User multi-dimensional comfort calculation method based on subjective and objective weight separation

Abstract

The invention discloses a user multi-dimensional comfort calculation method based on subjective and objective weight separation. The method comprises the following steps: calculating a thermal comfort index of a user based on predicted average evaluation; a visual comfort index and an air quality comfort index are constructed based on the Weber-Fechner law, the visual comfort index of a user is calculated according to subjective evaluation of the building user for different illumination intensities and building light environment parameters, and the air quality comfort index of the user is calculated according to building air environment parameters and pollutant parameters. Calculating an air quality comfort index of the user; normalizing the thermal comfort index, the visual comfort index and the air quality comfort index of the user to obtain a thermal comfort normalized index, a visual comfort normalized index and an air quality comfort normalized index, and determining the influence of objective intensity factors on the comfort of the user; and according to the preference of the user for the comfort, setting the weight of each dimension, constructing a human body sensory comfort model integrating various sensory indexes, and determining the influence of subjective preference factors on the comfort of the user.

Description

Technical Field

[0001] The invention relates to the field of building energy optimization, and in particular to a method for calculating multi-dimensional comfort of users with subjective and objective weight separation. Background Art

[0002] Comfort index refers to an index that is formulated under the comprehensive effect of various environmental factors to evaluate the degree of influence of one or more factors in a specific environment on the human body. Comfort index is an important indicator in the process of building energy optimization, which is used to coordinate the contradiction between energy cost and human comfort in the process of energy optimization. At present, there are two problems with the comfort index in the existing building energy optimization methods:

[0003] 1. The influencing factors of comfort index are monotonous, and the scientific relationship between "stimulus intensity-human perception" is not established. Specifically, when constructing the comfort index, the nominal (physical) intensity that affects the comfort is used as an indicator, and the impact of stimulus intensity on human perception is not further considered. For example, the light intensity itself is used as a visual comfort indicator, and the CO2 concentration itself is used as a visual comfort indicator. There is a lack of modeling process for stimulus intensity and human perception; at the same time, the index construction in existing research is relatively simple, and there is a lack of comprehensive consideration of the impact of various dimensional indicators on human comfort, which affects the optimization effect of building energy use.

[0004] Second, the weight setting principle is not clear when considering multi-dimensional comfort factors. Specifically, in the existing comfort index setting for energy optimization, the comfort loss caused by comfort satisfaction is not distinguished from the subjective needs of users, and each dimension of comfort is not optimized at the same level. Instead, fuzzy weights are used to balance the level of each dimension with the priority of user needs, which makes it difficult to modify the weights in the specific optimization process and cannot dynamically reflect the user's preference for different comfort levels in different time and space environments. Therefore, the existing comfort calculation method for measuring multi-dimensional comfort has the problem of unclear weight setting principle.

[0005] The above problems affect the actual effectiveness of the calculated human sensory comfort and the design and arrangement of building energy optimization strategies. Summary of the invention

[0006] Purpose of the invention: The present invention aims to provide a user comfort calculation method that separates the subjective and objective weights of human sensory comfort by considering the multi-dimensional comfort of users in a building.

[0007] Technical solution: The method for calculating user multi-dimensional comfort with subjective and objective weight separation of the present invention comprises the following steps:

[0008] According to the water vapor pressure P of the environment where the residents are located a , the temperature inside the building where the human body is located t a; The surface temperature of the building after being radiated is t r , the user's thermal comfort index PMV is calculated based on the predicted mean evaluation;

[0009] The visual comfort index is constructed based on the Weber-Fechner law, and the user's visual comfort index V is calculated according to the building user's subjective evaluation of different light intensities and the building light environment parameters;

[0010] Based on the Weber-Fechner law and indoor air quality standards, the air quality comfort index is constructed, and the user's air quality comfort index AQCI is calculated according to the building air environment parameters and pollutant parameters;

[0011] The user's thermal comfort index PMV, visual comfort index V and air quality comfort index AQCI are normalized to obtain the thermal comfort index PMV, visual comfort index V and air quality comfort index AQCI. Normalized index of visual comfort and normalized index of air quality comfort Determine the impact of objective intensity factors on user comfort;

[0012] According to the user's preference for comfort, the weight of each dimension is set to build a human sensory comfort model that integrates multiple sensory indicators Determine the impact of subjective preference factors on user comfort.

[0013] Furthermore, the user's visual comfort index V is

[0014] V(E t , ΔE, UGR)

[0015] =[a0+a1lnE t +a2(lnE t ) 2 +a3(lnE t ) 3 +C]·δ(ΔE)·ε(UGR)

[0016]

[0017] Among them, E t is the indoor light intensity; ΔE is the absolute value of the change in light intensity during instantaneous adjustment; UGR is the unified glare value; a0, a1, a2, a3 are the coefficients of C is the polynomial model of visual comfort; δ(·) is the correction function of the instantaneous change of light intensity to comfort; ε(·) is the correction function of the unified glare value to comfort; E limit The upper limit of indoor light intensity acceptable to users; UGR limit The lower limit of the unified glare value attenuation; UGR max It is the acceptable upper limit of the unified glare value.

