Adaptive thermal comfort prediction model construction method

A prediction model and construction method technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as underestimation of thermal discomfort, deterioration of prediction performance, unreasonable adaptability factors, etc., to achieve accuracy and reliability High degree of accuracy, predictive performance improvement effect

Active Publication Date: 2019-10-01
香港城市大学成都研究院
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  • Application Information

AI Technical Summary

Problems solved by technology

[0022] The purpose of the present invention is to provide a method for constructing an adaptive thermal comfort prediction model to overcome the problem that aPMV’s prediction performance deteriorates as it deviates from thermal neutrality and underestimates thermal discomfort due to the unreasonable adaptive factors introduced by aPMV, so as to achieve The purpose of improving thermal comfort prediction performance

Method used

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  • Adaptive thermal comfort prediction model construction method
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  • Adaptive thermal comfort prediction model construction method

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Embodiment 1

[0059] Such as figure 1 As shown, a method for constructing an adaptive thermal comfort prediction model disclosed in the present invention, the adaptive thermal comfort prediction model naPMV is PMV and The ratio of , using the following formula to calculate the adaptability factor

[0060]

[0061] In the formula, PMV is the predicted average voting value of the PMV model; TSV is the actual average thermal sensation voting value; n is the total number of acquired PMV and TSV data sets; i is the i-th group of PMV and TSV data sets.

[0062] In this example, the fitness factor is calculated The process is as follows:

[0063] (a) First get the modeling purpose of naPMV:

[0064]

[0065] Equation (8) essentially adds a new weight w to the deviation between naPMV and TSV i,p :

[0066]

[0067] Get the new weight w according to formula (9) i,p The expression of is as follows:

[0068]

[0069] Since naPMV is used to accurately predict TSV, the new weight ...

Embodiment 2

[0100] The PMV and TSV data sets in this embodiment come from air-conditioned buildings. The air-conditioned building is an office building in Seoul, South Korea. PMV is based on physical measurements, calculated according to Fanger's formula, and TSV is derived from subjective thermal sensation voting. For specific PMV and TSV calculations, refer to the article by Kim et al.: Kim JT, Lim JH, Cho SH, Yun GY. 2015. Development of the adaptive PMV model for improving prediction performances. Energy and Buildings, 98, 100-105.

[0101] Based on the PMV and TSV data sets, Kim et al. calculated aPMV. For the calculation process of specific aPMV, please refer to the article by Kim et al.: Kim JT, Lim JH, Cho SH, Yun GY. 2015. Development of the adaptive PMV model for improving prediction performances. Energy and Buildings, 98, 100-105.

[0102] exist image 3 Among them, the average absolute error MAE of PMV predicting TSV is 0.66, and the error standard deviation SD is 0.34; the...

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Abstract

The invention discloses an adaptive thermal comfort prediction model construction method, and the method comprises the steps: enabling an adaptive thermal comfort prediction model naPMV to be the ratio of PMV to PMV, and employing the following formula (shown in the specification) to calculate the adaptive factor phi, wherein in the formula, PMV is the predicted average voting value of a PMV model, the TSV is an actual average thermal sensation voting value, n is the total number of the acquired PMV and TSV data sets, i is the ith PMV and TSV data set. Furthermore, the calculation steps of theadaptive factor phi are as follows: (1) dividing the acquired PMV and TSV data groups into three types according to three conditions that PMV = 0, PMV< 0, PMV> 0; (2) if the PMV=0, enabling the calculated naPMV to be equal to the PMV; if the PMV < 0, enabling the result calculated according to the formula of the adaptive factor phi to be PMV <naPMV<0, and if the PMV>0, the result calculated according to the formula of the adaptive factor phi is 0<naPMV<PMV. Experiments prove that the method is high in accuracy and reliability, and the thermal comfort prediction performance is improved.

Description

technical field [0001] The invention relates to the field of indoor thermal comfort prediction, in particular to a method for constructing an adaptive thermal comfort prediction model. Background technique [0002] Providing indoor thermal comfort is an important task in free-running buildings and air-conditioned buildings. Accurate and reliable thermal comfort prediction is a necessary condition for creating a comfortable indoor environment. Inaccurate and unreliable predictions of thermal comfort can lead to uncomfortably hot or cold rooms and waste energy. [0003] There are two main types of existing thermal comfort prediction models, namely thermal balance models and thermal adaptation models. [0004] Fanger's predictive mean model, or PMV model, is a widely used heat balance model. PMV is calculated from four indoor environmental parameters (air temperature, velocity, relative humidity, and mean radiant temperature) and two personnel-related parameters (activity in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 林章张胜程勇
Owner 香港城市大学成都研究院
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