Airport terminal air conditioning system control method giving consideration to heat and humidity comfort and air quality

Abstract

The invention discloses an airport terminal air conditioning system control method giving consideration to heat and humidity comfort and air quality, and belongs to the field of heating ventilation air conditioning control. A model prediction control algorithm is adopted for double-layer optimization control, information such as outdoor meteorological data, the number of passengers and air conditioner operation parameters in a current prediction time domain is collected in real time, prediction of the future operation state of the system is combined, double-layer control is carried out, a heat and humidity control layer optimizes the heat and humidity comfort degree and the energy consumption target, and the energy consumption is reduced. And the fresh air control layer optimizes the air quality and the energy consumption target. According to the method, the energy consumption of the air conditioning system is remarkably reduced, and meanwhile it is ensured that the regional temperature, the relative humidity and the carbon dioxide concentration are kept within the set comfortable range. According to the method, the heat and humidity comfort level and the air quality target are fully considered, energy-saving optimization control over the multiple areas of the terminal air conditioning system is achieved through the double-layer model prediction control method, the energy efficiency and comfort level of the air conditioning system are greatly improved, and the method has wide application prospects.

Description

Technical Field

[0001] The present invention belongs to the field of HVAC control, and in particular relates to a method for controlling an air conditioning system of an airport terminal taking both thermal and humidity comfort and air quality into consideration. Background Art

[0002] As a large public transportation building, the airport terminal has an average annual energy consumption of about 180kW·h / m 2 , which is about 2-3 times that of ordinary public buildings. The HVAC system accounts for about 40%-80% of the total energy consumption. Its energy consumption characteristics and energy-saving potential are of important research value.

[0003] In airport terminals, passenger flow is unevenly distributed in time and space, and regional occupancy is greatly affected by factors such as flight schedules and passenger travel arrangements. Passengers in different areas have different comfort needs, resulting in an unbalanced thermal and humid environment. The high density of people in airports requires a large amount of outdoor air to dilute regional air pollutants and provide acceptable regional air quality. According to relevant surveys, due to excessive fresh air supply and unorganized infiltration, the average carbon dioxide volume fraction in the airport's personnel activity area is only 500-600ppm, which is significantly lower than the 1000ppm limit requirement in the regional air quality standard, resulting in unnecessary energy waste.

[0004] At present, the air conditioning control method mainly considers the control of regional temperature to reduce the energy consumption of the system, but usually ignores the influence of humidity and fresh air, making it difficult to take into account both thermal and humid comfort and air quality goals. Thermal and humid environment and air quality are two important factors affecting passenger comfort, including three main regional air quality parameters: temperature, humidity, CO 2 Therefore, it is urgent to provide a new control method for the terminal air conditioning system that takes into account both thermal and humidity comfort and air quality. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects in the prior art and provide a terminal air conditioning system control method that takes into account both thermal and humid comfort and air quality. The control method achieves the coordinated optimization of thermal and humid environment and air quality through a double-layer control strategy, while reducing energy consumption, providing an energy-saving and comfortable environment for the terminal.

[0006] The specific technical solutions adopted by the present invention are as follows:

[0007] The present invention provides a terminal air conditioning system control method taking into account both thermal and humid comfort and air quality, the method comprising: the upper thermal and humid control layer calculates the optimal control sequence of air supply state parameters by optimizing regional thermal and humid comfort and air conditioning system operating energy consumption, and transmits the result to the lower layer; the lower fresh air control layer calculates the optimal control sequence of fresh air ratio by optimizing regional air quality and air conditioning system operating energy consumption; a double-layer control model is constructed by a double-layer optimization control strategy; based on the double-layer control model, a rolling optimization strategy is adopted to continuously adjust the control parameters according to the current operating state and external environmental changes; at each control moment, the double-layer control model is optimized by a model predictive control algorithm according to the latest meteorological data, the number of passengers, regional temperature and humidity, and air quality data, to obtain the optimal control sequence of air conditioning in the prediction time domain, and execute the control action in the control time domain.

[0008] Preferably, the control method is as follows:

[0009] S1: According to the air-conditioning control area of ​​the terminal, establish the regional heat and humidity balance model and regional air quality model considering the passenger occupancy rate and the air-conditioning system operation energy consumption model;

[0010] S2: Set the parameters of the two-layer control model and obtain the data in the prediction time domain;

[0011] S3: Based on the regional heat and humidity balance model and the air conditioning system operation energy consumption model, a rolling optimization model of the heat and humidity control layer is constructed, and the heat and humidity comfort tracking and the air conditioning system operation energy consumption in the prediction time domain in S2 are taken as optimization targets, the weight coefficient of the upper layer objective function is set, the upper layer constraint conditions are clarified, and the optimal control sequence of the air supply state parameters in the prediction time domain is obtained by optimization and solution, and the results are fed back to the fresh air control layer;

[0012] S4: Based on the regional air quality model and the air conditioning system operation energy consumption model, a rolling optimization model of the fresh air control layer is constructed, the optimization result of the heat and humidity control layer in S3 is used as input, the regional air quality and the air conditioning system operation energy consumption in the predicted time domain in S2 are used as optimization targets, the weight coefficient of the lower layer objective function is set, the lower layer constraint conditions are clarified, the optimal control sequence of the fresh air ratio in the predicted time domain is obtained through optimization and solution, and the results are fed back to the heat and humidity control layer;

[0013] S5: Execute the control action within the control time domain, update the state information of the two-layer control model, and feed back the updated state information to the next control time domain; repeat S2 to S4 for rolling optimization until the scheduling cycle ends.

[0014] Preferably, in S1, the regional heat and moisture balance model includes a regional heat balance model and a regional moisture balance model, and the construction method is as follows:

[0015] For the regional heat balance model, the change in regional air sensible heat is equal to the difference between the regional sensible heat load and the air conditioning cooling capacity, which is expressed as:

[0016]

[0017] The regional sensible heat load includes internal heat sources and external heat sources. The internal heat sources include passengers, equipment and lighting, and the external heat sources include enclosure structures, solar radiation and infiltration. p represents the specific heat capacity of regional air, kJ / (kg·K); ρ a Indicates the regional air density, kg / m 3 ; V represents the volume of the region, m 3 ; T in represents the regional air temperature, ℃; t represents the current time, s; T oa represents the outdoor air temperature, ℃; j represents the enclosure structure type of the area; S represents the enclosure structure set of the area; A j represents the area of ​​the enclosure structure j, m 2 ;k j represents the heat transfer coefficient of the enclosure structure j, W / (m 2 ℃); G sa Indicates the air supply volume flow rate, m 3 / s;T sa Indicates the supply air temperature, °C; Q person Indicates the sensible heat dissipation of passengers, kW; Q other represents the heat generated by other heat sources, kW;

[0018] Q other =Q solar +Q infilt +Q device +Q light (2)

[0019]

[0020] Among them, Q solar is the solar radiation heat, kW; Q infilt is the air penetration heat transfer, kW; Q device Heat dissipation for equipment, kW; Q light Heat dissipation for lighting, kW; Q person represents the sensible heat dissipation of passengers, kW; N represents the number of passengers in the area, people;

[0021] For the regional moisture balance model, the change in regional air latent heat is equal to the difference between the regional latent heat load and the air conditioning dehumidification capacity, which is expressed as:

[0022]

[0023] M infilt =A inf Vρ a (W oa -W in ) (6)

[0024] The regional latent heat load includes the infiltration and fresh air humidity load, and the passenger latent heat dissipation load; W in Indicates regional air humidity, g / kg; M person Indicates the passenger latent heat dissipation load, g / s; M infilt Indicates the wet load caused by air infiltration, g / s; W sa Indicates the air supply humidity, g / kg; N indicates the number of passengers in the area, people; Δh vap represents the specific enthalpy of evaporation, water is 2257 J / g; A inf Indicates the number of infiltration winds, s -1 ; W oa Indicates outdoor air humidity, g / kg.

