JP7264359B1 - Heat loss coefficient estimation system, heat loss coefficient estimation method, and program - Google Patents

Abstract

An object of the present invention is to provide a heat loss coefficient estimation system capable of estimating a Q value reflecting the actual state of a building. [Solution] A simulation that predicts room temperature using measured outside air temperature data, weather data, and other data including information on the shape of the building, etc., while changing the index value (C value) representing airtightness performance Execute to select the C value that provides the best match between the obtained predicted room temperature data and the measured room temperature data, and estimate the Q value based on the C value thus selected. [Selection drawing] Fig. 4

Description

The present invention relates to a heat loss coefficient estimation system for effectively estimating the heat loss coefficient of a building.

In recent years, the stock of existing houses has tended to increase, and evaluation of the thermal environment using indices such as the heat loss coefficient (Q value) has become more important for evaluation after distribution and insulation improvement. is coming.

Here, the Q value is a numerical value that represents the thermal environment of a building, and is an index that considers the thermal insulation performance (U value) and airtight performance (ventilation frequency) of the building. In other words, it is the sum of the ease of heat escape due to heat insulation performance and the ease of heat escape due to airtight performance, and is an index that comprehensively represents the heat insulation performance and airtight performance of a building.

More specifically, the Q value is represented by Equation (1) below.
Q value = (heat loss amount of each part + heat loss amount due to ventilation) / total floor area (1)

And a common way to find the Q factor is to do the calculation based on the building specifications. In addition, as in Patent Document 1, there is also a method in which a heat transfer model is used to estimate the Q value.

Furthermore, Patent Document 2 proposes a method of estimating the Q value by equalizing the room temperature with a heater.

JP 2013-221772 A JP 2010-79580 A

However, when the Q value is obtained by the general method described above, the result of calculation based on the specifications of the building often does not necessarily reflect the actual condition of the house. For example, with regard to insulation performance, we usually input based on the design documents, but in reality, the specifications are different from the design documents due to deterioration over time and poor construction, etc., and the insulation performance is lower than the specifications in the design documents. There may be

Regarding the airtight performance, a fixed ventilation rate (for example, 0.5 times/h or more in a living room of a house) is usually set, but in reality, it depends on the accuracy of construction when building a house. It depends on the area of the gap, etc., and does not have a fixed uniform ventilation frequency. In order to grasp such gaps and the like, it is generally necessary to perform measurement using large-scale equipment.

In addition, the heat transfer model used in the method of estimating the Q value as in the above-mentioned Patent Document 1 simply treats the building frame and room air as one unit for each room, and the room has a total heat capacity and a uniform temperature , the relationship between temperature and heat capacity is approximate, and the estimated Q value may not match the actual situation of the current building.

In addition, in the method of estimating the Q value as in Patent Document 2, the power of the heater that emits thermal energy, the stirring unit that stirs the heat of the heater, the heat control unit that controls the heat of the heater, and the stirring unit is measured and stored. Therefore, it is necessary to introduce a power data storage unit that can store data, which requires large-scale equipment and work.

SUMMARY OF THE INVENTION Accordingly, an object of the present invention is to provide a heat loss coefficient estimation system capable of estimating a Q value that reflects the actual state of a building.

Another object of the present invention is to provide a heat loss coefficient estimating system capable of estimating a Q value that reflects the actual state of a building through simple operations such as temperature measurement and field survey.

The present invention provides the following heat loss coefficient estimation system, heat loss coefficient estimation method, and program.

The invention according to the first embodiment of the present invention has the following configuration.
Measured room temperature data receiving means (for example, measured room temperature a data receiving unit 151);
Using data (e.g., simulation data 52) including an index value (e.g., C value 52a) representing the airtight performance of the room, a simulation is performed to predict the room temperature of the room in the building, and at the plurality of measurement timings a simulation execution means (for example, simulation execution unit 154) that acquires corresponding predicted room temperature data (for example, predicted room temperature data 55);
A simulation execution control means (for example, a simulation execution control unit 155) that changes the index value and controls to repeatedly execute the simulation;
Predicted room temperature data selection means (predicted room temperature data selection unit 156) that compares each of the predicted room temperature data obtained as a result of the repeatedly executed simulation with the measured room temperature data and selects predicted room temperature data with the highest degree of agreement; ,
Heat loss coefficient estimation for estimating the heat loss coefficient of the building based on the optimal value (e.g., optimal C value 56) that is the index value used in the simulation when the selected predicted room temperature data was obtained. A heat loss coefficient estimating system (heat loss coefficient estimating system 1) having means (for example, a heat loss coefficient estimating unit 158).

With such a configuration of the present invention, it is possible to estimate the Q value that reflects the actual condition of the building by simple work such as temperature measurement and field survey, and as a result, it is possible to obtain a highly accurate Q value. .

The invention according to the second embodiment of the present invention has the following configuration in the first embodiment.
The heat loss coefficient estimating means estimates the heat loss coefficient of the building using the ventilation volume calculated based on the optimum value and predetermined data (for example, ventilation volume calculation data (standard conditions) 53b). configured to

With such a configuration of the present invention, the ventilation volume according to the standard conditions is used for estimating the Q value. Intrinsic Q values can be estimated.

