CN109340899B - Method for predicting indoor thermal comfort temperature in winter in severe cold region based on thermal adaptability - Google Patents
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Abstract
A method for predicting indoor thermal comfort temperature in winter in severe cold regions based on thermal adaptability belongs to the field of energy conservation and environmental protection. The method solves the problems that the prior method only carries out adaptive thermal comfort evaluation on the whole heating period and uses the thermal sensing voting of each temperature interval to average during evaluation, so that the indoor comfort temperature prediction in winter in severe cold regions is inaccurate and the heating energy consumption is large. The method divides the whole heating season into 3 stages for evaluation, and can provide important reference for heating design and operation regulation in severe cold areas; compared with the existing method, the adaptive thermal comfort model provided by the invention has the advantages that the weight analysis method is adopted, the larger weight is given to the higher temperature distribution frequency and the more heat sensation votes, the human thermal adaptability is considered in the obtained indoor comfort temperature prediction values of different heating stages in winter in the severe cold area, and the heating energy consumption can be reduced by 10%. The invention can be applied to the field of energy conservation and environmental protection.
Description
Technical Field
The invention belongs to the field of energy conservation and environmental protection, and particularly relates to a method for predicting indoor thermal comfort temperature in winter in a severe cold area.
Background
In severe cold areas, the winter is long, and the heating period is more than half a year. In severe cold areas, the outdoor temperature is low and has large variation range in winter, the average outdoor temperature is generally-20-5 ℃ in the heating period, and the current indoor heating design temperature is 18 ℃. At present, the room temperature of various buildings in severe cold regions is maintained at a value in the whole heating season, and the room temperature of partial buildings is higher, so that the heating energy consumption is increased, and the thermal comfort and the health of human bodies are not facilitated.
The thermal neutral temperature of a severe cold region in winter is close to the average room temperature, and the fact that the past thermal experience of people has a remarkable influence on the thermal neutral temperature is proved. In the long winter, along with the change of outdoor air temperature, the indoor air temperature should be adjusted along with the change of outdoor air temperature to satisfy the requirements of thermal comfort, health and energy conservation of human bodies.
The existing evaluation method for the indoor thermal environment and the thermal comfort is mainly based on a thermal comfort model provided by professor Denmark Fanger, and a prediction average voting value PMV and a prediction dissatisfaction percentage PPD index provided by the thermal comfort model, wherein the index is suitable for evaluating the thermal comfort of the air-conditioning environment. At present, an adaptive thermal comfort model is adopted for winter heating environment evaluation in a severe cold area, but the adaptive thermal comfort model evaluates the indoor thermal environment in the whole heating period and does not divide heating stages according to the outdoor temperature of the severe cold area to respectively evaluate the adaptive thermal comfort, so that the evaluation has certain unreasonable property and the indoor comfortable temperature in the severe cold area in winter cannot be accurately predicted.
Moreover, the current adaptive thermal comfort model generally adopts a Bin method, namely, the thermal sensing voting of each temperature interval is averaged. Because the indoor thermal environment parameter distribution and the human body thermal sensation distribution are generally normal distribution, namely, in the tested temperature range, the low temperature and the higher temperature occur less frequently; in each temperature interval, the human body heat sensation votes are unequal, the votes corresponding to higher and lower temperatures are fewer, and the votes corresponding to the middle temperature are larger. Therefore, the method of averaging by using the thermal sensing votes for each temperature interval may cause inaccurate prediction of indoor comfortable temperature in winter in severe cold regions and large heating energy consumption.
Disclosure of Invention
The invention aims to solve the problems of inaccurate indoor comfortable temperature prediction in winter in severe cold regions and high heating energy consumption caused by carrying out adaptive thermal comfort evaluation on the whole heating period and averaging by using thermal sensing voting of each temperature interval during evaluation in the conventional method.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a method for predicting indoor thermal comfort temperature in winter in severe cold regions based on thermal adaptability comprises the following steps:
step one, collecting daily outdoor daily average temperature in a heating season of a severe cold area;
dividing the heating season into 3 stages of an initial heating stage, a middle heating stage and a final heating stage according to daily outdoor daily average temperature change in the heating season;
continuously monitoring the indoor air temperature and the indoor air relative humidity in the heating process, and intermittently testing the indoor air flow rate and the indoor black ball temperature;
step four, investigating the heat sensation of the subject according to the heat response voting scale in the heat comfort standard;
and fifthly, respectively establishing weighted linear regression models of average heat feeling voting values and indoor air temperatures at the initial stage of heating, the middle stage of heating and the final stage of heating to obtain heat neutral temperatures at the initial stage of heating, the middle stage of heating and the final stage of heating, and calculating a heat comfort temperature interval by using the heat neutral temperatures.
