Air conditioner adjustable resource aggregation and control method and system

Abstract

The invention discloses an air conditioner adjustable resource aggregation and control method and system, and the system comprises a smart energy unit, an air conditioner management module and an air conditioner control module communication, the smart energy unit is used as basic equipment, is connected with the air conditioner management module through an RS485 serial port line, collects the environment temperature and humidity of the air conditioner management module, communicates with the air conditioner control module through 5G / RS-485, and is connected with the air conditioner control module through an RS485 serial port line. The air conditioner control module is in butt joint with a host remote control terminal to achieve air conditioner load bottom guarantee control under the power grid emergency state, the air conditioner load in a transformer area is monitored in real time through a measuring unit or a user general meter, data information is gathered into the intelligent energy unit, and the algorithm module integrates power supply power of a power grid through a resource aggregation algorithm. According to the method, the adjustable capacity of the air conditioner is calculated and issued, flexible regulation and control of the air conditioner are achieved, dependence on traditional energy is reduced, interaction with an intelligent power grid is achieved to optimize energy distribution and relieve power grid pressure, good compatibility and expansibility are achieved, integration with other intelligent devices is facilitated, and comfortable, energy-saving, environment-friendly and reliable air conditioner use experience is provided for users.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy regulation, and particularly relates to an adjustable resource aggregation and control method and system for air conditioners. Background Art

[0002] With the continuous growth of global energy demand and the increasingly severe environmental problems, improving energy utilization efficiency and achieving sustainable development have become top priorities. In the field of air conditioners, traditional air conditioning systems often suffer from problems such as energy waste, low operating efficiency, and lack of intelligent control. On the one hand, air conditioning systems consume a large amount of energy during operation, especially during peak electricity consumption periods, which brings a huge pressure to the power grid. At the same time, traditional air conditioning systems usually cannot dynamically adjust energy consumption according to actual needs, resulting in unnecessary energy waste. On the other hand, most existing air conditioning systems adopt simple methods such as temperature setting and timing switches in control, lacking precise perception and intelligent control of the indoor environment. This not only affects the comfort experience of users but also makes it difficult to achieve efficient energy utilization.

[0003] Currently, many air conditioning systems can only operate based on simple temperature settings or timing switches and cannot dynamically adjust according to actual situations, resulting in energy waste. Moreover, although some existing intelligent air conditioners have certain intelligent control functions, they may be insufficient in terms of personalization and accuracy. In recent years, although the source-end optimization control of central air conditioners has become more and more mature, the lack of management and control means at the air conditioner end has become a serious problem. The air conditioner end includes equipment such as fan coils and air handling units, which directly affect the comfort of the indoor environment. If effective management and control are lacking, it will pose a huge obstacle to the overall energy efficiency improvement of the central air conditioning system and the control of environmental comfort. Therefore, the overall perception and management of the air conditioner end are the key to the future intelligent improvement of air conditioners. Summary of the Invention

[0004] The present invention aims to solve various technical problems. First is the problem of energy utilization efficiency. Traditional air conditioners cannot dynamically adjust energy consumption according to demand, resulting in energy waste. The invention needs to achieve precise energy distribution, automatically adjust power according to factors such as environment, personnel, and grid load, and improve energy efficiency. Second is the problem of intelligent control. Existing air conditioner controls are single and lack personalization. This invention aims to achieve intelligent control, automatically adjust parameters according to user needs, provide a comfortable environment, and at the same time achieve remote and automated control to improve convenience. Third is the problem of resource aggregation. Currently, air conditioner resources are scattered, and it is necessary to effectively aggregate resources such as electricity and air, integrate renewable energy, achieve multi-device collaboration, and improve system efficiency. Finally, there are the problems of environmental comfort control and system reliability. It is necessary to solve the deficiencies of traditional air conditioners in environmental control, precisely perceive and control temperature, humidity, air quality, etc., and at the same time improve system reliability and stability through fault warning diagnosis and extend the service life of equipment.