The invention according to the third embodiment of the present invention has the following configuration in the second embodiment.
The simulation execution control means executes the simulation using the index value and the ventilation volume calculated based on data different from the predetermined data (for example, ventilation volume calculation data (actual measurement conditions) 53a). configured to control.

With such a configuration of the present invention, room temperature can be predicted more realistically because ventilation is used under conditions based on actual measurement values in prediction of room temperature by simulation.

The invention according to the fourth embodiment of the present invention has the following configuration.
a measured room temperature data receiving step of receiving measured room temperature data obtained by measuring the temperature of a building room at a plurality of measurement timings;
a simulation execution step of performing a simulation for predicting the room temperature of a room of the building using data including an index value representing the airtightness performance of the room, and obtaining predicted room temperature data corresponding to the plurality of measurement timings;
a simulation execution control step of changing the index value and controlling to repeatedly execute the simulation;
a predicted room temperature data selection step of comparing each of the predicted room temperature data obtained as a result of the repeatedly executed simulation with the measured room temperature data and selecting the predicted room temperature data with the highest degree of agreement;
a heat loss coefficient estimation step of estimating the heat loss coefficient of the building based on the optimum value that is the index value used in the simulation when the selected predicted room temperature data was obtained. Method.

With such a configuration of the present invention, it is possible to estimate the Q value that reflects the actual condition of the building by simple work such as temperature measurement and field survey, and as a result, it is possible to obtain a highly accurate Q value. .

The invention according to the fifth embodiment of the present invention has the following configuration.
the computer,
Measured room temperature data receiving means for receiving measured room temperature data obtained by measuring the temperature of a building room at a plurality of measurement timings;
Simulation execution means for executing a simulation for predicting the room temperature of a room in the building using data including an index value representing the airtightness performance of the room, and acquiring predicted room temperature data corresponding to the plurality of measurement timings;
Simulation execution control means for controlling to repeatedly execute the simulation by changing the index value;
Predicted room temperature data selection means for comparing each of the predicted room temperature data obtained as a result of the repeatedly executed simulation with the measured room temperature data and selecting the predicted room temperature data with the highest degree of agreement;
A program operated as heat loss coefficient estimating means for estimating the heat loss coefficient of the building based on the optimum value which is the index value used in the simulation when the selected predicted room temperature data was obtained.

With such a configuration of the present invention, it is possible to estimate the Q value that reflects the actual condition of the building by simple work such as temperature measurement and field survey, and as a result, it is possible to obtain a highly accurate Q value. .

The invention according to the sixth aspect of the present invention has the following configuration in the first aspect.
The heat loss coefficient estimating means is configured to estimate the heat loss coefficient of the building using the ventilation volume calculated based on the optimum value and the data obtained without using the measured room temperature data. be.

With such a configuration of the present invention, the ventilation volume according to the standard conditions is used for estimating the Q value. Intrinsic Q values can be estimated.

The invention according to the seventh aspect of the present invention has the following configuration in the first aspect.
The simulation execution control means is configured to control execution of the simulation using the ventilation volume calculated based on the index value and the parameter obtained using the actually measured room temperature data.

With such a configuration of the present invention, room temperature can be predicted more realistically because ventilation is used under conditions based on actual measurement values in prediction of room temperature by simulation.

The heat loss coefficient estimation system according to the present invention can estimate the Q value reflecting the actual condition of the building. Moreover, as a result, a highly accurate Q value can be obtained.

In addition, the heat loss coefficient estimation system according to the present invention makes it possible to estimate the Q value reflecting the actual condition of the building by simple operations such as temperature measurement and field survey.

BRIEF DESCRIPTION OF THE DRAWINGS It is the schematic for demonstrating the outline|summary of the heat loss coefficient estimation system which concerns on this invention. FIG. 3 is a schematic diagram showing an example of a hardware configuration of a computer that constitutes a user terminal of the heat loss coefficient estimation system according to the present invention; FIG. 4 is a functional block diagram of a user terminal of the heat loss coefficient estimation system according to the present invention; It is a schematic diagram showing the relationship between processing and data in the heat loss coefficient estimation system according to the present invention. It is a figure which shows an example of the equation used by the simulation utilized in the heat-loss-coefficient estimation system which concerns on this invention, and estimation of Q value. 4 is a flowchart for explaining the flow of processing in the heat loss coefficient estimation system according to the present invention; 4 is a graph showing, in chronological order, predicted room temperature data of a room obtained by simulation and actually measured room temperature data obtained by measuring the temperature of the room in the heat loss coefficient estimation system according to the present invention.

First, an outline of a heat loss coefficient estimation system according to one embodiment of the present invention will be described with reference to FIG. Although the heat loss coefficient estimation system 1 according to one embodiment of the present invention is shown here as a stand-alone system using a user terminal 10, for example, a plurality of computers installed at the same site are connected by a network. A variety of configurations can be used, such as a configuration in which multiple computers distributed at different sites are connected via the Internet.

The user terminal 10 shown in FIG. 1 includes actually measured room temperature data 51 obtained by measuring a predetermined room in a building at a plurality of measurement timings, and a simulation room temperature data 51 for predicting the room temperature of the building by executing a simulation. Input data 52, ventilation volume used for simulation, ventilation volume calculation data 53 for calculating ventilation volume for estimating Q value, and output Q value 54 estimated using these data do.

