A variable air volume system summer fresh air optimization control method

Abstract

The application discloses a kind of variable air volume system summer fresh air optimization control methods, it is characterized in that, by comparing indoor and outdoor air enthalpy, when outdoor air enthalpy is higher, according to the number of area, find the most unfavorable area of the largest fresh air demand, when the most unfavorable area fresh air demand changes greater than set value, using multi-objective genetic optimization control algorithm to solve the most unfavorable area room temperature set value, and with the value as the basis to calculate VAV system total fresh air quantity, and accordingly as basis adjustment fresh air valve, return air valve and exhaust valve to achieve fresh air control.The control method is suitable for fresh air ratio control in variable air volume system, and the control strategy has good robustness, and has outstanding effect in improving building multi-area indoor air quality and saving operating energy consumption.

Description

Technical Field

[0001] This invention relates to the field of building ventilation control technology, specifically to a method for optimizing the control of fresh air in summer for a variable air volume system. Background Technology

[0002] With social development and the improvement of people's living standards, people have increasingly higher requirements for indoor environmental comfort. Currently, large buildings typically install fresh air systems to supply and exchange indoor air, thereby improving comfort. However, the fresh air control strategy of existing fresh air systems, namely variable air volume (VAV) air conditioning systems, usually adopts the principle of directly adjusting the system's fresh air ratio based on the CO2 concentration of a fixed selected area. For example, a fresh air control method disclosed in CN202211128154.X and a fresh air control method and system for air conditioning in integrated energy-saving buildings disclosed in CN201210109722.1 are both based on this method of controlling fresh air by detecting CO2 concentration.

[0003] This conventional method of controlling fresh air intake by detecting CO2 concentration is currently still largely used in variable air volume (VAV) air conditioning systems, which employ constant air volume (CAV) systems. This involves installing CO2 sensors in fixed characteristic areas to control the CO2 concentration in those areas below a set value (1000 PPM). However, this method doesn't reduce the fresh air volume when the number of people in non-characteristic areas decreases. Similarly, when people suddenly leave an area, the CO2 concentration in that area doesn't decrease accordingly. Therefore, this control method cannot adapt to the dynamic environment of a building, resulting in significant energy waste during VAV system operation while trying to maintain indoor air quality.

[0004] Therefore, how to design a fresh air control method that is more adaptable to the flow of people in a building, has better robustness, can better improve the indoor air quality in multiple areas of a building, and saves operating energy consumption has become a problem that needs to be considered and solved by those skilled in the art. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the technical problem to be solved by the present invention is: how to provide a summer fresh air optimization control method for a variable air volume system that can better adapt to changes in the flow of people in a building, has better robustness, can better improve indoor air quality in multiple areas of a building, and saves operating energy consumption.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A method for optimizing fresh air control in a variable air volume (VAV) system during summer is characterized by comparing the enthalpy values ​​of indoor and outdoor air. When the outdoor air enthalpy value is higher, the method calculates the most unfavorable area with the highest fresh air demand based on the number of people in the area. When the change in fresh air demand in the most unfavorable area exceeds the set value, a multi-objective genetic optimization control algorithm is used to solve for the room temperature set value in the most unfavorable area. This value is then used as the basis for adjusting the total fresh air volume of the VAV system (i.e., variable air volume air conditioning system). Based on this, the fresh air valve, return air valve, and exhaust air valve are adjusted to achieve fresh air control.

[0008] This method specifically includes the following steps:

[0009] Step 1: Collect, calculate, and compare the outdoor air enthalpy value with the indoor air enthalpy value. When the outdoor air enthalpy value is lower than the indoor air enthalpy value, activate the enthalpy difference control mode to ensure the minimum fresh air ratio of the system and adjust the opening of the fresh air valve to meet the fresh air volume required by the number of people in the room (in this way, when the outdoor enthalpy value is low, the indoor heat and humidity load can be eliminated as much as possible by utilizing the indoor and outdoor enthalpy difference).

[0010] Step 2: When the outdoor air enthalpy is higher than the indoor air enthalpy, perform the following steps:

[0011] 1) Communicate with the personnel counters in each area and calculate the fresh air demand L for each area according to formulas (2) and (3). x,i Total fresh air demand of the system (L) x .

