JP4693689B2 - Comfort evaluation method - Google Patents

Description

  The present invention relates to a comfort evaluation method for evaluating the indoor comfort of buildings such as office buildings and residential buildings.

  In general, a thermal environment evaluation index PMV (Predicted Mean Vote) in which a thermal load of a human body is combined with a thermal sensation of a human is known as an index indicating comfort in a room of a building. This PMV is determined by a total of six factors including indoor air temperature, humidity, average radiation temperature, airflow velocity, activity amount such as a person sitting or standing, and clothing amount.

Conventionally, it has been proposed to predict and evaluate indoor comfort using these elements, and to calculate and control a set temperature of an air conditioner (see, for example, Patent Document 1). According to this, by calculating and controlling the set temperature of the air conditioner based on the predicted indoor comfort, it is possible to ensure comfort in the room and to save energy.
JP 2002-213895 A

  By the way, since the heat insulating property of the window provided in the building is lower than that of the wall and the floor, especially in winter, even if the window is closed, cold air flows into the room through the window and flows on the floor. In winter, cold air that has flowed into the room from, for example, a ventilation opening provided in a building tends to accumulate on the floor. For this reason, in winter, the temperature of the feet of a human being in the room is lower than the actually measured room temperature, resulting in a decrease in comfort due to a decrease in the skin temperature of the feet.

  In the conventional comfort evaluation method, when evaluating the comfort in the room, the indoor environment in winter in which a temperature difference occurs in the room or a flow of cool air occurs in the feet is not considered. For this reason, the indoor comfort in winter cannot be accurately evaluated.

  Accordingly, an object of the present invention is to provide a comfort evaluation method capable of accurately evaluating the indoor comfort in winter.

In order to solve the above-mentioned problem, the invention according to claim 1 is characterized in that a reported value indicating comfort in the room obtained from a subject by an indoor experiment, a temperature in the room, an average radiation temperature, Using the values of each of the three environmental elements of the floor temperature, the coefficient of each environmental element of the linear model represented by the comfort variable as the dependent variable and each environmental element as the independent variable is obtained by the method of multiple regression analysis. In the comfort evaluation method for calculating and calculating a value indicating the comfort by an arithmetic expression represented by the linear model using each of the coefficients obtained by the calculation, the three environmental elements include the room By adding the floor temperature and the floor wind speed, there are five environmental elements, and the floor temperature and floor wind speed of the room are the room temperature and the wind speed at the foot position of the subject.

  According to the first aspect of the present invention, the value indicating the comfort in the room is calculated using an arithmetic expression in which each element of the room floor temperature and the floor wind speed is included as an independent variable. By substituting the values of the indoor temperature, average radiation temperature, room floor temperature, room floor temperature, and floor wind speed, which are the objects of comfort evaluation, into the room, considering the floor temperature and wind speed respectively. A value indicating comfort can be determined. As a result, when the cold air flowing into the room through windows and ventilation openings accumulates on the floor or flows on the floor as in winter, the temperature of human feet in the room is lower than the actually measured room temperature. However, it is possible to accurately evaluate the comfort in the room considering that.

  The present invention will be described with reference to the illustrated embodiments.

  FIG. 1 shows an example in which the present invention is applied to evaluate comfort in a room 11 provided with a window 10.

  In the comfort evaluation method according to the present invention, the reported value indicating comfort obtained from the subject 13 through the experiment in the room 11, the temperature in the room 11, the average radiation temperature, the temperature of the floor 14 in the room 11, and the floor A total of five environmental elements, temperature and wind speed on the floor, are used as elements for evaluating comfort in the room 10, respectively.

  The reported value from the subject 13 is obtained by conducting the following experiment.

As shown in FIG. 1, the subject 13 seated on the chair 15 arranged in the room 11 is notified of the comfort felt when the room temperature, the average radiation temperature, the floor temperature, the floor temperature, and the wind speed on the floor are changed. . In the illustrated example, the room temperature is changed within a range of 20 to 25 ° C., the average radiation temperature is changed within a range of 19 ° C. to 24 ° C., the floor temperature is changed within a range of 12 to 22 ° C. The experiment is performed under a total of 32 patterns of environmental conditions. Further, the room temperature is measured at a height position of 110 cm from the floor 14, the floor temperature is measured at a height position of 10 cm from the floor 14, and the wind speed on the floor is measured at a height position of 10 cm from the floor 14. That is, the floor temperature is the room temperature at the foot of the subject 13 sitting on the chair 15, and the wind speed on the floor is the wind speed at the foot of the subject 13.

  When the comfort in the room 11 is reported, as shown in FIG. 2, the comfort is divided into seven stages, and a numerical value indicating each comfort is selected. For example, “+3” is selected when the user feels very comfortable, and “−1” is selected when the user feels uncomfortable. In this embodiment, the average value of the reported values obtained from 5 to 6 subjects 13 in each environmental condition pattern is obtained, and this average value (hereinafter referred to as an actual measurement value) is the comfort level in the room 10. Used as an element for evaluation.

  Further, in the comfort evaluation method according to the present invention, a comfort prediction formula for predicting comfort is derived using the actual measurement value in each environmental condition pattern and each environmental element.