FIG. 2 is a diagram showing the hardware configuration of the user terminal 10 shown in FIG. 1. As shown in FIG. The user terminal 10 includes a CPU (Central Processing Unit) 101, a memory 102, an audio output device 103, a display controller 104, a display 105, an input device interface 106, a keyboard 107, a mouse 108, an external storage device 109, an external recording medium interface 110, and a bus 111 connecting these components together. In addition, the user terminal 10 is configured to include a network interface 120 for connecting to other computers via a network 140 such as the Internet when it is necessary to transmit and receive data to and from other computers. be.

The CPU 101 controls the operation of each component of the user terminal 10 and executes predetermined processing under the control of the OS. In this embodiment, for example, processes such as execution of simulation and estimation of Q value are executed.

The memory 102 is a non-volatile memory, and includes a mask ROM (Read Only Memory), a ROM including flash memory, and a RAM (Random Access Memory), which is a volatile memory. The mask ROM stores programs and the like that are executed when the user terminal 10 is started. The flash memory and RAM temporarily store programs executed by the CPU 101 and data created and used during execution of those programs.

The audio output device 103 is, for example, a device such as a speaker, receives audio data from an application operating under the OS, and outputs audio.

A display controller 104 is a dedicated controller for actually processing drawing commands issued by the CPU 101 . The drawing data processed by the display controller 104 is, for example, once written in a graphic memory and then output to the display 105 . The display 105 is, for example, a display device configured by an LCD (Liquid Crystal Display) or the like. In this embodiment, the display device can display, for example, an estimated Q value.

Input device interface 106 receives a signal input from an input device such as keyboard 107 or mouse 108 and transmits a predetermined command to CPU 101 in accordance with the signal pattern. Note that when the display 105 is configured by a touch panel, the input device interface 106 detects a touch on the display by the user and transmits a detection signal of the touch to the CPU 101 . In this embodiment, by operating this input device, the user can input, for example, measured room temperature data 51, simulation data 52, ventilation volume calculation data 53, etc. These data will be described later. It is stored in the external storage device 109 or the like.

The external storage device 109 is, for example, a storage device such as a hard disk drive (HDD), and the above-described programs and data according to the present invention are recorded in this device. flash memory or RAM.

The external recording medium interface 110 accesses the portable external recording medium 130 , reads data recorded therein, and transfers the read data to the external storage device 109 or memory 102 . The external recording medium interface 110 includes, for example, a driving device that accesses the recording surface of a CD (Compact Disc) or a DVD (Digital Versatile Disc), or accesses data stored in a USB memory or a device connected via a USB cable. A USB interface is included. In this embodiment, for example, measured room temperature data 51, simulation data 52, and ventilation volume calculation data 53 can be input via a portable external recording medium 130 such as a CD or USB memory. These data are stored in the external storage device 109 or the like.

Network interface 120 connects to network 140 and controls data transmission and reception via network 140 .

A program for realizing each function according to the present invention can be recorded in the external recording medium 130 . Data recorded in the external recording medium 130 is read through the external recording medium interface 110 and then stored in the external storage device 109. If it is a program, it is loaded into the RAM of the memory 102 when executed.

Also, a program for realizing each function according to the present invention may be provided from another computer via the network interface 120 described above and a network such as the Internet.

FIG. 3 is a functional block diagram representing functions performed by the user terminal 10. As shown in FIG. The user terminal 10 includes a measured room temperature data reception unit 151, a simulation data reception unit 152, a ventilation volume calculation data reception unit 153, a simulation execution unit 154, a simulation execution control unit 155, a predicted room temperature data selection unit 156, and a ventilation volume calculation unit. 157 , a heat loss coefficient estimation unit 158 , a display control unit 159 and an input control unit 160 . Also, when the user terminal 10 connects to the network 140 via the network interface 120 shown in FIG. include.

The user terminal 10 also stores measured room temperature data 51, simulation data 52, ventilation volume calculation data 53, and predicted room temperature data 55 in the storage device 170 (corresponding to the external storage device 109 in FIG. 2).

Here, under the control of the input control unit 160, the measured room temperature data receiving unit 151 receives the measured room temperature data input by the user using the keyboard or mouse, and stores it in the storage device 170 as the measured room temperature data 51. do. Also, the actually measured room temperature data may be received from the network 140 via the network interface unit, or may be received from an external recording medium 130 such as a USB memory. The actually measured room temperature data 51 is a group of data obtained as a result of measuring the room temperature at a plurality of measurement timings for a room in the building to be diagnosed.

Under the control of the input control unit 160, the simulation data receiving unit 152 receives simulation data input by the user using the keyboard 107 or the mouse 108, and stores the data in the storage device 170 as the simulation data 52. . Also, the simulation data may be received from the network 140 via the network interface unit, or may be received from an external recording medium 130 such as a USB memory. Among the data for simulation, the weather data acquired from the Meteorological Agency is subjected to conversion processing so that it can be used for simulation by a weather data conversion unit included in the data reception unit for simulation 152 as necessary. .

