A method for regulating a high-efficiency central air conditioning system and the system thereof

Abstract

This invention relates to the field of air conditioning control systems, and specifically discloses a high-efficiency central air conditioning system control method and system, including data acquisition, database establishment, algorithm model establishment, result acquisition, and control equipment; based on the operating data of the central air conditioning system, a total energy consumption algorithm model is established regarding the energy efficiency relationship of the central air conditioning system, and the optimal solution for the overall energy efficiency of the system is obtained based on the total energy consumption algorithm model to operate the system equipment, so as to minimize the energy consumption of the central air conditioning system during operation, reduce energy consumption, and save energy.

Description

Technical Field

[0001] This invention relates to the field of air conditioning control systems, and particularly to a method and system for controlling central air conditioning. Background Technology

[0002] Currently, the adjustment of existing central air conditioning systems mainly relies on manual control. While knowledgeable and responsible operators may make appropriate adjustments during operation, some systems employ PID feedback to adjust chilled water temperature based on changes in outdoor temperature. However, some systems operate directly based on factory-set temperature parameters, with operators only controlling the start and stop of the equipment. A common drawback of these systems is that the energy efficiency of the central air conditioning equipment is often low, or the energy supply exceeds actual demand. Even with adjustments to reduce energy waste, comfort levels cannot be adjusted promptly, resulting in a lag in system energy supply.

[0003] The technical problem to be solved by this application is: how to solve the problem of low energy efficiency of equipment in central air conditioning systems and achieve optimal energy efficiency of the overall equipment in central air conditioning systems. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a high-efficiency central air conditioning system regulation method and system.

[0005] The technical solution adopted in this invention is: a method for regulating a high-efficiency central air conditioning system, comprising:

[0006] Data Acquisition: The control host collects and stores the operating data of the central air conditioning system;

[0007] Database creation: The control host establishes a database of function models related to device operation;

[0008] Establish the algorithm model: Call the function model of the database and establish an algorithm model for the total energy consumption of the central air conditioning system per unit time;

[0009] Results obtained: Based on the total energy consumption algorithm model, the results of the highest efficiency of the central air conditioning system and the flow rate of each device in the central air conditioning system are obtained;

[0010] Regulation equipment: The control unit regulates the flow rate of various devices in the central air conditioning system.

[0011] The control host establishes a unique total energy consumption algorithm model for the central air conditioning system based on the actual operating data of the central air conditioning system and the database. This model calculates the highest efficiency result of the central air conditioning system and the flow rate of each device in the system. The control host then adjusts the flow rate of each device in the central air conditioning system according to the results to achieve high energy efficiency operation of the central air conditioning system.

[0012] In some implementations, the total energy consumption algorithm model is: Q 室内 =K·Q 制冷 =F(X) yields F[f(η) 总 )]=F[f(η1+η2+η3+η4)];

[0013] Q 室内 For the cooling load that needs to be cooled indoors, Q 制冷 Let X be the cooling capacity of the air conditioning system main unit, X be the total power consumption of the system, and K be the coefficient relationship between the cooling load retrieved from the database and the cooling capacity of the air conditioning system main unit. Since the total power consumption of the system is related to the energy efficiency of the air conditioning system main unit, cooling pump, chilled water pump, and cooling tower, the database is accessed to establish the relationship between indoor cooling efficiency per unit time and the flow rate of individual equipment. η1 represents the energy efficiency relationship of the air conditioning system main unit, η2 represents the energy efficiency relationship of the cooling pump, η3 represents the energy efficiency relationship of the chilled water pump, and η4 represents the energy efficiency relationship of the cooling tower. 总 Let F represent the total energy efficiency of the entire air conditioning system, F represent the functional relationship between the total power consumption of the system and the cooling capacity of the air conditioning unit, and f represent the functional relationship between the total energy efficiency and the total power consumption of the system.

[0014] Under the same flow rate across all devices, η is achieved. 总 To maximize X and thus minimize it, the host computer controls X based on η. 总 Control the flow rate of each device in the central air conditioning system.

