A method and system for controlling adjustable electrical load of a central air conditioner

Abstract

The present application relates to the technical field of control, and especially relates to a central air conditioner adjustable load control method and system, comprising the following steps: obtaining the load peak period and the power load value to be reduced issued by the power grid dispatching center, and generating a power pressure evaluation value; for normal grade power pressure, using time series decomposition technology to extract load trend components, and calculating overall load stability indexes; collecting the operation parameters of the central air conditioner system in real time; taking the power load reduction demand and the system comprehensive energy efficiency optimization as double targets, generating a regulation and control strategy, including adjusting the chilled water outlet temperature set value, the chilled water pump frequency and the terminal air valve opening; using feature fusion technology to combine the frequency domain energy distribution and the difference sequence into a power load reduction demand and system comprehensive energy efficiency multi-dimensional feature vector; and executing the regulation and control strategy according to the air conditioner load reduction amount before the load peak period. The present application has the advantage of improving the overall load adjustment efficiency.

Description

Technical Field

[0001] This invention relates to the field of control technology, and in particular to a method and system for controlling the adjustable electrical load of a central air conditioning system. Background Technology

[0002] Air conditioners are used frequently in summer and winter, especially during summer evenings and winter days. Peak usage periods put pressure on power regulation. Without affecting user usage, load can be controlled according to the load curve to achieve peak shaving and valley filling in the power system.

[0003] Air conditioners, as widely used appliances with large heat storage capacity, are an important controllable load. Most existing air conditioner load control methods rely solely on temperature information, resulting in limited data collection and consequently, limited control effectiveness.

[0004] Therefore, a central air conditioning system with adjustable electrical load control method and system is proposed, which can improve the overall electrical load adjustment efficiency and meet the real-time requirements of smart grid for operation and maintenance adjustment. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a central air conditioning adjustable electrical load control method and system, which can improve the overall electrical load adjustment efficiency and meet the real-time requirements of smart grid for operation and maintenance adjustment.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for controlling adjustable electrical load in a central air conditioning system includes the following steps: Obtain the peak load periods and the power load values ​​that need to be reduced as released by the power grid dispatch center, and generate power pressure assessment values; Based on the power pressure assessment value, the charging effect is divided into high voltage level, normal level and low voltage level; For normal power pressure, time series decomposition technology is used to extract load trend components and calculate the overall load stability index. Real-time collection of central air conditioning system operating parameters, including chilled water supply and return water temperature difference, main unit power, ambient temperature and humidity, and indoor temperature and humidity. concentration; A basic distribution map of operating parameters is generated using a spatial interpolation algorithm for subsequent real-time data acquisition. By integrating power pressure assessment values ​​and overall load stability indicators, the system outputs operating power adjustment commands. With the dual objectives of reducing grid load and optimizing overall system energy efficiency, control strategies are generated, including adjusting the chilled water outlet temperature setpoint, chilled water pump frequency, and terminal air valve opening. Feature fusion technology is used to merge frequency domain energy distribution and differential sequences into a multi-dimensional feature vector of grid load reduction demand and system comprehensive energy efficiency; Input the grid load reduction demand and the multi-dimensional feature vector of the system's comprehensive energy efficiency into a pre-trained classification neural network model to output the air conditioning load reduction amount; Control strategies are implemented based on the reduction in air conditioning load before peak load periods.

[0007] As a preferred option, the input variables also include indoor and outdoor temperature difference, population density, and historical energy consumption curves; The output variable is the air conditioning load reduction amount, and the output vector group is corrected by a fuzzy controller.

[0008] As a preferred option, the chilled water flow rate is adjusted based on dynamic feedback of the supply and return water temperature difference, with the temperature difference threshold set at 3–5℃. Terminal air valve opening degree and The concentration change rate is linked; when the concentration change rate exceeds the limit, the fresh air volume is increased.