[0025] Preferably, in S1, the method for constructing the regional air quality model is as follows:

[0026] The carbon dioxide concentration is used to measure the regional air quality. According to the mass balance equation, the regional carbon dioxide concentration balance model is obtained:

[0027]

[0028] C ma =drC oa +(1-dr)C in (8)

[0029] G in =G person N (9)

[0030] Among them, C in Indicates the regional carbon dioxide volume fraction, ppm; C ma Indicates the volume fraction of carbon dioxide in mixed air, ppm; C oa Indicates outdoor carbon dioxide volume fraction, ppm; G sa Represents the regional air supply volume flow rate, m 3 / s; V represents the spatial volume of the region, m 3 ; A inf Indicates the number of infiltration winds, s -1 ; dr represents fresh air ratio; G in represents the total rate of carbon dioxide produced by regional passengers, m 3 / s; G person represents the passenger carbon dioxide generation rate, m 3 / (s·person); N represents the number of passengers in the area, person;

[0031] Carbon dioxide release rate per passenger G person The calculation formula is expressed as:

[0032]

[0033] Among them, A D represents the DuBois surface area, m 2 ; M is the physical activity level in Met, which is set according to the type of activities of passengers in the terminal; RQ stands for respiratory quotient, which is the CO released by respiration 2 and absorb O 2 The molecular ratio of .

[0034] Preferably, in S1, the method for constructing the air conditioning system operation energy consumption model is as follows:

[0035] The energy consumption of air conditioning system operation includes refrigeration energy consumption and fan energy consumption. The total energy consumption P(t) is expressed as:

[0036] P(t)=P c (t)+P f (t) (11)

[0037] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW;

[0038] Refrigeration energy consumption P c The calculation formula of (t) is as follows:

[0039]

[0040] h ma (t) = (1-dr(t))h ra (t)+dr(t)h oa (t) (13)

[0041] Among them, ρ a Indicates the regional air density, kg / m 3 ; G sa (t) represents the air volume flow rate, m 3 / s;h ma (t) represents the mixed air enthalpy value, kJ / kg; h sa (t) represents the supply air enthalpy, kJ / kg; dr(t) represents the fresh air ratio; h ra (t) represents the return air enthalpy value, kJ / kg; h oa (t) represents the fresh air enthalpy, kJ / kg; COP represents the refrigeration energy efficiency ratio;

[0042] Fan energy consumption P f The calculation formula of (t) is as follows:

[0043] P f (t) = σG sa (t) 2 (14)

[0044] Where σ represents the penalty coefficient of the wind turbine power function.

[0045] Preferably, in S2, the parameter settings of the double-layer control model include regional parameters, control parameters, and initial parameters of decision variables; the control parameters include a control time domain and a prediction time domain, the prediction time domain data include outdoor meteorological data, number of passengers, air conditioning operation parameters, and other thermal disturbance data, the outdoor meteorological parameters include outdoor temperature, outdoor relative humidity, and solar radiation intensity, and the other thermal disturbance data include heat and humidity loads caused by infiltration, solar radiation, equipment, and lighting.

[0046] Preferably, S3 is as follows:

[0047] The constraints of the heat and humidity control layer include regional temperature change constraints, regional relative humidity change constraints, supply air volume flow constraints, supply air temperature constraints and supply air humidity constraints;

[0048] The regional temperature change constraint is:

[0049]

[0050] in, Indicates the lower limit of the regional temperature, °C; Indicates the upper limit of the regional temperature, °C; T in Indicates the regional temperature, °C;

[0051] The regional relative humidity change constraint is:

[0052]

[0053] in, Indicates the lower limit of relative humidity in the area; Indicates the upper limit of relative humidity in the area; Indicates the relative humidity of the area;

[0054] The supply air volume flow constraint is:

[0055]

[0056] in, Indicates the lower limit of the air supply volume flow rate, m 3 / s; Indicates the upper limit of the air supply volume flow rate, m3 / s; G sa Indicates the air supply volume flow rate, m 3 / s;

[0057] The supply air temperature constraint is:

[0058]

[0059] in, Indicates the lower limit of the supply air temperature, °C; Indicates the upper limit of the supply air temperature, °C; T sa Indicates the supply air temperature, °C;

[0060] Supply air humidity constraint is:

[0061]

[0062] in, Indicates the lower limit of air supply humidity, g / kg; Indicates the upper limit of air supply humidity, g / kg; W sa Indicates the air supply humidity, g / kg;

[0063] Thermal comfort tracking target J 1 It is expressed as:

[0064]

[0065] Among them, t represents the current time, h; T in (t) represents the regional temperature, °C; T set (t) represents the zone temperature setting value, °C; Indicates the relative humidity of the area; Indicates the relative humidity setting value of the area;

[0066] Relative humidity is the temperature T in and humidity W in The calculation formula is as follows:

[0067]

[0068] Among them, P w,s Indicates the partial pressure of saturated water vapor, kPa; P w represents the partial pressure of water vapor, kPa; B represents the atmospheric pressure, which is 101.325 kPa;

[0069] Air conditioning system operation energy consumption target J 2 It is expressed as:

[0070] J 2 =P c (t)+Pf (t) (24)

[0071] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW;

[0072] The rolling optimization model of the upper heat and moisture control layer is as follows:

[0073]

[0074] Where i represents the current control time, h; L represents the prediction time domain length, h; ω 1 The weight coefficient of the thermal comfort tracking target of the thermal and humidity control layer; J 1 (t) represents the thermal and moisture comfort tracking target value at time t; ω 2 The weight coefficient of the heat and humidity control layer system operation energy consumption target; J 2 (t) represents the target energy consumption value of the system at time t.

[0075] Preferably, the S4 is as follows:

[0076] The constraints of the fresh air control layer include carbon dioxide concentration constraints and fresh air ratio constraints;

[0077] Regional CO2 concentration constraints:

[0078]

[0079] in, Indicates the lower limit of regional carbon dioxide concentration, ppm; Indicates the upper limit of regional carbon dioxide concentration, ppm; C in Indicates the regional carbon dioxide concentration, ppm;

[0080] Fresh air ratio constraints:

[0081] dr min ≤dr≤dr max (27) Among them, dr min Indicates the lower limit of the fresh air ratio; dr max Indicates the upper limit of the fresh air ratio; dr indicates the fresh air ratio;

[0082] Regional Air Quality Tracking TargetJ 3 It is expressed as:

[0083] J 3 =(C in (t)-C min ) 2 (28)

[0084] Among them, Cin (t) represents the regional carbon dioxide concentration, ppm; C min Indicates the lower limit of the carbon dioxide volume fraction, that is, the outdoor carbon dioxide volume fraction.