[0015] Because the connection methods, models, and service lives of each central air conditioning unit are different, it is necessary to collect operational data from each unit and, based on the function relationships accessed from the database, to initially establish an operational algorithm model for each unit in the central air conditioning system. Since central air conditioning primarily regulates indoor temperature, the cooling load Q... 室内 The amount of heat that needs to be removed to cool the room to a specified temperature is the cooling capacity Q of the air conditioning system's main unit. 制冷 There will be some losses during the process, thus Q 制冷 With Q 室内 There is a certain coefficient relationship between them; the cooling capacity Q of the air conditioning system main unit 制冷 The total system power consumption X is also subject to some loss during operation, and there is energy loss during the conversion of total power consumption X into heat energy, so Q 制冷There is a certain functional relationship between X and the total power consumption of the main equipment in the central air conditioning system, which is related to the air conditioning unit, cooling pump, chilled water pump, and cooling tower. To achieve a low total power consumption X, the sum of the energy efficiency of each device must be maximized. Since the flow rate of each device is consistent during the operation of the central air conditioning system, by setting the relationship between cooling efficiency and the flow rate of individual devices, the energy efficiency relationship of each device can be obtained. Based on the energy efficiency relationship of each device, the energy efficiency of Q can be calculated. 室内 Under the same conditions, η 总 The maximum energy consumption is achieved, thus minimizing the total system power consumption X, thereby optimizing the overall energy efficiency of the central air conditioning system.

[0016] In some implementations, the total energy consumption algorithm model includes:

[0017] η1=f1(t) 供 t 回 G 冷冻 );

[0018] η2=f2(G 冷却 );

[0019] η3=f3(G) 冷冻 );

[0020] η4=f4(T) 供 T 回 G 冷却 );

[0021] Where t 供 t is the temperature of the chilled water supply. 回 T represents the chilled water return temperature. 供 T is the cooling water supply temperature. 回 G represents the return temperature of the cooling water. 冷冻 G is the chilled water flow rate. 冷却 To obtain the cooling water flow rate, the database is called to retrieve f1, f2, f3, and f4, where f1 is the result of η1 and t. 供 t 回 G 冷冻 The functional relationship between them; f2 is the relationship between η2 and G. 冷却 The functional relationship between them; f3 is the relationship between η3 and G. 冷冻 The functional relationship between them, f4 is η4 and T 供 T 回 G 冷却 The functional relationship between them; the host control controls the temperature and flow rate of each device.

[0022] Since the energy consumption of the air conditioning system's main unit, cooling pump, chilled water pump, and cooling tower is related to the temperature or flow rate of the cooling or chilled water they pass through, there is a certain functional relationship between their energy efficiency and the temperature or flow rate of the cooling or chilled water they pass through. In order to obtain a more accurate energy efficiency relationship for each device, a functional relationship between each device and temperature and flow rate is established.

[0023] In some implementations, the algorithm model also includes a time lag model, which is as follows:

[0024] ΔT=h·f h1 (Δt);

[0025] Where ΔT is the indoor temperature rise difference, Δt is the host unit temperature difference, h is the set temperature time, and f is obtained by calling the database. h1 f h1 Let ΔT and Δt be the functional relationship between them and h.

[0026] Because there is a certain time lag between the central air conditioning system and the building itself—that is, after the central air conditioning system is turned on, the cooling process begins with the building itself cooling down to the same temperature as the indoor air, and then the indoor air temperature is cooled down as a whole—different buildings exhibit different heat consumption, meaning their cooling time lags are different. By establishing the relationship between the main unit temperature difference Δt and the set temperature time h required to reach the indoor temperature rise difference ΔT, and statistically calculating the set temperature time h required to reach different indoor temperature rise differences ΔT, the heat consumption of the building can be calculated. This further obtains unique data on the performance of individual central air conditioning systems and also helps operators directly know the time required to reach the set temperature when starting the central air conditioning system, reducing the lag in system energy supply.

[0027] In some implementations, data acquisition also includes collecting data from outdoor weather stations and local weather stations; the database includes a climate data function model; and the algorithm model includes an outdoor data interferometry model.