[0018] Furthermore, the user's air quality comfort index AQCI is

[0019] AQCI=AQCI * ·ε(e CO )·ε(e PM2.5 )

[0020]

[0021] Among them, AQCI * Calculate the overall air quality comfort index for the air quality sub-index; e CO is the actual CO emission rate of the building; e PM2.5 is the actual PM2.5 emission rate of the building; e CO,max is the CO target emission rate standard; e PM2.5,max It is the target emission rate standard for PM2.5.

[0022] Furthermore, the air quality sub-indicators are used to calculate the overall air quality comfort index AQCI * for

[0023]

[0024] The air quality comfort index of the i-th pollutant x is

[0025] IAQCI i (x) = [b i,0 +b i,1 i i +b i,2 (lnI i ) 2 +b i,3 (lnI i ) 3 +C i ]

[0026] Among them, b i,j With C i is the polynomial model coefficient of the i-th air quality comfort index, j = 0, 1, 2, 3, I i is the factor intensity of the i-th pollutant.

[0027] Furthermore, the thermal comfort for

[0028]

[0029] Furthermore, the visual comfort normalization index for

[0030]

[0031] Among them, E best It is the maximum value of the visual comfort index.

[0032] Furthermore, the air quality comfort normalization index for

[0033]

[0034] Furthermore, the human sensory comfort model integrating multiple sensory indicators for

[0035]

[0036] Among them, ω thermal,t represents the thermal comfort weight at time t, ω light,t represents the visual comfort weight at time t, ω air,t Represents the air quality comfort weight at time t.

[0037] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention uses predicted average evaluation to measure user thermal comfort; introduces the Weber-Fechner law to construct a visual and air quality comfort model, and obtains corresponding polynomial model coefficients based on user subjective evaluation, thereby accurately reflecting the relationship between nominal (physical) intensity and various types of comfort, accurately measuring user visual and air quality comfort, and solving the problem of converting stimulus intensity into human perception; 2. When calculating the human sensory comfort of aggregated multi-dimensional comfort, the present invention uses a normalization method based on reasonable acceptable range selection and per-unit value selection, so that each dimensional comfort can be optimized at the same level. In the aggregation process, the comfort loss is distinguished from the subjective needs of users, the comfort loss is represented by the normalized comfort values ​​of each dimension, and the subjective preferences of users are represented by the comfort weights of each dimension, so as to solve the problem of difficulty in modifying weights in a specific optimization process. The user's preferences for different comfort levels in different time and space environments are dynamically reflected through flexible setting of weights, which solves the problem of unclear weight setting principles in existing comfort calculation methods for measuring multi-dimensional comfort. 3. The present invention also better reflects the multi-dimensional comfort perception of users in the building, takes indoor temperature, light intensity and air quality indicators into consideration, and ensures the real effectiveness of the human sensory comfort obtained by the comfort calculation method, which is beneficial to the design and arrangement of building energy optimization strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the building energy optimization results using the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] The method for calculating user multi-dimensional comfort with subjective and objective weight separation of the present invention comprises the following steps:

[0041] (1) Based on the actual operation of the building and the data that can be collected by the IoT devices, the building's thermal, light, and air environment parameters are collected, including: the water vapor pressure P of the environment in which the residents live a ; Temperature inside the building where the human body is located t a ; The surface temperature of the building after being radiated is t r ; Indoor light intensity E t ; Indoor relative humidity hum; Indoor carbon dioxide concentration c CO2 ; Carbon monoxide concentration c CO ; Formaldehyde concentration c HCHO ; Inhalable particle concentration c PM wait.

[0042] (2) Based on the predicted average evaluation and the collected building thermal environment parameters P a , t a , t r , calculate the user’s thermal comfort index PMV.

[0043] (3) The subjective evaluation of the building users on different light intensities was collected, and the visual comfort index model V(E) was obtained by combining the third-order polynomial model of the Weber-Fechner law to reflect the relationship between light intensity and user visual comfort. t , ΔE, UGR), based on the building light environment parameter E t Calculate ΔE, UGR and other data and input them into this indicator to calculate the user's visual comfort index V.