[0085] Air conditioning system operation energy consumption target J 4 It is expressed as:

[0086] J 4 =P c (t)+P f (t) (29)

[0087] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW.

[0088] The rolling optimization model of the lower fresh air control layer is as follows:

[0089]

[0090] Where i represents the current control time domain, h; L represents the length of the prediction time domain, h; ω 3 The weight coefficient of the air quality target in the fresh air control layer area; J 3 (t) represents the regional air quality target value at time t; ω 4 The weight coefficient of the air conditioning operation energy consumption target of the fresh air control layer; J 4 (t) represents the target energy consumption value of air conditioning operation at time t.

[0091] Compared with the prior art, the present invention has the following beneficial effects:

[0092] 1) The present invention adopts a two-layer model predictive control method, and divides the control task into an upper heat and humidity control layer and a lower fresh air control layer. The upper heat and humidity control layer optimizes the regional heat and humidity comfort and the operating energy consumption of the air conditioning system. By predicting the changes in heat and humidity comfort in the future, the optimal control sequence of the air supply state parameters (supply air temperature, supply air humidity and supply air volume flow) is calculated. The lower fresh air control layer optimizes the regional air quality and the operating energy consumption of the air conditioning system, and calculates the optimal control sequence of the fresh air ratio by predicting the changes in carbon dioxide concentration in the future.

[0093] 2) The present invention fully considers the impact of passenger flow, air supply status, and fresh air on regional temperature, humidity, and carbon dioxide concentration, takes into account both thermal and humidity comfort and air quality goals, effectively solves the problems of pollutant aggregation and thermal and humidity imbalance caused by passenger flow distribution in different areas, realizes energy-saving control of the terminal air-conditioning system, and is suitable for terminal air-conditioning control systems in various scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0095] Figure 1 A control flow chart of a terminal air conditioning system control method taking into account both thermal and humid comfort and air quality is provided for a preferred embodiment of the present invention.

[0096] Figure 2 A regional carbon dioxide concentration model diagram of a preferred embodiment is provided for the present invention. DETAILED DESCRIPTION

[0097] The present invention is further described and illustrated below in conjunction with the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0098] like Figure 1 As shown, a terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality is provided by the present invention, and the control method includes:

[0099] The upper heat and humidity control layer calculates the optimal control sequence of air supply state parameters by optimizing the regional heat and humidity comfort and the energy consumption of the air-conditioning system, and passes it to the lower layer; the lower fresh air control layer calculates the optimal control sequence of fresh air ratio by optimizing the regional air quality and the energy consumption of the air-conditioning system; a double-layer control model is constructed through a double-layer optimization control strategy; based on the obtained double-layer control model, a rolling optimization strategy is adopted to continuously adjust the control parameters according to the current operating status and changes in the external environment; at each control moment, the obtained double-layer control model is optimized through the model predictive control algorithm according to the latest meteorological data, number of passengers, regional temperature and humidity, and air quality data, to obtain the optimal control sequence of air conditioning in the prediction time domain, and execute the control action in the control time domain.

[0100] As a preferred embodiment of the present invention, the control method of the present invention specifically includes the following steps:

[0101] S1: According to the air-conditioning control area of ​​the terminal, establish a regional heat and humidity balance model and a regional air quality model considering the passenger occupancy rate as well as an air-conditioning system operation energy consumption model.

[0102] In actual use, the construction method of the regional heat and moisture balance model is as follows:

[0103] The regional heat and moisture balance model includes the regional heat balance model and the regional moisture balance model.

[0104] For the regional heat balance model, the change in regional air sensible heat is equal to the difference between the regional sensible heat load and the air conditioning cooling capacity, which is expressed as:

[0105]

[0106] The regional sensible heat load includes internal heat sources and external heat sources. The internal heat sources include passengers, equipment and lighting, and the external heat sources include enclosure structures, solar radiation and infiltration. p represents the specific heat capacity of regional air, kJ / (kg·K); ρ a Indicates the regional air density, kg / m 3 ; V represents the volume of the region, m 3 ; T in represents the regional air temperature, ℃; t represents the current time, s; T oa represents the outdoor air temperature, ℃; j represents the enclosure structure type of the area; S represents the enclosure structure set of the area; A j represents the area of ​​the enclosure structure j, m 2 ;k j represents the heat transfer coefficient of the enclosure structure j, W / (m 2 ℃); G sa Indicates the air supply volume flow rate, m 3 / s;T sa Indicates the supply air temperature, °C; Q person Indicates the sensible heat dissipation of passengers, kW; Q other represents the heat generated by other heat sources, kW;

[0107] Q other =Q solar +Q infilt +Q device +Q light (2)

[0108]

[0109] Among them, Q solar is the solar radiation heat, kW; Q infilt is the air penetration heat transfer, kW; Q device Heat dissipation for equipment, kW; Q light Heat dissipation for lighting, kW; Q person represents the sensible heat dissipation of passengers, kW; N represents the number of passengers in the area, people;

[0110] For the regional moisture balance model, the change in regional air latent heat is equal to the difference between the regional latent heat load and the air conditioning dehumidification capacity, which is expressed as:

[0111]

[0112] M infilt =A inf Vρ a (W oa -W in ) (6)

[0113] The regional latent heat load includes the infiltration and fresh air humidity load, and the passenger latent heat dissipation load; W in Indicates regional air humidity, g / kg; M person Indicates the passenger latent heat dissipation load, g / s; M infilt Indicates the wet load caused by air infiltration, g / s; W sa Indicates the air supply humidity, g / kg; N indicates the number of passengers in the area, people; Δh vap represents the specific enthalpy of evaporation, water is 2257 J / g; A inf Indicates the number of infiltration winds, s -1 ; W oa Indicates outdoor air humidity, g / kg.

[0114] In actual use, the construction method of the regional air quality model is as follows:

[0115] There are many types of pollutants emitted in the terminal area, which are difficult to monitor and control. The amount of pollutants emitted by the human body is roughly proportional to the concentration of carbon dioxide emitted by breathing. Carbon dioxide concentration is used to measure regional air quality. According to the mass balance equation, the regional carbon dioxide concentration balance model is obtained:

[0116]

[0117] C ma =drC oa +(1-dr)C in (8)

[0118] G in =G person N (9)

[0119] Among them, C in Indicates the regional carbon dioxide volume fraction, ppm; C ma Indicates the volume fraction of carbon dioxide in mixed air (return air and fresh air), ppm; G oa Indicates outdoor carbon dioxide volume fraction, ppm; G sa Represents the regional air supply volume flow rate, m 3 / s; V represents the spatial volume of the region, m 3 ; A inf Indicates the number of infiltration winds, s -1 ; dr represents fresh air ratio; G inrepresents the total rate of carbon dioxide produced by regional passengers, m 3 / s; G person represents the passenger carbon dioxide generation rate, m 3 / (s·person); N represents the number of passengers in the area, person;

[0120] Carbon dioxide release rate per passenger G person The calculation formula is expressed as:

[0121]

[0122] Among them, A D represents the DuBois surface area, m 2 , with an average of 1.8m 2 ; M is the physical activity level in Met, which is set according to the type of activities of passengers in the terminal; RQ stands for respiratory quotient, which is the CO released by respiration 2 and absorb O 2 The molecular ratio of is generally 0.83 under light physical labor.

