CN109340899B - Method for predicting indoor thermal comfort temperature in winter in severe cold region based on thermal adaptability - Google Patents

Abstract

A prediction method of indoor thermal comfort temperature in winter in severe cold regions based on thermal adaptability belongs to the field of energy saving and environmental protection. The invention solves the inaccuracy of indoor comfort temperature prediction in severe cold areas in winter caused by the existing methods that only perform adaptive thermal comfort evaluation for the entire heating period, and use the thermal sensation vote of each temperature interval to take the average value during evaluation. energy consumption problem. The present invention divides the whole heating season into three stages for evaluation, which can provide important reference for the heating design and operation adjustment in severe cold areas; the adaptive thermal comfort model of the present invention adopts the weight analysis method, and compared with the existing method, the present invention The invention gives greater weight to those with higher temperature distribution frequency and more thermal sensation votes, and the obtained indoor comfortable temperature prediction values in different heating stages in severe cold regions in winter consider the thermal adaptability of the human body, and can reduce heating energy consumption by 10%. The invention can be applied to the field of energy saving and environmental protection.

Description

Prediction method of indoor thermal comfort temperature in winter in severe cold regions based on thermal adaptability

technical field

The invention belongs to the field of energy saving and environmental protection, and particularly relates to a method for predicting indoor thermal comfort temperature in winter in severe cold regions.

Background technique

In severe cold areas, the winter is long, and the heating period is more than half a year. In severe cold regions, the outdoor temperature in winter is low and varies greatly. The average outdoor temperature during the heating period is generally -20 to 5 °C, while the current indoor heating design temperature is 18 °C. At present, the room temperature of various buildings in severe cold areas is maintained at the same value throughout the heating season, and the room temperature of some buildings is too high, which will lead to increased heating energy consumption and is not conducive to human thermal comfort and health.

The thermoneutral temperature in winter in severe cold regions is close to the average room temperature, proving that people's past thermal experience has a significant impact on the thermoneutral temperature. In the long winter, as the outdoor air temperature changes, the indoor air temperature should also be adjusted to meet the requirements of human thermal comfort, health and energy saving.

At present, the evaluation method of indoor thermal environment and thermal comfort is mainly based on the thermal comfort model proposed by Professor Fanger of Denmark, as well as the predicted average voting value PMV and predicted dissatisfaction percentage PPD index proposed by him, which is suitable for thermal comfort evaluation of air-conditioned environment. The current evaluation of the heating environment in severe cold regions in winter adopts the adaptive thermal comfort model, but the adaptive thermal comfort model evaluates the indoor thermal environment during the entire heating period, and does not divide the heating stages according to the outdoor temperature in the severe cold region to carry out adaptive thermal comfort separately. Therefore, there is a certain irrationality in the evaluation, and the indoor comfortable temperature in winter in severe cold regions cannot be accurately predicted.

Moreover, the current adaptive thermal comfort model generally adopts the Bin method, that is, the average value of the thermal sensation votes in each temperature range. Since the distribution of indoor thermal environment parameters and the distribution of human thermal sensation are generally normal distributions, that is, in the tested temperature range, low and high temperatures occur less frequently; in each temperature range, the number of votes for human thermal sensation is not equal. Higher and lower temperatures corresponded to fewer votes, while intermediate temperatures corresponded to larger votes. Therefore, the method of taking the average of the thermal sensation votes in each temperature interval will lead to inaccurate prediction of the indoor comfortable temperature in winter in severe cold regions and the problems of large heating energy consumption.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve the inconsistency of indoor comfort temperature prediction in severe cold areas in winter caused by only evaluating the adaptive thermal comfort during the whole heating period and taking the average value of the thermal sensation voting in each temperature interval in the existing method. Accurate and large heating energy consumption.