[0009] As a preferred option, the optimal total energy consumption is calculated based on the equipment model, and the revised energy-saving control strategy is sent to the local controller. When the actual energy consumption deviates from the predicted value by more than 10%, an abnormal alarm mechanism is triggered.

[0010] A central air conditioning system with adjustable electrical load control includes: The load forecasting module is used to obtain the peak load periods and the power load values ​​that need to be reduced as released by the power grid dispatch center, and generate power pressure assessment values. Based on the power pressure assessment value, the charging effect is divided into high voltage level, normal level and low voltage level; For normal power pressure, time series decomposition technology is used to extract load trend components and calculate the overall load stability index. The data acquisition module is used to collect real-time operating parameters of the central air conditioning system, including chilled water supply and return temperature difference, main unit power, ambient temperature and humidity, and indoor temperature and humidity. concentration; A basic distribution map of operating parameters is generated using a spatial interpolation algorithm for subsequent real-time data acquisition. The optimized control module integrates power pressure assessment values ​​and overall load stability indicators to output operating power adjustment commands. With the dual objectives of reducing grid load and optimizing overall system energy efficiency, control strategies are generated, including adjusting the chilled water outlet temperature setpoint, chilled water pump frequency, and terminal air valve opening. Feature fusion technology is used to merge frequency domain energy distribution and differential sequences into a multi-dimensional feature vector of grid load reduction demand and system comprehensive energy efficiency; Input the multi-dimensional feature vectors of grid load reduction demand and system comprehensive energy efficiency into a pre-trained classification neural network model to output the potential value of air conditioning load reduction. The execution module is used to implement control strategies based on the potential for reducing air conditioning load before peak load periods.

[0011] As a preferred option, the optimized control module specifically includes: The system extracts light intensity correlation vectors based on temporal features and automatically switches the air conditioner's operating mode. The temperature and humidity settings are dynamically adjusted based on the results of the perceived temperature analysis.

[0012] As a preferred option, the execution module controls the airflow in the ventilation ducts in zones. Each zone is equipped with a temperature sensor and an electric regulating valve. The airflow is adjusted independently by the control device. When the temperature in a certain zone reaches the standard, the opening of the air valve in that zone is reduced to prioritize the high-demand zone.

[0013] As a preferred option, a cloud platform monitoring module is also included, which is used to visualize energy consumption curves, system efficiency and fault records; Control parameters can be modified via a remote interface, supporting graphical programming and online simulation.

[0014] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described central air conditioning adjustable electrical load control method.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described central air conditioning adjustable electrical load control method.

[0016] The beneficial effects of using the present invention are as follows: 1. Based on a pre-trained deep neural network, heterogeneous feature separation is performed on the air conditioning current and voltage signals, outlet water temperature setpoint, water pump frequency, and terminal air valve opening in the standardized data to obtain the armature transient features and temperature change features of the standardized data, including: The air conditioning current and voltage signals, outlet water temperature setpoint, water pump frequency, and terminal air valve opening in the standardized data are respectively input into the convolutional paths set in parallel in the pre-trained deep neural network. Based on the convolution path, multi-scale spatiotemporal feature extraction is performed on the air conditioner current and voltage signals, the outlet water temperature setpoint, the water pump frequency, and the terminal air valve opening to obtain the current and voltage depth features and temperature change depth features of the standardized data. The current-voltage depth features and temperature change depth features are cross-integrated to obtain a joint feature representation of the standardized data. The joint feature representation is decoupled and separated to obtain the initial armature transient features and initial temperature change features of the standardized data; The initial armature transient characteristics and initial temperature change characteristics are subjected to feature enhancement optimization to obtain the armature transient characteristics and temperature change characteristics of the standardized data.

[0017] Control strategies are implemented based on the reduction in air conditioning load before peak load periods.