[0123] In actual use, the construction method of the air conditioning system operation energy consumption model is as follows:

[0124] The energy consumption of air conditioning system operation includes refrigeration energy consumption and fan energy consumption. The total energy consumption P(t) is expressed as:

[0125] P(t)=P c (t)+P f (t) (11)

[0126] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW;

[0127] Refrigeration energy consumption P c The calculation formula of (t) is as follows:

[0128]

[0129] h ma (t) = (1-dr(t))h ra (t)+dr(t)h oa (t) (13)

[0130] Among them, ρ a Indicates the regional air density, kg / m 3 ; G sa (t) represents the air volume flow rate, m 3 / s;h ma (t) represents the mixed air enthalpy value, kJ / kg; h sa(t) represents the supply air enthalpy, kJ / kg; dr(t) represents the fresh air ratio; h ra (t) represents the return air enthalpy value, kJ / kg; h oa (t) represents the fresh air enthalpy, kJ / kg; COP represents the refrigeration energy efficiency ratio;

[0131] Fan energy consumption P f The calculation formula of (t) is as follows:

[0132] P f (t) = σG sa (t) 2 (14)

[0133] Where σ represents the penalty coefficient of the wind turbine power function.

[0134] S2: Set the parameters of the two-layer control model and obtain the data in the prediction time domain.

[0135] In actual use, the parameter settings of the two-layer control model include regional parameters, control parameters, and initial parameters of decision variables; among them, the control parameters include the control time domain and the prediction time domain. The prediction time domain data includes outdoor meteorological data, number of passengers, air-conditioning operation parameters, and other thermal disturbance data. Outdoor meteorological parameters include outdoor temperature, outdoor relative humidity, and solar radiation intensity. Other thermal disturbance data include infiltration, solar radiation, equipment, and heat and humidity loads caused by lighting.

[0136] S3: Based on the regional heat and moisture balance model and the air conditioning system operation energy consumption model in S1, a rolling optimization model of the heat and moisture control layer is constructed. The heat and moisture comfort tracking and the air conditioning system operation energy consumption in the prediction time domain in S2 are taken as optimization goals. The weight coefficient of the upper-level objective function is set, the upper-level constraints are clarified, and the optimal control sequence of the supply air state parameters (including supply air temperature, supply air humidity, and supply air volume) in the prediction time domain is obtained through optimization and solution, and the results are fed back to the fresh air control layer.

[0137] In actual use, the constraints of the heat and humidity control layer include regional temperature change constraints, regional relative humidity change constraints, supply air volume flow constraints, supply air temperature constraints and supply air humidity constraints, as follows:

[0138] The regional temperature change constraint is:

[0139]

[0140] in, Indicates the lower limit of the regional temperature, °C; Indicates the upper limit of the regional temperature, °C; T in Indicates the regional temperature, °C.

[0141] The regional relative humidity change constraint is:

[0142]

[0143] in, Indicates the lower limit of relative humidity in the area; Indicates the upper limit of relative humidity in the area; Indicates the relative humidity of the area.

[0144] The supply air volume flow constraint is:

[0145]

[0146] in, Indicates the lower limit of the air supply volume flow rate, m 3 / s; Indicates the upper limit of the air supply volume flow rate, m 3 / s; G sa Indicates the air supply volume flow rate, m 3 / s.

[0147] The supply air temperature constraint is:

[0148]

[0149] in, Indicates the lower limit of the supply air temperature, °C; Indicates the upper limit of the supply air temperature, °C; T sa Indicates the supply air temperature, ℃.

[0150] Supply air humidity constraint is:

[0151]

[0152] in, Indicates the lower limit of air supply humidity, g / kg; Indicates the upper limit of air supply humidity, g / kg; W sa Indicates the supply air humidity, g / kg.

[0153] In S3, the optimization objectives are thermal and humidity comfort tracking and air conditioning system operation energy consumption within the prediction time domain, as follows:

[0154] Thermal comfort tracking target J 1 It is expressed as:

[0155]

[0156] Among them, t represents the current time, h; T in (t) represents the regional temperature, °C; T set (t) represents the zone temperature setting value, °C; Indicates the relative humidity of the area; Indicates the relative humidity setpoint for the zone.

[0157] Relative humidity is the temperature T in and humidity W in The calculation formula is as follows:

[0158]

[0159] Among them, P w,s Indicates the partial pressure of saturated water vapor, kPa; P w represents the partial pressure of water vapor, kPa; B represents the atmospheric pressure, which is 101.325 kPa;

[0160] Air conditioning system operation energy consumption target J 2 It is expressed as:

[0161] J 2 =P c (t)+P f (t) (24)

[0162] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW.

[0163] The rolling optimization model of the upper heat and moisture control layer is as follows:

[0164]

[0165] st Formula (1)-Formula (6), Formula (11)-Formula (24)

[0166] Where i represents the current control time, h; L represents the prediction time domain length, h; ω 1 The weight coefficient of the thermal comfort tracking target of the thermal and humidity control layer; J 1 (t) represents the thermal and moisture comfort tracking target value at time t; ω 2 The weight coefficient of the heat and humidity control layer system operation energy consumption target; J 2 (t) represents the target energy consumption value of the system at time t.

[0167] S4: Based on the regional air quality model and air conditioning system operation energy consumption model in S1, a rolling optimization model of the fresh air control layer is constructed. The optimization results of the heat and humidity control layer in S3 are used as input, and the regional air quality and air conditioning system operation energy consumption in the predicted time domain in S2 are used as optimization targets. The weight coefficient of the lower-level objective function is set, the lower-level constraints are clarified, and the optimal control sequence of the fresh air ratio in the predicted time domain is obtained through optimization and solution, and the results are fed back to the heat and humidity control layer.

[0168] In actual use, the fresh air control layer constraints include carbon dioxide concentration constraints and fresh air ratio constraints, as follows:

[0169] Regional CO2 concentration constraints:

[0170]

[0171] in, Indicates the lower limit of regional carbon dioxide concentration, ppm; Indicates the upper limit of regional carbon dioxide concentration, ppm; C in Indicates the regional carbon dioxide concentration, ppm.

[0172] Fresh air ratio constraints:

[0173] dr min ≤dr≤dr max (27)

[0174] Among them, dr min Indicates the lower limit of the fresh air ratio; dr max Indicates the upper limit of the fresh air ratio; dr represents the fresh air ratio.

[0175] Regional Air Quality Tracking TargetJ 3 It is expressed as:

[0176] J 3 =(C in (t)-C min ) 2 (28)

[0177] Among them, C in (t) represents the regional carbon dioxide concentration, ppm; C min Indicates the lower limit of the carbon dioxide volume fraction, that is, the outdoor carbon dioxide volume fraction.

[0178] Air conditioning system operation energy consumption target J 4 It is expressed as:

[0179] J 4 =P c (t)+P f (t) (29)

[0180] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW.

[0181] The rolling optimization model of the lower fresh air control layer is as follows:

[0182]

[0183] st formula (7)-formula (14), formula (26)-formula (29)

[0184] Where i represents the current control time domain, h; L represents the length of the prediction time domain, h; ω 3 The weight coefficient of the air quality target in the fresh air control layer area; J 3 (t) represents the regional air quality target value at time t; ω 4 The weight coefficient of the air conditioning operation energy consumption target of the fresh air control layer; J 4 (t) represents the target energy consumption value of air conditioning operation at time t.

