CN111046536A - Thermal comfort modeling method based on variable adaptive factors and predicted average voting value - Google Patents
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Abstract
The invention discloses a thermal comfort modeling method based on a changed adaptive factor and a predicted average voting value, which comprises the following steps of:
constructing a thermal comfort model naPMV, wherein lambda is an adaptive factor which linearly changes with the reciprocal of the ambient temperature T:
wherein p and q are constant parameters, and both are calculated according to the following formulas:
in the formula PMVi、TSViAnd TiRespectively the ith thermal sensation prediction average voting value and the thermal sensation actual average voting value collected in the field researchAnd ambient temperature; acquiring n PMV, TSV and ambient temperature T data sets in total;
are each PMVi、TSViA thermally-sensed predicted average vote value and a thermally-sensed actual average vote value that are both greater than zero after the preprocessing. The invention combines an adaptability method and an inference method, introduces a changed adaptability factor into an inference method model PMV, and thereby constructs a new thermal comfort model naPMV. The napMV model improves the capability of the model PMV in explaining thermal adaptation, and can predict thermal comfort more accurately and reliably.
Description
Technical Field
The invention relates to the field of indoor thermal comfort prediction, in particular to a thermal comfort modeling method based on a changed adaptive factor and a predicted average voting value.
Background
Thermal comfort is one of important indexes for evaluating indoor environment quality, and influences the health and working efficiency of indoor personnel. Thermal adaptation is defined as the phenomenon in which the human body gradually responds to repeated stimuli of the thermal environment in a decreasing manner, and includes three aspects, namely physiology, psychology and behavior. Thermal adaptation helps the body to regain thermal comfort when thermal discomfort is caused by a change in the thermal environment.
The thermal adaptation can widen the thermal comfort zone, thereby being beneficial to energy conservation. Thermal adaptation can raise the upper limit of acceptable indoor temperature (operational temperature) from about 27 ℃ to 32 ℃ as specified by the thermal comfort standard ASHRAE 55. However, the existing research shows that the energy consumption of the air conditioner can be reduced by about 5-10% when the indoor temperature is increased by 1 degree. Therefore, a thermal comfort model considering thermal adaptation is a guarantee for developing thermal comfort, low energy consumption buildings.
At present, the thermal comfort modeling method for people can be divided into an adaptive approach (adaptive approach) and a rational approach (rational approach).
The adaptive method establishes a thermal comfort model based on field research, and aims to analyze a thermal comfort state in a real environment. The adaptive approach can take into account the impact of thermal adaptation on thermal comfort. The adaptive method linearly relates thermal comfort to ambient temperature (indoor or outdoor temperature). The slope of the linear model characterizes the degree of thermal adaptation, and the slope and intercept collectively characterize thermal adaptation under the influence of various factors, such as cultural background, climate zone, and building type. Thus, thermal comfort under thermal adaptation is dynamically a function of ambient temperature. However, the obvious drawback of the adaptive approach is that it ignores the important influence of human heat balance related factors such as indoor air velocity, humidity and human metabolic rate on thermal comfort.
The inference method realizes thermal comfort prediction by associating human body thermal balance with thermal regulation. The Predicted Mean Vote value (Predicted Mean volume-PMV) is a widely adopted thermal comfort model established based on an inference method, and is adopted by domestic and foreign thermal comfort standards at present, such as national standard GB/T50785, international standard ISO 7730, united states standard ashore 55, european standard EN 15251 and the like. The input to the PMV includes four environmental parameters (air temperature, air velocity, radiation temperature and humidity) and two personnel parameters (metabolic rate and clothing thermal resistance). Thus, the inference method can consider the impact of more parameters on thermal comfort than the adaptive method. However, the reasoning method is established based on environmental bin experiments, and thermal adaptation cannot be fully explained.
In order to expand the ability of the reasoning method to explain the thermal adaptation, the Yaohun Mingzhou university of Chongqing proposed a thermal comfort model aPMV. The Yaohun Ming teach is based on the negative feedback control theory, introduces an adaptive factor into the PMV model to explain the thermal adaptation caused by social, economic and cultural backgrounds, thermal experience, thermal expectation and other factors, thereby forming aPMV. Currently, the aPMV model is adopted by the national standard GB/T50785-2012.