[0018] 2. This invention achieves efficient and accurate identification of power grid load reduction demand data by combining deep neural networks with graph attention mechanisms. This method can perform deep heterogeneous feature separation on the multi-dimensional feature vectors of power grid load reduction demand and system comprehensive energy efficiency, effectively extracting air conditioning current and voltage signals, outlet water temperature setpoints, pump frequencies, and terminal air valve openings. Through cross-domain adaptive fusion technology, features from different sources and of different properties are unified into a comprehensive data representation, significantly improving the completeness and robustness of data feature expression.

[0019] 3. The formula for calculating the data acquisition efficiency of input variables is:

[0020] in, Indicates the first Data acquisition efficiency of individual air conditioning units; Indicates the first The reliability of the air conditioning unit; Indicates the first The number of testing devices in each air conditioning unit; Indicates the first The first air conditioning unit The amount of data per testing device; Indicates the first The first air conditioning unit The sampling frequency of each detection device; Indicates the first The first air conditioning unit The link distance between each detection device and its connected slave device; This indicates the maximum permissible link distance between the detection device and the connected slave device; Indicates the first The first air conditioning unit The power of each testing device; The standard deviation of noise; Indicates the first Background noise of the testing equipment.

[0021] 4. In the data transmission delay calculation algorithm, a dynamic management mechanism is introduced, and a waiting coefficient is used to reflect the queuing time of data packets in the network; the specific formula is as follows:

[0022] in, This indicates the latency of the device's communication connection to the detection device; Indicates to Summing each data packet; Indicates the first The size of each data packet; Indicates the first The importance of each data packet; Indicates the first Response time requirements for each data packet; Indicates the first The transmission rate of each data packet; Indicates the first The waiting factor for each data packet; Indicates the total transmission rate; Indicates infinity; This indicates the status flag of the receiving host.

[0023] These features and advantages of the present invention will be disclosed in detail in the following specific embodiments and accompanying drawings. Attached Figure Description

[0024] The invention will be further described below with reference to the accompanying drawings: Figure 1 This is a schematic flowchart of the electrical load control method according to Embodiment 1 of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0026] The concepts involved in this application will first be explained with reference to the accompanying drawings. It should be noted that the following explanation of each concept is only to make the content of this application easier to understand and does not imply any limitation on the scope of protection of this application. Example

[0027] A method for controlling the adjustable electrical load of a central air conditioning system, such as Figure 1 As shown, it includes the following steps: Obtain the peak load periods and the power load values ​​that need to be reduced as released by the power grid dispatch center, and generate power pressure assessment values; Based on the power pressure assessment value, the charging effect is divided into high voltage level, normal level and low voltage level; For normal power pressure, time series decomposition technology is used to extract load trend components and calculate the overall load stability index. Real-time collection of central air conditioning system operating parameters, including chilled water supply and return water temperature difference, main unit power, ambient temperature and humidity, and indoor temperature and humidity. concentration; A basic distribution map of operating parameters is generated using a spatial interpolation algorithm for subsequent real-time data acquisition. By integrating power pressure assessment values ​​and overall load stability indicators, the system outputs operating power adjustment commands. With the dual objectives of reducing grid load and optimizing overall system energy efficiency, control strategies are generated, including adjusting the chilled water outlet temperature setpoint, chilled water pump frequency, and terminal air valve opening. Feature fusion technology is used to merge frequency domain energy distribution and differential sequences into a multi-dimensional feature vector of grid load reduction demand and system comprehensive energy efficiency; Input the grid load reduction demand and the multi-dimensional feature vector of the system's comprehensive energy efficiency into a pre-trained classification neural network model to output the air conditioning load reduction amount; Based on a pre-trained deep neural network, heterogeneous feature separation is performed on the air conditioning current and voltage signals, outlet water temperature setpoint, water pump frequency, and terminal air valve opening in the standardized data to obtain the armature transient features and temperature change features of the standardized data, including: The air conditioning current and voltage signals, outlet water temperature setpoint, water pump frequency, and terminal air valve opening in the standardized data are respectively input into the convolutional paths set in parallel in the pre-trained deep neural network. Based on the convolution path, multi-scale spatiotemporal feature extraction is performed on the air conditioner current and voltage signals, the outlet water temperature setpoint, the water pump frequency, and the terminal air valve opening to obtain the current and voltage depth features and temperature change depth features of the standardized data. The current-voltage depth features and temperature change depth features are cross-integrated to obtain a joint feature representation of the standardized data. The joint feature representation is decoupled and separated to obtain the initial armature transient features and initial temperature change features of the standardized data; The initial armature transient characteristics and initial temperature change characteristics are subjected to feature enhancement optimization to obtain the armature transient characteristics and temperature change characteristics of the standardized data.