[0028] A=f 温1 (a);

[0029] Where A represents the outdoor weather station data at this time, a represents the local weather station data at this time, and f is obtained by calling the database. 温1 f 温1 This is the functional relationship between outdoor weather station data A and local weather station data a at this time;

[0030] Given forecast data b for the next hour and forecast data c for the next two hours from the local weather station, based on A=f 温1(a) Predict the data B of the outdoor weather station one hour from now and the data C two hours from now; record the actual outdoor weather station data B1 and the local weather station data b1 one hour from now, verify whether B and B1 are consistent, establish B1=F(b1), and predict the data C1 of the outdoor weather station one hour from now based on B1=F(b1).

[0031] Record the actual outdoor weather station data C2 and the local weather station data c2 two hours later to verify whether C, C1 and C2 are consistent.

[0032] By establishing outdoor weather stations and comparing data from outdoor weather stations with local weather station data, it is possible to establish changes in outdoor temperature in buildings equipped with central air conditioning systems and further predict the outdoor temperature. Since outdoor temperature is related to the heat dissipation of cooling towers, predicting the outdoor temperature allows for advance adjustment of the cooling tower temperature and flow rate. This also helps the control unit set a more suitable indoor temperature for human comfort and dynamically adjusts the indoor temperature and equipment operating status. By establishing temperature relationships across different time intervals, it is possible to obtain accurate predictions of outdoor temperature for a specific duration based on local weather station data.

[0033] In some implementations, data simulation is also included. This simulation involves matching simulated data derived from a total energy consumption algorithm model with the actual operating data of the central air conditioning system. A matching rate exceeding 90% is required for the system to be put into use. To ensure that a relatively complete individual central air conditioning system has been established, the simulated data derived from the total energy consumption algorithm model based on the database and equipment operating data must match the actual equipment operating data with a matching rate exceeding 90% before the system can be put into use.

[0034] In some implementations, the cooling load is calculated by the control unit based on the temperature difference between the indoor temperature and a set target indoor temperature, or based on the temperature difference between the indoor temperature and a suitable human body temperature matching the outdoor temperature. The cooling load is the heat generated by the temperature difference between the indoor temperature and the set temperature. The set temperature can be obtained in two ways: either manually or by the control unit matching the outdoor temperature. These two different methods are provided to the operators in a user-friendly and intelligent manner.

[0035] A central air conditioning control system, employing any of the above-mentioned high-efficiency central air conditioning system control methods, includes a control host comprising a data acquisition module, a network communication module, an adaptive algorithm module, and a storage module. The control host is communicatively connected to the central air conditioning system. The storage module includes a database and a storage server for storing the central air conditioning system's operational data. The adaptive algorithm module is used to call function models from the database and establish a total energy consumption algorithm model. The control host collects operational data from each device through the network communication module and the data acquisition module, stores the operational data in the storage module, calls device operational data and functions from the storage module, and calculates a personalized algorithm model for each individual central air conditioning system through the adaptive algorithm module. The various modules complement each other and work together effectively.

[0036] The beneficial effects of this invention are as follows:

[0037] This high-efficiency central air conditioning system regulation method establishes the optimal solution for the overall energy efficiency of the differentiated central air conditioning system to operate the system equipment, so as to minimize the energy consumption of each device in the system, reduce energy consumption, save energy, and achieve the energy conservation and environmental protection effect advocated by the state. Attached Figure Description

[0038] Figure 1 This is a schematic diagram showing the connection between the various modules of the control host of the present invention and the central air conditioning system;

[0039] Figure 2 This is a schematic diagram showing the relationship between indoor cooling efficiency per unit time and the flow rates of the air conditioning system's main unit, cooling pump, chilled water pump, and cooling tower.

[0040] Figure 3 This is a schematic diagram summarizing the relationship between indoor cooling efficiency per unit time and the flow rates of the air conditioning system main unit, cooling pump, chilled water pump, and cooling tower.