However, the fitness factor for aPMV is a constant value for a given climate zone and building type, indicating that aPMV assumes that the thermal fitness is static. But in a real-world environment, thermal adaptation is dynamic. Therefore, the existing thermal comfort model aPMV cannot fully explain thermal adaptation, that is, cannot consider the dynamic property of thermal adaptation, so that the dynamic thermal comfort under the action of dynamic thermal adaptation cannot be accurately predicted.
Disclosure of Invention
The invention aims to provide a thermal comfort modeling method based on a changed adaptive factor and a predicted average voting value so as to integrate the advantages of the adaptive method and the reasoning method, so that the reasoning method can comprehensively explain thermal adaptation (namely the dynamic property of the thermal adaptation), and further improve the thermal comfort prediction performance.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the thermal comfort modeling method based on the changed adaptive factors and the predicted average voting value is based on a Fanger predicted average voting value PMV model, and a new thermal comfort model naPMV is constructed according to the following formula:
where λ is an adaptation factor that varies linearly with the inverse of the ambient temperature T:
in the formula, q and p are normal parameters.
The constant parameters q and p are respectively calculated according to the following formulas:
in the formula, PMVi、TSViAnd TiRespectively collecting the ith heat sensation prediction average voting value, the heat sensation actual average voting value and the environment temperature in field research; acquiring n PMV, TSV and ambient temperature T data sets in total;
are each PMVi、TSViA thermally-sensed predicted average vote value and a thermally-sensed actual average vote value that are both greater than zero after the preprocessing.
The aPMV developed by the teachings of yaohun was formed by introducing an adaptive factor λ into PMV, as shown in equation 1. The adaptability factor lambda is the ratio of the thermal adaptation K and the thermal environment stimulation delta, such as the formula2, respectively. However, the fitness factor λ of aPMV developed by professor yao is a constant value and does not account for the dynamics of thermal fitness. According to the adaptive method, the present invention linearly relates the thermal adaptation K to the ambient temperature T, as shown in equation 3. The thermal environment stimulus δ may be an ambient temperature T, as shown in equation 4. By integrating the formulas 2-4, the invention provides a dynamically changing adaptive factor, namely the adaptive factor is linearly related to the reciprocal of the ambient temperature T:
the dynamic adaptability factor can explain the dynamic of thermal adaptation with the change of ambient temperature. The ambient temperature T here may be an indoor temperature such as an air temperature or an operating temperature (operating temperature), or an outdoor temperature such as a monthly average outdoor air temperature or an outdoor average operating temperature (running mean outdoor operating temperature).
K＝pT+q(3)
δ＝T (4)
In the formula
In the process, q and p are determined according to data collected by field research, and the process is as follows:
the collected thermal sensing prediction average vote value PMV and the actual thermal sensing average vote value TSV are first preprocessed to a value greater than zero. The preprocessing is to eliminate the negative influence of the inconsistency of PMV and TSV signs on the model performance. Since the absolute values of PMV and TSV are typically less than 5, the pre-processing of PMV and TSV is as shown in equations 5 and 6. The preprocessing of the newly developed model naPMV is equation 7. Thus the preprocessed naPMV can be expressed as equation 8. Then the objective function
(shown in equation 9) to determine q and p. Equation 9 can be converted to equation 10. Because in equation 10
Tending towards 1, the minimization objective function is essentially to minimize the difference between the preconditioned naPMV and the preconditioned TSV. Combining equation 8 and equation 10, the objective function
And may be further converted into equation 11. In order to minimize the objective function
The derivatives of q and p of formula 11 should both be 0, so that the respective calculation formulas for q and p can be obtained (as described above).
After the constant parameters q and p are obtained through calculation, according to a formula:
and a new thermal comfort model naPMV can be constructed.