[0185] S5: Execute the control action within the control time domain, update the state information of the two-layer control model, and feed back the updated state information to the next control time domain; repeat S2 to S4 for rolling optimization until the scheduling cycle ends.

[0186] The method and effects of the present invention will be specifically described below through examples.

[0187] Example

[0188] In the prior art, air conditioning control methods mainly use regional temperature as the optimization control target to reduce the energy consumption of the system, but usually ignore the influence of humidity and fresh air. It is difficult to take into account both thermal and humidity comfort and air quality targets at the same time, which may lead to problems such as thermal and humidity imbalance and regional pollutant accumulation.

[0189] As a preferred implementation mode of the present invention, this embodiment takes one day as the scheduling cycle, that is, the scheduling cycle is 24 hours, sets the control time domain to 1 hour, and the prediction time domain to 6 hours: data collection and optimization solution are performed every hour, and the uncertainty of changes in outdoor weather, passenger occupancy rate, etc. is taken into account. The model predictive control algorithm is used to predict the system state changes in the next 6 hours, and the optimal control sequence is solved. The optimization is continuously rolled out over time until the end of the scheduling cycle.

[0190] This embodiment provides a terminal air conditioning system control method that takes into account both thermal and humidity comfort and air quality. The control process is as follows: Figure 1 The specific implementation steps are as follows:

[0191] S1: According to the air-conditioning control area of ​​the terminal, establish a regional heat and humidity balance model and a regional air quality model considering the passenger occupancy rate as well as an air-conditioning system operation energy consumption model.

[0192] S11: Regional heat and moisture balance model

[0193] Specifically, the regional heat and moisture balance model includes the regional heat balance model and the regional moisture balance model. The regional sensible heat load is mainly composed of internal heat sources (passengers, equipment, lighting) and external heat sources (enclosure structure, solar radiation, infiltration). The change value of the regional air sensible heat is equal to the difference between the regional sensible heat load and the air conditioning cooling capacity, and the regional heat balance model is obtained, which is expressed as:

[0194]

[0195] Among them, C p represents the specific heat capacity of regional air, and its value is 1.005 kJ / (kg·K); ρ a Indicates the regional air density, the value is 1.205kg / m 3 ; V represents the volume of the region, m 3 ; T in represents the regional air temperature, ℃; t represents the current time, s; T oa represents the outdoor air temperature, ℃; j represents the enclosure structure type of the area; S represents the enclosure structure set of the area; A j represents the area of ​​the enclosure structure j, m 2 ;k j represents the heat transfer coefficient of the enclosure structure j, W / (m 2 ℃); G sa Indicates the air supply volume flow rate, m 3 / s;T sa Indicates the supply air temperature, °C; Q person Indicates the sensible heat dissipation of passengers, kW; Q other Represents the heat generated by other heat sources, kW.

[0196] Q other =Q solar +Q infilt +Q device +Q light (2)

[0197]

[0198] Among them, Q solar is the solar radiation heat, kW; Q infilt is the air penetration heat transfer, kW; Q device Heat dissipation for equipment, kW; Q light Heat dissipation for lighting, kW; Q person represents the sensible heat dissipation of passengers, kW; N represents the number of passengers in the area, people.

[0199] Furthermore, Q other The data can be obtained by sensor detection, simulation or mathematical model. In this embodiment, the power density of the equipment is set to 15W / m2 , the lighting power density is set to 10W / m 2 .

[0200] The regional latent heat load is mainly composed of infiltration and fresh air humidity load, and passenger latent heat dissipation load. The change value of regional air latent heat is equal to the difference between the regional latent heat load and the air conditioning dehumidification capacity, and the regional moisture balance model is obtained, which is expressed as:

[0201]

[0202] M infilt =A inf Vρ a (W oa -W in ) (6)

[0203] The regional latent heat load includes the infiltration and fresh air humidity load, and the passenger latent heat dissipation load; W in Indicates regional air humidity, g / kg; M person Indicates the passenger latent heat dissipation load, g / s; M infilt Indicates the wet load caused by air infiltration, g / s; W sa Indicates the air supply humidity, g / kg; N indicates the number of passengers in the area, people; Δh vap represents the specific enthalpy of evaporation, water is 2257 J / g; A inf Indicates the number of infiltration winds, s -1 ; W oa Indicates outdoor air humidity, g / kg.

[0204] S12: Regional Air Quality Model

[0205] There are many types of pollutants emitted in the terminal area, which are difficult to monitor and control. The amount of pollutants emitted by the human body is roughly proportional to the concentration of carbon dioxide emitted by breathing. Carbon dioxide concentration is used to measure regional air quality, such as Figure 2 As shown, according to the mass balance equation, the regional carbon dioxide concentration balance model is obtained:

[0206]

[0207] C ma =drC oa +(1-dr)C in (8)

[0208] G in =G person N (9)

[0209] Among them, C in Represents the regional carbon dioxide volume fraction; C maIndicates the volume fraction of carbon dioxide in mixed air (return air and fresh air); C oa represents the outdoor carbon dioxide volume fraction, which is 400×10 -6 (i.e. 400ppm); G sa Represents the regional air supply volume flow rate, m 3 / s; V represents the spatial volume of the region, m 3 ; A inf Indicates the number of infiltration winds, s -1 ; dr represents fresh air ratio; G in represents the total rate of carbon dioxide produced by regional passengers, m 3 / s; N represents the number of passengers in the area; G person represents the passenger carbon dioxide generation rate, m 3 / (s·person).

[0210] Furthermore, the calculation formula for the average carbon dioxide release rate per passenger is expressed as:

[0211]

[0212] Among them, G person represents the per capita carbon dioxide generation rate of passengers, m 3 / (s·person), A D represents the DuBois surface area, m 2 , with an average of 1.8m 2 ; M is the physical activity level in Met, which is set according to the type of activities of passengers in the terminal; RQ stands for respiratory quotient, which is the CO released by respiration 2 and absorb O 2 The molecular ratio of is generally 0.83 under light physical labor.

[0213] S13: Air conditioning system operation energy consumption model

[0214] Specifically, the energy consumption of air-conditioning system operation mainly includes refrigeration energy consumption and fan energy consumption. The total energy consumption P(t) is expressed as:

[0215] P(t)=P c (t)+P f (t) (11)

[0216] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW.

[0217] Furthermore, the cooling energy consumption P c (t) The calculation formula is as follows:

[0218]

[0219] h ma (t) = (1-dr(t))h ra (t)+dr(t)h oa (t) (13)

[0220] Among them, ρ a Indicates air density, kg / m 3 ; G sa (t) represents the air volume flow rate, m 3 / s;h ma (t) represents the enthalpy of mixed air (supply air and return air), kJ / kg; h sa (t) represents the supply air enthalpy, kJ / kg; dr(t) represents the fresh air ratio; hra(t) represents the return air enthalpy, kJ / kg; h oa (t) represents the fresh air enthalpy, kJ / kg; COP represents the refrigeration energy efficiency ratio.

[0221] Furthermore, the fan energy consumption P f (t) The calculation formula is as follows:

[0222] P f (t) = σG sa (t) 2 (14)

[0223] Where, σ represents the penalty coefficient of the wind turbine power function; G sa (t) represents the air volume flow rate, m 3 / s.

[0224] S2: Set the parameters of the two-layer control model and obtain the data in the prediction time domain.

