CN114877502B - Distributed model-based temperature and humidity control method and system for multi-zone air conditioning system - Google Patents
Distributed model-based temperature and humidity control method and system for multi-zone air conditioning system Download PDFInfo
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Abstract
The invention belongs to the technical field of air conditioner control, and provides a temperature and humidity control method and a temperature and humidity control system of a multi-zone air conditioning system based on a distributed model, wherein the multi-zone air conditioning system is decomposed into a plurality of sub-air conditioning systems on the basis of considering thermal coupling among rooms caused by a heat transfer process, so that the problem of reduced control performance due to neglecting interaction among subsystems is solved; meanwhile, on the basis of constructing the air-conditioning system dynamic model of each sub air-conditioning system, the establishment and prediction of the prediction model of the sub air-conditioning system realize the purpose of decomposing the centralized control into a plurality of groups of sub control units which are communicated with each other, so that the method can be used for a large-scale dynamic coupling system; in addition, an optimization objective problem is established according to the predicted value of the output variable of each subsystem and the set values of the temperature and the humidity of each subsystem, the optimal control variable is obtained by utilizing the optimization objective function, and compared with a centralized structure, the calculation complexity is obviously reduced.
Description
Technical Field
The invention belongs to the technical field of air conditioner control, and particularly relates to a temperature and humidity control method and system of a multi-zone air conditioning system based on a distributed model.
Background
In recent years, the demand for comfort in indoor environment has been increasing, and air conditioning systems have become an indispensable and important component of modern buildings. Since multi-zone air conditioning systems in large buildings are complex in structure, have a large number of variables, and are coupled seriously, temperature and humidity control of multi-zone air conditioning systems for large buildings has been a challenge.
The inventor finds that, in the prior art, a centralized model predictive control strategy, a distributed model predictive control strategy and the like are mainly used for temperature and humidity control of a multi-zone air conditioning system of a large building; in the two control measurements, the distributed model predictive control strategy has the problem of steady-state tracking error, and the centralized model predictive control strategy has lower calculation speed.
Disclosure of Invention
The invention aims to solve the problems and provides a temperature and humidity control method and system of a multi-zone air conditioning system based on a distributed model, which are beneficial to improving the control performance of the existing distributed model predictive control strategy, which is reduced because the interaction among subsystems is ignored, and decomposing centralized control into a plurality of groups of sub-control units which are communicated with each other, so that the centralized control can be used for a large-scale dynamic coupling system.
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention provides a temperature and humidity control method for a multi-zone air conditioning system based on a distributed model, including:
decomposing the multi-zone air conditioning system into a plurality of sub air conditioning systems which are in a thermal coupling relationship with each other;
the method comprises the following steps of constructing an air conditioning system dynamic model of each sub air conditioning system by taking the air temperature at the outlet of an evaporator, the indoor air temperature, the air temperature at the tail end of a dry cooling area in the evaporator, the temperature of the wall surface of the evaporator and the indoor air humidity as state variables, taking the mass flow of a refrigerant and the volume flow of supplied air as input variables, and taking the temperature and the humidity as output variables;
discretizing the dynamic model of the air conditioning system, and establishing a prediction model of the sub air conditioning system;
obtaining a predicted value of an output variable of each subsystem through the prediction model;
establishing an optimization target problem according to the respective output variable prediction value of each subsystem and the respective set values of the temperature and the humidity of each subsystem;
and optimizing to obtain an optimal control variable according to the established optimization target problem, and controlling the multi-region air conditioning system by using the obtained optimal control variable.
Furthermore, the multi-zone air conditioning system comprises an outdoor unit and a plurality of indoor units; the outdoor unit comprises a compressor and a condenser, and the indoor unit comprises an expansion valve, an evaporator and a fan;
the indoor units and the outdoor units are connected through refrigerant pipelines to form a closed loop, and an expansion valve is arranged on the refrigerant pipeline at the inlet of each indoor unit.