[0041] Figure 4 This is a diagram illustrating the matching and comparison between simulated data and actual data. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] This invention provides a technical solution:

[0044] A method and system for regulating a high-efficiency central air conditioning system, including a control unit and a central air conditioning system, please refer to [link / reference]. Figure 1 The control host includes a data acquisition module, a network communication module, an adaptive algorithm module, and a storage module. The storage module includes a database storing function models of equipment operation and a storage server storing operational data of each device in the central air conditioning system. The control host collects operational data from each device in the central air conditioning system through the network communication module and the data acquisition module, and stores this data in the storage module. It then retrieves the operational data and functions from the storage module and uses the adaptive algorithm module to calculate a personalized algorithm model for each individual central air conditioning system. The specific implementation method is as follows:

[0045] Based on the on-site conditions of the central air conditioning system equipment, we organized, confirmed, and sorted out the equipment for which data needs to be collected (divided into read-only data and controllable data), the operating time of the central air conditioning system, and its operating habits.

[0046] Data collection for central air conditioning system equipment and the addition of sensor devices to detect factors affecting the operation of the central air conditioning system; the main factors affecting the efficiency of the central air conditioning system, in addition to the performance of the equipment itself, include the main unit temperature affecting the supply and return temperatures of chilled water in the system pipeline, the frequency of chilled pumps and cooling pumps affecting the flow rate and pressure in the pipeline, and the frequency and number of cooling towers corresponding to the heat dissipation of cooling water; the above data are collected, and remote communication connections are established for each device.

[0047] All related equipment (including main equipment and sensors) of the central air conditioning system are connected to the control host for communication. Through communication protocols, the PLC collects and integrates data, and the wireless network transmits data. All operating data of the central air conditioning system are stored on the storage server.

[0048] To meet the operational requirements of the central air conditioning system, the operating algorithm of the central air conditioning system is customized and adjusted. Based on the function model of the control host calling the database, and combined with the operating data of the central air conditioning system in the storage server, an algorithm model for the total energy consumption of the central air conditioning system per unit time is established.

[0049] Please see Figure 4 The control host uses the initial data to simulate the total energy consumption algorithm model to obtain simulated data; the actual data is obtained by extracting the actual collected data based on the total energy consumption algorithm model and calculating it. The simulated data is compared with the actual data. If the matching rate is over 90%, it can be put into actual use.

[0050] The control host obtains the highest efficiency result of the central air conditioning system and the flow rate results of each device in the central air conditioning system based on the total energy consumption algorithm model.

[0051] The control unit then adjusts the flow rate of each device in the central air conditioning system based on the results.

[0052] The total energy consumption algorithm model is: Q室内 =K·Q 制冷 =F(X) yields F[f(η) 总 )]=F[f(η1+η2+η3+η4)];

[0053] Q 室内 For the cooling load that needs to be cooled indoors, Q 制冷 X represents the cooling capacity of the air conditioning system's main unit, X represents the total power consumption of the system, and K represents the coefficient relationship between the cooling load retrieved from the database and the cooling capacity of the air conditioning system's main unit; please refer to [link / reference]. Figure 2 and Figure 3 Since the total power consumption of the system is related to the energy efficiency of the air conditioning system main unit, cooling pump, chilled water pump, and cooling tower, the database is accessed to establish the relationship between indoor cooling efficiency and the flow rate of individual devices per unit time. η1 represents the energy efficiency of the air conditioning system main unit, η2 represents the energy efficiency of the cooling pump, η3 represents the energy efficiency of the chilled water pump, and η4 represents the energy efficiency of the cooling tower. 总 Let F represent the total energy efficiency of the entire air conditioning system, F represent the functional relationship between the total power consumption of the system and the cooling capacity of the air conditioning unit, and f represent the functional relationship between the total energy efficiency and the total power consumption of the system.

[0054] Under the same flow rate across all devices, η is achieved. 总 To maximize X and thus minimize it, the host computer controls X based on η. 总 Control the flow rate of each device.

[0055] The cooling load is calculated by the control unit based on the temperature difference between the indoor temperature and the set target indoor temperature, or based on the temperature difference between the indoor temperature and the suitable human body temperature that matches the outdoor temperature.