[0225] Specifically, the model parameter settings include regional parameters, control parameters (control time domain, prediction time domain), and initial parameters of decision variables. The prediction time domain data include outdoor meteorological data, number of passengers, air conditioning operation parameters, and other thermal disturbance data. The outdoor meteorological parameters include outdoor temperature, outdoor relative humidity, and solar radiation intensity. The other thermal disturbance data include heat and humidity loads caused by infiltration, solar radiation, equipment, and lighting.

[0226] Furthermore, since the scheduling period of this embodiment is 24 hours, when the predicted time domain at the current control moment exceeds the end of the scheduling period, the shortened rolling time domain method is adopted. During the backward rolling of the control time domain, the predicted time domain length is gradually shortened until the control time domain reaches the end of the scheduling period.

[0227] S3: Based on the regional heat and moisture balance model and the air conditioning system operation energy consumption model in S1, a rolling optimization model of the heat and moisture control layer is constructed. The heat and moisture comfort tracking and the air conditioning system operation energy consumption in the prediction time domain in S2 are taken as optimization goals. The weight coefficient of the upper-level objective function is set, the upper-level constraints are clarified, and the optimal control sequence of the supply air state parameters (including supply air temperature, supply air humidity, and supply air volume) in the prediction time domain is obtained through optimization and solution, and the results are fed back to the fresh air control layer.

[0228] S31: Determine constraints

[0229] Specifically, the constraints of the heat and humidity control layer include regional temperature change constraints, regional relative humidity change constraints, air supply volume flow constraints, air supply temperature constraints, and air supply humidity constraints, which are expressed as follows:

[0230] The regional temperature change constraint is:

[0231]

[0232] in, Indicates the lower limit of the regional temperature, °C; Indicates the upper limit of the zone temperature, °C.

[0233] The regional relative humidity change constraint is:

[0234]

[0235] in, Indicates the lower limit of relative humidity in the area; Indicates the upper limit of relative humidity in an area.

[0236] The supply air volume flow constraint is:

[0237]

[0238] in, Indicates the lower limit of the air supply volume flow rate, m 3 / s; Indicates the upper limit of the air supply volume flow rate, m 3 / s.

[0239] The supply air temperature constraint is:

[0240]

[0241] in, Indicates the lower limit of the supply air temperature, °C; Indicates the upper limit of the supply air temperature, °C.

[0242] Supply air humidity constraint is:

[0243]

[0244] in, Indicates the lower limit of air supply humidity, g / kg; Indicates the upper limit of supply air humidity, g / kg.

[0245] S32: Determine optimization goals

[0246] Specifically, the heat and humidity control layer takes the weight distribution function of heat and humidity comfort tracking in the prediction time domain and the energy consumption of the air conditioning system as the optimization target, which is as follows:

[0247] Thermal comfort tracking target J 1 It is expressed as:

[0248]

[0249] Among them, t represents the current time, h; T in (t) represents the regional temperature, °C; T set (t) represents the zone temperature setting value, °C; Indicates the relative humidity of the area; Indicates the relative humidity setpoint for the zone.

[0250] Furthermore, relative humidity is the temperature T in and humidity W in The calculation formula is as follows:

[0251]

[0252] Among them, P w,s Indicates the partial pressure of saturated water vapor, kPa; P w represents the partial pressure of water vapor, kPa; B represents the atmospheric pressure, which is 101.325 kPa;

[0253] Air conditioning system operation energy consumption target J 2 It is expressed as:

[0254] J 2 =P c (t)+P f (t) (24)

[0255] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW.

[0256] S31: Determine the decision variables

[0257] The decision variables are divided into control variables and state variables. The control variable of the heat and humidity control layer is the supply air temperature T that affects the temperature and humidity of the area. sa, Supply air humidity W sa , air supply volume flow rate G sa , the state variable is the regional temperature T in , Regional relative humidity

[0258] S33: Building a rolling optimization model

[0259] Specifically, the rolling optimization model of the upper heat and moisture control layer is as follows:

[0260]

[0261] st Formula (1)-Formula (6), Formula (11)-Formula (24)

[0262] Where i represents the current control time, h; L represents the prediction time domain length, h; ω 1 The weight coefficient of the thermal comfort tracking target of the thermal and humidity control layer; J 1 (t) represents the thermal and moisture comfort tracking target value at time t; ω 2 The weight coefficient of the heat and humidity control layer system operation energy consumption target; J 2 (t) represents the target energy consumption value of the system at time t.

[0263] The heat and humidity control layer solves the optimal control sequence of decision variables (supply air temperature, supply air humidity, supply air volume flow) within the prediction time domain of 6 hours, and feeds the optimization results back to the lower fresh air control layer. The lower layer further optimizes the fresh air ratio based on the optimization results of the upper layer as constraints.

[0264] S4: Based on the regional air quality model and air conditioning system operation energy consumption model in S1, a rolling optimization model of the fresh air control layer is constructed. The optimization results of the heat and humidity control layer in S3 are used as input, and the regional air quality and air conditioning system operation energy consumption in the predicted time domain in S2 are used as optimization targets. The weight coefficient of the lower-level objective function is set, the lower-level constraints are clarified, and the optimal control sequence of the fresh air ratio in the predicted time domain is obtained through optimization and solution, and the results are fed back to the heat and humidity control layer.

[0265] S41: Determine constraints

[0266] Specifically, the fresh air control layer constraints include carbon dioxide concentration constraints and fresh air ratio constraints, as follows:

[0267] Regional CO2 concentration constraints:

[0268]

[0269] in, Indicates the lower limit of regional carbon dioxide concentration, ppm; Indicates the upper limit of regional carbon dioxide concentration, ppm.

[0270] Fresh air ratio constraints:

[0271] dr min ≤dr≤dr max (27)

[0272] Among them, dr min Indicates the lower limit of the fresh air ratio; dr max Indicates the upper limit of the fresh air ratio.

[0273] S42: Determine optimization goals

[0274] Specifically, the fresh air control layer takes the weight distribution function of the regional air quality and the air conditioning system operating energy consumption in the predicted time domain as the optimization goal, which is as follows:

[0275] Regional Air Quality Tracking TargetJ 3 It is expressed as:

[0276] J 3 =(C in (t)-C min ) 2 (28)

[0277] Where C(t) represents the regional carbon dioxide concentration, ppm; C min represents the lower limit of the carbon dioxide volume fraction, that is, the outdoor carbon dioxide volume fraction, which is set to 400ppm in this embodiment.

[0278] Air conditioning system operation energy consumption target J 4 It is expressed as:

[0279] J 4 =P c (t)+P f (t) (29)

[0280] Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW.

[0281] It should be noted that in the calculation of the operating energy consumption of the air-conditioning system at the fresh air control layer, the supply air state parameters adopt the optimization results of the upper layer, including supply air temperature, supply air humidity, and supply air volume flow rate.

[0282] S42: Determine the decision variables

[0283] The decision variables are divided into control variables and state variables. The control variable of the fresh air control layer is the fresh air ratio dr that affects the regional carbon dioxide concentration, and the state variable is the regional carbon dioxide concentration C in .