[0012] L x,i =n i ×L x,sta (2)

[0013] In the formula:

[0014] L X,sta -Based on hygiene requirements, the per capita fresh air volume standard is [m]. 3 / h;

[0015] L x,i - The required fresh air volume for the i-th region, m 3 / h;

[0016] n i - The number of people indoors in the i-th region;

[0017]

[0018] In the formula:

[0019] L X -Total fresh air volume required by the system, m 3 / h;

[0020] L x,i- The required fresh air volume for the i-th region, m 3 / h;

[0021] m - The number of areas covered by the VAV system;

[0022] 2) Communicate with the terminal controllers of the VAV systems in each area to obtain the actual air supply volume L in each area. s,i and the total air volume of the system (L) s ;

[0023]

[0024] In the formula:

[0025] L s - Total system air volume, m 3 / h;

[0026] L s,i - The air supply volume at the terminal of the i-th regional system, m 3 / h;

[0027] m - The number of areas covered by the VAV system;

[0028] 3) Calculate the fresh air demand ratio Z for each area. t,i The region with the highest demand ratio is identified as the most unfavorable region, and its fresh air ratio Z is obtained. t ;

[0029]

[0030] In the formula:

[0031] L x,i - The required fresh air volume for the i-th region, m 3 / h;

[0032] L s,i - The air supply volume at the terminal of the i-th regional system, m 3 / h;

[0033] Z t,i -The fresh air demand ratio for the i-th region at the current moment, %;

[0034] Z t -The current most unfavorable area's fresh air demand ratio, %;

[0035] 4) Identify the most unfavorable area of ​​the system at fixed time intervals. When the most unfavorable area changes from the previous moment, and the change in the required fresh air ratio of the most unfavorable area at this moment compared with the fresh air ratio at the previous moment reaches or exceeds the set threshold ratio, execute the following steps. Otherwise, the system will not change the control and will still execute according to the original control parameters.

[0036] 5) A multi-objective genetic algorithm is used to optimize the controller and solve for the room temperature setpoint t in the most unfavorable region. new，set ;

[0037] 6) Reset the room temperature setpoint t for the most unfavorable area according to the calculation results in step 5). new，set This serves as the basis for its control and regulation;

[0038] 7) Recalculate the total fresh air volume of the VAV system and use it as the basis for adjusting the fresh air supply valve, return air valve and exhaust valve (the specific adjustment process is existing technology and will not be detailed here).

[0039] Furthermore, in step 1, outdoor temperature and humidity are collected and outdoor air enthalpy is calculated by using temperature and humidity sensors installed outdoors, and return air temperature and humidity are collected and indoor air enthalpy is calculated by using temperature and humidity sensors installed at the return air vent of the indoor VAV system.

[0040] This method of collecting data from the return air vents of the indoor VAV system allows for a better reflection of the indoor enthalpy. The process of calculating the air enthalpy based on temperature and humidity is existing technology and will not be detailed here.

[0041] Furthermore, in step 4), identification is performed every fixed 20 minutes. Too short a time will increase the computational load, while too long a time will lead to a lag in response.

[0042] Further, in step 4), the set threshold ratio is 10%, i.e., |Z t -Z t-1 If the threshold is greater than 0.1, proceed with the next step. Setting this threshold to a proportional value of 10% allows for better control of the system's robustness.

[0043] Furthermore, step 5) involves using a multi-objective genetic algorithm to optimize the controller solution, which includes the following steps:

[0044] First, set the constraints as follows:

[0045] (1) When setting the indoor temperature setpoint constraint for the most unfavorable zone, refer to the provisions in the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" (GB 50736-2012) and limit the indoor temperature setpoint for the most unfavorable zone to between 24-28℃:

[0046] 24≤T N ≤28 (6)

[0047] (2) When setting indoor thermal comfort constraints, the indoor thermal comfort of the most unfavorable zone should be limited to ±0.5 according to the OSI (International Organization for Standardization) recommended value for PMV:

[0048] -0.5≤PMV≤0.5 (7)

[0049] (3) When setting indoor air quality constraints, the indoor CO2 concentration in each area shall be limited to below 1000 ppm in accordance with the provisions of GB / T 18883-2022 "Indoor Air Quality Standard".