[0044] V(E t , ΔE, UGR)

[0045] =[a0+a1lnE t +a2(lnE t ) 2 +a3(lnE t ) 3 +C]·δ(ΔE)·ε(UGR)

[0046]

[0047]

[0048] Among them, E t is the indoor light intensity; E limit The upper limit of indoor light intensity that users can accept; a i(i=0,1,2,3,…) and C are the coefficients of the visual comfort polynomial model; ΔE is the absolute value of the change in illumination intensity during instantaneous adjustment; UGR is the unified glare value; UGR limit The lower limit of the unified glare value attenuation; UGR max It is the acceptable upper limit of the unified glare value.

[0049] (4) Construct the air quality sub-index IAQCI based on the main air quality pollutants measured during building operation i (I i ), based on the air quality index model, the air quality comfort index model AQCI (AQCI * ,e CO ,e PM2.5 ), input building air environment parameters hum, c CO2 , c CO , c HCHO , c PM Etc., calculate the user's air quality comfort index AQCI.

[0050] First, the components of the air quality sub-index are determined by combining the data collected by sensors in the building with the main pollutants in the building. The air quality comfort sub-index of a single pollutant is constructed as follows:

[0051] IAQCI i (I i )=[b i,0 +b i,1 i i +b i,2 (lnI i ) 2 +b i,3 (lnI i ) 3 +C i ]

[0052] Among them, b i,j (j=0,1,2,3,…) and C i is the polynomial model coefficient of the i-th air quality comfort index, I i is the strength of the i-th factor.

[0053] Then, the overall air quality comfort index is calculated based on the air quality sub-indicators. The construction method is as follows: AQCI * (I1,I2,I3,I4,I5,...)=min[IAQCI1(hum),IAQCI2(c CO2 ),IAQCI3(c CO ),

[0054] IAQCI4(c HCHO),IAQCI(c PM )5,……]

[0055] Next, considering the impact of pollutant emission rate on the breathing quality comfort index, taking CO and PM2.5 as examples, the original air quality comfort index is revised as follows:

[0056] AQCI * ,e CO ,e PM2.5 )=AQCI * ·ε(e CO )·ε(e PM2.5 )

[0057]

[0058] Among them, e CO is the actual CO emission rate of the building; e PM2.5 is the actual PM2.5 emission rate of the building; e CO,max is the CO target emission rate standard; e PM2.5,max It is the target emission rate standard for PM2.5.

[0059] (5) The calculated thermal comfort index PMV, visual comfort index V and air quality comfort index AQCI are normalized to obtain the normalized thermal comfort index Normalized index of visual comfort Normalized index with air quality comfort

[0060] First, the thermal comfort index is normalized. Based on my country's relevant regulations on PMV, the recommended value of PMV is -1≤PMV≤+1. If the PMV value exceeds this range, the thermal environment cannot meet the thermal needs of most users, that is, they are dissatisfied with the thermal environment. Therefore, the thermal comfort index is modified as follows:

[0061]

[0062] Then, the visual comfort index is normalized. Without considering the influence of light flicker or glare on comfort, the maximum value of the visual comfort index is E best Corresponding values, so the normalization for visual comfort is as follows:

[0063]

[0064] Finally, for air quality comfort, the comfort index value range is [0, +1]. When AQCI = 0, the residents are unacceptable to the air quality conditions, that is, they are dissatisfied with the air quality. When AQCI = 1, the residents are satisfied with the air quality. Therefore, the air quality comfort index has met the normalization requirements:

[0065]

[0066] (6) Based on the user's preference for comfort in different situations, determine the comfort weight of each dimension at each moment, ω thermal，t ,ω light，t With ω air，t , substitute the normalized indicators of each dimension To obtain the human sensory comfort index Complete the calculation.

[0067]

[0068] Among them, ω thermal,t represents the thermal comfort weight at time t, ω light,t represents the visual comfort weight at time t, ω air,t It represents the air quality comfort weight at time t. The weight setting can be dynamically adjusted according to the change of user preferences.

[0069] An office building in southern China was used as a calculation scenario to evaluate the comfort calculation method. During the experiment, the present invention was used as a component of the optimization objective function to optimize the energy consumption behavior of the building in the next day. The basic situation of the building is as follows: the building area is 528.08 square meters, there are two floors above ground, and the building height is 7.2 meters. The main function of the building is to provide office and meeting space. The building is an energy-saving building. Photovoltaic tiles and photovoltaic railings are installed as photovoltaic power generation equipment, and energy storage equipment is configured. The building envelope has been energy-saving transformed, and insulation measures have been adopted for the exterior walls, roofs, doors and windows. The operating status of HVAC and energy storage equipment can be controlled by Internet of Things devices. The building energy optimization method will optimize the operating strategies of various flexible energy-consuming equipment in the next 24 hours, with an optimization time interval of 15 minutes.

