Heating ventilation air conditioner control system considering multi-person thermal comfort and person behaviors

Abstract

The invention discloses a heating ventilation air conditioner control system considering multi-person thermal comfort and person behaviors, and belongs to the field of intelligent energy management. The control system comprises an information module, a model adaptation module and a heating ventilation air conditioner control module. The information module is in communication connection with the model adaptation module and the heating ventilation air conditioner control module and is connected with an external system at the same time. The model adaptation module comprises a heating ventilation and air conditioning dynamic model, a thermal comfort model and a heating ventilation and air conditioning control model, the heating ventilation and air conditioning dynamic model is in communication connection with the thermal comfort model and the heating ventilation and air conditioning control model, and the thermal comfort model is in communication connection with the heating ventilation and air conditioning control model. The energy consumption of the heating ventilation air conditioning system is reduced while the heat comfort of indoor personnel is considered; indoor personnel are allowed to carry out user-defined control on the heating ventilation air conditioning system according to specific requirements, and the special requirements of the indoor personnel can also be considered while the requirements of all the indoor personnel on thermal comfort are considered in the control process.

Description

Technical Field

[0001] The present invention relates to a heating, ventilation and air conditioning (HVAC) control system considering multi-person thermal comfort and human behavior, and belongs to the field of intelligent energy management. Background Art

[0002] In the composition of China's energy consumption, building energy consumption accounts for more than 40% of the total energy consumption, and HVAC energy consumption accounts for more than 50% of building energy consumption. Therefore, implementing control on the HVAC system is of great significance for improving the energy efficiency of the HVAC system and reducing building energy consumption.

[0003] Model-based predictive control technology is the main method to improve the energy efficiency of HVAC from the control perspective. This method applies the dynamic model of the HVAC system to predict the future development of indoor conditions in a building within a certain range, and implements a dynamic control strategy for the building's HVAC system in a predictable manner.

[0004] While improving the energy efficiency of buildings, the HVAC control system also needs to meet the thermal comfort requirements of occupants. Currently, the average predicted vote model or its simplified version is usually used to control thermal comfort. This model is based on four physical variables: air temperature, mean radiant temperature, relative air velocity, and air humidity, and two personal parameters: metabolic rate and clothing insulation rate. Since it is impossible to collect these two personal parameters, clothing insulation and metabolic rate, in a dynamic manner, existing models often assume or simplify their values, thus destroying the prediction accuracy of the models. In addition, even if all input variables can be accurately obtained, since existing models are aggregated models used to predict the average comfort of a large number of people, when used for a small number of samples, existing models all show poor prediction performance.

[0005] Existing average predicted vote models are often preset models that can only input predefined variables and do not allow input of other variable sets that may affect the actual results. Therefore, new variables that may be potentially related to the thermal comfort of people in a specific environment, such as gender, body mass index, time of day, age, health status, etc., cannot be incorporated into the model, thereby reducing the prediction accuracy and the immediate understanding of the influencing factors on the environment and human thermal comfort. This is an inherent defect of the average predicted vote model.

[0006] Finally, existing HVAC control systems rarely consider the behavior of indoor occupants, resulting in a mismatch between the expected energy demand and the actual energy consumption in practical applications, that is, a performance gap.

[0007] It can be seen that the existing HVAC control systems have the following disadvantages or deficiencies: they cannot take into account the thermal comfort of multiple individuals; they lack flexibility and are difficult to cope with changes in influencing factors in a specific environment; they ignore the influence of the behavior of building occupants, resulting in a performance gap. Summary of the Invention

[0008] In view of the problems existing in the prior art, the present invention discloses a heating, ventilation and air conditioning (HVAC) control system considering multi-person thermal comfort and human behavior. The inventor establishes a multi-person thermal comfort model according to the actual control requirements and indoor human behavior, and incorporates it into the model predictive control system to implement real-time control of the HVAC system.