[0050] 0≤C i ≤1000 (8)

[0051] (4) When setting energy consumption constraints for the wind system, no specific numerical limit is set for the energy consumption of the wind system during the optimization process. Referring to commonly used multi-objective optimization methods, the objective function is required to be minimized when editing the algorithm.

[0052] 0≤J pow ≤J pow,max (9)

[0053] Then, an optimization objective function is used (considering the thermal comfort of the most unfavorable area, the indoor air quality of all areas, and the energy consumption of the ventilation system, the following function formula is adopted):

[0054]

[0055] Where: PMV j —Comfort index for the most unfavorable area;

[0056] J pow —Energy consumption of variable air volume (VAV) air conditioning system, kW·h;

[0057] W fan,s — Blower energy consumption, kW·h;

[0058] W fan,p —Exhaust fan energy consumption, kW·h;

[0059] W AHU —Energy consumption of air handling units, kW·h.

[0060] —CO2 concentration index in the most unfavorable area;

[0061] Finally, the most unfavorable temperature setpoint t is obtained by using an artificial intelligence genetic algorithm according to the above formula (10). new，set The artificial intelligence genetic algorithm mentioned above is an existing algorithm. Its specific calculation process is common knowledge and is a mature artificial intelligence genetic algorithm, so it will not be described in detail here.

[0062] This control method has the following characteristics and advantages: 1. It can ensure air quality in all areas of the VAV system while maximizing the optimization of energy consumption and thermal comfort in the most unfavorable area of ​​the multi-zone variable air volume (VAV) air conditioning system. The control method establishes a multi-zone ventilation equation, resets the most unfavorable area using a personnel counting system, and calculates the optimal room temperature setpoint in the most unfavorable area through a genetic optimization controller, thereby achieving active optimization of the fresh air ratio of the VAV unit. 2. The genetic optimization controller comprehensively considers air quality, indoor thermal comfort, and energy consumption of the air supply system within the supply area. It incorporates a non-dominated sorting genetic algorithm combined with an approximation-ideal-solution sorting method to solve for the optimal indoor temperature setpoint in the most unfavorable area. This indoor temperature setpoint is then assigned to the VAV terminal (VAVBOX) in the most unfavorable area, achieving self-tuning of the PID parameters of the VAV terminal.

[0063] Therefore, this invention proposes a VAV system ventilation control strategy based on resetting the temperature of the most unfavorable zone. This strategy refines the VAV ventilation system according to the dynamic changes in the number of people in each area of ​​the building and the actual fresh air demand of each area. The control method first uses the NSGA-II multi-objective optimization algorithm to find the optimal indoor temperature in the most unfavorable zone, and then calculates the fresh air ratio of the VAV system based on this temperature value to optimize the VAV ventilation system control. The optimized control method ensures stable system operation. It not only solves the problem of insufficient fresh air supply in the most unfavorable zone but excessive fresh air supply in other areas, but also effectively ensures indoor thermal comfort while reducing the energy consumption of the ventilation system.

[0064] Therefore, this control method is applicable to the fresh air ratio control in variable air volume systems. The control strategy has good robustness and outstanding effects in improving indoor air quality in multiple areas of a building and saving operating energy consumption. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the control flow of the invention method.

[0066] Figure 2 This is a schematic diagram of the algorithm flow for solving the problem using an artificial intelligence genetic algorithm in the method of the present invention.

[0067] Figure 3 This is a schematic diagram showing the change in the number of people in three areas during a comparative experiment between this invention and two other ventilation control strategies.

[0068] Figure 4 This is a schematic diagram showing the change curve of thermal comfort (PMV value) in the most unfavorable area of ​​Strategy A when conducting a comparative experiment between the present invention and two other ventilation control strategies.

[0069] Figure 5 This is a schematic diagram showing the change curve of thermal comfort (PMV value) in the most unfavorable area of ​​strategy B when conducting a comparative experiment between the present invention and two other ventilation control strategies.