The ventilation volume calculation data receiving unit 153 receives the ventilation volume calculation data input by the user using the keyboard 107 or the mouse 108 under the control of the input control unit 160, and stores the ventilation volume calculation data in the storage device 170. stored as data 53 for use. In addition, the ventilation volume calculation data may be received from the network 140 via the network interface unit, or may be received from an external recording medium 130 such as a USB memory.

The simulation execution unit 154 predicts the room temperature of the building based on the simulation data 52 specified by the simulation execution control unit 155 and stores the result in the storage device 170 as predicted room temperature data 55 . The predicted room temperature data 55 is a data group obtained as a result of predicting the room temperature of each room in the building to be diagnosed at timings corresponding to the measurement timings of the actually measured room temperature data 51 .

The simulation execution control unit 155 selects a plurality of C values 52a (an index value representing the airtight performance, equivalent opening area [cm ² /m ² ] per total floor area) from the data stored as the simulation data 52. ) is taken out, and the simulation is repeatedly executed for each C value 52a. As a result of this simulation, the storage device 170 stores predicted room temperature data 55 corresponding to each C value 52a.

After the simulation for each C value 52a is executed, the predicted room temperature data selector 156 selects predicted room temperature data 55 and measured room temperature data 51 corresponding to each C value 52a stored in the storage device 170 at each timing. to determine the optimum C value 56 corresponding to the predicted room temperature data 55 that is closest (highly matched) to the measured room temperature data 51 .

When the simulation execution control unit 155 passes the simulation data 52 to the simulation execution unit 154 and instructs execution, the ventilation volume calculation unit 157 calculates the corresponding ventilation volume based on the C value 52a set at the time of execution. Calculate Measured conditions based on the actually measured environmental conditions are used to calculate the ventilation volume at this time. Further, when the optimum C value 56 is determined by the predicted room temperature data selection unit 156, the ventilation volume calculation unit 157 calculates the corresponding ventilation volume based on the determined optimum C value 56. Standard conditions based on standard/average environmental conditions are used to calculate the ventilation volume at this time.

Based on the optimum C value 56 determined by the predicted room temperature data selection unit 156 (that is, based on the ventilation volume calculated using the standard conditions in the ventilation volume calculation unit 157), the heat loss coefficient estimation unit 158 A heat loss coefficient (Q value) is estimated and output (for example, displayed on the display 105).

The display control unit 159 displays an input screen for inputting the measured room temperature data 51, the simulation data 52, and the ventilation volume calculation data 53 on the display 105, and displays the estimated Q value on the display 105. to control.

The input control unit 160 controls input according to the operation of the keyboard 107 and the mouse 108 on the screen displayed on the display 105 of the user terminal 10 .

Next, with reference to FIGS. 4 and 5A, processing in the heat loss coefficient estimation system 1 according to the present invention will be described together with data input/output relationships.

[Measurement of temperature inside and outside the building]
In this embodiment, actually measured room temperature data 51 obtained by measuring the room temperature of a room in a building is received, and further, actually measured outside temperature data 52b obtained by measuring the outside temperature outside the building is received.

In this embodiment, when determining the optimum airtightness performance (optimal C value 56) by comparing the above-mentioned measured room temperature data 51 and the predicted room temperature data 55 obtained as a result of the simulation, the determination is performed with high accuracy. The following measurement conditions (1) and (2) are set so as to be carried out.

Measurement conditions (1)
The measurement conditions that greatly affect the airtight performance are set as follows.
・Airtight performance, that is, the effect on room temperature due to the difference in ventilation volume increases depending on the temperature difference between inside and outside the building. To make it easy to observe the change in temperature due to the influence of the airtightness performance by setting a state in which the is large.
・Specifically, for example, in winter, the room temperature is kept constant by sufficiently heating the room at night, then the heating is stopped, and the process of the room temperature decreasing until dawn is measured. At this time, the measurement timing can be set at various intervals such as every hour or every 10 minutes.
・For heating, use a wall-mounted air conditioner or a fan heater to heat the indoor air.

Measurement conditions (2)
As described below, factors other than airtightness should be excluded as possible as factors for changes in room temperature.
・Because the room temperature fluctuates greatly due to solar radiation, measurements are taken at night after sunset so as to exclude the effects of solar radiation.
・In order to limit heat transfer between the heating room and adjacent rooms or corridors, the door of the heating room should be closed during measurement.

In the present embodiment, the simulation is performed using a model in which the temperature of the indoor air is uniform, and temperature unevenness in the room is not taken into consideration. For this purpose, for example, to measure the average room temperature, measure the room temperature near the center of the height of the room, or measure the room temperature at multiple points and take the average temperature of those room temperatures. .

[Estimation of airtight performance using measured room temperature data]
In this embodiment, simulation data 52 shown in FIG. 4 (that is, C value 52a, which is an index value representing the airtightness of the building, measured outside temperature data 52b, weather data 52c, and various conditions of the building where the actual measurement was performed are A room temperature simulation is performed by setting other parameters 52d) including the room temperature, and the predicted room temperature data 55 obtained as a result of the simulation and the actually measured room temperature data 51 are compared. At this time, the C value 52a is set as a variable parameter, and, for example, the simulation is repeated while changing the numerical value with a constant variation width.

