembly, unit bus, and housing joint The airtightness performance prediction device according to claim 1 or 2, which is one. The airtightness performance prediction device according to any one of claims 1 to 3, wherein the prediction unit predicts, as the airtightness performance, at least one report of an equivalent clearance area and a total equivalent clearance area of a building.