CN109340899A - The prediction technique of severe cold area winter indoor thermal comfort temperature based on hot adaptability - Google Patents
The prediction technique of severe cold area winter indoor thermal comfort temperature based on hot adaptability Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
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Abstract
The prediction technique of severe cold area winter indoor thermal comfort temperature based on hot adaptability, it belongs to energy conservation and environmental protection.The present invention solves the problems, such as that the inaccuracy of severe cold area winter indoor comfortable temperature prediction caused by being only averaged to hotness ballot when carrying out adaptive thermal comfort evaluation during entire heating and evaluating using each temperature range existing for existing method, heating energy consumption are big.Entire heating season is divided into 3 stages to evaluate by the present invention, can provide important references for the Heating Design and runing adjustment of severe cold area；Adaptive thermal comfort model of the invention uses weight analysis method, compared with the conventional method, the present invention is larger for Temperature Distribution frequency, hotness is voted more gives biggish weight, obtained severe cold area winter difference heating stage indoor comfortable temperature prediction value considers human body hot adaptability, and heating energy consumption can be reduced by 10%.Present invention could apply to energy conservation and environmental protections.
Description
Technical field
The invention belongs to energy conservation and environmental protections, and in particular to a kind of prediction side of severe cold area winter indoor thermal comfort temperature
Method.
Background technique
Severe cold area winter is very long, and heating period is up to more than half a year.Severe cold area winter outside air temperature is low and amplitude of variation
Greatly, outdoor temperature on average is generally -20~5 DEG C between heating period, and indoor heating design temperature all uses 18 DEG C at present.It is tight at present
The cold all kinds of building room temperatures in area all maintain a value in entire heating season, and part building room temperature is higher, will lead to confession in this way
Warm energy consumption increases, and is unfavorable for human thermal comfort and health.
The thermal neutral temperature in severe cold area winter and average room temperature be close, it was demonstrated that the past thermal history of people is to neutral
Temperature has a significant impact.In very long winter, with the variation of outside air temperature, indoor air temperature should also carry out therewith
It adjusts, to meet human thermal comfort, health and energy-efficient requirement.
The evaluation method of indoor thermal environment and thermal comfort is based primarily upon the thermal comfort mould of Denmark professor Fanger proposition at present
Type and prediction averagely ballot value PMV and predicted percentage of dissatisfied PPD index that he is proposed, the index are suitable for air-conditioning
Environmental thermal comfort evaluation.Current severe cold area heat supply in winter environmental evaluation uses adaptive thermal comfort model, but adaptability heat
Comfortable model is evaluated indoor thermal environment entire heating period, and there is no divide to supply according to severe cold area outside air temperature
The warm stage to carry out adaptive thermal comfort evaluation respectively, therefore is evaluated there are certain irrationality, it is tight out to be unable to Accurate Prediction
Cold area winter indoor comfort temperature.
And current adaptive thermal comfort model generally uses Bin method, i.e., throws the hotness of each temperature range
Ticket is averaged.Since indoor thermal environment parameter distribution and human thermal sensation's distribution are generally normal distribution, i.e., in the temperature of test
Section is spent, the frequency that low temperature and higher temperature occur is less；In each temperature range, human thermal sensation's votes are unequal,
Higher temperature and the corresponding votes of lower temperature are less, and the corresponding votes of medium temperature are larger.Therefore, each temperature is utilized
The method that the hotness ballot in degree section is averaged, will lead to and severe cold area winter indoor comfortable temperature prediction is not allowed
Really and the larger problem of heating energy consumption.
Summary of the invention
The purpose of the present invention is comment for existing for solution existing method progress adaptive thermal comfort during entire heating
Severe cold area winter indoor comfortable temperature caused by hotness ballot when valence and evaluation using each temperature range is averaged
Spend the big problem of the inaccuracy predicted, heating energy consumption.
The technical solution adopted by the present invention to solve the above technical problem is:
The prediction technique of severe cold area winter indoor thermal comfort temperature based on hot adaptability, this method include following step
It is rapid:
Step 1: outdoor daily mean temperature daily in acquisition severe cold area heating season；
Step 2: heating season is divided into In The Initial Period Of Heating according to outdoor daily mean temperature variation daily in heating season, is supplied
Warm mid-term and heating 3 stages of latter stage；
Step 3: being continuously monitored in heating process to indoor air temperature and indoor air relative humidity, to interior
Air velocity and indoor black ball temperature carry out interval test；
Step 4: being investigated according to the thermal response ballot scale in hot comfortable standard the hotness of subject；
Step 5: establishing the average hotness ballot value and Interior Space of In The Initial Period Of Heating, heating mid-term and latter stage of heating respectively
The weighed regression model of temperature degree obtains the thermal neutral temperature of In The Initial Period Of Heating, heating mid-term and latter stage of heating, and utilizes heat
Thermoneutrality finds out thermal comfort temperature section.