Each simulation uses a calculated ventilation volume based on the corresponding C value 52a. As a result of performing the above simulation using a plurality of C values 52a, a plurality of predicted room temperature data 55 are obtained. The C value when (the degree of matching is high) is obtained as the optimum C value 56 .

[Simulation specifications and simulation data]
In this embodiment, an unsteady dynamic heat load calculation program is used as a simulation for predicting the room temperature. This program treats a building with multiple rooms, considers the heat capacity of each room and each part, and can calculate the room temperature of each room by considering the heat in and out due to various factors. The computational model for this simulation is common in architectural environmental engineering. In this example, as described above, the simulation is performed using the known unsteady dynamic heat load calculation program, but the simulation may be performed using other calculation models and programs.

In the calculation model of the simulation in this embodiment, the relationship of the heat balance at room temperature is roughly represented by Equation (2) shown in FIG. 5(A). That is, the heat capacity multiplied by the change in room temperature during time delta t equals the sum of convection from indoor surfaces, ventilation, internal heat generation, and heating and cooling. "Heat capacity" is the heat capacity of indoor air and household goods. Here, standard values for household goods are set in three stages of "large", "medium", and "small", and one of them can be selected. shall be "Convection from indoor surfaces" is the calculated and summed heat input due to convection from indoor surfaces for each indoor surface.

"Ventilation" means the calculated and totaled amount of heat that has flowed in from each air inflow path for each of the air inflow paths. The value obtained using ventilation volume. Here, 24-hour ventilation by mechanical ventilation is not used, and natural ventilation by gaps is assumed. In addition, it is assumed that the airtight performance is the same throughout the building.

“Internal heat generation” is the convective component of indoor heat generation, such as the human body, lighting, and appliances, which can be dynamically set. Also, if hearing is possible, it is also possible to set heat generation according to a schedule based on the results of that hearing. The “heating/cooling” is, for example, sensible heat load due to heating such as air conditioners and fan heaters.

In addition, the calculation model for the simulation in this embodiment includes a model that represents the heat balance on the indoor surface. It is the sum of the "heat flow due to radiation". The "heat flow due to convection" is obtained by multiplying the difference between the indoor surface temperature and the room temperature by the convective heat transfer coefficient of the indoor surface.

Furthermore, the calculation model for the simulation in this embodiment includes a model representing the heat balance on the outdoor surface. and "heat flow due to solar radiation" are added, and "heat flow due to nighttime radiation" is subtracted. The "heat flow due to convection/radiation" is obtained based on the outside air temperature, but in this embodiment, the actually measured outside air temperature data 52b is used. In addition, the "heat flow due to solar radiation" is obtained based on the "incident solar radiation amount on the outdoor surface", and this "incident solar radiation amount on the outdoor surface" is obtained from the weather data 52c, etc. It is obtained from each amount of solar radiation of "solar radiation" and "reflected solar radiation".

The above-mentioned "heat flow from the outdoor surface to the inside of the wall" affects the room temperature due to the heat transfer of the wall. It can be grasped by the heat conduction equation.

In addition, the calculation model for the simulation in this embodiment includes a model representing the heat balance of the window. "Heat gain by transmitted solar radiation" is added. The "heat transfer amount" is obtained based on the difference between the ambient temperature outside the window and the room temperature, but the measured outside air temperature data 52b can be used as the ambient temperature outside the window. Also, the "absorbed solar radiation heat gain" and the "heat gain by transmitted solar radiation" are obtained based on the amount of incident solar radiation on the outdoor surface, the shielding of the window, and the like.

In the present embodiment, the C value 52a and the actually measured outside air temperature data 52b are used when performing the simulation as described above, but the simulation is performed using other simulation data 52. FIG.

For example, the weather data 52c regarding the location of the building to be diagnosed is used. The weather data 52c is, for example, data such as the amount of solar radiation and the wind speed and direction. However, in this embodiment, in order to use these data in the simulation, predetermined weather data conversion processing is executed as necessary.

The other parameters 52d of the simulation data 52 are, for example, the floor plan, shape, dimensions, orientation of the building to be diagnosed, and the surrounding environment of the site (solar obstacles such as neighboring buildings, etc.). These pieces of information are input to the heat loss coefficient estimation system 1 based on design documents, for example. In addition, it is possible to input the actual situation or information closer to the actual situation into the heat loss coefficient estimation system 1 by conducting interviews or the like.

As other parameters 52d, the specifications of the part of the building to be diagnosed (material thickness, thermal physical property values (thermal conductivity, volume ratio, solar reflectance, emissivity), etc.) and opening specifications are specified in the design document. based on the heat loss coefficient estimation system 1. Moreover, it is also possible to input more accurate information to the heat loss coefficient estimation system 1 by conducting a field survey or the like.

[Weather data conversion processing]
As described above, the weather data 52c of the simulation data 52 shown in FIG. 4 undergoes a predetermined weather data conversion process for use in the simulation.

The simulation using the non-stationary dynamic heat load calculation program in this embodiment requires outside air temperature, nighttime radiation, direct solar radiation, sky solar radiation, and wind direction/speed as weather data. Here, as the outside air temperature, the actually measured outside air temperature (measured outside air temperature data 52b shown in FIG. 4) is used.