Further, the dynamic model of the air conditioning system is as follows:
wherein a, w, z, wet and dry respectively represent air, evaporator wall, indoor environment, evaporator wet and cold area and evaporator dry and cold area; i denotes an air conditioning system subsystem; c represents specific heat capacity; ρ represents a density; v represents a spatial volume; t is a unit of e Represents the evaporator outlet air temperature; t is z Represents the indoor air temperature; v f The volume flow of the supplied air of the fan is represented; q s,load Represents the indoor sensible heat load; r is ij Representing the thermal resistance between the air conditioning system subsystem i and the air conditioning system subsystem j; h is fg Represents the latent heat of water evaporation; w z Represents the indoor air humidity; q l,load Indicating chamberInternal latent heat loading; v dry Represents the volume of the dry cooling zone inside the evaporator; t is dry Representing the air temperature at the boundary of the dry cooling zone and the wet cooling zone in the evaporator; alpha represents a heat exchange coefficient; a represents a heat exchange area; h is a total of e Represents the enthalpy of air at the evaporator outlet; t is w Represents the evaporator wall temperature; m r Represents the mass flow of the refrigerant; Δ h represents the enthalpy difference of the refrigerant at the evaporator inlet and outlet.
Further, the thermal resistance between the sub air conditioning system i and the sub air conditioning system j satisfies R ij ＝R ji 。
Furthermore, a differential equation numerical solving method is utilized to discretize the dynamic model of the air conditioning system and establish a prediction model of the sub air conditioning system.
Furthermore, an optimization target problem is established by combining constraint conditions.
Further, solving the optimization problem of the prediction model of each sub air-conditioning system by using an interior point method; and applying the first item of control input solved by the prediction model of each sub air-conditioning system to the multi-region air-conditioning system to obtain the real output value of the multi-region air-conditioning system at the next sampling moment, and correcting the prediction model of each sub air-conditioning system by using the real output value feedback.
In a second aspect, the present invention further provides a distributed model-based multi-zone air conditioning system temperature and humidity control system, including:
a sub air conditioning system division module configured to: decomposing a multi-zone air conditioning system into a plurality of sub air conditioning systems which are in a thermal coupling relationship with each other;
a dynamic model building module configured to: the method comprises the following steps of constructing an air conditioning system dynamic model of each sub air conditioning system by taking the air temperature at the outlet of an evaporator, the indoor air temperature, the air temperature at the tail end of a dry cooling area in the evaporator, the temperature of the wall surface of the evaporator and the indoor air humidity as state variables, taking the mass flow of a refrigerant and the volume flow of supplied air as input variables, and taking the temperature and the humidity as output variables;
a predictive model building module configured to: discretizing the dynamic model of the air conditioning system, and establishing a prediction model of the sub air conditioning system;
a prediction module configured to: obtaining a predicted value of an output variable of each subsystem through the prediction model;
an optimization objective function establishment module configured to: establishing an optimization target problem according to the respective output variable prediction value of each subsystem and the respective set values of the temperature and the humidity of each subsystem;
a control module configured to: and optimizing to obtain an optimal control variable according to the established optimization target problem, and controlling the multi-region air conditioning system by using the obtained optimal control variable.
In a third aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for controlling temperature and humidity of a multi-zone air conditioning system based on a distributed model according to the first aspect.
In a fourth aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of the temperature and humidity control method for a distributed model-based multi-zone air conditioning system according to the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the invention decomposes the multi-area air conditioning system into a plurality of sub air conditioning systems on the basis of considering the thermal coupling among all rooms caused by the heat transfer process, thereby improving the problem of reduced control performance due to neglecting the interaction among the sub systems; meanwhile, on the basis of constructing the air-conditioning system dynamic model of each sub air-conditioning system, the establishment and prediction of the prediction model of the sub air-conditioning system realize the purpose of decomposing centralized control into a plurality of groups of sub control units which are communicated with each other, so that the method can be used for a large-scale dynamic coupling system; in addition, on the basis of the prediction model, the optimization objective problem is established according to the respective output variable prediction value of each subsystem and the respective temperature and humidity set values of each subsystem, the optimal control variable is obtained by utilizing the optimization objective function to control the multi-region air-conditioning system, and compared with a centralized structure, the calculation complexity is obviously reduced.
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The accompanying drawings, which form a part hereof, are included to provide a further understanding of the present embodiments, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present embodiments and together with the description serve to explain the present embodiments without unduly limiting the present embodiments.