[0005] To achieve the above object, the technical solution of the present invention is as follows: A control system for an air conditioner adjustable resource aggregation and control method includes a smart energy unit, an air conditioner management module, and an air conditioner control module for communication. Based on the smart energy unit as the basic device, it is connected to the air conditioner management module through an RS485 serial cable to collect the ambient temperature and humidity of the air conditioner management module, and communicate with the air conditioner control module through 5G / RS-485. The air conditioner control module realizes the guaranteed control of the air conditioner load under the emergency state of the power grid by docking the remote control terminal of the host, monitors the air conditioner load in the substation area in real time through the measurement unit or the user master meter, converges the data information into the smart energy unit, and its algorithm module uses the resource aggregation algorithm to integrate the power supply power of the power grid, calculates the adjustable capacity of the air conditioner and issues it, realizing the flexible control of the air conditioner.

[0006] As an improvement of the present invention, the load management system master station issues the configuration parameters of the flexible control unit through 5G or radio, which are used for the temperature and humidity, power, voltage, current of the air conditioner, including the measurement point number, communication rate and port number, communication address, user major category number and user minor category number of the air conditioner. Among them, in order to ensure the uniqueness of the air conditioner host address in the master station system, the 12-bit communication address is composed of the lower 10 bits of the household number + 2 bits of the host address. The host address starts from 01 and is used for the MODBUS communication address of the air conditioner.

[0007] As an improvement of the present invention, the master station automatically generates the total addition group number of the air conditioner regulation operation parameters and the association relationship with the host unit according to the established rules. The rules include the generation rule of the measurement point: the measurement point number is based on the device serial number and is obtained by adding on this basis; the generation rule of the air conditioner total addition group number is as follows: the default value of the total addition group number is set to 8, and the measured point numbers under its jurisdiction are default to the measurement point numbers of the air conditioner measurement unit. In addition, regarding the range of the measured point numbers to be total added, it has been clearly stipulated that it should be restricted within the range of 5 to 20.

[0008] As an improvement of the present invention, the acquisition module of the smart energy unit collects data such as the operation mode, switch state, and limit current ratio of the central air conditioner host based on the Modbus protocol according to the parameters issued by the master station, collects the ambient temperature and humidity of the temperature and humidity sensor, and collects data such as voltage, current, power, and switch state of the measurement unit, and uploads them to the data center.

[0009] As an improvement of the present invention, the intelligent energy unit algorithm module analyzes the adjustable capacity of the air-conditioning load according to data such as power. The adjustable capacity of the air-conditioning load is divided into two levels. The high-level adjustable capacity is applicable to the minute-level demand response service, the adjustment process duration is less than 5 minutes, and the sustainable duration is not more than 1 hour. The low-level adjustable capacity is applicable to the energy-saving operation scenario of air conditioners under the condition of orderly power consumption, the sustainable duration exceeds 2 hours, and there may be multiple time periods within a day. The intelligent energy unit calculates the load adjustable capacities of the user-side air-conditioning equipment corresponding to the above two levels respectively through an algorithm model based on the air-conditioning load data, the set temperature of the air conditioner, the environmental temperature data, and other operating states and parameters.

[0010] As an improvement of the present invention, for directly controllable air-conditioning equipment, by docking with service applications such as demand response, it supports the flexible regulation of the air-conditioning load. The intelligent energy unit control module generates equipment control instructions based on the received demand response signal and the pre-set strategy in the unit, or the received air-conditioning operation parameter control command, and performs flexible load regulation on the air-conditioning equipment. According to the content of the demand response signal issued by the master station, select a suitable demand response strategy, and generate a control instruction set according to the communication protocol supported by the air-conditioning equipment / system or the air-conditioning control unit, including equipment start / stop instructions, air-conditioning temperature setting instructions, circulating water temperature setting instructions for central air-conditioning units, etc. At the same time, according to the monitored changes in the operating load of the equipment, changes in the operating state, and other information fed back on-site, dynamically adjust the strategy and continue to issue instructions to achieve a closed-loop demand response control. According to the effective time range in the demand response signal or the air-conditioning control instruction and the requirements for restoring the operating parameters of the equipment, after the flexible load control ends, restore the operating parameters of the air-conditioning equipment.