  The comfort prediction formula is obtained by multiple regression analysis. As is well known in the art, the multiple regression analysis is a method for analyzing or learning a certain variable by expressing it as a linear combination of a plurality of variables that are considered as factors determining the variable. In general, if y is the dependent variable that results, and x1, x2,..., Xr are the independent variables considered to be the cause, the linear model for predicting and learning y from x1, x2,. It is expressed as follows.

y = a0 + a1 · x1 + a2 · x2 + ·· + ar · xr
In the above equation, a0, a1, a2,... Ar are the coefficients of each independent variable.

Therefore, room temperature, average radiation temperature, floor temperature, floor temperature and floor wind speed are independent variables, and room temperature: Ta (° C), average radiation temperature: Tr (° C), floor temperature: Tf (° C), floor temperature: Assuming that Ta ₁₀₀ (° C.) and wind speed on the floor: V ₁₀₀ (m / s) and the dependent variable is comfort, a linear model that predicts and learns comfort is expressed by the following equation. In the illustrated example, since the subject 13 is seated as described above, the activity amount of the subject 13 is almost zero and does not contribute to the comfort evaluation. Therefore, the activity amount of the subject 13 is set as an independent variable. Do not handle.

(Comfort) = a0 + a1 · Ta + a2 · Tr + a3 · Tf + a4 · Ta 100 + a5 · V 100
... (Formula 1)
The measured values in each environmental condition pattern are substituted into (Comfort) of Equation 1, and the values of the environmental elements when each measured value is obtained are Ta, Tr, Tf in Equation 1 for each environmental condition pattern. , Ta ₁₀₀ and V ₁₀₀ are substituted respectively to obtain five equations and solve simultaneous equations using these equations. Thus, the values of the coefficients a0 to a5 are obtained as a0 = -10.957, a1 = 0.149, a2 = 0.187, a3 = 0.030, a4 = 0.190, and a5 = −2.317, respectively. . By substituting these coefficients into Formula 1, a comfort prediction formula can be obtained as follows.

(Comfort) =-10.957 + 0.149 · Ta + 0.187 · Tr + 0.030 · Tf
+ 0.190 · Ta ₁₀₀ -2.317 · V ₁₀₀ (Formula 2)
FIG. 3 is a graph showing the relationship between the measured value of comfort and the predicted value of comfort obtained from the comfort prediction formula using the value of each environmental element when the measured value is measured. The horizontal axis indicates the actual measurement value, and the vertical axis indicates the predicted value based on the comfort prediction formula.

  As a result of comparing the actually measured value and the predicted value, the predicted value agrees with the actually measured value within an error range of approximately ± 0.5. In addition, as a result of calculating the multiple correlation coefficient R of the comfort prediction formula, R = 0.919, which indicates that the correlation between the predicted value and the actually measured value is high. Further, as a result of investigating the probability that the hypothesis that the comfort cannot be accurately predicted by the comfort prediction formula, that is, the significant probability is large between the actually measured value and the predicted value, the significance probability is 0%. The significance level probability was smaller than 5%. As a result, the above-mentioned hypothesis can be rejected, and it can be seen that the predicted value obtained from the comfort prediction formula substantially matches the actually measured value.

  Therefore, it is possible to determine that the values of the coefficients a0 to a5 obtained by the multiple regression analysis are appropriate and that the comfort in the room 11 can be predicted with high accuracy using the comfort prediction formula. it can.

According to the present embodiment, as described above, the predicted value of comfort in the room 11 is calculated using the comfort prediction formula in which the elements of the floor temperature of the room 11 and the wind speed of the floor are included as independent variables. Therefore, the values of the temperature, the average radiation temperature, the floor temperature of the room 11, the floor temperature of the room 11 and the wind speed of the floor of the room 11 for which the comfort is to be evaluated are calculated as Ta, Tr, Tf of the comfort prediction formula. By substituting for Ta ₁₀₀ and V ₁₀₀ , it is possible to obtain a predicted value indicating comfort in the room 11 in consideration of the floor temperature and the wind speed, respectively.

  As a result, the cold air flowing into the room through the window 10 accumulates on the floor 14 or flows on the floor 14 as in the winter, so that the temperature of the human foot in the room 11 is lower than the actually measured room temperature. Even when it becomes low, the comfort in the room 11 in consideration of that can be accurately evaluated.

It is explanatory drawing which shows roughly the environmental condition at the time of the experiment for measuring a measured value with the comfort evaluation method which concerns on this invention. It is explanatory drawing which shows roughly the relationship between various comfort and the numerical value which shows each comfort. It is a graph which shows the relationship between the measured value of comfort, and the predicted value of comfort calculated | required from a comfort prediction formula using the value of each environmental element when this measured value is measured.

Explanation of symbols

11 rooms 13 subjects Ta room temperature Tr average radiation temperature Tf floor temperature Ta ₁₀₀ floor temperature V ₁₀₀ floor wind speed a0, a1, a2, a3, a4, a5 coefficient

Claims (1)

Using the declared value indicating the comfort of the room obtained from the subject by the experiment in the room, and the values of the three environmental elements of the room temperature, the average radiation temperature, and the floor temperature of the room, The coefficient of each environmental element of the linear model represented by the sex as a dependent variable and each environmental element as an independent variable is calculated by a method of multiple regression analysis, and each coefficient obtained by the calculation is used. In the comfort evaluation method for calculating a value indicating the comfort by an arithmetic expression represented by a linear model,
The above-mentioned three environmental elements are added to the floor temperature of the room and the wind speed on the floor to make a total of five environmental elements, and the floor temperature and the wind speed of the room are the room temperature and the wind speed at the foot position of the subject. A comfort evaluation method characterized by that.

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