Energy-saving control method and system for air conditioning system based on steady-state recognition

Abstract

The invention relates to the technical field of process control, in particular to an air conditioning system energy-saving control method based on steady state recognition. The method comprises the following steps that S10, steady-state recognition is conducted on air conditioner operation data through a DBSI steady-state recognition method based on a difference equation, so that a steady-state data interval is obtained; s20, starting to traverse and search each steady-state data interval, searching a COP maximum point of the air conditioning system under different steady-state working conditions under fixed-frequency operation, and taking chilled water and cooling water operation data under the COP maximum point as an optimal set point; and S30, the optimal set points are controlled through the MPC algorithm, and the performance of the air conditioner system is improved while energy consumption is reduced. According to the method, the problems of multi-device coupling operation and control difficulty of the air conditioning system are solved by utilizing the steady state identification method of the operation state of the air conditioning system, traversing and optimizing the COP operation optimal point aiming at the identified steady state interval based on the operation data and then utilizing the multi-input multi-output control characteristic predicted by the model.

Description

Technical Field

[0001] The present invention relates to the technical field of process control, and in particular to an energy-saving control method for an air-conditioning system based on steady-state identification. Background Art

[0002] DBSI: A differential-signal based state identification method (DBSI).

[0003] Model Predictive Control: Model Predictive Control (MPC) is an online optimization controller: at the sampling time, the current state is used as the initial state, the system runs the model to predict the output changes in the future time domain, solve the optimal problem in the time domain, obtain the optimal sequence, and use the first value of the sequence as the input for control.

[0004] COP: The ratio of the cooling (heating) amount generated in the refrigeration (heating) cycle to the power consumed for cooling (heating).

[0005] OPC protocol: OPC is the abbreviation of OLE for Process Control, which is an industrial standard protocol for process control. The OPC protocol is based on Microsoft's OLE (Object Linking and Embedding) technology, and its formulation is completed by providing a set of standard OLE / COM interfaces. OLE2 technology is used in OPC technology. OLE allows multiple microcomputers to exchange documents, graphics and other objects. The OPC protocol includes a complete set of standard interfaces, properties and methods. The OPC protocol is widely used in process control systems to provide interoperability between equipment and software from different suppliers.

[0006] Under the current market situation and energy conservation needs, the energy-saving optimization control problem of air-conditioning systems has become a key research hotspot in the construction industry at home and abroad. The current problems of air-conditioning systems are that the air-conditioning circulating water system in the air-conditioning system consumes a lot of energy, and the air-conditioning system is in a constant flow operation state for a long time, which shows that there are still many unreasonable places in the actual operation of the air-conditioning system. Because the water system lacks advanced control and regulation functions, when the water system is running in a constant flow state, the load required by the water system and the operating load do not match, resulting in a certain consumption of energy. During the operation of the air-conditioning system, due to the gradual change of temperature, the response speed of the system load demand is slow, and the cooling water delivery of the water circulation system is often slower than the demand for changes in the cooling capacity of the chilled water, which may cause large fluctuations in the operating parameters of the air-conditioning control system and affect the control accuracy of the water system. In order to solve the above problems, an energy-saving control method for air-conditioning systems based on steady-state identification is proposed. Summary of the invention

[0007] In order to solve the technical problems existing in the above-mentioned prior art, the present invention provides an air-conditioning system energy-saving control method and system based on steady-state identification.

[0008] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0009] In a first aspect, in an embodiment provided by the present invention, a method for energy-saving control of an air-conditioning system based on steady-state identification is provided, the method comprising the following steps:

[0010] S10, performing steady-state identification on the air-conditioning operation data using a steady-state identification method of DBSI based on a difference equation to obtain a steady-state data interval;

[0011] S20, start traversing and searching for each steady-state data interval, find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and use the chilled water and cooling water operation data at the COP maximum point as the optimal setting point;

[0012] S30. Use the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy.

[0013] As a further solution of the present invention, the steady-state identification method of DBSI based on the differential equation in S10 performs steady-state identification on the air-conditioning operation data to obtain a steady-state data interval, including:

[0014] S101, reading operating data of air conditioning system sensors;

[0015] S102, selecting load Q from the operating data of the air conditioning system sensor as load data;

[0016] S103, performing steady-state identification on the load data Q of the day to obtain a steady-state data interval.