[0011] As an improvement of the present invention, a method for aggregating and controlling adjustable air-conditioning resources implemented by the system includes the following steps:

[0012] Step 1, the intelligent energy unit monitors the operating state and energy demand of the air conditioner in real time, combines the external energy supply situation, dynamically adjusts the power distribution according to the established energy demand prediction model, predicts the air-conditioning energy demand in the next period of time based on historical data and current environmental parameters, and conducts energy allocation in advance;

[0013] Step 2, automatically adjust the operating mode and parameters of the air conditioner according to the indoor sensor data, and at the same time establish an air-conditioning performance model, and continuously optimize the control parameters to adapt to different climate conditions and usage scenarios;

[0014] Step 3: Use machine learning algorithms to predict the user's usage habits and the changing trends of the indoor environment. On the one hand, analyze historical data to establish user behavior models and environmental change models, and predict future air-conditioning demands and indoor environmental states. On the other hand, according to the prediction results, start or adjust the operation of the air conditioner in advance.

[0015] As an improvement of the present invention, in step 1, according to P total =P solar +P grid +P backup dynamically adjust the power distribution, where P total represents the total power of the air conditioner, P solar represents the solar power supply, P grid represents the power grid power supply, P backup represents the backup power supply.

[0016] As an improvement of the present invention, the temperature control formula in step 2: T set =T ambient +K×(E desired -E actual ), where T set represents the set temperature, T ambient represents the environmental temperature, K represents the control coefficient, E desired represents the desired comfort index, and E actual represents the actual comfort index.

[0017] As an improvement of the present invention, the prediction model formula in step 3: Y(t + △t)=f(X(t), θ), where Y(t + △t) represents the system state at a future moment, X(t) represents the system input at the current moment, θ represents the model parameters, and f represents the prediction function, so as to adjust the operation state of the air conditioner in advance.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: An air-conditioning adjustable resource aggregation and control method and system have multiple beneficial effects. In terms of energy utilization, it can dynamically adjust the operation parameters of the air conditioner to achieve high energy efficiency, integrate renewable energy, reduce the dependence on traditional energy, and interact with the smart grid to optimize energy distribution and relieve the grid pressure. In terms of intelligent control, it provides a personalized comfort experience, adjusts the temperature, wind speed, etc. according to user preferences and environmental changes, and has self-learning and self-adaptive capabilities. Remote control and monitoring also facilitate users to adjust and understand the state of the air conditioner at any time. In terms of environment and health, it can optimize air quality, precisely control humidity, and reduce the noise level. In terms of system reliability, it has a fault warning and diagnosis function, and the redundant design improves the reliability of key components. Good compatibility and scalability facilitate integration with other intelligent devices, providing users with a comfortable, energy-saving, environmentally friendly and reliable air-conditioning usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a system architecture diagram of the present invention;

[0020] Figure 2 This is a flow chart of step D of a specific system architecture implementation scheme of the present invention;

[0021] Figure 3 This is a flowchart of step E of the specific system architecture implementation plan of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0023] Embodiment: This embodiment introduces a method and system for aggregating and controlling adjustable resources of an air conditioner. The system can regulate the parameters of each module of the air conditioner in the most efficient operation mode, so as to achieve the best balance between the comfort demand and the energy saving demand. At the same time, the system realizes intelligent control, thereby effectively aggregating renewable energy and improving system efficiency.

[0024] The specific system architecture of this embodiment is as follows: a method and system for aggregating and controlling adjustable resources of an air conditioner, which uses a smart energy unit as the basic device, is connected to the air conditioner management module via an RS485 serial port line, collects the ambient temperature and humidity of the air conditioner management module, and communicates with the air conditioner control module via 5G / RS-485. The air conditioner control module achieves minimum control of the air conditioner load under emergency conditions of the power grid by docking with the host remote control terminal; at the same time, the air conditioner load in the substation is monitored in real time through the measurement unit or the user's total meter, and the data information is aggregated into the smart energy unit. Its algorithm module uses the resource aggregation algorithm to integrate the power supply of the power grid, calculates the adjustable capacity of the air conditioner and sends it down, so as to achieve flexible control of the air conditioner. In addition, the smart energy unit can use a radio or 5G module to inform the user of the adjustment results, as shown in the attached Figure 1 The system architecture diagram is described.