[0070] Figure 6 This is a schematic diagram of the thermal comfort (PMV value) change curve in the area most unfavorable to strategy C when conducting comparative experiments with the present invention and two other ventilation control strategies.

[0071] Figure 7 The diagram shows the indoor carbon dioxide concentration variation curves in each area of ​​Strategy A when conducting a comparative experiment with the present invention and two other ventilation control strategies.

[0072] Figure 8 This is a schematic diagram of the indoor carbon dioxide concentration change curves in each area of ​​Strategy B when conducting a comparative experiment between the present invention and two other ventilation control strategies.

[0073] Figure 9 The diagram shows the indoor carbon dioxide concentration variation curves in each area of ​​Strategy C when conducting a comparative experiment with the present invention and two other ventilation control strategies.

[0074] Figure 10 The diagram shows the energy consumption change curve of the fresh air system in Strategy A when conducting a comparative experiment with the present invention and two other ventilation control strategies.

[0075] Figure 11 The diagram shows the energy consumption change curve of the fresh air system in Strategy B when conducting a comparative experiment with the present invention and two other ventilation control strategies.

[0076] Figure 12 The diagram shows the energy consumption change curve of the fresh air system in Strategy C when conducting a comparative experiment with the present invention and two other ventilation control strategies.

[0077] Figure 13 The diagram shows the structure of the VAV system used in the comparative experiment of this invention. Detailed Implementation

[0078] The present invention will now be described in further detail with reference to specific embodiments.

[0079] In specific implementation: A method for optimizing fresh air control in summer for a variable air volume (VAV) system is characterized by comparing the enthalpy values ​​of indoor and outdoor air. When the outdoor air enthalpy value is higher, the method calculates the most unfavorable area with the largest fresh air demand based on the number of people in the area. When the change in fresh air demand in the most unfavorable area exceeds the set value, a multi-objective genetic optimization control algorithm is used to solve for the room temperature set value in the most unfavorable area. This value is then used as the basis for adjusting the total fresh air volume of the VAV system (i.e., variable air volume air conditioning system). Based on this, the fresh air valve, return air valve, and exhaust air valve are adjusted to achieve fresh air control.

[0080] This method specifically includes the following steps, see [link to steps]. Figure 1 :

[0081] Step 1: Collect, calculate, and compare the outdoor air enthalpy value with the indoor air enthalpy value. When the outdoor air enthalpy value is lower than the indoor air enthalpy value, activate the enthalpy difference control mode to ensure the minimum fresh air ratio of the system and adjust the opening of the fresh air valve to meet the fresh air volume required by the number of people in the room (in this way, when the outdoor enthalpy value is low, the indoor heat and humidity load can be eliminated as much as possible by utilizing the indoor and outdoor enthalpy difference).

[0082] Step 2: When the outdoor air enthalpy is higher than the indoor air enthalpy, perform the following steps:

[0083] 1) Communicate with the personnel counters in each area and calculate the fresh air demand L for each area according to formulas (2) and (3). x,i Total fresh air demand of the system (L) x .

[0084] L x,i =n i ×L x,sta (2)

[0085] In the formula:

[0086] L X,sta -Based on hygiene requirements, the per capita fresh air volume standard is [m]. 3 / h;

[0087] L x,i - The required fresh air volume for the i-th region, m 3 / h;

[0088] n i - The number of people indoors in the i-th region;

[0089]

[0090] In the formula:

[0091] L X -Total fresh air volume required by the system, m 3 / h;

[0092] L x,i - The required fresh air volume for the i-th region, m 3 / h;

[0093] m - The number of areas covered by the VAV system;

[0094] 2) Communicate with the terminal controllers of the VAV systems in each area to obtain the actual air supply volume L in each area. s,i and the total air volume of the system (L) s ;

[0095]

[0096] In the formula:

[0097] L s - Total system air volume, m 3 / h;

[0098] L s,i - The air supply volume at the terminal of the i-th regional system, m 3 / h;

[0099] m - The number of areas covered by the VAV system;

[0100] 3) Calculate the fresh air demand ratio Z for each area. t,i The region with the highest demand ratio is identified as the most unfavorable region, and its fresh air ratio Z is obtained. t ;