[0017] As a further solution of the present invention, the step S103 of performing steady-state identification on the load data Q of the day to obtain a steady-state data interval includes:

[0018] S1031. Perform differential processing on the load data Q of the day to obtain differential data of the operating data;

[0019] S1032, determining the steady-state regional amplitude by using a dichotomy method on the differential data;

[0020] S1033. Eliminate erroneous points identified in the differential data using a quadratic difference method.

[0021] As a further solution of the present invention, the S20, starting to traverse and search for each steady-state data interval, finding the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal setting point; including:

[0022] S201, finding multiple steady-state interval data of the day;

[0023] S202, traversing COP data in each steady-state interval;

[0024] S203, finding the data points with the maximum COP in each steady-state interval;

[0025] S204, recording the maximum COP value of each steady-state interval.

[0026] As a further scheme of the present invention, the operating data at least includes the cooling tower outlet water temperature, outdoor temperature, cooling tower inlet water temperature, chiller operating power, chiller cooling water outlet water temperature, chiller inlet water temperature, chilled water outlet water temperature, chilled water inlet water temperature, and system operation power consumption data of each equipment in the air-conditioning system on a certain day.

[0027] As a further solution of the present invention, the S30, using the MPC algorithm to control each optimal set point, while saving energy consumption and improving the performance of the air conditioning system, includes:

[0028] S301, obtaining data of each node at each COP maximum value;

[0029] S302, using each node data as a device operation fixed point;

[0030] S303, using the MPC algorithm to control the reaching of the set point within each steady-state time of the day;

[0031] S304, determine whether the operation of the day is completed, if so, proceed to S305, if not, return to S303;

[0032] S305: Update operation data.

[0033] In a second aspect, in another embodiment provided by the present invention, an air conditioning system energy-saving control system based on steady-state identification is provided, and the system includes: a steady-state identification module, a traversal search module and an optimal control module.

[0034] The steady-state identification module performs steady-state identification on the air-conditioning operation data based on the steady-state identification method of DBSI of the differential equation to obtain a steady-state data interval.

[0035] The traversal search module starts traversal search for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and uses the chilled water and cooling water operation data at the COP maximum point as the optimal setting point.

[0036] The optimal control module uses the MPC algorithm to control each optimal set point, thereby improving the performance of the air-conditioning system while saving energy.

[0037] The technical solution provided by the present invention has the following beneficial effects:

[0038] The present invention utilizes a steady-state identification method for the operating status of an air-conditioning system, and based on operating data, traverses and searches for the optimal COP operating point for the identified steady-state interval, and then utilizes the multi-input and multi-output control characteristics predicted by the model to solve the problem of multi-device coupled operation and control difficulty of the air-conditioning system.

[0039] These and other aspects of the present invention will be more clearly understood in the following description of the embodiments. It should be understood that the above general description and the following detailed description are only exemplary and explanatory and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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 or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other embodiments can be obtained based on these drawings without paying creative work.

[0041] Figure 1 The present invention is a flowchart of an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0042] Figure 2 The present invention is a detailed flow chart of an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0043] Figure 3 This is a diagram of an original signal and its differential signal in an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0044] Figure 4 This is a diagram of a differential signal and a steady-state determination threshold value in an air-conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0045] Figure 5 This is a diagram of the original signal and the corrected state recognition result in an air-conditioning system energy-saving control method based on steady-state recognition according to an embodiment of the present invention.

[0046] Figure 6 This is a block diagram of an MPC control method for energy-saving control of an air-conditioning system based on steady-state identification according to an embodiment of the present invention.

[0047] Figure 7 This is a state recognition result diagram of load data in an air-conditioning system energy-saving control method based on steady-state recognition according to an embodiment of the present invention.

[0048] Figure 8 This is a cooling water temperature difference control diagram in an air conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0049] Fig. 9 This is a chilled water temperature difference control diagram in an air conditioning system energy-saving control method based on steady-state identification according to an embodiment of the present invention.

[0050] Fig.10 The figure shows a comparison of the total electric power of fixed frequency and global adaptation in an energy-saving control method for an air-conditioning system based on steady-state identification according to an embodiment of the present invention.