[0025] Specific system architecture implementation plan of this embodiment:

[0026] A. The new load management system master station sends the flexible control unit configuration parameters through 5G or radio, which are used for the temperature and humidity, power, voltage, and current of the air conditioner, including the air conditioner's measurement point number, communication rate and port number, communication address, user category number, and user category number. In order to ensure that the air conditioner host address master station system is unique, the 12-bit communication address consists of the lower 10 bits of the user number + 2 bits of the host address. The host address starts from 01 and is used for the MODBUS communication address of the air conditioner.

[0027] B. The master station automatically generates the total addition group number for air conditioner regulation and the association relationship between the main unit and the host unit according to the established rules as follows. Generation rule for measurement points: The measurement point number is based on the device serial number and is obtained by adding 4 to it. For example, if the device serial number of the air conditioner host is 21, then the corresponding measurement point number is the device serial number plus 4, that is, 21 + 4 = 25. Generation rule for the total addition group number of the air conditioner is as follows: The default value of the total addition group number is set to 8, and the measured point numbers to be total added under it default to the measurement point numbers of the air conditioning measurement unit. In addition, regarding the range of the measured point numbers to be total added, it has been clearly stipulated that it should be restricted within the interval of 5 to 20.

[0028] C. The intelligent energy unit acquisition module collects data such as the operation mode, switch status, and limit current ratio of the central air conditioner host based on the parameters issued by the master station according to the Modbus protocol, collects the ambient temperature and humidity of the temperature and humidity sensors, and collects data such as voltage, current, power, and switch status of the measurement unit, and uploads them to the data center.

[0029] D. The intelligent energy unit algorithm module analyzes the adjustable capacity of the air conditioner load based on data such as power. The adjustable capacity of the air conditioner load is divided into two levels. The high-level adjustable capacity is applicable to the minute-level demand response service, the adjustment process duration is less than 5 minutes, and the sustainable duration is not more than 1 hour; the low-level adjustable capacity is applicable to the energy-saving operation scenario of the air conditioner under the condition of orderly power use, the sustainable duration exceeds 2 hours, and there may be multiple time periods in a day. The intelligent energy unit calculates the load adjustable capacity of the user-side air conditioner equipment corresponding to the above two levels through the algorithm model based on the air conditioner load data, the air conditioner set temperature and ambient temperature data, and other operation states and parameters. The specific process is shown in the appendix Figure 2 。

[0030] E. For directly controllable air conditioner equipment, by docking with business applications such as demand response, it supports the flexible regulation of the air conditioner load; the intelligent energy unit control module generates device control instructions based on the received demand response signal and the preset strategy in the unit, or the received air conditioner operation parameter control command, and conducts flexible load regulation on the air conditioner equipment. According to the content of the demand response signal issued by the master station, select the appropriate demand response strategy, and generate a control instruction set according to the communication protocol supported by the air conditioner equipment / system or the air conditioner control unit, including device start / stop instructions, air conditioner temperature setting instructions, circulating water temperature setting instructions for the central air conditioner unit, etc. At the same time, according to the monitored changes in the operation load and operation state of the equipment and other information feedback on-site, dynamically adjust the strategy and continue to issue instructions to achieve a closed-loop demand response control. According to the effective time range in the demand response signal or the air conditioner control instruction and the requirement for restoring the operation parameters of the equipment, after the flexible load control ends, restore the operation parameters of the air conditioner equipment. The specific process is shown in the appendix Figure 3 。

[0031] Specific control method of this embodiment:

[0032] I. Dynamic energy distribution algorithm

[0033] The intelligent energy unit monitors the operating status and energy demand of the air conditioner in real time, combines the external energy supply situation (such as grid load, electricity price fluctuations, etc.), and according to P total =P solar +P grid +P backup dynamically adjusts the power distribution, where P total represents the total power of the air conditioner, P solar represents the solar power supply, P grid represents the grid power supply, P backup represents the standby power supply. Establish an energy demand prediction model, predict the air conditioner's energy demand in the next period of time based on historical data and current environmental parameters, and carry out energy allocation in advance. Consider the priority and cost-effectiveness of different energy sources, such as preferentially using renewable energy (such as solar energy, wind energy, etc.), and switching to traditional energy when renewable energy is insufficient.