The invention has the beneficial effects that: the invention provides a method for predicting indoor thermal comfort temperature in winter in severe cold regions based on thermal adaptability. Compared with the prior art, the thermal neutral temperature and thermal comfort temperature range obtained by the method is more reasonable, can provide important reference for heating design and operation regulation in severe cold areas, and meets the requirements of thermal comfort, health and energy conservation of human bodies; and the adaptive thermal comfort model adopts a weight analysis method, compared with the existing method of averaging the thermal sensing votes of each temperature interval, the adaptive thermal comfort model gives larger weight to the temperature with larger distribution frequency and more thermal sensing votes, and the obtained indoor comfort temperature predicted values of different heating stages in the severe cold area in winter take the thermal adaptability of the human body into consideration, so that the heating energy consumption can be reduced by 10%.
Drawings
FIG. 1 is a flow chart of a method for predicting indoor thermal comfort temperature in winter in severe cold regions based on thermal adaptability according to the present invention;
FIG. 2 is a schematic diagram of the outdoor temperature change and heating stage division during the heating period of the present invention;
fig. 3 is a schematic diagram of the adaptive thermal comfort model for the initial stage of heating, the middle stage of heating, and the final stage of heating according to the present invention.
Detailed Description
The first embodiment is as follows: as shown in fig. 1, the method for predicting a thermal comfort temperature in a winter season in a severe cold region based on thermal adaptability according to the present embodiment specifically includes the following steps:
step one, collecting daily outdoor daily average temperature in a heating season of a severe cold area;
dividing the heating season into 3 stages of an initial heating stage, a middle heating stage and a final heating stage according to daily outdoor daily average temperature change in the heating season;
continuously monitoring the indoor air temperature and the indoor air relative humidity in the heating process, and intermittently testing the indoor air flow rate and the indoor black ball temperature;
step four, carrying out subjective investigation on the heat sensation of the subject according to the heat response voting scale in the heat comfort standard;
the thermal response voting scale is specifically as follows: -3 for cold, -2 for cold, -1 for slightly cold, 0 for neutral, 1 for slightly warm, 2 for warm, 3 for hot.
And fifthly, respectively establishing weighted linear regression models of average heat feeling voting values and indoor air temperatures at the initial stage of heating, the middle stage of heating and the final stage of heating to obtain heat neutral temperatures at the initial stage of heating, the middle stage of heating and the final stage of heating, and calculating a heat comfort temperature interval by using the heat neutral temperatures.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: the specific process of the second step is as follows:
dividing the heating season into 3 stages of an initial heating stage, a middle heating stage and a final heating stage according to the daily outdoor daily average temperature change of the heating season;
the average outdoor daily temperature averaged in 5 days of sliding is used as a division index;
if the average values of 2 continuous sliding 5 balances in a certain day are all lower than 5 ℃, starting to enter the initial heating stage;
after the initial stage of heating, if the average values of 2 continuous sliding 5 balances on a certain day are all lower than-10 ℃, starting to enter the middle stage of heating;
and after the heating is performed in the middle stage, if the average values of 2 continuous sliding 5 scales on a certain day are not lower than-10 ℃, judging to enter the final stage of heating until the average values of 2 continuous sliding 5 scales on a certain day are not lower than 5 ℃, and ending the final stage of heating.
The third concrete implementation mode: the second embodiment is different from the first embodiment in that: and step three, intermittently testing the indoor air flow rate and the indoor black ball temperature, wherein the method comprises the following specific steps: testing the indoor air flow rate and the indoor black ball temperature every 2-3 weeks, wherein the testing time of the indoor air flow rate is 3-5 minutes, and the testing time of the indoor black ball temperature is 10-20 minutes.
The environmental parameters of the field test include indoor air temperature, relative humidity, air flow rate, black ball temperature. Wherein, the indoor temperature and humidity are continuously monitored. The data acquisition module for continuous monitoring is placed in a room where the subject stays frequently, and the temperature and humidity of the room are continuously recorded. Air flow rate and black ball temperature were measured using a batch test. And arranging a data acquisition module in the center of the room close to the subject every 2-3 weeks, and testing the temperature and the air flow rate of the black ball at the height of 1.1m from the ground.