The technical scheme that the present invention takes for solving the above-mentioned technical problems is:

A prediction method of indoor thermal comfort temperature in winter in severe cold regions based on thermal adaptability, the method includes the following steps:

Step 1. Collect the daily average outdoor temperature during the heating season in severe cold areas;

Step 2, divide the heating season into three stages: the initial heating stage, the middle heating stage and the final heating stage according to the daily average outdoor temperature change in the heating season;

Step 3: Continuously monitor indoor air temperature and indoor air relative humidity during the heating process, and intermittently test indoor air flow rate and indoor black bulb temperature;

Step 4: Investigate the subject's thermal sensation according to the thermal response voting scale in the thermal comfort standard;

Step 5: Establish a weighted linear regression model of the average thermal sensation vote value and indoor air temperature in the initial heating, mid heating and final heating stages respectively, and obtain the thermal neutral temperature in the initial heating, mid heating and final heating stages, and use the thermal neutral temperature Find the thermal comfort temperature range.

The beneficial effects of the present invention are as follows: the present invention provides a method for predicting the indoor thermal comfort temperature in severe cold regions based on thermal adaptability, and the present invention divides the entire heating season into the initial heating period, the middle heating period and the final heating period to carry out adaptive thermal comfort respectively. Evaluation. Compared with the existing method, the thermally neutral temperature and thermally comfortable temperature range obtained by the present invention are more reasonable, can provide an important reference for heating design and operation adjustment in severe cold areas, and better meet the requirements of human thermal comfort, health and energy saving. And the adaptive thermal comfort model of the present invention adopts the weight analysis method, compared with the existing method of averaging the thermal sensation votes of each temperature interval, the present invention has a larger frequency of temperature distribution and more thermal sensation votes. The predicted values of indoor comfort temperature in winter in different heating stages in severe cold regions take into account the thermal adaptability of the human body, which can reduce heating energy consumption by 10%.

Description of drawings

1 is a flowchart of a method for predicting indoor thermal comfort temperature in severe cold regions in winter based on thermal adaptability of the present invention;

Fig. 2 is the schematic diagram of the outdoor temperature change during the heating period of the present invention and the division of the heating stage;

FIG. 3 is a schematic diagram of the adaptive thermal comfort model of the present invention in the early stage of heating, the middle stage of heating and the final stage of heating.

Detailed ways

Embodiment 1: As shown in FIG. 1 , the method for predicting indoor thermal comfort temperature in severe cold regions in winter based on thermal adaptability described in this embodiment specifically includes the following steps:

Step 1. Collect the daily average outdoor temperature during the heating season in severe cold areas;

Step 2, divide the heating season into three stages: the initial heating stage, the middle heating stage and the final heating stage according to the daily average outdoor temperature change in the heating season;

Step 3: Continuously monitor indoor air temperature and indoor air relative humidity during the heating process, and intermittently test indoor air flow rate and indoor black bulb temperature;

Step 4. According to the thermal response voting scale in the thermal comfort standard, conduct a subjective survey on the subject's thermal sensation;

The thermal response voting scale is specifically: -3 represents cold, -2 represents cool, -1 represents slightly cool, 0 represents neutral, 1 represents slightly warm, 2 represents warm, and 3 represents hot.

Step 5: Establish a weighted linear regression model of the average thermal sensation vote value and indoor air temperature in the initial heating, mid heating and final heating stages respectively, and obtain the thermal neutral temperature in the initial heating, mid heating and final heating stages, and use the thermal neutral temperature Find the thermal comfort temperature range.

Embodiment 2: The difference between this embodiment and Embodiment 1 is that the specific process of step 2 is:

The heating season is divided into three stages: the initial heating stage, the middle heating stage and the final heating stage according to the daily average outdoor temperature change in the heating season;

Using the sliding 5-day average outdoor daily average temperature as the division index;

If the average value of 2 consecutive sliding 5-days on a certain day is lower than 5℃, it will start to enter the early stage of heating;

After entering the early stage of heating, if the average value of two consecutive sliding 5-days on a certain day is lower than -10℃, it will start to enter the middle stage of heating;

After entering the middle stage of heating, if the average value of 2 consecutive sliding 5-days on a certain day is not lower than -10℃, it is judged to enter the end heating period, until the average value of 2 consecutive sliding 5-days on a certain day is not lower than 5℃. The end of the heating period.

Embodiment 3: The difference between this embodiment and Embodiment 2 is that in step 3, the indoor air flow rate and the indoor black bulb temperature are intermittently tested. For black bulb temperature, the test time for each indoor air flow rate is 3 to 5 minutes, and the test time for each indoor black bulb temperature is 10 to 20 minutes.