The beneficial effects of the present invention are: the present invention provides the severe cold area winter indoor thermal comfort temperature based on hot adaptability
Entire heating season is divided into In The Initial Period Of Heating, heating mid-term and heating latter stage to fit respectively by the prediction technique of degree, the present invention
Answering property comfort evaluation.Compared with the conventional method, the thermal neutral temperature and thermal comfort temperature range that the present invention obtains are more reasonable,
Important references can be provided for the Heating Design and runing adjustment of severe cold area, more meet human thermal comfort, health and energy conservation
Requirement；And adaptive thermal comfort model of the invention uses weight analysis method, with the existing heat to each temperature range
Feel that the method that is averaged of ballot is compared, the present invention is larger for Temperature Distribution frequency, hotness is voted it is more give compared with
Big weight, obtain severe cold area difference heating winter in stage indoor comfortable temperature prediction value consider human body hot adaptability,
Heating energy consumption can be reduced by 10%.
Detailed description of the invention
Fig. 1 is the process of the prediction technique of the severe cold area winter indoor thermal comfort temperature of the invention based on hot adaptability
Figure；
Fig. 2 is the schematic diagram of the variation of heating period outdoor temperature and heating divided stages of the invention；
Fig. 3 is the schematic diagram of the adaptive thermal comfort model of In The Initial Period Of Heating of the invention, heating mid-term and latter stage of heating.
Specific embodiment
Specific embodiment 1: as shown in Figure 1, severe cold area winter room described in present embodiment based on hot adaptability
The prediction technique of interior thermal comfort temperature, this method specifically includes the following steps:
Step 1: outdoor daily mean temperature daily in acquisition severe cold area heating season；
Step 2: heating season is divided into In The Initial Period Of Heating according to outdoor daily mean temperature variation daily in heating season, is supplied
Warm mid-term and heating 3 stages of latter stage；
Step 3: being continuously monitored in heating process to indoor air temperature and indoor air relative humidity, to interior
Air velocity and indoor black ball temperature carry out interval test；
Step 4: carrying out subjective survey to the hotness of subject according to the thermal response ballot scale in hot comfortable standard；
The thermal response is voted scale specifically: -3 represent cold, and -2 represent cool, and -1 represents slightly cool, and 0 represents neutrality, 1 representative
Slightly warm, 2 representatives are warm, and 3 represent heat.
Step 5: establishing the average hotness ballot value and Interior Space of In The Initial Period Of Heating, heating mid-term and latter stage of heating respectively
The weighed regression model of temperature degree obtains the thermal neutral temperature of In The Initial Period Of Heating, heating mid-term and latter stage of heating, and utilizes heat
Thermoneutrality finds out thermal comfort temperature section.
Specific embodiment 2: the present embodiment is different from the first embodiment in that: the detailed process of step 2 are as follows:
According to heating season, heating season is divided into In The Initial Period Of Heating, heating mid-term and confession by daily outdoor daily mean temperature variation
Warm 3 stages of latter stage；
Using 5 days average outdoor daily mean temperatures of sliding as Classification Index；
If certain day 5 day averages of continuous 2 slidings are below 5 DEG C, In The Initial Period Of Heating is initially entered；
Into after In The Initial Period Of Heating, if certain day 5 day averages of continuous 2 slidings are below -10 DEG C, heating is initially entered
Mid-term；
Into after heating mid-term, if certain day 5 day averages of continuous 2 slidings are not less than -10 DEG C, judge to enter confession
In warm latter stage, until certain day 5 day averages of continuous 2 slidings are not less than 5 DEG C, heating latter stage terminates.
Specific embodiment 3: present embodiment is unlike specific embodiment two: to room air in step 3
Flow velocity and indoor black ball temperature carry out interval test, concrete mode are as follows: every 2~3 weeks room air flow velocitys of test and room
Interior black ball temperature, testing time of each room air flow velocity are 3 to 5 minutes, every time the testing time of interior black ball temperature be
10 to 20 minutes.
The environmental parameter of on-the-spot test includes indoor air temperature, relative humidity, air velocity, black ball temperature.Wherein, room
Interior temperature and humidity is using continuous monitoring.The data acquisition module continuously monitored is placed on the room that subject often stays, continuously
Record the temperature and humidity in room.Air velocity and black ball temperature are tested using interval.Data acquisition module was arranged in every 2~3 weeks
Black ball temperature and air velocity are tested at subject in room center at liftoff 1.1m height.