[0051] Fig.11 The structure block diagram of an air-conditioning system energy-saving control system based on steady-state identification according to an embodiment of the present invention.

[0052] In the figure: 100-steady-state identification module, 200-traversal search module, 300-optimal control module. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0055] It should be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0056] Specifically, the embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0057] See also Figure 1 , Figure 1 is a flow chart of an air conditioning system energy-saving control method based on steady-state identification provided by an embodiment of the present invention, such as Figure 1 As shown, the air conditioning system energy-saving control method based on steady-state identification includes steps S10 to S30.

[0058] S10. Perform steady-state identification on the air-conditioning operation data using a steady-state identification method of DBSI based on a differential equation to obtain a steady-state data interval.

[0059] In an embodiment of the present invention, the steady-state identification method of DBSI based on the differential equation in S10 performs steady-state identification on the air-conditioning operation data to obtain a steady-state data interval, including:

[0060] S101, reading operating data of air conditioning system sensors;

[0061] The operating data at least include the cooling tower outlet water temperature, outdoor temperature, cooling tower inlet water temperature, chiller operating power, chiller cooling water outlet water temperature, chiller inlet water temperature, chilled water outlet water temperature, chilled water inlet water temperature, system operation power consumption and other data of each equipment in the air-conditioning system on a certain day.

[0062] S102, selecting load Q from the operating data of the air conditioning system sensor as load data;

[0063] S103, performing steady-state identification on the load data Q of the day to obtain a steady-state data interval.

[0064] In the embodiment of the present invention, the step S103 of performing steady-state identification on the load data Q of the day to obtain a steady-state data interval includes:

[0065] S1031. Perform differential processing on the load data Q of the day to obtain differential data of the operating data.

[0066] Exemplarily, S1031, performing differential processing on the load data Q of the day to obtain differential data of the operating data includes:

[0067] The electric energy W differential data is obtained by removing the burrs and missing points in the electric energy consumption data; then performing linear interpolation to supplement the missing points; and then performing forward difference on the processed power consumption data to obtain its differential data. The electric energy W and the electric energy differential data We are as follows: Figure 3 shown.

[0068] S1032. Determine the steady-state region amplitude by using a dichotomy method on the differential data.

[0069] Exemplarily, S1032, determining the steady-state region amplitude by using a dichotomy method on the differential data, includes:

[0070] The specific implementation is to take THa=0 and THb=max|Xe| (where Xe is We) as the initial conditions, and count the data ratio P of the electric energy increment as (THa+THb) / 2, then make THa=(THa+THb) / 2, otherwise make THb=(THa+THb) / 2, when |PP set |<StopError The judgment area continues to change. The judgment area amplitude is determined to be TH=THb. Figure 4 is the determination result of the TH determination threshold.

[0071] S1033, using the quadratic difference method to eliminate the erroneous points identified in the differential data. The upper horizontal line (corresponding value is 1) and the lower horizontal line (corresponding value is 0) of the flag signal Flag of the steady-state identification result correspond to the transition state and the steady state respectively.

[0072] Exemplarily, the step S1033, using a secondary difference method to eliminate erroneous points identified in the differential data, includes:

[0073] S10331, obtaining a differential signal We sequence of the steady-state determination signal Flag, extracting a point P in the amplitude sequence of the differential signal We that is equal to 1;

[0074] S10332, for the point P sequence obtained in the first step, find the point Q corresponding to the steady-state flag Flag, and take out the number of 1s in the M points before and after the point. If the number of these points is greater than M, change the value of point Q to 1, otherwise set it to 0;

[0075] S10333. Repeat steps S10331 and S10332 to update until the steady-state signal Flag does not change.

[0076] like Figure 5 , is the final steady-state identification result; when Flag = 2000, the system is in an unsteady state; when Flag = 0, the system is in a steady state.

[0077] S20, start traversing and searching for each steady-state data interval, find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed-frequency operation, and use the chilled water and cooling water operation data at the COP maximum point as the optimal setting point.

[0078] In an embodiment of the present invention, S20, starting to traverse and search for each steady-state data interval, finding the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal setting point; including:

[0079] S201, finding multiple steady-state interval data of the day;

[0080] S202, traversing COP data in each steady-state interval;

[0081] S203, finding the data points with the maximum COP in each steady-state interval;

[0082] S204, recording the maximum COP value of each steady-state interval.