The nighttime radiation, direct solar radiation, and sky solar radiation are calculated based on the measurement data of the day at the meteorological observation points near the building published by the Japan Meteorological Agency. For example, the night radiation amount is estimated from the outside air temperature (measured outside air temperature data 52b) and the hours of sunshine, relative humidity, and rainfall in the weather data 52c.

In addition, the amount of direct solar radiation and the amount of solar radiation in the sky are separated from the total solar radiation estimated from the outside temperature (measured outside temperature data 52b) and the sunshine hours, relative humidity, and precipitation amount of the weather data 52c. Calculated by doing.

Of the simulation data 52, the data converted from the measured outside air temperature data 52b and the weather data 52c as described above are time-series data that change over time. The result of the simulation (predicted room temperature data 55) executed by inputting these time-series data as parameters also changes with the passage of time.

[Calculation of ventilation volume]
In the present embodiment, two methods are used to convert an index (C value) representing airtightness into ventilation volume or ventilation frequency.

The first method is to use the ventilation volume calculation data (actual measurement conditions) 53a when calculating the ventilation volume from the C value 52a shown in FIG.

In past research, there is a method for simply estimating the ventilation volume due to natural ventilation according to the C value. The amount is obtained by multiplying the sum of the component proportional to the temperature difference between the inside and outside of the building and the component proportional to the square of the outside wind speed, and the C value.
Ventilation [m ³ /h] = C value x (coefficient 1 x temperature difference between inside and outside + coefficient 2 x wind pressure coefficient difference x outside wind speed ² )
... (3)

Here, the ventilation amount calculation data (actual measurement conditions) 53a is a part of "coefficient 1×inside/outside temperature difference+coefficient 2×wind pressure coefficient difference×external wind speed ² ". The temperature difference between inside and outside is obtained as the temperature difference by referring to the measured room temperature data 51 and the measured outside temperature data 52b. etc.) can be obtained from the wind speed, and the external wind speed can be obtained, for example, from the wind speed of the weather data 52c (see the dotted arrow in FIG. 4). The wind direction and wind speed of the weather data 52c are data measured on the day at a weather observation point near the construction site published by the Meteorological Agency.

In this way, one set C value 52a is used in one simulation, but the ventilation volume obtained based on the C value 52a and input to the simulation is determined according to the temperature difference between inside and outside. , will change from time to time.

The second method is to use data for calculating the ventilation volume (standard conditions) 53b when calculating the ventilation volume from the optimum C value 56 shown in FIG.

When calculating the ventilation volume from the optimum C value 56, it is possible to use the same method as the first method described above, but the ventilation volume obtained in this way is the inside and outside at the time the room temperature was measured. It is a value that fluctuates under the influence of temperature difference, wind direction and wind speed, and does not necessarily represent the unique performance of the building. Therefore, here, the ventilation volume is calculated as shown by the following formula (4).
Ventilation [m ³ /h] = Optimal C value x (Coefficient 1 x Standard temperature difference between inside and outside + Coefficient 2 x Standard wind pressure coefficient difference x Standard outside wind speed ² )
... (4)

Here, the ventilation amount calculation data (standard conditions) 53b is a part of "coefficient 1*standard temperature difference between inside and outside+coefficient 2*standard wind pressure coefficient difference*standard outside wind speed ² ". At this time, it is conceivable to set the standard inside/outside temperature difference by, for example, two methods. The first method is condition setting centered on winter. That is, the room temperature is assumed to be 20° C., and the outside temperature is assumed to be the average temperature in January calculated based on the measurement data at the meteorological observation points near the building published by the Meteorological Agency.

The second method is condition setting for evaluation throughout the year. In other words, the room temperature is set to 24°C (average value when winter is set to 20°C and summer is set to 28°C), and the outside temperature is calculated based on the measurement data at the meteorological observation point near the building published by the Japan Meteorological Agency. is the annual average temperature.

The standard wind pressure coefficient difference is the wind pressure coefficient difference under standard conditions. Here, the wind pressure coefficient difference is obtained from the environment around the building (for example, whether it is an urban area, whether it is open, etc.) and the wind speed, and when obtaining the standard wind pressure coefficient difference, for example, the annual average wind speed is used as the wind speed. Also, the standard external wind speed is the annual average wind speed ascertained from the weather data 52c.

In this way, by setting the standard temperature difference between inside and outside, the standard wind pressure coefficient difference, and the standard outside wind speed as standard conditions for evaluating the airtightness performance of a building, the influence on the environment at the time of measurement of room temperature and outside temperature can be minimized. The ventilation not received can be calculated. Note that the ventilation volume calculation data (standard conditions) 53b can be set according to various criteria other than the above-described first method and second method.

The ventilation rate thus calculated is then divided by the air volume (indoor capacity) to obtain the ventilation rate, and the Q value 54 is estimated using the ventilation rate thus determined.

Next, the flow of processing in the heat loss coefficient estimation system 1 according to the present invention will be described in more detail with reference to FIGS. 5(B), 6, and 7. FIG.

