Method and device for determining air conditioning load aggregate regulation parameters, and electronic device

Abstract

The application discloses a kind of air conditioner load aggregate regulation parameter determination method, device and electronic equipment. Among them, the method comprises: obtaining the operating state parameter of air conditioner and the environment condition parameter of the environment where air conditioner is located;Based on operating state parameter and environment condition parameter, the load equivalent heat parameter model of air conditioner is constructed;Based on the preset comfort temperature range and the load equivalent heat parameter model of air conditioner, the load aggregate model of air conditioner is constructed, wherein the preset comfort temperature range is used to indicate the comfort temperature range of user preset;Based on load aggregate model, determine the regulation parameter of air conditioner, wherein the regulation parameter is used to represent the regulation potential of the load aggregate of air conditioner.The present application solves the technical problems of low accuracy and efficiency in determining the regulation parameter of air conditioner load aggregate in related technologies.

Description

Technical Field

[0001] This invention relates to the field of air conditioning load aggregation, and more specifically, to a method, apparatus, and electronic device for determining the control parameters of air conditioning load aggregation. Background Technology

[0002] With the development of new power system technologies and the continuous increase in the proportion of air conditioning load in the total power load, traditional power management and demand response strategies are facing severe challenges. Air conditioning load is affected by various factors such as ambient temperature and compressor operating status, making aggregated modeling of air conditioning load a complex and difficult task.

[0003] In related technologies, methods for aggregating and assessing the adjustability potential of central air conditioning loads typically involve establishing an air conditioning load model to evaluate the total amount, characteristics, and adjustability potential of the load. For example, by establishing a first-order equivalent thermal parameter model, a mechanistic model of a single air-conditioned room is obtained, and then an air conditioning cluster control strategy is established. Simulation experiments are conducted on the air conditioning cluster to obtain the controllable proportional coefficient and actual demand response of the air conditioning load. However, the central air conditioning load aggregation model established in this way is often quite coarse and difficult to accurately reflect the complexity and diversity of the actual system. The accuracy of air conditioning load aggregation is low, and different users have different needs and preferences for temperature-controlled loads such as air conditioning, making it difficult to meet users' personalized needs. This results in low accuracy and efficiency in determining the control parameters of the air conditioning load aggregation in related technologies.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for determining the control parameters of an air conditioning load aggregate, in order to at least solve the technical problems of low accuracy and efficiency in determining the control parameters of an air conditioning load aggregate in related technologies.

[0006] According to one aspect of the embodiments of this application, a method for determining the control parameters of an air conditioning load aggregation is provided, comprising: acquiring the operating status parameters of the air conditioner and the environmental condition parameters of the environment in which the air conditioner is located; constructing a load equivalent thermal parameter model of the air conditioner based on the operating status parameters and the environmental condition parameters; constructing a load aggregation model of the air conditioner based on a preset comfort temperature range and the load equivalent thermal parameter model, wherein the preset comfort temperature range is used to represent a pre-set comfort temperature range for the user; and determining the control parameters of the air conditioner based on the load aggregation model, wherein the control parameters are used to characterize the control potential of the air conditioner load aggregation.

[0007] Optionally, based on operating status parameters and environmental condition parameters, an equivalent thermal parameter model of the air conditioner load is constructed, including: based on operating status parameters and environmental condition parameters, constructing the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation of the air conditioner; and based on the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation, constructing an equivalent thermal parameter model of the load.

[0008] Optionally, based on operating status parameters and environmental condition parameters, a relationship between room temperature and cooling capacity of the air conditioner is constructed, including: determining the differential equation of the equivalent thermal parameter model of the air conditioner based on the cooling capacity parameter in the operating status parameters, the indoor parameter in the environmental condition parameters, and the outdoor parameter in the environmental condition parameters; and discretizing the differential equation of the equivalent thermal parameter model to obtain the relationship between room temperature and cooling capacity.

[0009] Optionally, based on a preset comfort temperature range and a load equivalent thermal parameter model, a load aggregation model for the air conditioner is constructed, including: determining the state of charge parameters based on the preset comfort temperature range and the load equivalent thermal parameter model; determining the relationship between the power and state of charge of the air conditioner based on the state of charge parameters and the operating state parameters; and constructing the load aggregation model based on the relationship between the power and state of charge.

[0010] Optionally, based on a preset comfort temperature range and a load equivalent thermal parameter model, the state of charge parameters are determined, including: determining the control quantity parameters and control capacity parameters of the air conditioner based on the preset comfort temperature range and the load equivalent thermal parameter model; and determining the state of charge parameters based on the ratio of the control quantity parameters and the control capacity parameters.