[0083] S30. Use the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy.

[0084] In an embodiment of the present invention, S30, using the MPC algorithm to control each optimal set point, while saving energy consumption and improving the performance of the air conditioning system, includes:

[0085] S301, obtaining data of each node at each COP maximum value;

[0086] S302, using each node data as a device operation fixed point;

[0087] S303, using the MPC algorithm to control the reaching of the set point within each steady-state time of the day;

[0088] In an embodiment of the present invention, the MPC algorithm is used to control reaching the set point, and the control process includes model prediction, rolling optimization and feedback correction.

[0089] In an embodiment of the present invention, the model prediction includes:

[0090] The prediction model is obtained through the operation data of the air-conditioning system equipment. The step response equation of the model is as follows:

[0091] X k+1 =AX k +Bu k (1)

[0092] Y k+1 =CX k+1 (2)

[0093] The unit step response model obtains the sampling value a of the step response i =a(iT), i=1, 2, ..., where T is the sampling period. When the step response is at a certain time t N =NT and then becomes stable, a = [a1…a N ] Tis the model vector, and N is the modeling step length. At time k, assuming that the control system has initial predicted values Y0(k+i∣k) for the outputs in the next N time instants, i = 1, …, N, under the action of M consecutive control increments Δu(k), Δu(k+1), …, Δu(k+m-1) starting from the current time instant, M is called the control step length, and the predicted values of the outputs in the next P time instants are Y M is Equation (3):

[0094]

[0095] The predicted value should be as close as possible to the set value r p (k+i), i = 1, …, P, usually M < P < N. Expressing the above equation in vector form gives Equation (4)

[0096] Y(k) = Y0(k) + AΔu m (k) (4)

[0097] In Equation (5), A is the unit step response coefficient a i forming a P×N matrix

[0098]

[0099] In the embodiment of the present invention, the rolling optimization includes:

[0100] During MPC control, the real-time output Y(k+1) and the predicted output Y1(k+1|k) are collected at this time to obtain the output error e(k+1), as shown in Equation (6).

[0101] e(k+1) = Y(k+1) - Y1(k+1|k) (6)

[0102] Then, the error e(k+1) is weighted to obtain an error sequence to correct the MPC model, and its calculation expression is as follows (7):

[0103] y cor (k+1) = y N1 (k) + he(k+1) (7)

[0104] where y cor (k+1) is the predicted value of the corrected model output at the next time instant; h is an N-dimensional error weighting sequence. The online optimization problem at time K is that, under the action of Δu(k), Δu(k+1), …, Δu(k+m-1), the control system gradually reaches the set value in the next P time instants while ensuring that the system is in a stable state. The control performance index is written in vector form, that is, the performance optimization index can be expressed as the following Equation (8). Where N and Q are weight matrices, and by adjusting the sizes of N and Q, the set error and energy consumption in the algorithm can be optimized

[0105]

[0106] In an embodiment of the present invention, the feedback correction includes:

[0107] Finally, y cor (k+1) Get y by shifting matrix A N0 (k+1) is used as the initial value of the model prediction at the next moment, and its calculation expression (9) is as follows:

[0108] y N0 (k+1)=Ay cor (k+1) (9)

[0109] At this time, a0 in the shift matrix A will act on the device as the real-time control quantity Δu(k), controlling the operation of the device so that the system runs to the set temperature.

[0110] S304, determine whether the operation of the day is completed, if so, proceed to S305, if not, return to S303;

[0111] S305: Update operation data.

[0112] The present invention uses a steady-state identification algorithm to realize the identification of the operating status of the air-conditioning system, and a model predictive control algorithm is used to control the air-conditioning operating equipment to achieve energy-saving effects; the data is divided into steady-state data and transition state data through a differential method and a dichotomy method, and then the error points identified in the steady-state data are eliminated using a quadratic difference method; the state space equation of the equipment data is constructed through a system identification method to realize a multi-input and multi-output prediction model.

[0113] Exemplarily, the global steady-state optimization control process is described by taking the building system operation data points in Table 1 below as an example.