Environmental parameters tested in the field include indoor air temperature, relative humidity, air velocity, and black bulb temperature. Among them, the indoor temperature and humidity are continuously monitored. The continuous monitoring data acquisition module was placed in the room where the subjects often stayed, and the temperature and humidity of the room were continuously recorded. Air flow rate and black bulb temperature were tested intermittently. Every 2 to 3 weeks, the data acquisition module was arranged in the center of the room near the subject, and the black bulb temperature and air velocity were tested at a height of 1.1 m above the ground.

Embodiment 4: The difference between this embodiment and Embodiment 3 is that the specific process of step 5 is:

The distribution of indoor thermal environment parameters is generally normal distribution, that is, in the tested temperature range, low temperature and high temperature occur less frequently. The distribution of human body thermal sensation is generally also normal distribution, that is, in each temperature interval, the number of votes for human thermal sensation is not equal, the number of votes corresponding to the higher temperature and the lower temperature is less, and the number of votes corresponding to the middle temperature is more. If a conventional linear regression model is used, the results will have a certain bias. Therefore, the present embodiment uses the distribution frequency of the number of samples of the human body thermal sensation vote in each temperature interval as the weight of the weighted regression model analysis.

For different heating stages in severe cold regions, according to the number of samples in the temperature distribution interval of each heating stage, weighted linear regression is performed on the thermal sensation vote and indoor air temperature in the building environment, and the thermal neutral temperature in different heating stages can be obtained.

The weighted linear regression model is:

y _i =a+bx _i +e _i /w _i

Among them: x _i is the indoor air temperature, _yi is the average thermal sensation voting value corresponding to each indoor air temperature, e _i is the residual error, a and b are the linear regression coefficients, and _wi is the voting sample size corresponding to each air temperature;

为室内空气温度；in:

is the indoor air temperature;

Then the residual _ei is expressed as,

According to the principle of the least squares method, in order to obtain the estimates of a and b, select a and b so that the residual sum of squares Q is the smallest, and the residual sum of squares Q is expressed as:

Among them: N is the number of temperature intervals;

According to the extreme value principle, to minimize the Q value, take the partial derivatives of the linear regression coefficients a and b and set them equal to 0:

Then the weighted linear regression coefficient and the coefficient of determination of the regression equation are expressed as follows:

Where: R ² is the coefficient of determination of the regression equation;

According to the established thermal sensation survey results and the weighted linear regression model of indoor air temperature, the thermoneutral temperature in the initial heating period, the middle heating period and the final heating period were obtained respectively. According to the weighted linear regression model, the average thermal sensation is set as -0.5 to 0.5, and the corresponding temperature interval is obtained, which is the thermal comfort temperature interval.

Further, this thermal comfort temperature can be used to guide heating design and operational regulation.

Example

The following is further clarified based on the 2013-2014 thermal comfort temperature forecast in Harbin heating season. In this example, a residential building is used as an example, and the adaptive thermal comfort model is given in the initial heating stage, the middle heating stage and the final heating stage.

Step 1: Determine the heating stage according to the outdoor temperature. Taking the 2013-2014 heating season in Harbin as an example, the heating season is divided into the initial heating period, the middle heating period, and the final heating period. The results are shown in Figure 2.

As can be seen from Figure 2, from November 22, 2013, the 5-day moving average outdoor temperature in Harbin dropped to -10°C, and by March 2, 2014, the outdoor temperature rose to above -10°C;

These two time nodes are defined as the start and end time of the mid-heating period. The outdoor temperature in the mid-heating period is the lowest, and the average outdoor temperature is -16 °C. The average outdoor air temperature at the initial stage of heating and the final stage of heating were 1.4°C and 2.2°C, respectively.

Step 2: In this example, 10 houses in Harbin urban area were selected as the survey samples, with a total of 20 subjects. Through the data acquisition module, the environmental information parameters of these 10 households are converted into digital signals.

At the same time, through the questionnaire collection module, the subjective thermal sensations of 20 subjects were converted into digital information.

Step 3: Input the above information into the evaluation module. Statistical data, environmental parameters and subjective thermal sensations are obtained, as shown in Table 1.