[0101]

[0102] In the formula:

[0103] L x,i - The required fresh air volume for the i-th region, m 3 / h;

[0104] L s,i - The air supply volume at the terminal of the i-th regional system, m 3 / h;

[0105] Z t,i -The fresh air demand ratio for the i-th region at the current moment, %;

[0106] Z t -The current most unfavorable area's fresh air demand ratio, %;

[0107] 4) Identify the most unfavorable area of ​​the system at fixed time intervals. When the most unfavorable area changes from the previous moment, and the change in the required fresh air ratio of the most unfavorable area at this moment compared with the fresh air ratio at the previous moment reaches or exceeds the set threshold ratio, execute the following steps. Otherwise, the system will not change the control and will still execute according to the original control parameters.

[0108] 5) A multi-objective genetic algorithm is used to optimize the controller and solve for the room temperature setpoint t in the most unfavorable region. new，set ;

[0109] 6) Reset the room temperature setpoint t for the most unfavorable area according to the calculation results in step 5). new，set This serves as the basis for its control and regulation;

[0110] 7) Recalculate the total fresh air volume of the VAV system and use this as the basis for adjusting the fresh air supply valve, return air valve, and exhaust valve (specifically, step 7). This can be done as follows: Recalculate the total fresh air volume of the VAV system; determine the fresh air valve opening based on the selected fresh air valve opening-flow rate relationship and adjust the fresh air valve accordingly; determine the return air volume based on the formula: return air volume = supply fan volume - fresh air volume; determine the return air valve opening based on the selected return air valve opening-flow rate relationship and adjust the return air valve accordingly; determine the exhaust air volume as 90% of the fresh air volume, and determine the exhaust valve opening based on the selected exhaust valve opening-flow rate relationship and adjust the exhaust valve accordingly.

[0111] In the first step of implementation, outdoor temperature and humidity are collected and outdoor air enthalpy is calculated by using temperature and humidity sensors installed outdoors, and return air temperature and humidity are collected and indoor air enthalpy is calculated by using temperature and humidity sensors installed at the return air vent of the indoor VAV system.

[0112] This method of collecting data from the return air vents of the indoor VAV system allows for a better reflection of the indoor enthalpy. The process of calculating the air enthalpy based on temperature and humidity is existing technology and will not be detailed here.

[0113] During implementation, in step 4), identification is performed every fixed 20 minutes. Too short a time will increase the computational load, while too long a time will lead to a lag in response.

[0114] In implementation, in step 4), the set threshold ratio is 10%, i.e., |Z t -Z t-1 If the threshold is greater than 0.1, proceed with the next step. Setting this threshold to a proportional value of 10% allows for better control of the system's robustness.

[0115] In implementation, step 5) involves using a multi-objective genetic algorithm to optimize the controller solution, which includes the following steps:

[0116] First, set the constraints as follows:

[0117] (1) When setting the indoor temperature setpoint constraint for the most unfavorable zone, refer to the provisions in the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" (GB 50736-2012) and limit the indoor temperature setpoint for the most unfavorable zone to between 24-28℃:

[0118] 24≤T N ≤28 (6)

[0119] (2) When setting indoor thermal comfort constraints, the indoor thermal comfort of the most unfavorable zone should be limited to ±0.5 according to the OSI (International Organization for Standardization) recommended value for PMV:

[0120] -0.5≤PMV≤0.5 (7)

[0121] (3) When setting indoor air quality constraints, the indoor CO2 concentration in each area shall be limited to below 1000 ppm in accordance with the provisions of GB / T 18883-2022 "Indoor Air Quality Standard".

[0122] 0≤C i ≤1000 (8)

[0123] (4) When setting energy consumption constraints for the wind system, no specific numerical limit is set for the energy consumption of the wind system during the optimization process. Referring to commonly used multi-objective optimization methods, the objective function is required to be minimized when editing the algorithm.