FIG. 6 is a flowchart for explaining the flow of processing in the heat loss coefficient estimation system 1 according to the present invention, and FIG. 4 is a graph showing predicted room temperature data and actually measured room temperature data obtained by measuring the room temperature.

Based on the user's instruction on the user terminal 10, the process shown in the flowchart of FIG. 6 is started. First, in step S01, under the control of the simulation execution control unit 155, it is determined whether or not the simulation for all the C values 52a set as the simulation data 52 has been completed. In the present embodiment, as a candidate for the C value 52a, for example, a plurality of values, including a representative value known empirically, are set by sliding up and down at regular intervals, or a plurality of representative values are set. A plurality of C values 52a can be set by various methods such as individually setting the value of .

If it is determined that the simulation has been completed for all C values 52a (YES in step S01), the process proceeds to step S06. On the other hand, if it is determined that the simulation has not been completed for all the C values 52a (NO in step S01), one of the unused C values 52a is selected in step S02, and the simulation is performed with that C value 52a. Execute.

Next, in step S03, the ventilation volume calculation unit 157 calculates the ventilation volume based on the set C value 52a and the ventilation volume calculation data (actual measurement conditions) 53a. The method of calculating the ventilation volume based on the C value 52a and the ventilation volume calculation data (actual measurement conditions) 53a is as described above.

Next, in step S04, the simulation execution control unit 155 executes a simulation using the ventilation amount calculated in step S03, the measured outside air temperature data 52b, the weather data 52c, and other parameters 52d. The instruction is given to the unit 154 .

Next, in step S05, the simulation execution control unit 155 stores the predicted room temperature data obtained as a result of the simulation as the predicted room temperature data 55 in the storage device 170 in association with the C value 52a. The predicted room temperature obtained by the simulation changes with time as the input ventilation volume, the measured outside air temperature data 52b, and the weather data 52c change over time. In this embodiment, for example, by providing an input value that changes hourly, hourly predicted room temperature data 55 corresponding to the input value is acquired for a certain period of time.

After step S05, the process returns to step S01, and under the control of the simulation execution control unit 155, it is repeatedly determined whether or not the simulation has been completed for all the C values 52a.

In step S06, the predicted room temperature data selector 156 compares the predicted room temperature data 55 stored in the storage device 170 and the actually measured room temperature data 51 with temperatures at corresponding timings. Since the predicted room temperature data 55 are stored for each C value 52a, this comparison process is performed for each predicted room temperature data 55. FIG. Then, as a result of the comparison processing, the predicted room temperature data 55 that most closely matches the measured room temperature data 51 is selected, and the C value 52 a corresponding to the predicted room temperature data 55 is determined as the optimum C value 56 .

Here, as a criterion for selecting the optimum predicted room temperature data 55, for example, the predicted room temperature data 55 and the actually measured room temperature data 51 are compared at corresponding temperatures, and the one with the smallest total squared temperature difference is selected. , is selected as the predicted room temperature data 55 with the highest degree of agreement. Of course, various other criteria can be employed for the degree of matching evaluation.

Here, referring to FIG. 7, predicted room temperature data 55 of the room obtained by simulation and actual room temperature data 51 obtained by measuring the temperature of the room are shown in a graph. The solid polygonal line is the measured room temperature, and the dotted polygonal line is the predicted room temperature. In the present embodiment, for example, the period during which the predicted room temperature data 55 is acquired is (in winter) from the time when the heating is stopped at night (represented by t0 in FIG. 7), when the sun rises at dawn and the temperature rises. until the time (indicated by t12 in FIG. 7) before the start of the operation.

During the predicted room temperature acquisition period (t0 to t12), the predicted room temperature data 55 and the actually measured room temperature data 51 are compared. In FIG. 7, the temperature difference is indicated by double arrows. In the comparison process described above, the square of the difference between the predicted room temperature data 55 and the measured room temperature data 51 is totaled at each timing, and such total calculation is repeated by the set number of C values 52a. When the total is calculated for the C value 52a of , the C value 52a corresponding to the predicted room temperature data 55 with the smallest total value is set as the optimum C value 56.

Returning to the flowchart of FIG. 6, in step S07, the ventilation volume calculation unit 157 calculates the ventilation volume based on the optimum C value 56 and the ventilation volume calculation data (standard conditions) 53b. The method of calculating the ventilation volume based on the optimum C value 56 and the ventilation volume calculation data (standard conditions) 53b is as described above.

Next, in step S08, the heat loss coefficient estimator 158 estimates the heat loss coefficient (Q value) based on the calculated ventilation volume. The Q value is an index of the thermal environment that takes into account both heat insulation performance and airtightness performance, and is estimated by the above-described formula (1). Presumed. The Q value indicates the amount of heat (in units of [W]) that escapes per ^square meter of floor space from the entire building when the temperature difference between indoors and outdoors is 1°C.

In equation (5), the Q value is obtained by dividing the amount of heat that escapes from the building (unit: [W/K]) by the total floor area (unit: [m ² ]).

Here, the amount of heat escaping from the building is the sum of the amount of heat loss in each part of the building and the amount of heat loss due to ventilation. As shown in FIG. 5B, the heat loss amount of each part of the building is obtained by multiplying the area of the part, the U value, and the temperature difference coefficient, opening, etc.).