[0284] S43: Building an optimization model

[0285] Specifically, the rolling optimization model of the lower fresh air control layer is as follows:

[0286]

[0287] st formula (7)-formula (14), formula (26)-formula (29)

[0288] Where i represents the current control time domain, h; L represents the length of the prediction time domain, h; ω 3 The weight coefficient of the air quality target in the fresh air control layer area; J 3 (t) represents the regional air quality target value at time t; ω 4 The weight coefficient of the air conditioning operation energy consumption target of the fresh air control layer; J 4 (t) represents the target energy consumption value of air conditioning operation at time t.

[0289] The fresh air control layer solves the optimal control sequence of the fresh air ratio within the prediction time domain of 6 hours, and feeds the optimization results back to the heat and humidity control layer, and finally obtains the optimal control solution that takes into account both the heat and humidity comfort and the regional air quality goals, that is, the supply air temperature, supply air humidity, supply air volume flow rate and fresh air ratio within the current control time domain of 1 hour. By optimizing and solving different air conditioning control areas of the terminal, the optimal control sequence of different areas is obtained.

[0290] S5: Execute the control action within the control time domain, update the state information of the control system, and feed back the updated state information to the next moment, repeating the above steps for double-layer rolling optimization until the scheduling cycle ends.

[0291] Specifically, the control actions include controlling the supply air temperature, supply air humidity, supply air volume flow rate and fresh air ratio within the time domain of 1 hour. The state of the control system includes controlling the regional temperature, regional humidity, carbon dioxide concentration and number of passengers within the time domain of 1 hour, which are fed back to the next control moment as the initial value.

[0292] Furthermore, a double-layer rolling optimization strategy is adopted to continuously adjust the control parameters according to the current operating status and changes in the external environment. At each control moment, the system optimizes the air conditioning operating parameters within the predicted time domain based on the latest meteorological data, number of passengers, regional temperature and humidity, and air quality data through the model predictive control algorithm, and performs corresponding control actions.

[0293] The present invention constructs a regional heat and humidity balance model and a regional air quality model considering the volatility of passengers based on the spatial and temporal distribution of the number of passengers in the terminal. The model predictive control algorithm is used for double-layer optimization control. By real-time collection of outdoor meteorological data, the number of passengers, air conditioning operating parameters and other information in the current prediction time domain, combined with the prediction of the future operation state of the system, double-layer control is performed. The heat and humidity control layer controls and optimizes the heat and humidity comfort and energy consumption targets, and the fresh air control layer controls and optimizes the air quality and energy consumption targets. The model predictive control algorithm can dynamically predict and optimize the operation state of the air conditioning system on the basis of considering comfort and air quality, solve the optimal solution of the air conditioning operating parameters in the prediction time domain, and execute the corresponding control actions in the control time domain. The method significantly reduces the energy consumption of the air conditioning system, while ensuring that the regional temperature, relative humidity and carbon dioxide concentration are maintained within the set comfort range. Different from the traditional single-area temperature control method, the present invention fully considers the heat and humidity comfort and regional air quality targets, and realizes the energy-saving optimization control of the terminal air conditioning system in multiple areas through the double-layer model predictive control method, which greatly improves the energy efficiency and comfort of the air conditioning system and has broad application prospects.

[0294] The above-described embodiment is only a preferred solution of the present invention, but it is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for controlling an air conditioning system of an airport terminal taking into account both thermal and humidity comfort and air quality, characterized in that: The upper heat and humidity control layer calculates the optimal control sequence of air supply state parameters by optimizing the regional heat and humidity comfort and the operating energy consumption of the air-conditioning system, and passes it to the lower layer; the lower fresh air control layer calculates the optimal control sequence of fresh air ratio by optimizing the regional air quality and the operating energy consumption of the air-conditioning system; a double-layer control model is constructed through a double-layer optimization control strategy; based on the double-layer control model, a rolling optimization strategy is adopted to continuously adjust the control parameters according to the current operating status and changes in the external environment; at each control moment, the double-layer control model is optimized through the model predictive control algorithm according to the latest meteorological data, number of passengers, regional temperature and humidity, and air quality data, to obtain the optimal control sequence of air conditioning in the prediction time domain, and execute control actions in the control time domain.

2. The terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality according to claim 1, characterized in that: The control method is specifically as follows: S1: According to the air-conditioning control area of ​​the terminal, establish the regional heat and humidity balance model and regional air quality model considering the passenger occupancy rate and the air-conditioning system operation energy consumption model; S2: Set the parameters of the two-layer control model and obtain the data in the prediction time domain; S3: Based on the regional heat and humidity balance model and the air conditioning system operation energy consumption model, a rolling optimization model of the heat and humidity control layer is constructed, and the heat and humidity comfort tracking and the air conditioning system operation energy consumption in the prediction time domain in S2 are taken as optimization targets, the weight coefficient of the upper layer objective function is set, the upper layer constraint conditions are clarified, and the optimal control sequence of the air supply state parameters in the prediction time domain is obtained by optimization and solution, and the results are fed back to the fresh air control layer; S4: Based on the regional air quality model and the air conditioning system operation energy consumption model, a rolling optimization model of the fresh air control layer is constructed, the optimization result of the heat and humidity control layer in S3 is used as input, the regional air quality and the air conditioning system operation energy consumption in the predicted time domain in S2 are used as optimization targets, the weight coefficient of the lower layer objective function is set, the lower layer constraint conditions are clarified, the optimal control sequence of the fresh air ratio in the predicted time domain is obtained through optimization and solution, and the results are fed back to the heat and humidity control layer; S5: Execute the control action within the control time domain, update the state information of the two-layer control model, and feed back the updated state information to the next control time domain; repeat S2 to S4 for rolling optimization until the scheduling cycle ends.

3. The terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality according to claim 2, characterized in that: In S1, the regional heat and moisture balance model includes a regional heat balance model and a regional moisture balance model, and the construction method is as follows: For the regional heat balance model, the change in regional air sensible heat is equal to the difference between the regional sensible heat load and the air conditioning cooling capacity, which is expressed as: The regional sensible heat load includes internal heat sources and external heat sources. The internal heat sources include passengers, equipment and lighting, and the external heat sources include enclosure structures, solar radiation and infiltration. p represents the specific heat capacity of regional air, kJ / (kg·K); ρ a Indicates the regional air density, kg / m 3 ; V represents the volume of the region, m 3 ; T in represents the regional air temperature, ℃; t represents the current time, s; T oa represents the outdoor air temperature, ℃; j represents the enclosure structure type of the area; S represents the enclosure structure set of the area; A j represents the area of ​​the enclosure structure j, m 2 ;k j represents the heat transfer coefficient of the enclosure structure j, W / (m 2 ℃); G sa Indicates the air supply volume flow rate, m 3 / s;T sa Indicates the supply air temperature, °C; Q person Indicates the sensible heat dissipation of passengers, kW; Q other represents the heat generated by other heat sources, kW; Q other =Q solar +Q infilt +Q device +Q light (2) Among them, Q solar is the solar radiation heat, kW; Q infilt is the air penetration heat transfer, kW; Q device Heat dissipation for equipment, kW; Q light Heat dissipation for lighting, kW; Q person represents the sensible heat dissipation of passengers, kW; N represents the number of passengers in the area, people; For the regional moisture balance model, the change in regional air latent heat is equal to the difference between the regional latent heat load and the air conditioning dehumidification capacity, which is expressed as: M infilt =A inf Vρ a (W oa -W in ) (6) Among them, the regional latent heat load includes infiltration and fresh air humidity load, passenger latent heat dissipation load; W in Indicates regional air humidity, g / kg; M person Indicates the passenger latent heat dissipation load, g / s; M infilt Indicates the wet load caused by air infiltration, g / s; W sa represents the air supply humidity, g / kg; N represents the number of passengers in the area, people; Δh vap represents the specific enthalpy of evaporation, water is 2257 J / g; A inf Indicates the number of infiltration winds, s -1 ; W oa Indicates outdoor air humidity, g / kg.