[0070] like Figure 1 As shown in Figure 1, the optimization results of the above building energy optimization method show the components of the building load in the next 24 hours after optimization. Table 1 compares the above building energy optimization method with a day-ahead optimization method and user autonomous adjustment effects. The above results show that this comfort calculation method can accurately reflect the user's comfort needs, which is conducive to the design and arrangement of building energy optimization strategies.

[0071] Table 1

[0072]

[0073] The present invention considers the thermal comfort, visual comfort and air quality comfort of users in the building, constructs a single comfort index model based on the predicted average evaluation and the Weber-Fechner law, and calculates the comfort index of each dimension according to the building environment parameters. The index of each dimension can truly reflect the relationship between the intensity of stimulation and human perception. The present invention combines user demand preferences to aggregate into a human sensory comfort index, and realizes the use of one indicator to accurately quantify the multi-dimensional comfort of users in a specific environment, and makes the quantification result accurately reflect the objective comfort loss and the subjective needs of users; at the same time, the comfort weight of each dimension at each moment can be adjusted separately based on the subjective changes of users, and separated from the objective comfort loss, which is conducive to the design and arrangement of building energy optimization strategies.

Claims

1. A method for calculating user multi-dimensional comfort by separating subjective and objective weights, characterized in that: The following steps are involved: Calculate the thermal comfort index PMV of users based on the predicted mean evaluation; The visual comfort index is constructed based on the Weber-Fechner law, and the user's visual comfort index V is calculated according to the building user's subjective evaluation of different light intensities and the building light environment parameters; Based on the Weber-Fechner law and indoor air quality standards, the air quality comfort index is constructed, and the user's air quality comfort index AQCI is calculated according to the building air environment parameters and pollutant parameters; The user's thermal comfort index PMV, visual comfort index V and air quality comfort index AQCI are normalized to obtain the thermal comfort index PMV, visual comfort index V and air quality comfort index AQCI. Normalized index of visual comfort and normalized index of air quality comfort Determine the impact of objective intensity factors on user comfort; According to the user's preference for comfort, the weight of each dimension is set to build a human sensory comfort model that integrates multiple sensory indicators Determine the impact of subjective preference factors on user comfort.

2. The method for calculating user multi-dimensional comfort with subjective and objective weight separation according to claim 1, characterized in that: The user's visual comfort index V is V(E t , ΔE, UGR) = [a0 + a1lnE t +a2(lnE t ) 2 +a3(lnE t ) 3 +C]·δ(ΔE)·ε(UGR) Among them, E t is the indoor light intensity; ΔE is the absolute value of the change in light intensity during instantaneous adjustment; UGR is the unified glare value; a0, a1, a2, a3 are the coefficients of C is the polynomial model of visual comfort; δ(·) is the correction function of the instantaneous change of light intensity to comfort; ε(·) is the correction function of the unified glare value to comfort; E limit The upper limit of indoor light intensity acceptable to users; UGR limit The lower limit of the unified glare value attenuation; UGR max It is the acceptable upper limit of the unified glare value.

3. The method for calculating user multi-dimensional comfort with subjective and objective weight separation according to claim 1, characterized in that: The user's air quality comfort index AQCI is DOWN=DOWN * ·ε(e CO )·ε(e PM2.5 ) Among them, AQCI * Calculate the overall air quality comfort index for the air quality sub-index; e CO is the actual CO emission rate of the building; e PM2.5 is the actual PM2.5 emission rate of the building; e CO,max is the CO target emission rate standard; e PM2.5,max It is the target emission rate standard for PM2.

5.

4. The method for calculating user multi-dimensional comfort with subjective and objective weight separation according to claim 3 is characterized in that: Air quality sub-index calculation of overall air quality comfort index AQCI * for The air quality comfort index of the i-th pollutant x is IAQCI i (x)=[b i,0 +b i,1 lnI i +b i,2 (lnI i ) 2 +b i,3 (lnI i ) 3 +C i ] Among them, b i,j With C i is the polynomial model coefficient of the i-th air quality comfort index, j = 0, 1, 2, 3, I i is the factor intensity of the i-th pollutant.

5. The method for calculating user multi-dimensional comfort with subjective and objective weight separation according to claim 1, characterized in that: The thermal comfort for 6. The method for calculating user multi-dimensional comfort with subjective and objective weight separation according to claim 2, characterized in that: The normalized index of visual comfort for Among them, E best It is the maximum value of the visual comfort index.

7. The method for calculating user multi-dimensional comfort with subjective and objective weight separation according to claim 1, characterized in that: The normalized index of air quality comfort for 8. The method for calculating user multi-dimensional comfort with subjective and objective weight separation according to claim 1, characterized in that: The human sensory comfort model integrating multiple sensory indicators for Among them, ω thermal,t represents the thermal comfort weight at time t, ω light,t represents the visual comfort weight at time t, ω air,t Represents the air quality comfort weight at time t.

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