The area of each part can be obtained, for example, from the shape and dimensions of the building included in the other parameters 52d, and the U value (heat transmission coefficient) is expressed as 1/thermal resistance value, and this thermal resistance value is It can be obtained from the specifications (thickness and thermal conductivity) of the part included in the parameter 52d. Also, the temperature difference coefficient is a coefficient determined by the type of outside air with which the part is in contact.

Heat loss due to ventilation is a fixed value of 0.35 multiplied by the air change rate and air volume. Here, the fixed value 0.35 is a coefficient related to specific heat. The ventilation frequency is obtained by dividing the ventilation volume obtained in step S07 of FIG. 6 by the air volume based on the optimum C value 56 determined by the present invention. The air volume is the total amount of air in the room, and can be obtained from the shape and dimensions of the building included in the other parameters 52d.

By estimating the Q value using the ventilation rate (ventilation frequency) that matches the actual situation, an index of thermal performance that reflects the actual situation of the building (insulation performance, airtightness performance, and actual living style) can be obtained. will be

After that, in step S09, the estimated heat loss coefficient (Q value) is displayed on a display device such as the display 105 under the control of the display control unit 159, or is displayed on the storage device under the control of the heat loss coefficient estimation unit 158. 170.

In this embodiment, as shown in FIG. 6, the optimum C value 56 is determined after the simulation for all the prepared C values 52a is completed. In the stage, the predicted room temperature data 55 and the measured room temperature data 51 are compared, and when the result of the comparison process falls within a predetermined range (a range of values considered to have a sufficiently high degree of agreement), subsequent simulations are performed. It may be configured to stop and determine the C value 52 a corresponding to the predicted room temperature data 55 as the optimum C value 56 .

Further, in the present embodiment, basically, the configuration is such that actually measured room temperature data 51 is obtained for one room (heating room) of the building, and the actually measured room temperature data 51 is compared with predicted room temperature data 55 obtained by simulation. It is assumed that the measured room temperature data 51 is obtained for a plurality of rooms in the building, the predicted room temperature data 55 obtained by the simulation performed for each room is compared with the measured room temperature data 51 of the corresponding room, and each room It is also possible to determine the optimum C value 56 by calculating and evaluating the degree of matching in .

So far, the heat loss coefficient estimation system according to the present invention has been described with reference to the drawings, but these are only examples, and the technical idea of the present invention can be realized by various other configurations.

1... Heat loss coefficient estimation system 10... User terminal

Claims (5)

a measured room temperature data receiving means for receiving measured room temperature data obtained by measuring the temperature of a building room at a plurality of measurement timings;
a simulation execution means for executing a simulation for predicting the room temperature of a room in the building using data including an index value representing the airtightness performance of the room, and obtaining predicted room temperature data corresponding to the plurality of measurement timings;
simulation execution control means for controlling to repeatedly execute the simulation by changing the index value;
Predicted room temperature data selection means for comparing each of the predicted room temperature data obtained as a result of the repeatedly executed simulation with the measured room temperature data and selecting the predicted room temperature data with the highest degree of agreement;
heat loss coefficient estimating means for estimating the heat loss coefficient of the building based on the optimum value, which is the index value used in the simulation when the selected predicted room temperature data was obtained. system. The heat loss coefficient according to claim 1, wherein the heat loss coefficient estimating means estimates the heat loss coefficient of the building using the ventilation volume calculated based on the optimum value and predetermined data. estimation system. 3. The simulation execution control means according to claim 2, wherein said simulation execution control means performs control to execute said simulation using said index value and a ventilation volume calculated based on data different from said predetermined data. Heat loss coefficient estimation system. a measured room temperature data receiving step of receiving measured room temperature data obtained by measuring the temperature of a building room at a plurality of measurement timings;
a simulation execution step of performing a simulation for predicting the room temperature of a room of the building using data including an index value representing the airtightness performance of the room, and obtaining predicted room temperature data corresponding to the plurality of measurement timings;
a simulation execution control step of changing the index value and controlling to repeatedly execute the simulation;
a predicted room temperature data selection step of comparing each of the predicted room temperature data obtained as a result of the repeatedly executed simulation with the measured room temperature data and selecting the predicted room temperature data with the highest degree of agreement;
a heat loss coefficient estimation step of estimating the heat loss coefficient of the building based on the optimum value that is the index value used in the simulation when the selected predicted room temperature data was obtained. Method. the computer,
Measured room temperature data receiving means for receiving measured room temperature data obtained by measuring the temperature of a building room at a plurality of measurement timings;
Simulation execution means for executing a simulation for predicting the room temperature of a room in the building using data including an index value representing the airtightness performance of the room, and acquiring predicted room temperature data corresponding to the plurality of measurement timings;
Simulation execution control means for controlling to repeatedly execute the simulation by changing the index value;
Predicted room temperature data selection means for comparing each of the predicted room temperature data obtained as a result of the repeatedly executed simulation with the measured room temperature data and selecting the predicted room temperature data with the highest degree of agreement;
A program operated as heat loss coefficient estimating means for estimating the heat loss coefficient of the building based on the optimum value which is the index value used in the simulation when the selected predicted room temperature data was obtained.

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