[0009] A heating, ventilation and air conditioning (HVAC) control system considering multi-person thermal comfort and human behavior includes an information module, a model adaptation module and an HVAC control module. The information module is communicatively connected to the model adaptation module and the HVAC control module respectively, and is also communicatively connected to an external system at the same time, and is described in detail as follows:

[0010] 1. The information module is used to implement the information operations involved in the HVAC control system, including the collection, storage and processing of system operation information and system external information, the execution of information interaction between the HVAC control system and the external environment, and the information communication with the model adaptation module and the HVAC control module. Further explanations are as follows:

[0011] 1.1 The system operation information is the information that can reflect the operation state of the HVAC system, including the working state of the HVAC, the lighting state and the cooling power provided by the air supply.

[0012] 1.2 The system external information includes indoor human physiological information, indoor human behavior information, indoor environmental information and time information, and the detailed explanations are as follows:

[0013] 1.2.1 The indoor human physiological information is the information that can reflect the physiological state and individual differences of indoor people, including skin temperature, pulse rate, body mass index.

[0014] 1.2.2 The indoor human behavior information is the information that will affect the indoor environment and the thermal comfort requirements, including the number of people in the room and the behavior of indoor people adding / removing clothes.

[0015] 1.2.3 The environmental information is the information that can reflect the current indoor environmental conditions, including indoor temperature, relative humidity, carbon dioxide concentration, wind speed.

[0016] 1.2.4 The time information is the current moment.

[0017] 1.3 The information interaction between the HVAC control system and the external environment refers to inputting the system external information into the HVAC control system through the existing data collection methods.

[0018] 1.4 The information interaction among the internal information module, model adaptation module, and HVAC control module of the HVAC control system refers to inputting the external information collected by the information module and the operation information of the HVAC system into the model adaptation module, inputting the operation variables generated by the model adaptation module into the HVAC control module, and inputting the control instructions into the HVAC equipment;

[0019] 2. The model adaptation module consists of an HVAC dynamics model, a thermal comfort model, and an HVAC control model. Among them, the HVAC dynamics model is communicatively connected to the thermal comfort model and the HVAC control model respectively, and the thermal comfort model and the HVAC control model are communicatively connected. The HVAC dynamics model is used to predict the indoor temperature at a certain moment. On this basis, the thermal comfort model is used to analyze the thermal comfort of indoor occupants. According to the analysis results, the HVAC control model is used to generate an optimal control strategy or a custom control strategy based on a preset temperature. The specific contents are as follows:

[0020] 2.1 The HVAC dynamics model adopts the following prediction model to predict the indoor temperature information at the next moment according to the system operation information, occupant behavior information, and environmental information:

[0021] T o (t + 1) = f 1 (Q sa (t), hod(t), os(t), T o (t), n occ (t))

[0022] Among them, T o (t + 1) is the predicted result of the indoor temperature at the next moment, f 1 is the said prediction model, Q sa (t) is the cooling power provided by the HVAC system supply air at the current moment, hod(t) is the time of day at the current moment, os(t) is the operation and lighting state of the HVAC system at the current moment, T o (t) is the indoor temperature at the current moment, n occ (t) is the number of indoor occupants at the current moment, and there is a fixed time interval between the current moment and the next moment;

[0023] 2.2 The thermal comfort model includes an occupant classification sub-model, an individual thermal comfort sub-model, and a multi-occupant thermal comfort sub-model; the occupant classification sub-model is used to classify indoor occupants, the individual thermal comfort sub-model analyzes the individual thermal comfort of indoor occupants, and on this basis, the multi-occupant thermal comfort sub-model analyzes the thermal comfort of indoor occupants. The details are as follows:

[0024] 2.2.1 The personnel classification model classifies indoor personnel into different categories based on the aforementioned personnel physiological information, personnel behavior information, and environmental information, using a suitable classification algorithm.