Table 1 Thermal environment parameters and subjective thermal sensation statistics

The adaptive thermal comfort model for three stages during heating can be calculated, as shown in Figure 3.

Therefore, the adaptive thermal comfort model and thermal neutral temperature of the three stages during residential heating in Harbin are shown in Table 2.

Table 2 Adaptive thermal comfort model and thermoneutral temperature in three stages of heating season

In the formula: x——indoor air temperature;

y——the predicted value of human thermal sensation;

The above calculation examples of the present invention are only to illustrate the calculation model and calculation process of the present invention in detail, but are not intended to limit the embodiments of the present invention. For those of ordinary skill in the art, on the basis of the above description, other different forms of changes or changes can also be made, and it is impossible to list all the embodiments here. Obvious changes or modifications are still within the scope of the present invention.

Claims (1)

1. A method for predicting indoor comfort temperature in severe cold regions in winter based on thermal adaptability, characterized in that the method comprises the following steps: Step 1. Collect the daily average outdoor temperature during the heating season in severe cold areas; Step 2, divide the heating season into three stages: the initial heating stage, the middle heating stage and the final heating stage according to the daily average outdoor temperature change in the heating season; The specific process of the second step is: The heating season is divided into three stages: the initial heating period, the middle heating period and the final heating period according to the daily average outdoor temperature change in the heating season: The sliding 5-day average outdoor daily average temperature is used as the division index: If the average value of 2 consecutive sliding 5-days on a certain day is lower than 5℃, it will start to enter the early stage of heating; After entering the early stage of heating, if the average value of two consecutive sliding 5-days on a certain day is lower than -10℃, it will start to enter the middle stage of heating; After entering the middle stage of heating, if the average value of 2 consecutive sliding 5-days on a certain day is not lower than -10℃, it is judged to enter the end heating period, until the average value of 2 consecutive sliding 5-days on a certain day is not lower than 5℃. The end of the heating period; Step 3: Continuously monitor indoor air temperature and indoor air relative humidity during the heating process, and intermittently test indoor air flow rate and indoor black bulb temperature; In the said step 3, the indoor air flow rate and the indoor black bulb temperature are intermittently tested, and the specific method is as follows: the indoor air flow rate and the indoor black bulb temperature are tested once every 2 to 3 weeks, and the test time of each indoor air flow rate is 3 to 3. 5 minutes, the test time of each indoor black bulb temperature is 10 to 20 minutes; Step 4: Investigate the subject's thermal sensation according to the thermal response voting scale in the thermal comfort standard; Step 5: Establish a weighted linear regression model of the average thermal sensation vote value and indoor air temperature in the initial heating, mid heating and final heating stages respectively, and obtain the thermal neutral temperature in the initial heating, mid heating and final heating stages, and use the thermal neutral temperature Find the comfort temperature range; The specific process of the step 5 is: The weighted linear regression model is: y _i =a+bx _i +e _i /w _i Among them: x _i is the indoor air temperature, _yi is the average thermal sensation voting value corresponding to each indoor air temperature, e _i is the residual error, a and b are the linear regression coefficients, and _wi is the voting sample size corresponding to each air temperature;

in:

is the estimated value of y _i ; Then the residual _ei is expressed as:

The residual sum of squares Q is expressed as:

Among them: N is the number of temperature intervals; According to the extreme value principle, the partial derivatives of the linear regression coefficients a and b are obtained:

Then the weighted linear regression coefficient and the coefficient of determination of the regression equation are expressed as follows:

Where: R ² is the coefficient of determination of the regression equation; According to the weighted linear regression model of the thermal sensation survey results and the indoor air temperature established respectively in the early, middle and late heating stages of heating, the thermal neutral temperature in the early stage of heating, the middle stage of heating and the final stage of heating is obtained. According to the weighted linear regression model, let the average The thermal sensation is -0.5 to 0.5, and the corresponding comfortable temperature range is obtained; The adaptive thermal comfort model in the early stage of heating is y=0.2327x-5.0232; The adaptive thermal comfort model in the middle heating period is y=0.1456x-3.4144; The adaptive thermal comfort model at the end of the heating period is y=0.1733x-3.9993.

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