Specific embodiment 4: present embodiment is unlike specific embodiment three: the detailed process of step 5 are as follows:
Indoor thermal environment parameter distribution is generally normal distribution, i.e., goes out in the temperature range of test, low temperature and higher temperature
Existing frequency is less.General human thermal sensation's distribution is also normal distribution, i.e., in each temperature range, human thermal sensation's votes
Unequal, higher temperature and the corresponding votes of lower temperature are less, and the corresponding votes of medium temperature are more.If using
Conventional linear regression model (LRM), as a result can generate certain deviation.Therefore, present embodiment is based on the people in each temperature range
Body heat feels the sample number distribution frequency of ballot, the weight as Weight Regression Model analysis.
It heats the stage for severe cold area difference, according to the sample number of each heating phase temperature distributed area, to building
In environment hotness ballot with indoor air temperature be weighted linear regression, can be obtained the different heating stages hanker it is warm-natured
Degree.
Weighed regression model are as follows:
yi=a+bxi+ei/wi
Wherein: xiFor indoor air temperature, yiFor the corresponding average hotness ballot value of each indoor air temperature, eiIt is residual
Difference, a and b are linear regression coeffficient, wiFor the corresponding ballot sample size of each air themperature；
Wherein:For indoor air temperature；
Then residual error eiIt is expressed as,
A, b are then chosen in order to acquire the valuation of a, b according to principle of least square method, so that residual sum of squares (RSS) Q is minimum, it is residual
Poor quadratic sum Q is indicated are as follows:
Wherein: N is temperature range number；
According to extremum principle, to make Q value minimum, partial derivative is asked to linear regression coeffficient a and b, and it is enabled to be equal to 0:
Then the coefficient of determination of weighted linear regression coefficient and regression equation is expressed as follows:
Wherein: R2For the coefficient of determination of regression equation；
According to the hotness investigation result of foundation and the weighed regression model of indoor air temperature, heating is respectively obtained
Initial stage, heating mid-term and heat latter stage thermal neutral temperature.According to weighed regression model, enable average hotness be -0.5 to
0.5, corresponding temperature range is found out, this temperature range is thermal comfort temperature section.
Further, this thermal comfort temperature available coach Heating Design and runing adjustment.
Embodiment
It is furtherd elucidate below in conjunction with the prediction of Harbin heating season 2013-2014 thermal comfort temperature.This example is with house
Example provides In The Initial Period Of Heating, heating mid-term, the adaptive thermal comfort model in latter stage of heating.
Step 1: determining the heating stage according to outside air temperature, by taking the 2013-2014 heating season of Harbin as an example, will heat
It is divided into In The Initial Period Of Heating, heating mid-term, heating latter stage season, as a result sees Fig. 2.
From Figure 2 it can be seen that the 5 days sliding average outside air temperatures in Harbin were down to -10 DEG C since on November 22nd, 2013, arrive
On March 2nd, 2014, outside air temperature went up to -10 DEG C or more；
This 2 timing nodes are defined as to the beginning and ending time of heating mid-term, heating mid-term outside air temperature is minimum, average outdoor
Temperature is -16 DEG C.In The Initial Period Of Heating and the average outdoor temperature in heating latter stage are respectively 1.4 DEG C and 2.2 DEG C.
Step 2: it is investigation sample that this example, which chooses 10 family house of regions in Harbin City, totally 20 subjects.It is adopted by data
Collect module, converts digital signal for the environmental information parameter at this 10 family.
Meanwhile by questionnaire collection module, digital information is converted by the Subjective Thermal Feeling of 20 subjects.
Step 3: above- mentioned information are input in evaluation module.Statistical data obtains environmental parameter and Subjective Thermal Feeling,
As shown in table 1.
1 thermal environment parameter of table and Subjective Thermal Feeling statistical data
The adaptive thermal comfort model of three phases during heating can be calculated, as shown in Figure 3.
Therefore, the adaptive thermal comfort model of three phases and thermal neutral temperature such as 2 institute of table during the house heating of Harbin
Show.
The adaptive thermal comfort model and thermal neutral temperature of 2 heating season three phases of table
In formula: x --- indoor air temperature；
Y --- human thermal sensation's predicted value；
Above-mentioned example of the invention only explains computation model and calculation process of the invention in detail, and is not to this
The restriction of the embodiment of invention.It for those of ordinary skill in the art, on the basis of the above description can be with
It makes other variations or changes in different ways, all embodiments can not be exhaustive here, it is all to belong to the present invention
The obvious changes or variations extended out of technical solution still in the scope of protection of the present invention.