FIG. 1 is a schematic view of a multi-zone air conditioning system according to embodiment 1 of the present invention;
FIG. 2 is a room layout and thermal coupling between rooms according to embodiment 1 of the present invention;
fig. 3 is a Control block diagram of a Distributed Model Predictive Control (DMPC) in embodiment 1 of the present invention;
fig. 4 is a flow chart of DMPC calculation in embodiment 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Example 1:
the embodiment provides a temperature and humidity control method of a multi-zone air conditioning system based on a distributed model, which comprises the following steps:
decomposing the multi-zone air conditioning system into a plurality of sub air conditioning systems which are in a thermal coupling relationship with each other;
constructing an air conditioning system dynamic model of each sub air conditioning system by taking the outlet air temperature of an evaporator, the indoor air temperature, the tail end air temperature of a dry cooling area in the evaporator, the wall surface temperature of the evaporator and the indoor air humidity as state variables, taking the mass flow of a refrigerant and the volume flow of supplied air as input variables and taking the temperature and the humidity as output variables;
discretizing the dynamic model of the air conditioning system, and establishing a prediction model of the sub air conditioning system;
obtaining a predicted value of an output variable of each subsystem through the prediction model;
establishing an optimization objective problem, namely establishing an optimization objective function, according to the respective output variable prediction value of each subsystem and the respective set values of the temperature and the humidity of each subsystem;
and optimizing to obtain an optimal control variable according to the established optimization target problem, and controlling the multi-region air conditioning system by using the obtained optimal control variable.
In this embodiment, as shown in fig. 1, the multi-zone air conditioning system may be understood as a multi-zone building air conditioning system, and the multi-zone air conditioning system may include an outdoor unit and a plurality of indoor units; the outdoor unit may include a compressor and a condenser, and the indoor unit may include an expansion valve, an evaporator, and a fan; the arrangement of the compressor, the condenser, the expansion valve, the evaporator and the fan in the room may be realized by conventional arrangements, which are not described in detail herein. The indoor unit and the outdoor unit are connected by means of the refrigerant pipeline to form a closed loop, the flow of the refrigerant entering each room is controlled by the expansion valve, the air supply rate of each room is controlled by the fan, and therefore the humidity and the temperature of the multiple rooms are controlled by matching with the damp and hot loads of different rooms.
For the multi-zone air conditioning system, in the embodiment, only the thermal coupling between the rooms caused by the heat transfer process is considered; therefore, the multi-zone air conditioning system can be regarded as being composed of a plurality of single-room air conditioning systems with thermal coupling, so that the multi-zone air conditioning system can be decomposed into a plurality of sub air conditioning systems according to the number of rooms, wherein the dynamic model of the thermal coupling sub air conditioning system i is considered as follows:
wherein, subscript i representsShowing the ith subsystem and i is epsilon {1,2,3}; subscripts a, w, z, wet, dry represent air, evaporator wall, indoor environment, evaporator wet and cold sections, respectively; c represents specific heat capacity; ρ represents a density; h is fg Represents the latent heat of water evaporation; alpha represents a heat exchange coefficient; v represents a spatial volume; a represents the heat exchange area; Δ h represents the enthalpy difference of the refrigerant at the inlet and outlet of the evaporator; v f Representing the volume flow of the wind supplied by the fan; m r Represents the mass flow of the refrigerant; t is a unit of z Represents the indoor air temperature; t is dry Representing the air temperature at the boundary of the dry cooling zone and the wet cooling zone in the evaporator; t is w Represents the evaporator wall temperature; w is a group of z Represents the indoor air humidity; q s,load Represents the indoor sensible heat load; q l,load Represents the indoor latent heat load; t is z,j Represents the air temperature of room j adjacent to room i; h is e Represents the enthalpy of air at the outlet of the evaporator and satisfies h e ＝C a T e +h fg W e Wherein, T e Represents the evaporator outlet air temperature; r is ij Represents the thermal resistance between the room i and the other rooms j and satisfies R ij ＝R ji (ii) a FIG. 2 shows a room distribution arrangement and thermal coupling between the rooms, R 12 ＝R 21 ,R 23 ＝R 32 。
The system of equations (1) for the sub-air conditioning system i can be expressed in a two-input two-output five-state non-linear state space representation:
y i ＝Cx i
wherein, the state variable of the sub air conditioning system i
Output variable of sub air conditioning system i
Input variables of sub-air conditioning system i/>
Disturbance variable in the sub-air conditioning system i>
Represents the thermal coupling between the other sub air conditioning systems and the sub air conditioning system i; f. of i ,g i ,d i Representing a function with respect to a state variable>
Based on the formula (2), the dynamic model of the sub air-conditioning system i is discretized by using a differential equation numerical solution method, namely a four-order Runge Kutta method, and x can be obtained i (t k ) And x i (t k+1 ) The recursive relation between the two, the prediction model is established:
wherein the content of the first and second substances,
where h denotes the integration step.