Compared with the prior art, the invention has the following beneficial effects:
the adaptive thermal comfort model naPMV with the variable adaptive factor lambda is constructed by adding the adaptive factor lambda which can be linearly changed along with the reciprocal of the ambient temperature T on the basis of a predicted average voting value PMV model of Fanger. The adaptability factor of the existing adaptive thermal comfort model aPMV is fixed, and the dynamic property of thermal adaptation cannot be explained; therefore, compared with the prior art, the thermal comfort model naPMV based on the changed adaptive factors and the predicted average voting value, which is constructed by the invention, can explain the dynamic property of thermal adaptation, so that the thermal comfort can be predicted more accurately.
Drawings
Fig. 1 is a schematic flow chart of the present invention for constructing a thermal comfort model.
FIG. 2 is a graph comparing the predicted thermal comfort effect of the newly developed thermal comfort model naPMV of example 1 of the present invention with the thermal comfort models aPMV of fixed fitness factor developed by YaoMing teach and thermal comfort model PMV of Fanger.
FIG. 3 is a graph comparing the predicted thermal comfort effect of the newly developed thermal comfort model naPMV of example 2 of the present invention with the thermal comfort models aPMV of fixed fitness factor developed by the teaching of YaoMing and Fanger.
Detailed Description
The present invention will be further described with reference to the following description and examples, which include but are not limited to the following examples.
Examples
The invention provides a novel thermal comfort model, which can comprehensively explain thermal adaptation so as to predict thermal comfort more accurately. The method of the present invention for constructing a thermal comfort model comprises three steps (as shown in fig. 1): first, preprocessing collected PMV, TSV and ambient temperatureT data set, making PMV and TSV are larger than zero (formula 12, 13); second, using the collected ambient temperature T and the pre-treated
Respectively calculating constant parameters q and p according to formulas 14 and 15; and thirdly, constructing a thermal comfort model naPMV according to a formula 16 by using the obtained constant parameters q and p.
The advantages of the invention are explained in two cases below. The case will compare the thermal comfort prediction effect of the newly developed thermal comfort model naPMV, the thermal comfort model aPMV of fixed adaptability factor developed by Yaojinggangzhi teaching and the thermal comfort model PMV of Fanger using the mean absolute error MAE and the error standard deviation SD. The average absolute error MAE is used for evaluating the accuracy of the thermal comfort prediction model and is calculated according to a formula 17; the smaller the MAE, the higher the accuracy of the thermal comfort prediction model. The error standard deviation SD is used for evaluating the reliability of the thermal comfort prediction model and is calculated according to a formula 18; the smaller the SD, the higher the reliability of the thermal comfort prediction model.
Errori＝Predictioni-TSVi(19)
Where Error is the Error, calculated according to equation 19, where Prediction may be naPMV, aPMV or PMV; the index i refers to the ith pair of data sets, for a total of n pairs of data sets.
The PMV, TSV and ambient temperature T data sets in case 1 come from a natural ventilation building, which is a five-story teaching building at the university of Chongqing. The PMV is obtained by calculation based on physical measurement and a Fanger formula; TSV is from subjective heat-sensation voting by college students; the ambient temperature T refers to the indoor air temperature. And calculating aPMV developed by the teaching of Yaojing according to the PMV and the TSV. For specific details regarding PMV, TSV, ambient temperature T and aPMV, reference is made to the literature of professor yaohun et al: yao R, LiB, Liu J.2009.A the organic adaptive model of thermal comfort-adaptive predicted Mean volume (aPMV). Building and Environment,44(10), 2089-.
Fig. 2 shows that aPMV is closer to TSV relative to PMV both in winter and summer, i.e., aPMV can better predict TSV. The mean absolute error MAE of the PMV predicted TSV is 0.59 and the standard deviation SD is 0.55. aPMV reduces the mean absolute error MAE to 0.33 and the standard deviation SD to 0.31. However, fig. 2 also clearly shows the problem of aPMV, i.e., the distance that aPMV deviates from the TSV increases with increasing TSV in summer. This indicates that a fixed adaptation factor lacks the ability to account for the dynamics of thermal adaptation. The constant parameters q and p of the varying adaptability factor, calculated from equations 14 and 15, as a function of the reciprocal of the ambient temperature T, are-0.684 and 0.036, respectively, based on the PMV, TSV and ambient temperature T. And calculating to obtain the newly developed thermal comfort model napMV according to the formula 16 by using the obtained q and p. The varying adaptability factor enables the newly developed naPMV model to account for the dynamics of thermal adaptation, thereby improving thermal comfort prediction performance. The average absolute error MAE of the predicted TSV of the naPMV is 0.16, and the standard error SD is 0.16; compared with aPMV, the accuracy of predicting TSV is further improved by 49.6% through naPMV, and the reliability of predicting TSV is improved by 46.6%.