Claims (4)
1. the prediction technique of the severe cold area winter indoor thermal comfort temperature based on hot adaptability, which is characterized in that this method packet
Include following steps:
Step 1: outdoor daily mean temperature daily in acquisition severe cold area heating season；
Step 2: heating season is divided into In The Initial Period Of Heating, in heating according to outdoor daily mean temperature variation daily in heating season
Phase and heating 3 stages of latter stage；
Step 3: being continuously monitored in heating process to indoor air temperature and indoor air relative humidity, to room air
Flow velocity and indoor black ball temperature carry out interval test；
Step 4: being investigated according to the thermal response ballot scale in hot comfortable standard the hotness of subject；
Step 5: establishing the average hotness ballot value and Interior Space temperature of In The Initial Period Of Heating, heating mid-term and latter stage of heating respectively
The weighed regression model of degree obtains the thermal neutral temperature of In The Initial Period Of Heating, heating mid-term and latter stage of heating, and utilizes neutral
Temperature finds out the section of thermal comfort temperature.
2. the prediction technique of the severe cold area winter indoor thermal comfort temperature according to claim 1 based on hot adaptability,
It is characterized in that, the detailed process of the step 2 are as follows:
Heating season is divided into In The Initial Period Of Heating, heating mid-term and heating end according to heating season daily outdoor daily mean temperature variation
3 stages of phase；
Using 5 days average outdoor daily mean temperatures of sliding as Classification Index；
If certain day 5 day averages of continuous 2 slidings are below 5 DEG C, In The Initial Period Of Heating is initially entered；
Into after In The Initial Period Of Heating, if certain day 5 day averages of continuous 2 slidings are below -10 DEG C, initially enter in heating
Phase；
Into after heating mid-term, if certain day 5 day averages of continuous 2 slidings are not less than -10 DEG C, judge to enter heating end
Phase, until certain day 5 day averages of continuous 2 slidings are not less than 5 DEG C, heating latter stage terminates.
3. the prediction technique of the severe cold area winter indoor thermal comfort temperature according to claim 2 based on hot adaptability,
It is characterized in that, carrying out interval test, concrete mode to room air flow velocity and indoor black ball temperature in the step 3 are as follows:
The room air flow velocity of test in every 2~3 weeks and indoor black ball temperature, the testing time of each room air flow velocity are 3 to 5 points
Clock, the testing time of interior black ball temperature is 10 to 20 minutes every time.
4. the prediction technique of the severe cold area winter indoor thermal comfort temperature according to claim 3 based on hot adaptability,
It is characterized in that, the detailed process of the step 5 are as follows:
Weighed regression model are as follows:
yi=a+bxi+ei/wi
Wherein: xiFor indoor air temperature, yiFor the corresponding average hotness ballot value of each indoor air temperature, eiFor residual error, a
It is linear regression coeffficient, w with biFor the corresponding ballot sample size of each air themperature；
Wherein:For yiEstimated value；
Then residual error eiIt indicates are as follows:
Residual sum of squares (RSS) Q is indicated are as follows:
Wherein: N is temperature range number；
According to extremum principle, partial derivative is asked to linear regression coeffficient a and b:
Then the coefficient of determination of weighted linear regression coefficient and regression equation is expressed as follows:
Wherein: R2For the coefficient of determination of regression equation；
According to the In The Initial Period Of Heating established respectively, the hotness investigation result of the mid-term that heats and latter stage of heating and indoor air temperature
Weighed regression model obtains the thermal neutral temperature of In The Initial Period Of Heating, heating mid-term and latter stage of heating, according to weighted linear regression
Model, enabling average hotness is -0.5 to 0.5, finds out corresponding thermal comfort temperature section.
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Cited By (4)
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| 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 |
| CN112395732A (en) * | 2020-06-12 | 2021-02-23 | 香港城市大学深圳研究院 | Thermal comfort prediction method and device for enhancing thermal neutral adaptability |
| CN113623719A (en) * | 2021-06-23 | 2021-11-09 | 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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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110298128A (en) * | 2019-07-04 | 2019-10-01 | 香港城市大学成都研究院 | A kind of adaptive thermal comfort prediction model construction method |
| CN110298128B (en) * | 2019-07-04 | 2023-05-30 | 香港城市大学成都研究院 | Construction method of adaptive thermal comfort prediction model |
| CN112395732A (en) * | 2020-06-12 | 2021-02-23 | 香港城市大学深圳研究院 | Thermal comfort prediction method and device for enhancing thermal neutral adaptability |
| 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 |
| CN113623719A (en) * | 2021-06-23 | 2021-11-09 | 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 |
| 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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