Through column writing of dynamic equations of a plurality of subsystems, a dynamic model of the whole multi-zone air conditioning system can be obtained, and a general state space expression is used for expressing as follows:
wherein the state variable
Output variable->
Input variable->
Disturbance variable
In the embodiment, for a multivariable, nonlinear and strongly coupled complex system such as a multi-zone air conditioning system, in order to realize simultaneous temperature and humidity control, a DMPC strategy is designed in the embodiment; the control block diagram of the DMPC is shown in fig. 3.
According to the topological structure of a multi-zone air-conditioning system and the design requirements and calculation consideration of a distributed control system, the multi-zone air-conditioning system can be decomposed into a plurality of sub air-conditioning systems according to the number of rooms, each sub air-conditioning system comprises five state variables of evaporator outlet air temperature, indoor air temperature, the tail end air temperature of a dry cooling zone in an evaporator, the temperature of the wall surface of the evaporator and indoor air humidity, input variables are refrigerant mass flow and volume flow of air supply in each room, and output variables are the temperature and the humidity of the air in each room. In the design of the DMPC strategy, the thermal coupling between adjacent subsystems is considered, each subsystem is provided with an MPC controller, and the optimization problem corresponding to the kth rolling optimization of the air-conditioning subsystem i is as follows:
in the above optimization problem, equation (5) represents an optimization objective function, equations (6) and (7) represent model constraints, equation (8) represents an initial value constraint, and equation (9) represents an input variable magnitude constraint; wherein subscript i denotes a sub air conditioning system i;
representing the solved optimal control variable; s (Delta) represents a piecewise constant function cluster with a sampling time of Delta; n is a radical of p Representing a prediction time domain; q represents the weight imposed on the output variable in the objective function; y is r,i Representing the expected value of the output variable of the subsystem i; />
Representing the state variable predicted value of the subsystem i; u. of i (t) represents the input variables of subsystem i; v. of i (t) represents the disturbance variable of subsystem i; />
A predicted value representing an output variable of the subsystem i; f, the number of the first and second groups,g, d, h represent functions on state variables; x is a radical of a fluorine atom i (t k ) Indicates that subsystem i is at t k The actual state value of the air conditioning system at the moment; omega u Representing a set of constraints on the input variables.
DMPC is particularly important in handling the coupling between subsystems and ensuring the global stability of the system. The DMPC algorithm helps to improve the control performance of the existing decentralized MPC algorithm, which is reduced by neglecting the interaction between subsystems, and the distributed control decomposes centralized control into a plurality of groups of sub-control units communicating with each other, so that it can be used in a large-scale dynamic coupling system, and in addition, the computational complexity is significantly reduced compared to a centralized structure.
The indoor temperature and humidity optimization control method based on the DMPC provided by the embodiment mainly comprises the following work:
decomposing the whole air conditioning system into a plurality of sub air conditioning systems according to the number of rooms of the multi-area air conditioning system, and establishing respective dynamic models for each sub air conditioning system by considering thermal coupling caused by a heat transfer process between the connected sub air conditioning systems; designing an independent MPC controller for each sub-air-conditioning system, namely discretizing a dynamic model of each sub-air-conditioning system to establish a prediction model, establishing a quadratic performance index function as an optimization objective function according to respective output variable prediction values and temperature and humidity set values of each sub-air-conditioning system, and establishing an optimization problem of each local MPC controller by combining constraint conditions; and solving the optimization problem of each local MPC controller by using an inner point method, applying a first item of control input solved by each local MPC controller to a multi-region air-conditioning system to obtain a real output value of the multi-region air-conditioning system at the next sampling moment, and performing feedback correction on each local MPC controller by using the real output value, namely using the output real value as an initial value of each local MPC controller at the next moment for the rolling optimization at the next moment. The specific algorithm steps of the DMPC are as follows:
off-line operation:
s1, decomposing subsystems of a multi-region air-conditioning system, namely decomposing the multi-region air-conditioning system into a plurality of subsystems according to the number of rooms, and establishing a dynamic model for each subsystem, namely formula (1);
s2, discretizing the dynamic model by using a differential equation numerical solution method, and establishing a prediction model, namely a formula (3);
and (3) online operation:
s3, initializing rolling optimization times N and predicting time domain N p Controlling the time domain N c K =1, initial state x i (t k )；
S4, when k is less than N (termination condition), executing a step S5, otherwise, executing a step S9;
a local controller:
s5, each local controller is based on x i (t k ) Predicting the output variable of the system at the future moment by using a prediction model, and determining a local sub-optimization objective function, namely a formula (5);
s6, searching for the optimal control variable in the current rolling window according to the optimization objective function and the constraint conditions in formulas (6) - (9)
S7, optimizing first elements of all control variables
Acting on the air conditioning system;
s8, k + +, updating the initial state information x in the next rolling window i (t k ) Returning to the step S4;
and S9, ending.