[0028] Control strategies are implemented based on the reduction in air conditioning load before peak load periods.

[0029] It also acquires the original current and voltage signals and the original partial discharge signal of the power cable in the air conditioning circuit; The original current and voltage signals and the original partial discharge signal are time-aligned to obtain the synchronization signal data of the power cable; The synchronization signal data is normalized to obtain the standardized data of the power cable.

[0030] Similarly, the pre-trained deep neural network performs heterogeneous feature separation on the current and voltage signals and partial discharge signals in the standardized data to obtain the armature transient features and insulation degradation features of the standardized data, including: The current and voltage signals and partial discharge signals in the standardized data are respectively input into the convolutional paths set in parallel in the pre-trained deep neural network; Based on the convolution path, multi-scale spatiotemporal feature extraction is performed on the current-voltage signal and the partial discharge signal to obtain the current-voltage depth feature and the partial discharge depth feature of the standardized data. The current-voltage depth features and the partial discharge depth features are cross-integrated to obtain a joint feature representation of the standardized data; The joint feature representation is decoupled and separated to obtain the initial armature transient features and initial insulation degradation features of the standardized data; The initial armature transient features and initial insulation degradation features are enhanced and optimized to obtain the standardized armature transient features and insulation degradation features. This invention achieves efficient and accurate identification of grid load reduction demand data by combining deep neural networks and graph attention mechanisms. This method can perform deep heterogeneous feature separation on the multi-dimensional feature vectors of grid load reduction demand and system comprehensive energy efficiency, effectively extracting air conditioning current and voltage signals, outlet water temperature setpoints, pump frequency, and terminal air valve opening. Through cross-domain adaptive fusion technology, features from different sources and of different properties are unified into a comprehensive data representation, significantly improving the completeness and robustness of data feature expression.

[0031] Input variables also include indoor-outdoor temperature difference, population density, and historical energy consumption curves; The output variable is the air conditioning load reduction amount, and the output vector group is corrected by a fuzzy controller.

[0032] The formula for calculating the data acquisition efficiency of input variables is:

[0033] in, express Data acquisition efficiency of individual air conditioning units; Indicates the first The reliability of the air conditioning unit; Indicates the first The number of testing devices in each air conditioning unit; Indicates the first The first air conditioning unit The amount of data per testing device; Indicates the first The first air conditioning unit The sampling frequency of each detection device; Indicates the first The first air conditioning unit The link distance between each detection device and its connected slave device; This indicates the maximum permissible link distance between the detection device and the connected slave device; Indicates the first The first air conditioning unit The power of each testing device; The standard deviation of noise; Indicates the first Background noise of the testing equipment.

[0034] The chilled water flow rate is adjusted based on dynamic feedback of the supply and return water temperature difference, with the temperature difference threshold set at 3–5℃. The opening degree of the terminal air valve is linked to the rate of change of CO2 concentration. When the rate of change of concentration exceeds the limit, the fresh air volume is increased.

[0035] The optimal total energy consumption is calculated based on the equipment model, and the corrected energy-saving control strategy is sent to the local controller. When the actual energy consumption deviates from the predicted value by more than 10%, an abnormal alarm mechanism is triggered.