[0025] 2.2.2 The individual thermal comfort model predicts the thermal comfort of a single indoor person:

[0026] Ts(t) = f 2 (T Z (t), RH(t), CO 2 (t), Av(t), T skin (t), PR(t), BMI, OB(t))

[0027] Among them, Ts(t) is the thermal comfort of a single indoor person at the current moment, and f 2 is the individual thermal comfort model. T Z (t) is the predicted result of the indoor temperature at the current moment by the HVAC dynamics model. RH(t) represents the relative humidity at time t. CO 2 (t) is the indoor carbon dioxide concentration at the current moment. Av(t) is the indoor wind speed at the current moment. T skin (t) is the skin temperature of a single indoor person at time t. PR(t) is the pulse rate of a single indoor person at time t. BMI is the body mass index of an individual. OB(t) is the behavior of whether the person adds clothing at time t. When OB(t) = 1, it means the indoor person has the behavior of adding clothing. When OB(t) = 0, it means the indoor person does not have the behavior of adding clothing.

[0028] 2.2.3 Based on the results of the indoor personnel classification model and the individual thermal comfort model, the following multi-person thermal comfort model is used to analyze the thermal comfort of multiple indoor people:

[0029]

[0030] Among them, T Sa,, (t) refers to the thermal comfort of multiple indoor people at the current moment. TS Si (t) refers to the individual thermal comfort of the representative person of indoor personnel category i at the current moment. h i refers to the proportion of indoor people of category i at the current moment among all indoor people, calculated by the following formula:

[0031]

[0032] Among them, N i is the number of indoor people of category i. n occ (t) is the total number of indoor people at the current moment. The representative person is a certain person in a specific category of people classified by the personnel classification model.

[0033] 2.3 The HVAC control model is established based on the corresponding objective function, constraints, and optimizer. This model provides control instructions for the HVAC equipment in the system and implements control, and the details are described as follows:

[0034] 2.3.1 The corresponding objective function represents the control objectives of the HVAC system, including the optimal control objective and the custom control objective;

[0035] Among them, the optimal control objective is: in the absence of external interference and while ensuring the thermal comfort of indoor occupants, to minimize the energy consumption of the HVAC system. The calculation formula is as follows:

[0036]

[0037] The custom control objective is that indoor members set different target indoor temperatures at different times, and the HVAC system switches or adjusts its operation according to the set target indoor temperature and outdoor environmental information. The calculation formula is as follows:

[0038]

[0039] Among them, J is the objective function, is the ratio of the normalized cooling and heating power, TZ represents the set temperature, TS represents the thermal state of the occupants, is the normalized TS index, OB is the energy-saving behavior of the occupants, W is the penalty factor, N is the prediction horizon, ε is the slack variable, and the subscript k is the time interval;

[0040] Among them, the ratio of the normalized cooling or heating power refers to the ratio of the instantaneous cooling or heating power to the cooling or heating capacity of the HVAC system:

[0041]

[0042] Among them, the normalized TS index is the ratio of the indoor TS measured within the TS comfort range:

[0043]

[0044] The normalized TZ index is the difference between the indoor temperature and the set temperature:

[0045]

[0046] 2.3.2 Establish the constraints of the HVAC control system according to the physical model constraints that the HVAC system needs to satisfy;

[0047] 2.3.3 The manipulated variables are obtained by solving the objective function under the constraints by selecting a suitable optimizer

[0048] In actual operation, according to the changes in the system operation information and external information feedback during the system operation, it is necessary to continuously update the HVAC dynamics model, thermal comfort model, and HVAC control model; the local controller receives the operation variables generated by the control model and generates control instructions based on the operation variables. Then, the HVAC system implements corresponding control on the HVAC equipment located in the system according to the control instructions generated by the local controller.

[0049] Beneficial effects

[0050] While taking into account the thermal comfort of indoor occupants, the present invention can reduce the energy consumption of the building's HVAC system and allows indoor occupants to customize the control of the HVAC system according to specific needs. During the control process, the needs of all indoor occupants for thermal comfort are considered, and at the same time, the individual needs of indoor occupants can also be taken into account. Brief description of the drawings

[0051] Figure 1 It is the overall block diagram of the present invention.

[0052] Figure 2 It is the schematic diagram of the mutual relationship of the model adaptation module of the present invention.

[0053] Figure 3 It is the schematic diagram of the HVAC control process of the present invention. Detailed implementation manners

[0054] The present invention will be further described in detail below in conjunction with the specification drawings and embodiments. It should be noted that the embodiments below are only used to explain the present invention, but do not constitute any limitation to the present invention. In addition, the embodiments are the actual applications of the present invention in an office building of a certain university.