[0034] II. Adaptive control algorithm

[0035] According to the sensor data such as indoor temperature, humidity, air quality, etc., automatically adjust the operating mode and parameters of the air conditioner to achieve a comfortable indoor environment. At the same time, establish an air conditioner performance model, and continuously optimize the control parameters to improve the energy efficiency ratio and stability of the air conditioner to adapt to different climate conditions and usage scenarios, such as summer cooling, winter heating, ventilation in transitional seasons, etc.

[0036] Temperature control formula: T set =T ambient +K×(E desired -E actual ), where T set represents the set temperature, T ambient represents the ambient temperature, K represents the control coefficient, E desired represents the desired comfort index, E actual represents the actual comfort index. Humidity control is the same.

[0037] III. Predictive control algorithm

[0038] Using machine learning algorithms, predict the user's usage habits and the changing trends of the indoor environment. The prediction model formula is: Y(t+△t) = f(X(t),θ), where Y(t+△t) represents the system state at a future time (such as temperature, humidity, etc.), X(t) represents the system input at the current time (such as environmental parameters, user behavior, etc.), θ represents the model parameters, and f represents the prediction function, so as to adjust the operating state of the air conditioner in advance, improve the response speed of the system and the energy-saving effect. On the one hand, analyze historical data to establish a user behavior model and an environmental change model, and predict the future air conditioner demand and indoor environmental state. On the other hand, according to the prediction results, start or adjust the operation of the air conditioner in advance to avoid unnecessary energy waste.

[0039] The innovation of the present invention lies in that by combining with the intelligent energy unit, renewable energy such as solar energy and wind energy can be fully utilized, reducing the dependence on traditional energy and lowering carbon emissions. At the same time, accurately regulate resource allocation according to actual needs to ensure the stable operation of the system and energy conservation and consumption reduction. For example, dynamically adjust the air conditioner parameters according to the indoor and outdoor environment and the personnel situation to achieve the optimal allocation of resources. At the same time, the system can comprehensively consider multiple parameters such as temperature, humidity, and energy consumption for coordinated control to find the best balance between comfort and energy conservation. It has an adaptive learning function, can predict demand based on user habits and environmental historical data, and provide a personalized comfortable experience. Moreover, it has a good response to the uncertainty on the demand side, manages and predicts user response behavior through advanced algorithms, optimizes the user selection strategy, reduces costs and user fatigue, and improves the reliability and stability of the system. Finally, the system architecture is flexible and highly compatible. It can adapt to air conditioner system environments of different scales and can also be seamlessly docked with other intelligent systems, such as the smart grid, to provide an integrated intelligent solution.

[0040] It should be noted that the above content only illustrates the technical idea of the present invention and cannot be used to limit the protection scope of the present invention. For those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements all fall within the protection scope of the claims of the present invention.

Claims

1. A system for aggregating and controlling adjustable air conditioning resources, characterized in that: It includes communication among smart energy unit, air conditioning management module and air conditioning control module. The smart energy unit is used as the basic equipment. It is connected to the air conditioning management module through RS485 serial port line to collect the ambient temperature and humidity of the air conditioning management module. It communicates with the air conditioning control module through 5G / RS-485. The air conditioning control module realizes the minimum control of air conditioning load in emergency state of power grid by docking with the host remote control terminal. The air conditioning load in the substation is monitored in real time through the measurement unit or the user's total meter, and the data information is aggregated to the smart energy unit. The algorithm module uses the resource aggregation algorithm to integrate the power supply of the power grid, calculates the adjustable capacity of the air conditioner and sends it down, so as to realize flexible regulation of the air conditioner.

2. The system for aggregating and controlling adjustable air conditioning resources according to claim 1, characterized in that: The load management system master station sends flexible control unit configuration parameters through 5G or radio, which are used for the temperature and humidity, power, voltage, and current of the air conditioner, including the measurement point number, communication rate and port number, communication address, user major category number, and user minor category number of the air conditioner. In order to ensure that the air conditioner host address master station system is unique, the 12-bit communication address consists of the lower 10 bits of the user number + 2 bits of the host address. The host address starts from 01 and is used for the MODBUS communication address of the air conditioner.

3. The system for aggregating and controlling adjustable air conditioning resources according to claim 2, characterized in that: The master station automatically generates the air conditioning control total group number of the operating parameters and the association relationship between the host unit according to the established rules. The rules include the generation rules of the measurement points: the measurement point number is based on the device serial number and is obtained by adding it on the basis of the device serial number; the generation rules of the air conditioning total group number are as follows: the default value of the air conditioning total group number is set, and the total measurement point number under it defaults to the measurement point number of the air conditioning measurement unit.