[0036] A central air conditioning system with adjustable electrical load control includes: The load forecasting module is used to obtain the peak load periods and the power load values ​​that need to be reduced as released by the power grid dispatch center, and generate power pressure assessment values. Based on the power pressure assessment value, the charging effect is divided into high voltage level, normal level and low voltage level; For normal power pressure, time series decomposition technology is used to extract load trend components and calculate the overall load stability index. The data acquisition module is used to collect the operating parameters of the central air conditioning system in real time, including the temperature difference between chilled water supply and return water, main unit power, ambient temperature and humidity, and indoor CO2 concentration. A basic distribution map of operating parameters is generated using a spatial interpolation algorithm for subsequent real-time data acquisition. The optimized control module integrates power pressure assessment values ​​and overall load stability indicators to output operating power adjustment commands. With the dual objectives of reducing grid load and optimizing overall system energy efficiency, control strategies are generated, including adjusting the chilled water outlet temperature setpoint, chilled water pump frequency, and terminal air valve opening. Feature fusion technology is used to merge frequency domain energy distribution and differential sequences into a multi-dimensional feature vector of grid load reduction demand and system comprehensive energy efficiency; Input the multi-dimensional feature vectors of grid load reduction demand and system comprehensive energy efficiency into a pre-trained classification neural network model to output the potential value of air conditioning load reduction. The execution module is used to implement control strategies based on the potential for reducing air conditioning load before peak load periods.

[0037] The optimization control module specifically includes: The system extracts light intensity correlation vectors based on temporal features and automatically switches the air conditioner's operating mode. The temperature and humidity settings are dynamically adjusted based on the results of the perceived temperature analysis.

[0038] The execution module controls the airflow in the ventilation ducts by zone. Each zone is equipped with a temperature sensor and an electric regulating valve. The airflow is adjusted independently by the control device. When the temperature in a certain zone reaches the standard, the opening of the air valve in that zone is reduced to prioritize the high-demand zone.

[0039] It also includes a cloud platform monitoring module, which is used to visualize energy consumption curves, system efficiency, and fault records; Control parameters can be modified via a remote interface, supporting graphical programming and online simulation.

[0040] In the data transmission delay calculation algorithm, a dynamic management mechanism is introduced, and a waiting coefficient is used to reflect the queuing time of data packets in the network; the specific formula is as follows:

[0041] in, This indicates the latency of the device's communication connection to the detection device; Indicates to Summing each data packet; Indicates the first The size of each data packet; Indicates the first The importance of each data packet; Indicates the first Response time requirements for each data packet; Indicates the first The transmission rate of each data packet; Indicates the first The waiting factor for each data packet; Indicates the total transmission rate; Indicates infinity; This indicates the status flag of the receiving host.

[0042] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described central air conditioning adjustable electrical load control method.

[0043] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described central air conditioning adjustable electrical load control method.

[0044] Those skilled in the art will recognize that the units (or modules, steps, etc., hereinafter the same) of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0045] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0046] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0047] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for controlling adjustable electrical load in a central air conditioning system, characterized in that, Includes the following steps: Obtain the peak load periods and the power load values ​​that need to be reduced as released by the power grid dispatch center, and generate power pressure assessment values; Based on the power pressure assessment value, the charging effect is divided into high voltage level, normal level and low voltage level; For normal power pressure, time series decomposition technology is used to extract load trend components and calculate the overall load stability index. Real-time collection of central air conditioning system operating parameters, including chilled water supply and return water temperature difference, main unit power, ambient temperature and humidity, and indoor temperature and humidity. concentration; A basic distribution map of operating parameters is generated using a spatial interpolation algorithm for subsequent real-time data acquisition. By integrating power pressure assessment values ​​and overall load stability indicators, the system outputs operating power adjustment commands. With the dual objectives of reducing grid load and optimizing overall system energy efficiency, control strategies are generated, including adjusting the chilled water outlet temperature setpoint, chilled water pump frequency, and terminal air valve opening. Feature fusion technology is used to merge frequency domain energy distribution and differential sequences into a multi-dimensional feature vector of grid load reduction demand and system comprehensive energy efficiency; Input the grid load reduction demand and the multi-dimensional feature vector of the system's comprehensive energy efficiency into a pre-trained classification neural network model to output the air conditioning load reduction amount; Control strategies are implemented based on the reduction in air conditioning load before peak load periods.