[0055] 1. Refer to the specification appendix Figure 1 , a HVAC control system considering the thermal comfort of multiple people and human behavior, includes the following three parts:

[0056] Q1 Information module: used to implement the information activities involved in the HVAC control system, including the collection, storage, and processing of system operation information and system external information, realizing the information interaction between the HVAC control system and the external environment, and realizing the information interaction between the internal information module, model adaptation model, and HVAC control module of the HVAC control system. The information module can be any application program such as computer software, mobile phone APP, etc. that can be used as an information carrier and realize information processing and information transmission.

[0057] Q2 Model Adaptation Module: The model adaptation module includes a heating, ventilation, and air conditioning (HVAC) dynamics model, a thermal comfort model, and an HVAC control model, which are used to generate control variables for the HVAC control system. Generally, the model adaptation module and the information module can be on the same carrier.

[0058] Q3 HVAC Control Module: The HVAC control module includes a local controller and an HVAC system, which are used to receive the operation variables generated by the model adaptation module and generate corresponding control instructions to control the HVAC system.

[0059] 2. The HVAC control system realizes external information interaction through the information module. This information is obtained through different data collection methods and is required by the system, including personnel psychological information, personnel behavior information, and environmental information. Taking the mobile APP as an example, the information module is a part of the mobile APP, serves as the information carrier of the HVAC system, and processes and transmits the information.

[0060] 2.1 The information module obtains physiological information such as the skin temperature, pulse rate, and body mass index of indoor personnel through bracelets, manual entry, etc.

[0061] 2.2 The information module is connected to action sensors to obtain information such as the number of people in the room and the behavior of people adding clothes.

[0062] 2.3 The information module, the smart gateway, are connected to human sensors, infrared sensors, temperature and humidity sensors, and environmental sensors to obtain information on the indoor environmental conditions, including indoor temperature, relative humidity, carbon dioxide concentration, and wind speed.

[0063] 2.4 The information module obtains the current time information through the mobile phone itself and the Internet, and inputs the above information into the HVAC control system to realize the information interaction between the HVAC control system and the external environment.

[0064] 3. The HVAC control system realizes internal information interaction through its information module, and is connected to the model adaptation module and the HVAC control module through wireless networks or other means, which is further described as follows;

[0065] 3.1 The information module obtains the operation information of the HVAC system in the HVAC control module, including the cooling power, operation status, and lighting status provided by the HVAC air supply, and inputs the operation information and the collected external information into the model adaptation module; the information module obtains the operation variables generated by the HVAC control model in the model adaptation module and inputs the operation variables into the HVAC control module;

[0066] 4. The HVAC control system processes and stores the information related to the system through the information module, including data cleaning, data repair, and standardization of the collected system operation information, personnel psychological information, personnel behavior information, and environmental information, and then stores them locally or in the cloud according to the information category and time node for other modules in the system to identify and use.

[0067] 5. Now, in combination with the attached drawings of the specification Figure 2 The model adaptation module in the HVAC control system will be described. This model adaptation module includes three models: the HVAC dynamics model M1, the thermal comfort model M2, and the HVAC control model M3, which are further described as follows:

[0068] HVAC dynamics model M1: This model receives the HVAC system operation information and external information input by the information module, predicts the indoor temperature at the next moment after implementing the HVAC control activity, and inputs the results into the thermal comfort model M2 and the HVAC control model M3;

[0069] Thermal comfort model M2: This model includes a personnel classification sub-model, an individual thermal comfort sub-model, and a multi-person thermal comfort sub-model, and predicts the thermal comfort of indoor personnel based on the results of the HVAC dynamics model M1;

[0070] HVAC control model M3: This model generates appropriate control parameters based on the results of the HVAC dynamics model M1 and the thermal comfort model M2, and transmits the control parameters to the control module;

[0071] Among them, the HVAC dynamics model uses the following prediction model to predict the indoor temperature information at the next moment based on the system operation information, personnel behavior information, and environmental information:

[0072] T o (t + 1) = f 1 (Q sa (t), hod(t), os(t), T o (t), n occ (t))

[0073] Among them, T o (t + 1) is the predicted result of the indoor temperature at the next moment, f 1 is the prediction model, and the NARX-ANN network with dynamic recursive non-linear autoregressive exogenous is used to predict the indoor temperature T Z Q sa (t) is the cooling power provided by the HVAC system supply air at the current moment, hod(t) is the time of day at the current moment, os(t) is the operation and lighting state of the HVAC system at the current moment, T o(t) is the indoor temperature at the current moment, n occ (t) is the number of indoor occupants at the current moment, and the time interval between the current moment and the next moment is fixed;

[0074] 6. The thermal comfort model in the HVAC control system model adaptation module realizes the prediction and analysis of indoor occupants' thermal comfort, including the occupant classification model, individual thermal comfort model, and multi-occupant thermal comfort model;

[0075] 6.1 The occupant classification model in the thermal comfort model is completed using the k-means algorithm, and the energy-consuming population is clustered according to the user's energy adjustment behavior, body mass index, outdoor air, and time point:

[0076]

[0077] The calculation methods of the data partition matrix and the cluster center update matrix are as follows:

[0078]

[0079] where δ ik represents the membership relationship of the data. If the data x i belongs to the k-th cluster, the corresponding element value δ ik is 1; otherwise, δ ik is 0;

[0080] 6.2 The individual thermal comfort model in the thermal comfort model predicts and analyzes the thermal comfort of a single individual according to the input occupant physiological information, occupant behavior information, and environmental information using a neural network:

[0081] Ts(t) = f 2 (T Z (t), RH(t), CO 2 (t), Av(t), T skin (t), PR(t), BMI, OB(t))

[0082] where Ts(t) is the thermal comfort of a single indoor occupant at the current moment, T Z (t) is the prediction result of the indoor temperature at the current moment by the HVAC dynamics model, f 2 is the analysis model, CO 2 (t) is the indoor carbon dioxide concentration at the current moment, Av(t) is the indoor wind speed at the current moment, T skin(t) is the skin temperature of a single indoor occupant at time t, PR(t) is the pulse rate of a single indoor occupant at time t, BMI is the body mass index of an individual, and OB(t) is the behavior of whether the occupant adds clothing at time t. Moreover, when the indoor occupant has the behavior of adding clothing, OB(t)=1; when the indoor occupant does not have the behavior of adding clothing, OB(t)=0.

[0083] 6.3 The multi - occupant thermal comfort model in the thermal comfort model predicts and analyzes the multi - occupant thermal comfort based on the results of the individual thermal comfort model and the occupant classification model. In this embodiment, the occupants are classified into three categories:

[0084] T Sa,, (t)=h 1 T S1 (t)+h 2 T S2 (t)+h ＝ T S＝ (t)

[0085] where T Sa,, (t) represents the multi - occupant thermal comfort in the room, T Si (t) represents the individual thermal comfort of the selected representative occupant, and hi i represents the proportion of the group where the representative occupant belongs in the whole:

[0086]

[0087] 7. Refer to the attached Figure 3 of the specification, the HVAC control model in the HVAC control system model adaptation module:

[0088] S1 Objective function: The corresponding objective function is used to represent the control objectives of the HVAC system, including the optimal control objective and the custom control objective:

[0089] The optimal control objective is to minimize the energy consumption of the HVAC system while ensuring the thermal comfort of indoor occupants in the absence of external interference:

[0090]

[0091] The custom control objective is that indoor members set different target indoor temperatures at different times, and the HVAC system switches or adjusts its operation according to the set target indoor temperature and outdoor environmental information:

[0092]

[0093] where J is the objective function, is the ratio of the standardized cooling and heating power, TS is the standardized index, OB is the energy-saving behavior of personnel, W is the penalty factor, N is the prediction range, ε is the slack variable, and the subscript k is the time interval;

[0094] Among them, the ratio of standardized cooling or heating power is the ratio of instantaneous cooling or heating power to the cooling or heating capacity of the HVAC system, and is calculated by the following formula:

[0095]

[0096] Among them, the standardized TS index is the ratio measured by the indoor TS within the TS comfort range:

[0097]

[0098] The standardized TZ index is the difference between the indoor temperature and the set temperature:

[0099]

[0100] In this embodiment of the present invention, the general penalty factor W 4S is set to 4, representing a higher weight for thermal comfort. Under this setting, the purpose of the control system is to keep the indoor environment close to thermal neutrality (TS = 0) to minimize cooling energy consumption; the penalty factor W 6 is set to 100,000, indicating that almost no violations are allowed; the penalty factor W 4Z is set to 4, representing a higher weight for temperature. Under this setting, the purpose of the control system is to keep the indoor temperature close to the set temperature;

[0101] S2 Constraint conditions: In the optimal control model and the custom control model, the constraint conditions refer to the physical model constraint conditions that the HVAC system needs to satisfy:

[0102] s.t.Q∈(0,Q max )

[0103] S3 Model Solving: An optimization method is used to solve the objective function under corresponding constraints to generate control variables. First, a global optimization method, the main optimizer, is used to search for a global rough solution, and then an auxiliary optimizer is used to search for the optimal solution around the initial solution. This method combines a global optimization method and a gradient-based optimization method, a hybrid optimization method, to solve the non-linear optimization problem of model predictive control (MPC). According to the solution results, operating variables are produced. In this embodiment, the office HVAC system is equipped with 4 air supply devices. The room temperature set point is set at 20 °C. The supply air temperature is not controlled by the thermostat but fluctuates between 18 and 22 °C according to the measurement results during the test. To retain the original control characteristics of the thermostat, its preset control parameters are not adjusted. The HVAC control system of the present invention realizes its function as an additional alternative control for the air conditioner, and the user can switch between the original thermostat control and the HVAC control system. When using the traditional control method, the total energy consumption is 237.8 kWh; while when using the control method of the present invention, the total energy consumption is about 192 kWh, a reduction of about 19.26%. At the same time, the comfort of the indoor staff is enhanced. It can be seen that the present invention not only improves the thermal comfort of the occupants but also greatly reduces the energy consumption.

[0104] 8. The model adaptation module in the HVAC control system updates the HVAC dynamics model, the thermal comfort model, and the HVAC control model according to the system operation information and external information collected by the information module.

[0105] 9. The local controller receives the operating variables generated by the control model and generates control instructions based on these operating variables. Then, the HVAC system implements corresponding control on the HVAC equipment in the system according to the control instructions generated by the local controller.

[0106] The present invention has been described in detail through embodiments. It should be noted that the above embodiments are only the preferred embodiments of the present invention, not all embodiments of the present invention, and do not constitute any limitation to the protection scope of the present invention. The protection scope of the present invention is determined by the claims of this application document. Those of ordinary skill in the art, without departing from the spirit and essence of the present invention, by equivalent substitution or other various transformations of the technical features of the present invention, the technical solutions or embodiments obtained all fall within the protection scope of the present invention.

Claims

1. A HVAC control system that takes into account the thermal comfort and behavior of multiple people, characterized in that: The control system includes an information module, a model adaptation module and a HVAC control module; wherein the information module is respectively connected to the model adaptation module and the HVAC control module in communication, and is also connected to an external system in communication.

2. A HVAC control system considering thermal comfort and behavior of multiple people as claimed in claim 1, characterized in that: The model adaptation module includes a HVAC dynamics model, a thermal comfort model, and a HVAC control model; wherein the HVAC dynamics model is communicatively connected to the thermal comfort model and the HVAC control model, respectively, and the thermal comfort model is communicatively connected to the HVAC control model.

3. A HVAC control system considering thermal comfort and behavior of multiple people as claimed in claim 2, characterized in that: The thermal comfort model includes a personnel classification sub-model, a personal thermal comfort sub-model, and a multi-person thermal comfort sub-model.