4. The system for aggregating and controlling adjustable air conditioning resources according to claim 3, characterized in that: According to the parameters sent by the master station, the smart energy unit acquisition module collects the operating mode, switch status, and current limit ratio data of the central air-conditioning host based on the Modbus protocol, collects the ambient temperature and humidity of the temperature and humidity sensor, and collects the voltage, current, power, and switch status data of the measurement unit, and sends them to the data center.

5. The system for aggregating and controlling adjustable air conditioning resources according to claim 4, characterized in that: The smart energy unit algorithm module analyzes the adjustable capacity of the air-conditioning load based on the collected data. The adjustable capacity of the air-conditioning load is divided into two levels. The high-level adjustable capacity is suitable for minute-level demand response services; the low-level adjustable capacity is suitable for air-conditioning energy-saving operation scenarios under orderly power consumption conditions. The smart energy unit calculates the load adjustable capacity of the user-side air-conditioning equipment corresponding to the above two levels through the algorithm model based on the air-conditioning load data, air-conditioning set temperature and ambient temperature data, as well as other operating conditions and parameters.

6. The system for aggregating and controlling adjustable air conditioning resources according to claim 5, characterized in that: For directly controllable air-conditioning equipment, it supports flexible regulation of air-conditioning load by connecting with business applications; the smart energy unit control module generates equipment control instructions based on the received demand response signal and the preset strategy in the unit, or the received air-conditioning operation parameter control command, and performs load flexible regulation on the air-conditioning equipment. According to the content of the demand response signal sent by the master station, the appropriate demand response strategy is selected, and the control instruction set is generated according to the communication protocol supported by the air-conditioning equipment / system or air-conditioning control unit. At the same time, according to the monitored changes in equipment operating load, operating status changes and other information fed back from the site, the strategy is dynamically adjusted, and instructions are continued to be issued to realize the demand response control closed loop. According to the effective time range and equipment operating parameter recovery requirements in the demand response signal or air-conditioning control instruction, the operating parameters of the air-conditioning equipment are restored after the load flexible control ends.

7. A method for aggregating and controlling adjustable air conditioning resources implemented by the system according to any one of claims 1 to 6, characterized in that: The steps include: Step 1: The smart energy unit monitors the operating status and energy demand of the air conditioner in real time, dynamically adjusts the power distribution based on the external energy supply, establishes an energy demand prediction model, predicts the air conditioner energy demand in the future based on historical data and current environmental parameters, and allocates energy in advance; Step 2: Automatically adjust the air conditioner's operating mode and parameters based on indoor sensor data, and establish an air conditioner performance model to continuously optimize control parameters to adapt to different climate conditions and usage scenarios; Step three: Use machine learning algorithms to predict user usage habits and indoor environmental change trends. On the one hand, analyze historical data, establish user behavior models and environmental change models, and predict future air conditioning needs and indoor environmental conditions. On the other hand, start or adjust air conditioning operation in advance based on the prediction results.

8. The method for aggregating and controlling adjustable air conditioning resources according to claim 7, characterized in that: In the step 1, according to P total =P solar +P grid +P backup Dynamically adjust power distribution, where P total Indicates the total power of the air conditioner, P solar Represents solar power supply power, P grid Represents the power supply of the power grid, P backup Indicates the backup power supply power.

9. The method for aggregating and controlling adjustable air conditioning resources according to claim 7, characterized in that: The temperature control formula in step 2 is: T set =T ambient +K×(E desired -E actual ), where T set Indicates the set temperature, T ambient represents the ambient temperature, K represents the control coefficient, E desired represents the expected comfort index, E actual Indicates the actual comfort index.

10. The method for aggregating and controlling adjustable air conditioning resources according to claim 7, characterized in that: The prediction model formula in step three is: Y(t+△t)=f(X(t),θ), where Y(t+△t) represents the system state at the future moment, X(t) represents the system input at the current moment, θ represents the model parameter, and f represents the prediction function, so as to adjust the operating state of the air conditioner in advance.

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