[0056] To further obtain a more specific algorithm model and better control the energy efficiency of each device, the total energy consumption algorithm model is η1=f1(t). 供 t 回 G 冷冻 );

[0057] η2=f2(G 冷却 );

[0058] η3=f3(G) 冷冻 );

[0059] η4=f4(T) 供 T 回 G 冷却 );

[0060] Where t 供 t is the temperature of the chilled water supply. 回 T represents the chilled water return temperature. 供 T is the cooling water supply temperature. 回 G represents the return temperature of the cooling water.冷冻 G is the chilled water flow rate. 冷却 To obtain the cooling water flow rate, the database is called to retrieve f1, f2, f3, and f4, where f1 is the result of η1 and t. 供 t 回 G 冷冻 The functional relationship between them; f2 is the relationship between η2 and G. 冷却 The functional relationship between them; f3 is the relationship between η3 and G. 冷冻 The functional relationship between them, f4 is η4 and T 供 T 回 G 冷却 The functional relationship between them; the control host controls the temperature and flow rate of each device in the central air conditioning system.

[0061] The control unit controls the temperature and flow rate of each device in the central air conditioning system.

[0062] To further reduce energy supply lag and understand building heat consumption, the algorithm model also includes a time lag model, which is as follows:

[0063] ΔT=h·f h1 (Δt);

[0064] Where ΔT is the indoor temperature rise difference, Δt is the host unit temperature difference, h is the set temperature time, and f is obtained by calling the database. h1 f h1 Let ΔT and Δt be the functional relationship with respect to h, where Δt = t 回 -t 供 .

[0065] To further determine the optimal indoor temperature and adjust the cooling tower's heat dissipation efficiency, an outdoor weather station was established for the building housing the central air conditioning system. A storage server collected data from the outdoor weather station and local weather station data. The database included a climate data function model, and the algorithm model included an outdoor data interferometry model. The outdoor data interferometry model is as follows:

[0066] A=f 温1 (a);

[0067] Where A represents the outdoor weather station data at this time, a represents the local weather station data at this time, and f is obtained by calling the database. 温1 f 温1 This is the functional relationship between outdoor weather station data A and local weather station data a at this time;

[0068] Given forecast data b for the next hour and forecast data c for the next two hours from the local weather station, based on A=f 温1(a) Predict the data B of the outdoor weather station one hour from now and the data C two hours from now; record the actual outdoor weather station data B1 and the local weather station data b1 one hour from now, verify whether B and B1 are consistent, establish B1=F(b1), and predict the data C1 of the outdoor weather station one hour from now based on B1=F(b1).

[0069] Record the actual outdoor weather station data C2 and the local weather station data c2 two hours later to verify whether C, C1 and C2 are consistent.

[0070] If the data matching degree between B and B1 is above 90%, that is, A=f(a) can predict the outdoor weather data of the building for the next hour based on the local weather station; if the data matching degree between C and C2 is above 90%, that is, A=f(a) can predict the outdoor weather data of the building for at least 2 hours based on the local weather station.

[0071] If the matching degree between B and B1 is below 90%, and the matching degree between C1 and C2 is above 90%, then B1=F(b1) can predict the outdoor weather data of the building for the next hour based on the local weather station. If the matching degree between B and B1 is below 90%, and the matching degree between C1 and C2 is below 90%, then there is no effective function that can predict the future outdoor weather data. In other words, the relationship between the outdoor weather station data and the local weather station data at this time needs to be recalculated.

[0072] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for regulating a high-efficiency central air conditioning system, characterized in that, include: Data Acquisition: The control host collects and stores the operating data of the central air conditioning system; Database creation: The control host establishes a database of function models related to device operation; Establish the algorithm model: Call the function model of the database and establish an algorithm model for the total energy consumption of the central air conditioning system per unit time; Results obtained: Based on the total energy consumption algorithm model, the results of the highest efficiency of the central air conditioning system and the flow rate of each device in the central air conditioning system are obtained; Regulating equipment: The control unit regulates the flow rate of various devices in the central air conditioning system; The algorithm model also includes a time lag model, which is as follows: ΔT=h·f h1 (Δt); Where ΔT is the indoor temperature rise difference, Δt is the host unit temperature difference, and h is the set temperature time. f is obtained by calling the database. h1 The f h1 Let ΔT and Δt be functions of h. The data acquisition also includes collecting data from outdoor weather stations and local weather stations. The database includes a climate data function model, and the algorithm model includes an outdoor data interferometry model. The outdoor data interferometry model is as follows: A=f 温1 (a); Where A represents the outdoor weather station data at this time, a represents the local weather station data at this time, and f is obtained by calling the database. 温1 f 温1 This is the functional relationship between outdoor weather station data A and local weather station data a at this time; Given the local weather station's forecast data b for the next hour and c for the next two hours, based on A=f 温1 (a) Predict the data B of the outdoor weather station one hour from now and the data C two hours from now; record the actual outdoor weather station data B1 and the local weather station data b1 one hour from now, verify whether B and B1 are consistent, establish B1=F(b1), and predict the data C1 of the outdoor weather station one hour from now based on B1=F(b1). Record the actual outdoor weather station data C2 and the local weather station data c2 two hours later to verify whether C, C1 and C2 are consistent.