[0114] Table 1 System operation data

[0115]

[0116] Since COP is generally used as the performance index of the air-conditioning system, the larger the COP, the better the performance of the air-conditioning system. Therefore, the main purpose of this algorithm is to find the maximum COP point in the steady state during the operation of the air-conditioning system. The specific execution steps are as follows:

[0117] 1. Read the sensor data of the system operation as shown in Table 1;

[0118] 2. Select the load Q in the air conditioning system operation data table as the algorithm operation data;

[0119] 3. Perform DBSI steady-state identification on load data Q;

[0120] 4. Assume that two steady-state intervals are found for the day (taking Table 1 as an example, the first three groups of data are steady-state 1, and the last three groups of data are steady-state 2. The following explanations are based on Table 4-1);

[0121] 5. Traverse the COP data in steady state 1 and steady state 2 in Table 1;

[0122] 6. Find the two COP maximum points 3.91 and 3.42;

[0123] 7. In steps 7 and 8 of the flowchart, the data of each node is obtained. The cooling tower outlet water temperature of the optimized control node group in interval 1 is 29.37 degrees, the chilled water temperature difference is 2.61 degrees, and the cooling water temperature difference is 3.12 degrees. Interval 2 is not repeated;

[0124] 8. In flowchart steps 9 and 10, use the data points in 7 as MPC set points;

[0125] 9. When the steady-state interval 1 time period is reached on the same day, the first MPC set point is run; when the steady-state interval 2 time period is reached on the same day, the second MPC set point is run

[0126] 10. If the steady-state interval of the day is not completed, continue to run; if the run is completed, end after updating the running data of the day.

[0127] The simulation time is set to 600 seconds, the sampling time is 0.4 seconds, and the building load data and the actual operating conditions of the MATLAB air-conditioning system are combined to change the air-conditioning system demand load. The air-conditioning system equipment operates at a fixed frequency. After automatic adjustment, the cooling water inlet and outlet temperatures, cooling water inlet and outlet temperatures, and air-conditioning system COP are recorded. The steady-state identification results of the air-conditioning system required load are shown in Figure 1. Figure 7 As shown, where n=25, M=50, TH=186.13.

[0128] From above Figure 7 It can be identified that the system operation time has two steady-state intervals, namely the sampling point 10-40 interval and the sampling point 51-329 interval. The data of the two intervals are optimized, and the maximum COP value is used as the optimization index to find the optimal performance point of the air-conditioning system as the system operation set point. Based on the optimization program, the optimal COP of the system in the two steady-state intervals is 4.42 and 4.05, the optimal setting point of the cooling water return temperature is 28.03℃ and 28.85℃, the optimal setting point of the cooling water temperature difference is 2.12℃ and 1.55℃, and the chilled water temperature difference is 1.77℃ and 1.50℃.

[0129] Then, according to the established MPC controller, based on the principle that the cooling tower adjusts the cooling water return temperature, the cooling pump adjusts the cooling water temperature difference, and the refrigeration pump adjusts the cooling water temperature difference, the air-conditioning system COP is optimized and the overall performance of the air-conditioning system is improved.

[0130] Down Figure 8 and 9 They are the control effect diagrams of global steady-state optimization control of cooling water temperature difference, freezing water temperature difference, etc.

[0131] like Fig.10 , showing the comparison of the total power consumption of the air-conditioning system equipment after fixed-frequency operation and global steady-state optimization control. The average total power consumed in the steady-state interval of the model predictive control method based on steady-state identification is 903.15kW, and the average total power consumed in the steady-state interval under fixed-frequency operation is 911.30kW.

[0132] It should be understood that, although described in a certain order, these steps are not necessarily performed in sequence in the above order. Unless there is clear explanation in this article, the execution of these steps does not have strict order restriction, and these steps can be performed in other orders. Moreover, a part of the steps of the present embodiment may include a plurality of steps or a plurality of stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a part of the steps or stages in other steps or other steps.

[0133] In one embodiment, see Fig.11 As shown, in an embodiment of the present invention, an air conditioning system energy-saving control system based on steady-state identification is also provided. The system includes a steady-state identification module 100, a traversal search module 200 and an optimal control module 300.