4. The terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality according to claim 2, characterized in that: In S1, the method for constructing the regional air quality model is as follows: The carbon dioxide concentration is used to measure the regional air quality. According to the mass balance equation, the regional carbon dioxide concentration balance model is obtained: C ma =drC oa +(1-dr)C in (8) G in =G person N (9) Among them, C in Indicates the regional carbon dioxide volume fraction, ppm; C ma Indicates the volume fraction of carbon dioxide in mixed air, ppm; C oa Indicates outdoor carbon dioxide volume fraction, ppm; G sa Represents the regional air supply volume flow rate, m 3 / s; V represents the spatial volume of the region, m 3 ; A inf Indicates the number of infiltration winds, s -1 ; dr represents fresh air ratio; G in represents the total rate of carbon dioxide produced by regional passengers, m 3 / s; G person represents the passenger carbon dioxide generation rate, m 3 / (s·person); N represents the number of passengers in the area, person; Carbon dioxide release rate per passenger G person The calculation formula is expressed as: Among them, A D represents the DuBois surface area, m 2 ; M is the physical activity level in Met, which is set according to the activity type of passengers in the terminal; RQ stands for respiratory quotient, which is the molecular ratio of CO2 released by respiration and O2 absorbed.

5. The terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality according to claim 2, characterized in that: In S1, the method for constructing the air conditioning system operation energy consumption model is specifically as follows: The energy consumption of air conditioning system operation includes refrigeration energy consumption and fan energy consumption. The total energy consumption P(t) is expressed as: P(t)=P c (t)+P f (t) (11) Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW; Refrigeration energy consumption P c The calculation formula of (t) is as follows: h ma (t)=(1-dr(t))h ra (t)+dr(t)h oa (t) (13) Among them, ρ a Indicates the regional air density, kg / m 3 ; G sa (t) represents the air volume flow rate, m 3 / s;h ma (t) represents the mixed air enthalpy value, kJ / kg; h sa (t) represents the supply air enthalpy, kJ / kg; dr(t) represents the fresh air ratio; h ra (t) represents the return air enthalpy value, kJ / kg; h oa (t) represents the fresh air enthalpy, kJ / kg; COP represents the refrigeration energy efficiency ratio; Fan energy consumption P f The calculation formula of (t) is as follows: P f (t)=σG sa (t) 2 (14) Where σ represents the penalty coefficient of the wind turbine power function.

6. The terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality according to claim 2, characterized in that: In S2, the parameter settings of the double-layer control model include regional parameters, control parameters, and initial parameters of decision variables; the control parameters include a control time domain and a prediction time domain, the prediction time domain data include outdoor meteorological data, the number of passengers, air conditioning operation parameters, and other thermal disturbance data, the outdoor meteorological parameters include outdoor temperature, outdoor relative humidity, and solar radiation intensity, and the other thermal disturbance data include infiltration, solar radiation, equipment, and heat and humidity loads caused by lighting.

7. The terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality according to claim 2, characterized in that: The S3 is as follows: The constraints of the heat and humidity control layer include regional temperature change constraints, regional relative humidity change constraints, supply air volume flow constraints, supply air temperature constraints and supply air humidity constraints; The regional temperature change constraint is: in, Indicates the lower limit of the regional temperature, °C; Indicates the upper limit of the regional temperature, °C; T in Indicates the regional temperature, °C; The regional relative humidity change constraint is: in, Indicates the lower limit of relative humidity in the area; Indicates the upper limit of relative humidity in the area; Indicates the relative humidity of the area; The supply air volume flow constraint is: in, Indicates the lower limit of the air supply volume flow rate, m 3 / s; Indicates the upper limit of the air supply volume flow rate, m 3 / s; G sa Indicates the air supply volume flow rate, m 3 / s; The supply air temperature constraint is: in, Indicates the lower limit of the supply air temperature, °C; Indicates the upper limit of the supply air temperature, °C; T sa Indicates the supply air temperature, °C; Supply air humidity constraint is: in, Indicates the lower limit of air supply humidity, g / kg; Indicates the upper limit of air supply humidity, g / kg; W sa Indicates the air supply humidity, g / kg; The thermal comfort tracking target J1 is expressed as: Among them, t represents the current time, h; T in (t) represents the regional temperature, °C; T set (t) represents the zone temperature setting value, °C; Indicates the relative humidity of the area; Indicates the relative humidity setting value of the area; Relative humidity is the temperature T in and humidity W in The calculation formula is as follows: Among them, P w,s Indicates the partial pressure of saturated water vapor, kPa; P w represents the partial pressure of water vapor, kPa; B represents the atmospheric pressure, which is 101.325 kPa; The air conditioning system operation energy consumption target J2 is expressed as: J2=P c (t)+P f (t) (24) Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW; The rolling optimization model of the upper heat and moisture control layer is as follows: Among them, i represents the current control time domain, h; L represents the prediction time domain length, h; ω1 represents the weight coefficient of the thermal and moisture comfort tracking target of the thermal and moisture control layer; J1(t) represents the thermal and moisture comfort tracking target value at time t; ω2 represents the weight coefficient of the air conditioning operation energy consumption target of the thermal and moisture control layer; J2(t) represents the air conditioning operation energy consumption target value at time t.

8. The terminal air conditioning system control method taking into account both thermal and humidity comfort and air quality according to claim 2, characterized in that: The S4 is specifically as follows: The constraints of the fresh air control layer include carbon dioxide concentration constraints and fresh air ratio constraints; Regional CO2 concentration constraints: in, Indicates the lower limit of regional carbon dioxide concentration, ppm; Indicates the upper limit of regional carbon dioxide concentration, ppm; C in Indicates the regional carbon dioxide concentration, ppm; Fresh air ratio constraints: dr min ≤dr≤dr max (27) Among them, dr min Indicates the lower limit of the fresh air ratio; dr max Indicates the upper limit of the fresh air ratio; dr indicates the fresh air ratio; The regional air quality tracking target J3 is expressed as: J3=(C in (t)-C min ) 2 (28) Among them, C in (t) represents the regional carbon dioxide concentration, ppm; C min Indicates the lower limit of carbon dioxide volume fraction, that is, outdoor carbon dioxide volume fraction; The air conditioning system operation energy consumption target J4 is expressed as: J4=P c (t)+P f (t) (29) Among them, P c (t) represents cooling energy consumption, kW; P f (t) represents the fan energy consumption, kW; The rolling optimization model of the lower fresh air control layer is as follows: Among them, i represents the current control time domain, h; L represents the prediction time domain length, h; ω3 represents the weight coefficient of the regional air quality target of the fresh air control layer; J3(t) represents the regional air quality target value at time t; ω4 represents the weight coefficient of the air conditioning operation energy consumption target of the fresh air control layer; J4(t) represents the air conditioning operation energy consumption target value at time t.

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