Example 2:
the embodiment provides a multizone air conditioning system atmospheric control system based on distributed model, includes:
a sub air conditioning system division module configured to: decomposing the multi-zone air conditioning system into a plurality of sub air conditioning systems which are in a thermal coupling relationship with each other;
a dynamic model building module configured to: the method comprises the following steps of constructing an air conditioning system dynamic model of each sub air conditioning system by taking the air temperature at the outlet of an evaporator, the indoor air temperature, the air temperature at the tail end of a dry cooling area in the evaporator, the temperature of the wall surface of the evaporator and the indoor air humidity as state variables, taking the mass flow of a refrigerant and the volume flow of supplied air as input variables, and taking the temperature and the humidity as output variables;
a predictive model building module configured to: discretizing the dynamic model of the air conditioning system, and establishing a prediction model of the sub air conditioning system;
a prediction module configured to: obtaining a predicted value of an output variable of each subsystem through the prediction model;
an optimization objective function establishment module configured to: establishing an optimization target problem according to the respective output variable prediction value of each subsystem and the respective set values of the temperature and the humidity of each subsystem;
a control module configured to: and optimizing to obtain an optimal control variable according to the established optimization target problem, and controlling the multi-region air conditioning system by using the obtained optimal control variable.
The working method of the system is the same as the temperature and humidity control method of the distributed model-based multi-zone air conditioning system in embodiment 1, and is not described again here.
Example 3:
the present embodiment provides a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the steps of the method for controlling temperature and humidity in a multi-zone air conditioning system based on a distributed model according to embodiment 1.
Example 4:
the embodiment provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor executes the program, and the steps of the method for controlling the temperature and humidity of the multi-zone air conditioning system based on the distributed model according to embodiment 1 are implemented.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present embodiment should be included in the protection scope of the present embodiment.
Claims (9)
1. A temperature and humidity control method of a multi-zone air conditioning system based on a distributed model is characterized by comprising the following steps:
decomposing the multi-zone air conditioning system into a plurality of sub air conditioning systems which are in a thermal coupling relationship with each other;
the method comprises the following steps of constructing an air conditioning system dynamic model of each sub air conditioning system by taking the air temperature at the outlet of an evaporator, the indoor air temperature, the air temperature at the tail end of a dry cooling area in the evaporator, the temperature of the wall surface of the evaporator and the indoor air humidity as state variables, taking the mass flow of a refrigerant and the volume flow of supplied air as input variables, and taking the temperature and the humidity as output variables;
discretizing the dynamic model of the air conditioning system, and establishing a prediction model of the sub air conditioning system;
obtaining a predicted value of an output variable of each subsystem through the prediction model;
establishing an optimization target problem according to the respective output variable prediction value of each subsystem and the respective set value of the temperature and the humidity of each subsystem;
optimizing to obtain an optimal control variable according to the established optimization target problem, and controlling the multi-region air conditioning system by using the obtained optimal control variable;
the dynamic model of the air conditioning system is as follows:
wherein a, w, z, wet and dry represent air, evaporator wall, indoor environment, evaporator wet and cold area and evaporator dry and cold area respectively; i denotes an air conditioning system subsystem; c represents specific heat capacity; ρ represents a density; v represents a volume in space; t is e Represents the evaporator outlet air temperature; t is z Represents the indoor air temperature; v f The volume flow of the supplied air of the fan is represented; q s,load Represents the indoor sensible heat load; r ij Representing the thermal resistance between the air conditioning system subsystem i and the air conditioning system subsystem j; h is fg Represents the latent heat of water evaporation; w z Represents the indoor air humidity; q l,load Represents the indoor latent heat load; v dry The volume of the dry cooling zone inside the evaporator; t is dry Representing the air temperature at the boundary of the dry cooling zone and the wet cooling zone in the evaporator; alpha represents a heat exchange coefficient; a represents the heat exchange area; h is e Represents the enthalpy of air at the evaporator outlet; t is w Represents the evaporator wall temperature; m is a group of r Represents the mass flow of the refrigerant; Δ h represents the enthalpy difference of the refrigerant at the evaporator inlet and outlet.