2. The method for regulating a high-efficiency central air conditioning system according to claim 1, characterized in that, The total energy consumption algorithm model is: Q 室内 =K·Q 制冷 =F(X) yields F[f(η) 总 )]=F[f(eta1+eta2+eta3+eta4)]; Q 室内 For the cooling load that needs to be cooled indoors, Q 制冷 Let X be the cooling capacity of the air conditioning system main unit, X be the total power consumption of the system, and K be the coefficient relationship between the cooling load retrieved from the database and the cooling capacity of the air conditioning system main unit. Since the total power consumption of the system is related to the energy efficiency of the air conditioning system main unit, cooling pump, chilled water pump, and cooling tower, the database is retrieved to establish the relationship between indoor cooling efficiency per unit time and the flow rate of a single device. η1 represents the energy efficiency relationship of the air conditioning system main unit, η2 represents the energy efficiency relationship of the cooling pump, η3 represents the energy efficiency relationship of the chilled water pump, and η4 represents the energy efficiency relationship of the cooling tower. 总 Let F be the total energy efficiency relationship of the entire air conditioning system, F be the functional relationship between the total power consumption of the system and the cooling capacity of the air conditioning unit, and f be the functional relationship between the total energy efficiency relationship and the total power consumption of the system. Under the same flow rate across all devices, η is achieved. 总 The maximum value is obtained by maximizing X, thereby obtaining the minimum value of X, and the control host is based on η. 总 Control the flow rate of each device in the central air conditioning system.

3. The method for regulating a high-efficiency central air conditioning system according to claim 2, characterized in that, The total energy consumption algorithm model includes: η1=f1(t 供 、t 回 、G 冷冻 ); η2=f2(G 冷却 ); η3=f3(G 冷冻 ); η4=f4(T 供 、T 回 、G 冷却 ); Where t 供 t is the temperature of the chilled water supply. 回 T represents the chilled water return temperature. 供 T is the cooling water supply temperature. 回 G represents the return temperature of the cooling water. 冷冻 G is the chilled water flow rate. 冷却 To obtain the cooling water flow rate, the database is called to retrieve f1, f2, f3, and f4, where f1 is the result of η1 and t. 供 t 回 G 冷冻 The functional relationship between them; f2 is the relationship between η2 and G. 冷却 The functional relationship between them; f3 is the relationship between η3 and G. 冷冻 The functional relationship between them, f4 is η4 and T 供 T 回 G 冷却 The functional relationship between them; the control host controls the temperature and flow rate of each device.

4. The method for regulating a high-efficiency central air conditioning system according to claim 1, characterized in that, It also includes data simulation, wherein the data simulation matching is the simulated data obtained based on the total energy consumption algorithm model, and the simulated data is matched with the actual operating data of the central air conditioning system. If the matching rate is higher than 90%, it can be put into use.

5. The method for regulating a high-efficiency central air conditioning system according to claim 2, characterized in that, The cooling load is calculated by the control host based on the temperature difference between the indoor temperature and the set specific target indoor temperature, or based on the temperature difference between the indoor temperature and the suitable human body temperature that matches the outdoor temperature.

6. A central air conditioning control system, employing the high-efficiency central air conditioning system control method as described in any one of claims 1-5, characterized in that, The control host includes a data acquisition module, a network communication module, an adaptive algorithm module, and a storage module. The control host is communicatively connected to the central air conditioning system. The storage module includes a database and a storage server for storing the operating data of the central air conditioning system. The adaptive algorithm module is used to call the function model of the database and establish a total energy consumption algorithm model.

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