Therefore, in the natural ventilation building, the thermal comfort model naPMV constructed according to the method effectively improves the accuracy and reliability of thermal comfort prediction.
The PMV, TSV ambient temperature T data set in case 2 is from the air conditioning building. The air-conditioning building is an office building of seoul in korea. PMV is obtained by calculation based on physical measurement and a formula of Fanger; TSV comes from subjective heat-sensation voting; the ambient temperature T refers to the indoor air temperature. And calculating aPMV developed by the teaching of the YaoMing according to the PMV and TSV data set. For specific details of PMV, TSV, ambient temperature T and aPMV, reference is made to Kim et al: kim JT, Lim JH, ChoSH, YungGY.2015. development of the adaptive PMV model for improving performance and Buildings,98, 100. 105.).
Fig. 3 shows that alpmv can better predict TSV versus PMV both in winter and summer. The mean absolute error MAE of the PMV predicted TSV is 1.03, and the standard deviation SD is 0.70. aPMV reduces the mean absolute error MAE to 0.23 and the standard deviation SD to 0.27. However, fig. 3 also clearly shows the problem of aPMV, i.e., the distance that aPMV deviates from the TSV increases as the TSV decreases in winter; the distance that the aPMV deviates from the TSV increases in summer as the TSV increases. This indicates that a fixed adaptation factor lacks the ability to account for the dynamics of thermal adaptation. The constant parameters q and p (equations 14 and 15) for the varying fitness factor, calculated from equations 14 and 15, to vary linearly with the reciprocal of the ambient temperature T are-5.326 and 0.240, respectively, based on the PMV, TSV and ambient temperature T. And calculating to obtain a newly developed thermal comfort model napMV according to a formula 16 by using the obtained q and p. The varying adaptability factor enables the newly developed model naPMV to explain the dynamics of thermal adaptation, thereby improving thermal comfort prediction performance. The average absolute error MAE of the predicted TSV of the naPMV is 0.17, and the standard error SD is 0.12; compared with aPMV, the accuracy of predicting TSV is further improved by 24.8% through naPMV, and the reliability of predicting TSV is improved by 56.6%.
Therefore, in the air-conditioning building, the thermal comfort model naPMV constructed according to the method effectively improves the accuracy and reliability of thermal comfort prediction.
In conclusion, the invention combines an adaptability method and an inference method to construct a new thermal comfort model naPMV; the thermal comfort model can fully explain thermal adaptation in terms of both natural ventilation buildings and air-conditioned buildings, thereby predicting thermal comfort more accurately. Compared with the prior art, the invention has obvious technical progress, and has outstanding substantive characteristics and remarkable progress.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or changes made within the spirit and scope of the main design of the present invention, which still solve the technical problems consistent with the present invention, should be included in the scope of the present invention.
Claims (2)
1. The thermal comfort modeling method based on the changed adaptive factors and the predicted average voting value is based on a Fanger predicted average voting value PMV model, and a thermal comfort model naPMV is constructed according to the following formula:
wherein λ is an adaptation factor; characterized in that the adaptability factor λ is linearly variable with the reciprocal of the ambient temperature T:
in the formula, q and p are normal parameters.
2. A thermal comfort modeling method based on varying fitness factors and predicted mean vote values according to claim 1, characterized in that constant parameters q and p are calculated according to the following formulas:
in the formula, PMVi、TSViAnd TiRespectively collecting the ith heat sensation prediction average voting value, the heat sensation actual average voting value and the environment temperature in field research; acquiring n PMV, TSV and ambient temperature T data sets in total;
are each PMVi、TSViA thermally-sensed predicted average vote value and a thermally-sensed actual average vote value that are both greater than zero after the preprocessing.
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