4. A HVAC control system considering thermal comfort and behavior of multiple people as claimed in claim 2, characterized in that: The HVAC dynamics model predicts the indoor temperature information at the next moment according to the following formula: T o (t+1)=f1(Q sa (t),hod(t),os(t),T o (t),n occ (t)) Among them, T o (t+1) is the prediction result of the indoor temperature at the next moment, f1 is the prediction model, Q sa (t) is the cooling power provided by the HVAC system at the current moment, hod(t) is the time of day at the current moment, os(t) is the operation and lighting status of the HVAC system at the current moment, T o (t) is the current indoor temperature, n occ (t) is the number of people in the room at the current moment.

5. A HVAC control system considering thermal comfort and behavior of multiple people as claimed in claim 3, characterized in that: The personnel classification sub-model classifies indoor personnel, the personal thermal comfort sub-model analyzes the personal thermal comfort of indoor personnel, and the multi-person thermal comfort sub-model analyzes the thermal comfort of indoor personnel; wherein the personal thermal comfort sub-model analyzes the personal thermal comfort of indoor personnel: Ts(t)=f2(T Z (t),RH(t),CO2(t),Av(t),T skin (t),PR(t),BMI,OB(t)) Where Ts(t) is the thermal comfort of a single indoor person at the current moment, f2 is the personal thermal comfort model, T Z (t) is the prediction result of the HVAC dynamics model for the current indoor temperature, RH(t) represents the relative humidity at time t, CO2(t) is the indoor carbon dioxide concentration at the current time, Av(t) is the indoor wind speed at the current time, T skin (t) is the skin temperature of a single indoor person at time t, PR(t) is the pulse rate of a single indoor person at time t, BMI is the individual's body mass index, OB(t) is whether the person has added clothes at time t, when OB(t) = 1, it means that the indoor person has added clothes, and OB(t) = 0, it means that the indoor person has not added clothes; the multi-person thermal comfort model analyzes the thermal comfort of multiple people indoors: Among them, T Sall (t) refers to the thermal comfort of multiple people in the room at the current moment, T Si (t) refers to the personal thermal comfort of the indoor occupant category i at the current moment, h i Refers to the proportion of people in the current indoor category i among all indoor people. The calculation formula is as follows: Among them, N i is the number of people in the room of category i, n occ (t) is the total number of people in the room at the current moment, and the representative person refers to a person in a specific category of people classified by the person classification model.

6. A HVAC control system considering thermal comfort and behavior of multiple people as claimed in claim 4, characterized in that: The HVAC control model is used to provide control instructions and implement control on the HVAC equipment in the system, including corresponding objective functions, constraints and optimizers; The objective function is used to represent the control target of the HVAC system, including the optimal control target and the custom control target: wherein the optimal control target refers to the minimum energy consumption of the HVAC system when there is no external interference and the thermal comfort of indoor personnel is guaranteed. The calculation formula is as follows: The custom control target refers to the indoor occupants setting different target indoor temperatures at different times. The HVAC system switches or adjusts its operation according to the set target indoor temperature and outdoor environmental information. The calculation formula is as follows: Where J is the objective function, It is the ratio of standardized cooling and heating power, TZ represents the set temperature, TS represents the thermal state of the personnel, is the standardized TS index, OB is the energy-saving behavior of personnel, W is the penalty factor, N is the prediction horizon, ε is the slack variable, and subscript k is the time interval; The normalized cooling and heating power ratio is the ratio of the instantaneous cooling power to the cooling or heating capacity of the HVAC system: The normalized TS index is the ratio of indoor TS measured within the TS comfort range: The normalized TZ index is the difference between the indoor temperature and the set temperature: The constraint condition refers to the corresponding physical model constraint condition that the HVAC system needs to satisfy; the optimizer solves the objective function under the constraint condition to produce the operating variable.

7. A HVAC control system considering thermal comfort and behavior of multiple people as claimed in claim 5, characterized in that: The information module may be any application program that can serve as an information carrier and implement information processing and transmission; the model adaptation module and the information module are located on the same carrier.

8. A HVAC control system considering thermal comfort and behavior of multiple people as claimed in claim 6, characterized in that: The information module includes computer software and mobile phone APP.

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