The fourth concrete implementation mode: the third difference between the present embodiment and the specific embodiment is that: the concrete process of the step five is as follows:
the distribution of the indoor thermal environment parameters is generally normal distribution, namely, in the tested temperature range, the low temperature and the higher temperature occur less frequently. The distribution of human thermal sensation is also normal, that is, in each temperature interval, the votes of human thermal sensation are unequal, the votes corresponding to higher and lower temperatures are less, and the votes corresponding to intermediate temperatures are more. If a conventional linear regression model is used, the results will be biased. Therefore, the present embodiment is based on the sample number distribution frequency of the human thermal sensation vote in each temperature section as the weight of the weighted regression model analysis.
Aiming at different heating stages of severe cold areas, according to the sample number of the temperature distribution interval of each heating stage, weighted linear regression is carried out on the heat feeling vote and the indoor air temperature in the building environment, and the heat neutral temperatures of the different heating stages can be obtained.
The weighted linear regression model is:
yi＝a+bxi+ei/wi
wherein: x is the number ofiIs the temperature of the indoor air, yiVoting value for average heat sensation corresponding to each indoor air temperature, eiAs residual, a and b are linear regression coefficients, wiVoting for each air temperatureThe amount of the sample;
wherein:
is the indoor air temperature;
residual error eiAs indicated by the general representation of the,
according to the principle of least square method, in order to obtain the estimation values of a and b, a and b are selected so that the sum of the squares of the residuals Q is minimum, and the sum of the squares of the residuals Q is expressed as:
wherein: n is the number of temperature intervals;
according to the extreme principle, to minimize the value of Q, the partial derivatives are calculated for the linear regression coefficients a and b and are made equal to 0:
the weighted linear regression coefficients and the determinant coefficients of the regression equation are then expressed as follows:
wherein: r2Determining coefficients for the regression equation;
and respectively obtaining the thermal neutral temperatures of the initial heating stage, the middle heating stage and the final heating stage according to the built thermal sensing survey result and the weighted linear regression model of the indoor air temperature. And (3) according to the weighted linear regression model, enabling the average thermal sensation to be-0.5, and calculating a corresponding temperature interval, wherein the temperature interval is a thermal comfort temperature interval.
Further, this thermal comfort temperature can be used as a guide for heating design and operational adjustments.
Examples
The following is further elucidated in connection with the prediction of the thermal comfort temperature of the harbin heating season 2013-. In this example, the house is taken as an example, and adaptive thermal comfort models of an initial heating stage, a middle heating stage, and an end heating stage are given.
The method comprises the following steps: the heating stage is determined according to the outdoor air temperature, taking the heating season of Harbin 2013-.
As can be seen from FIG. 2, from 11/22/2013, the sliding average outdoor temperature of Harbin was decreased to-10 ℃ in 5 days, and then increased to-10 ℃ or higher in 3/2/2014;
defining the 2 time nodes as the starting and ending time of the middle heating period, wherein the outdoor air temperature in the middle heating period is the lowest, and the average outdoor air temperature is-16 ℃. The average outdoor temperatures at the initial stage and the final stage of heating were 1.4 ℃ and 2.2 ℃, respectively.
Step two: in this example, 10 households in Harbin City were selected as survey samples, and 20 subjects were selected. The environment information parameters of the 10 users are converted into digital signals through a data acquisition module.
Meanwhile, subjective thermal sensation of 20 subjects was converted into digital information by a questionnaire collection module.
Step three: the information is input into an evaluation module. Data were counted to obtain environmental parameters and subjective thermal sensation as shown in table 1.
TABLE 1 thermal environmental parameters and subjective thermal sensation statistics
An adaptive thermal comfort model for the three stages of the heating period can be calculated as shown in fig. 3.
Thus, the adaptive thermal comfort model and thermal neutral temperature for the three phases during heating of a harbourine home are shown in table 2.
TABLE 2 adaptive thermal comfort model and thermal neutral temperature for three phases of the heating season
In the formula: x-indoor air temperature;
y is the human thermal sensation predicted value;
the above-described calculation examples of the present invention are merely to explain the calculation model and the calculation flow of the present invention in detail, and are not intended to limit the embodiments of the present invention. It will be apparent to those skilled in the art that other variations and modifications of the present invention can be made based on the above description, and it is not intended to be exhaustive or to limit the invention to the precise form disclosed, and all such modifications and variations are possible and contemplated as falling within the scope of the invention.