2. The distributed model-based multi-zone air conditioning system temperature and humidity control method according to claim 1, wherein the multi-zone air conditioning system comprises an outdoor unit and a plurality of indoor units; the outdoor unit comprises a compressor and a condenser, and the indoor unit comprises an expansion valve, an evaporator and a fan;
the indoor units and the outdoor units are connected through refrigerant pipelines to form a closed loop, and an expansion valve is arranged on the refrigerant pipeline at the inlet of each indoor unit.
3. The distributed model-based multi-zone air conditioning system temperature and humidity control method of claim 1, wherein a thermal resistance between a sub air conditioning system i and a sub air conditioning system j satisfies R ij ＝R ji 。
4. The distributed model-based multi-zone air conditioning system temperature and humidity control method according to claim 1, wherein a differential equation numerical solution method is used to discretize the air conditioning system dynamic model and establish a prediction model of the sub air conditioning system.
5. The distributed model-based multi-zone air conditioning system temperature and humidity control method of claim 1, wherein an optimization objective problem is established in combination with constraint conditions.
6. The distributed model-based temperature and humidity control method for the multi-zone air conditioning system according to claim 5, wherein an optimization objective problem of a prediction model of each sub air conditioning system is solved by using an interior point method; and applying the first item of control input solved by the prediction model of each sub air-conditioning system to the multi-region air-conditioning system to obtain the real output value of the multi-region air-conditioning system at the next sampling moment, and performing feedback correction on the prediction model of each sub air-conditioning system by using the real output value.
7. Multi-zone air conditioning system temperature and humidity control system based on distributed model, its characterized in that includes:
a sub air conditioning system division module configured to: decomposing the multi-zone air conditioning system into a plurality of sub air conditioning systems which are in a thermal coupling relationship with each other;
a dynamic model building module configured to: the method comprises the following steps of constructing an air conditioning system dynamic model of each sub air conditioning system by taking the air temperature at the outlet of an evaporator, the indoor air temperature, the air temperature at the tail end of a dry cooling area in the evaporator, the temperature of the wall surface of the evaporator and the indoor air humidity as state variables, taking the mass flow of a refrigerant and the volume flow of supplied air as input variables, and taking the temperature and the humidity as output variables;
a predictive model building module configured to: discretizing the dynamic model of the air conditioning system, and establishing a prediction model of the sub air conditioning system;
a prediction module configured to: obtaining a predicted value of an output variable of each subsystem through the prediction model;
an optimization objective function establishment module configured to: establishing an optimization target problem according to the respective output variable prediction value of each subsystem and the respective set value of the temperature and the humidity of each subsystem;
a control module configured to: optimizing to obtain an optimal control variable according to the established optimization target problem, and controlling the multi-region air conditioning system by using the obtained optimal control variable;
the dynamic model of the air conditioning system is as follows:
wherein a, w, z, wet and dry represent air, evaporator wall surface, and,Indoor environment, evaporator wet cooling area and evaporator dry cooling area; i denotes an air conditioning system subsystem; c represents specific heat capacity; ρ represents a density; v represents a spatial volume; t is a unit of e Represents the evaporator outlet air temperature; t is a unit of z Represents the indoor air temperature; v f Representing the volume flow of the wind supplied by the fan; q s,load Represents the indoor sensible heat load; r ij Representing the thermal resistance between the air conditioning system subsystem i and the air conditioning system subsystem j; h is fg Represents the latent heat of water evaporation; w z Represents the indoor air humidity; q l,load Represents the indoor latent heat load; v dry The volume of the dry cooling zone inside the evaporator; t is dry Representing the air temperature at the boundary of the dry cooling zone and the wet cooling zone in the evaporator; alpha represents a heat exchange coefficient; a represents the heat exchange area; h is e Represents the enthalpy of air at the evaporator outlet; t is w Represents the evaporator wall temperature; m is a group of r Represents the mass flow of the refrigerant; Δ h represents the enthalpy difference of the refrigerant at the evaporator inlet and outlet. .
8. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps of the distributed model-based multi-zone air conditioning system temperature and humidity control method according to any one of claims 1 to 6.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for controlling the temperature and humidity of a multi-zone air conditioning system based on a distributed model according to any one of claims 1 to 6 when executing the program.
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