[0134] The steady-state identification module 100 performs steady-state identification on the air-conditioning operation data based on the steady-state identification method of DBSI of the differential equation to obtain a steady-state data interval.

[0135] The traversal search module 200 starts traversal search for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and uses the chilled water and cooling water operation data at the COP maximum point as the optimal setting point.

[0136] The optimal control module 300 uses the MPC algorithm to control each optimal set point, thereby improving the performance of the air-conditioning system while saving energy consumption.

[0137] It should be understood that, as used herein, the singular form "a" or "an" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the items listed in association. The serial numbers of the embodiments disclosed in the above embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0138] A person skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the disclosure of the embodiments of the present invention (including the claims) is limited to these examples; under the concept of the embodiments of the present invention, the technical features in the above embodiments or different embodiments can also be combined, and there are many other changes in different aspects of the embodiments of the present invention as above, which are not provided in detail for the sake of simplicity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. An energy-saving control method for air-conditioning system based on steady-state identification, It is characterized in that The method includes: S10, performing steady-state identification on the air-conditioning operation data using a steady-state identification method of DBSI based on a difference equation to obtain a steady-state data interval; S20, start traversing and searching for each steady-state data interval, find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and use the chilled water and cooling water operation data at the COP maximum point as the optimal setting point; S30. Use the MPC algorithm to control the optimal set points, thereby improving the performance of the air-conditioning system while saving energy.

2. The air conditioning system energy-saving control method based on steady-state identification according to claim 1, It is characterized in that The step S10, the steady-state identification method of DBSI based on the differential equation, performs steady-state identification on the air-conditioning operation data to obtain a steady-state data interval, including: S101, reading operating data of air conditioning system sensors; S102, selecting load Q from the operating data of the air conditioning system sensor as load data; S103, performing steady-state identification on the load data Q of the day to obtain a steady-state data interval.

3. The air conditioning system energy-saving control method based on steady-state identification according to claim 2, It is characterized in that The step S103, performing steady-state identification on the load data Q of the day to obtain a steady-state data interval, includes: S1031. Perform differential processing on the load data Q of the day to obtain differential data of the operating data; S1032, determining the steady-state regional amplitude by using a dichotomy method on the differential data; S1033. Eliminate erroneous points identified in the differential data using a quadratic difference method.

4. The air conditioning system energy-saving control method based on steady-state identification according to claim 1, It is characterized in that S20, traversing and searching each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and using the chilled water and cooling water operation data at the COP maximum point as the optimal setting point; including: S201, finding multiple steady-state interval data of the day; S202, traversing COP data in each steady-state interval; S203, finding the data points with the maximum COP in each steady-state interval; S204, recording the maximum COP value of each steady-state interval.

5. The air conditioning system energy-saving control method based on steady-state identification according to claim 1, It is characterized in that The operating data at least includes the cooling tower outlet water temperature, outdoor temperature, cooling tower inlet water temperature, chiller operating power, chiller cooling water outlet water temperature, chiller inlet water temperature, chilled water outlet water temperature, chilled water inlet water temperature, and system operation power consumption data of each device in the air-conditioning system on a certain day.

6. The air conditioning system energy-saving control method based on steady-state identification according to claim 1, It is characterized in that S30, using the MPC algorithm to control each optimal set point, while saving energy consumption and improving the performance of the air conditioning system, includes: S301, obtaining data of each node at each COP maximum value; S302, using each node data as a device operation fixed point; S303, using the MPC algorithm to control the reaching of the set point within each steady-state time of the day; S304, determine whether the operation of the day is completed, if so, proceed to S305, if not, return to S303; S305: Update operation data.

7. An air conditioning system energy-saving control system based on steady-state identification, It is characterized in that The system includes a steady-state identification module, a traversal search module and an optimal control module. The steady-state identification module performs steady-state identification on the air-conditioning operation data based on the steady-state identification method of DBSI of the differential equation to obtain a steady-state data interval. The traversal search module starts traversal search for each steady-state data interval to find the COP maximum point of the air-conditioning system under different steady-state conditions under fixed frequency operation, and uses the chilled water and cooling water operation data at the COP maximum point as the optimal setting point. The optimal control module uses the MPC algorithm to control each optimal set point, thereby improving the performance of the air-conditioning system while saving energy.

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