[0124] 0≤J pow ≤J pow,max (9)

[0125] Then, an optimization objective function is used (considering the thermal comfort of the most unfavorable area, the indoor air quality of all areas, and the energy consumption of the ventilation system, the following function formula is adopted):

[0126]

[0127] Where: PMV j —Comfort index for the most unfavorable area;

[0128] J pow —Energy consumption of variable air volume (VAV) air conditioning system, kW·h;

[0129] W fan,s — Blower energy consumption, kW·h;

[0130] W fan,p —Exhaust fan energy consumption, kW·h;

[0131] W AHU —Energy consumption of air handling units, kW·h.

[0132] —CO2 concentration index in the most unfavorable area;

[0133] Finally, the most unfavorable temperature setpoint t is obtained by using an artificial intelligence genetic algorithm according to the above formula (10). new，set The artificial intelligence genetic algorithm mentioned above is an existing algorithm, and its specific calculation process is common knowledge. For details, please refer to [reference needed]. Figure 2 This is for your understanding, and will not be elaborated upon here.

[0134] This control method has the following characteristics and advantages: 1. It can ensure air quality in all areas of the VAV system while maximizing the optimization of energy consumption and thermal comfort in the most unfavorable area of ​​the multi-zone variable air volume (VAV) air conditioning system. The control method establishes a multi-zone ventilation equation, resets the most unfavorable area using a personnel counting system, and calculates the optimal room temperature setpoint in the most unfavorable area through a genetic optimization controller, thereby achieving active optimization of the fresh air ratio of the VAV unit. 2. The genetic optimization controller comprehensively considers air quality, indoor thermal comfort, and energy consumption of the air supply system within the supply area. It incorporates a non-dominated sorting genetic algorithm combined with an approximation-ideal-solution sorting method to solve for the optimal indoor temperature setpoint in the most unfavorable area. This indoor temperature setpoint is then assigned to the VAV terminal (VAVBOX) in the most unfavorable area, achieving self-tuning of the PID parameters of the VAV terminal.

[0135] To further demonstrate the advantages and effectiveness of the proposed method, the applicant conducted a comparative experiment. The VAV system used for the verification of the office building had three office areas. The specific system structure is described in [link to VAV system]. Figure 13 ; respectively according to as follows Figure 3 The changes in the number of personnel shown are controlled using three different control strategies.

[0136] Control Strategy A: The existing fresh air control strategy based on the maximum fresh air ratio;

[0137] Control Strategy B: Existing on-demand ventilation control strategy based on multi-zone ventilation equations;

[0138] Control Strategy C: The genetically optimized setpoint temperature reset control method proposed in this invention.

[0139] The comparison of the three different control strategies above yields the following curves showing the changes in indoor thermal comfort in the most unfavorable zone: Figure 4-6 As shown, strategy C, which starts by resetting the indoor set temperature in the most unfavorable area, performs best in controlling the indoor thermal comfort of the most unfavorable area.

[0140] The above three different control strategies were compared, and the indoor carbon dioxide concentration in each area was detected during the control process. The change curves are shown in the figure. Figure 7-9 As shown, strategy B resulted in localized CO2 concentration exceeding the standard, while strategies A and C both met the requirements.

[0141] The above three different control strategies are compared, and the changes in energy consumption of the fresh air system during the control process are detected. The change curves are shown in the figure. Figure 10-12 As shown, it can be seen that the energy-saving effect of strategy C is similar to that of strategy B, and it effectively achieves energy saving compared to strategy A.

[0142] As can be seen from the above comparative experiments, the control method proposed in this invention can not only meet the requirements of indoor air quality and thermal comfort, but also save energy consumption of the air supply system.