2. The method for controlling adjustable electrical load in a central air conditioning system according to claim 1, characterized in that, Input variables also include indoor-outdoor temperature difference, population density, and historical energy consumption curves; The output variable is the air conditioning load reduction amount, and the output vector group is corrected by a fuzzy controller.

3. The method for controlling adjustable electrical load in a central air conditioning system according to claim 1, characterized in that, The chilled water flow rate is adjusted based on dynamic feedback of the supply and return water temperature difference, with the temperature difference threshold set at 3–5℃. Terminal air valve opening degree and The concentration change rate is linked; when the concentration change rate exceeds the limit, the fresh air volume is increased.

4. The method for controlling adjustable electrical load in a central air conditioning system according to claim 1, characterized in that, The optimal total energy consumption is calculated based on the equipment model, and the corrected energy-saving control strategy is sent to the local controller. When the actual energy consumption deviates from the predicted value by more than 10%, an abnormal alarm mechanism is triggered.

5. A central air conditioning system with adjustable electrical load control, characterized in that, include: The load forecasting module is used to obtain the peak load periods and the power load values ​​that need to be reduced as released by the power grid dispatch center, and generate power pressure assessment values. Based on the power pressure assessment value, the charging effect is divided into high voltage level, normal level and low voltage level; For normal power pressure, time series decomposition technology is used to extract load trend components and calculate the overall load stability index. The data acquisition module is used to collect real-time operating parameters of the central air conditioning system, including chilled water supply and return temperature difference, main unit power, ambient temperature and humidity, and indoor temperature and humidity. concentration; A basic distribution map of operating parameters is generated using a spatial interpolation algorithm for subsequent real-time data acquisition. The optimized control module integrates power pressure assessment values ​​and overall load stability indicators to output operating power adjustment commands. With the dual objectives of reducing grid load and optimizing overall system energy efficiency, control strategies are generated, including adjusting the chilled water outlet temperature setpoint, chilled water pump frequency, and terminal air valve opening. Feature fusion technology is used to merge frequency domain energy distribution and differential sequences into a multi-dimensional feature vector of grid load reduction demand and system comprehensive energy efficiency; Input the multi-dimensional feature vectors of grid load reduction demand and system comprehensive energy efficiency into a pre-trained classification neural network model to output the potential value of air conditioning load reduction. The execution module is used to implement control strategies based on the potential for reducing air conditioning load before peak load periods.

6. The central air conditioning adjustable electrical load control system according to claim 5, characterized in that, The optimization control module specifically includes: The system extracts light intensity correlation vectors based on temporal features and automatically switches the air conditioner's operating mode. The temperature and humidity settings are dynamically adjusted based on the results of the perceived temperature analysis.

7. The central air conditioning adjustable electrical load control system according to claim 5, characterized in that, The execution module controls the airflow in the ventilation ducts in zones. Each zone is equipped with a temperature sensor and an electric regulating valve. The airflow is adjusted independently by the control device. When the temperature in a certain zone reaches the standard, the opening of the air valve in that zone is reduced to prioritize the high-demand zone.

8. The central air conditioning adjustable electrical load control system according to claim 5, characterized in that, It also includes a cloud platform monitoring module, which is used to visualize energy consumption curves, system efficiency, and fault records; Control parameters can be modified via a remote interface, supporting graphical programming and online simulation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the central air conditioning adjustable electrical load control method according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the central air conditioning adjustable electrical load control method according to any one of claims 1 to 4.

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