Claims (1)
1. The method for predicting the indoor comfortable temperature in the winter in the severe cold region based on the heat adaptability is characterized by comprising the following steps of:
step one, collecting daily outdoor daily average temperature in a heating season of a severe cold area;
dividing the heating season into 3 stages of an initial heating stage, a middle heating stage and a final heating stage according to daily outdoor daily average temperature change in the heating season;
the specific process of the second step is as follows:
dividing the heating season into 3 stages of an initial heating stage, a middle heating stage and a final heating stage according to the daily outdoor daily average temperature change of the heating season:
the average outdoor daily temperature averaged over 5 days was used as a division index:
if the average values of 2 continuous sliding 5 balances in a certain day are all lower than 5 ℃, starting to enter the initial heating stage;
after the initial stage of heating, if the average values of 2 continuous sliding 5 balances on a certain day are all lower than-10 ℃, starting to enter the middle stage of heating;
after entering the heating middle stage, if the average values of 2 continuous sliding 5 balances on a certain day are not lower than-10 ℃, judging to enter the heating final stage until the average values of 2 continuous sliding 5 balances on a certain day are not lower than 5 ℃, and ending the heating final stage;
continuously monitoring the indoor air temperature and the indoor air relative humidity in the heating process, and intermittently testing the indoor air flow rate and the indoor black ball temperature;
and in the third step, the indoor air flow rate and the indoor black ball temperature are intermittently tested, and the specific mode is as follows: testing the indoor air flow rate and the indoor black ball temperature every 2-3 weeks, wherein the testing time of the indoor air flow rate is 3-5 minutes, and the testing time of the indoor black ball temperature is 10-20 minutes;
step four, investigating the heat sensation of the subject according to the heat response voting scale in the heat comfort standard;
step five, respectively establishing weighted linear regression models of average heat feeling voting values and indoor air temperatures at the initial stage of heating, the middle stage of heating and the final stage of heating to obtain heat neutral temperatures at the initial stage of heating, the middle stage of heating and the final stage of heating, and calculating intervals of comfortable temperatures by using the heat neutral temperatures;
the concrete process of the step five is as follows:
the weighted linear regression model is:
yi＝a+bxi+ei/wi
wherein: x is the number ofiIs the temperature of the indoor air, yiVoting value for average heat sensation corresponding to each indoor air temperature, eiAs residual, a and b are linear regression coefficients, wiVoting sample amount corresponding to each air temperature;
wherein:
is yiAn estimated value of (d);
residual error eiExpressed as:
the residual sum of squares Q is expressed as:
wherein: n is the number of temperature intervals;
according to the extreme value principle, partial derivatives are obtained for the linear regression coefficients a and b:
the weighted linear regression coefficients and the determinant coefficients of the regression equation are then expressed as follows:
wherein: r2Determining coefficients for the regression equation;
according to the weighted linear regression models of the thermal sensation investigation results of the initial heating stage, the middle heating stage and the final heating stage and the indoor air temperature, which are respectively established, the thermal neutral temperatures of the initial heating stage, the middle heating stage and the final heating stage are obtained, the average thermal sensation is set to be-0.5 to 0.5 according to the weighted linear regression models, and the corresponding comfortable temperature interval is obtained;
the adaptive thermal comfort model at the initial heating stage is y-0.2327 x-5.0232;
the adaptive thermal comfort model in the middle heating period is 0.1456 x-3.4144;
the adaptive thermal comfort model at the end of heating is y 0.1733 x-3.9993.
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| CN112395732B (en) * | 2020-06-12 | 2024-04-02 | 香港城市大学深圳研究院 | Thermal comfort prediction method and device for enhancing thermal neutral adaptability |
| CN111780353A (en) * | 2020-06-24 | 2020-10-16 | 珠海格力电器股份有限公司 | Air conditioning unit control method, system and device and air conditioning unit |
| CN113623719B (en) * | 2021-06-23 | 2022-08-19 | 国家电投集团东北电力有限公司大连开热分公司 | Heat exchange station prediction control method based on effective room temperature detection |
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| CN104102789A (en) * | 2014-07-28 | 2014-10-15 | 重庆大学 | System and method for evaluating building hot and humid environment levels |
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| CN104102789A (en) * | 2014-07-28 | 2014-10-15 | 重庆大学 | System and method for evaluating building hot and humid environment levels |
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| 严寒地区人体热适应性研究（1）：住宅热环境与热适应性现场研究;王昭俊等;《暖通空调》;20151130;第45卷(第11期);73-79 * |
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