Claims

1. A method for optimizing and controlling fresh air intake in a variable air volume (VAV) system during summer, characterized in that, This method compares the enthalpy values ​​of indoor and outdoor air. When the outdoor air enthalpy value is higher, it calculates the most unfavorable area with the greatest demand for fresh air based on the number of people in the area. When the change in the fresh air demand of the most unfavorable area is greater than the set value, a multi-objective genetic optimization control algorithm is used to solve the room temperature set value of the most unfavorable area. This value is then used as the basis for adjustment to calculate the total fresh air volume of the VAV system. Based on this, the fresh air valve, return air valve, and exhaust air valve are adjusted to achieve fresh air control. The method includes the following steps: Step 1: Collect, calculate and compare the outdoor air enthalpy value and the indoor air enthalpy value. When the outdoor air enthalpy value is lower than the indoor air enthalpy value, start the enthalpy difference control mode to ensure the minimum fresh air ratio of the system and adjust the opening of the fresh air valve to meet the fresh air volume required by the number of people in the room. Step 2: When the outdoor air enthalpy is higher than the indoor air enthalpy, perform the following steps: 1) Communicate with the personnel counters in each area and calculate the fresh air demand of each area according to formulas (2) and (3). Total fresh air demand of the system ; In the formula: - The per capita fresh air volume standard determined according to hygiene requirements. ; -No. The required fresh air volume for each region ; -No. Number of people indoors in each area; In the formula: -Total fresh air volume required by the system ; -No. The required fresh air volume for each region ; - The number of areas covered by this VAV system; 2) Communicate with the terminal controllers of the VAV systems in each area to obtain the actual air supply volume in each area. and total system air volume ; In the formula: - Total system air volume ; -No. The air supply volume at the terminal of the regional system ; - The number of areas covered by this VAV system; 3) Calculate the fresh air demand ratio for each region. The region with the highest demand ratio is identified as the most unfavorable region, and its fresh air ratio is obtained. ; In the formula: -No. The required fresh air volume for each region ; -No. The air supply volume at the terminal of the regional system ; -Current moment The demand for fresh air in each region ; -The current most unfavorable area's demand for fresh air ratio, ; 4) Identify the most unfavorable area of ​​the system at fixed time intervals. When the most unfavorable area changes from the previous moment, and the change in the required fresh air ratio of the most unfavorable area at this moment compared with the fresh air ratio at the previous moment reaches or exceeds the set threshold ratio, execute the following steps. Otherwise, the system will not change the control and will still execute according to the original control parameters. 5) A multi-objective genetic algorithm is used to optimize the controller and solve for the room temperature setpoint in the most unfavorable region. ; 6) Reset the room temperature setpoint for the most unfavorable area according to the calculation results in step 5). This serves as the basis for its control and regulation; 7) Recalculate the total fresh air volume of the VAV system and use it as the basis for adjusting the fresh air supply valve, return air valve and exhaust valve.

2. The summer fresh air optimization control method for a variable air volume system as described in claim 1, characterized in that, In step 1, outdoor temperature and humidity are collected by outdoor temperature and humidity sensors and outdoor air enthalpy is calculated. In addition, indoor air temperature and humidity are collected by indoor temperature and humidity sensors at the return air vent of the VAV system and indoor air enthalpy is calculated.

3. The summer fresh air optimization control method for a variable air volume system as described in claim 1, characterized in that, In step 4), identification is performed every 20 minutes.

4. The summer fresh air optimization control method for a variable air volume system as described in claim 1, characterized in that, In step 4), the set threshold ratio is ,Right now Then proceed with the subsequent steps.

5. The summer fresh air optimization control method for a variable air volume system as described in claim 1, characterized in that, Step 5) The algorithm process for optimizing the controller using a multi-objective genetic algorithm includes the following steps: First, set the constraints as follows: (1) When setting the indoor temperature setpoint constraint for the most unfavorable zone, refer to the provisions in the design code and limit the indoor temperature setpoint for the most unfavorable zone to within a certain range. between: (2) Set indoor thermal comfort constraints to limit the indoor thermal comfort of the most unfavorable area to within ±0.5: (3) When setting indoor air quality constraints, the indoor CO2 in each area shall be limited to the specified limits in accordance with the standards. the following: (4) When setting energy consumption constraints for the wind system, the objective function should be minimized when editing the algorithm for the energy consumption of the wind system; Then, the objective function is optimized: In the formula: —Comfort index for the most unfavorable area; —Energy consumption of variable air volume (VAV) air conditioning systems ; —Energy consumption of the blower ; —Exhaust fan energy consumption ; —Energy consumption of air handling units ; —The most unfavorable zone Concentration index; Finally, the most unfavorable temperature setting value is obtained by using an artificial intelligence genetic algorithm according to the above formula (10). .

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