[0011] Optionally, the control parameters of the air conditioner are determined based on the load aggregation model, including: constructing an aggregate parameter identification objective function for the air conditioner based on the load aggregation model, wherein the aggregate parameter identification objective function is used to represent the objective function constructed based on the error between the actual data and the predicted data of the control parameters, and the predicted data is used to represent the data obtained by predicting the control parameters based on the load aggregation model; and searching the aggregate parameter identification objective function based on a preset biomimetic optimization algorithm to obtain the control parameters.

[0012] According to another aspect of the present invention, an apparatus for determining the control parameters of an air conditioning load aggregation is also provided, comprising: an acquisition module for acquiring operating state parameters of the air conditioner and environmental condition parameters of the environment in which the air conditioner is located; a first construction module for constructing a load equivalent thermal parameter model of the air conditioner based on the operating state parameters and environmental condition parameters; a second construction module for constructing a load aggregation model of the air conditioner based on a preset comfort temperature range and the load equivalent thermal parameter model, wherein the preset comfort temperature range represents a pre-set comfort temperature range for the user; and a determination module for determining the control parameters of the air conditioner based on the load aggregation model, wherein the control parameters characterize the control potential of the load aggregation of the air conditioner.

[0013] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0015] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0018] In this embodiment of the invention, a method for determining the control parameters of an air conditioning load aggregation is provided, comprising: acquiring the operating status parameters of the air conditioner and the environmental condition parameters of the environment in which the air conditioner is located; constructing a load equivalent thermal parameter model of the air conditioner based on the operating status parameters and the environmental condition parameters; constructing a load aggregation model of the air conditioner based on a preset comfort temperature range and the load equivalent thermal parameter model, wherein the preset comfort temperature range is used to represent a pre-set comfort temperature range for the user; and determining the control parameters of the air conditioner based on the load aggregation model, wherein the control parameters are used to characterize the control potential of the air conditioner load aggregation. It is noteworthy that by acquiring the operating status parameters and environmental condition parameters of the air conditioner, constructing a load equivalent thermal parameter model, and combining it with a preset comfort temperature range to construct a load aggregation model, this application can more accurately assess the adjustable potential of the air conditioning system. Considering that different users have different needs and preferences for temperature-controlled loads, this application, by using a preset comfort temperature range, can set a suitable temperature range according to the user's personalized needs, thereby improving the user's comfort experience. By acquiring the adjustable potential of the air conditioning load aggregation while taking into account user comfort, it is possible to better meet the personalized needs of users and increase user satisfaction. It can effectively improve the intelligent control capability of air conditioning systems, meet users' personalized needs, optimize energy utilization efficiency, improve system flexibility and response speed, and thus solve the technical problems of low accuracy and efficiency in determining the control parameters of air conditioning load aggregates in related technologies. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0020] Figure 1 This is a flowchart of a method for determining air conditioning load aggregation control parameters according to an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a search step for an aggregate parameter identification target function according to an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of a device for determining air conditioning load aggregation control parameters according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] According to an embodiment of the present invention, an embodiment of a method for determining the control parameters of an air conditioning load aggregate is provided. It can be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] Figure 1 This is a flowchart of a method for determining air conditioning load aggregation control parameters according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0027] Step S102: Obtain the operating status parameters of the air conditioner and the environmental condition parameters of the environment where the air conditioner is located.

[0028] In one optional embodiment, the operating status parameters and environmental condition parameters of the air conditioner can be obtained through real-time monitoring and data acquisition of the air conditioning system and its surrounding environment using various sensors and monitoring devices. Specifically, for the operating status parameters of the air conditioner, sensors installed on the air conditioning system can monitor its operating status in real time. The sensors can collect data periodically and transmit the data to the data acquisition system via a communication module. Environmental condition parameters may include parameters such as indoor and outdoor temperatures, solar radiation intensity, wind direction, and wind speed in the environment where the air conditioner is located. Environmental condition parameters can be monitored in real time by sensors installed inside and outside the building, or obtained through equipment such as weather stations. The data collected by these sensors can provide a more accurate understanding of the environmental conditions of the air conditioner. By accurately obtaining the operating status parameters and environmental condition parameters of the air conditioner, accurate data support can be provided for determining the subsequent air conditioning load aggregation control parameters. Accurately obtaining the operating status parameters and environmental condition parameters of the air conditioner can help the system better regulate the air conditioning load, achieve efficient energy utilization and conservation, and provide an important data foundation for determining the air conditioning load aggregation control parameters, thereby realizing intelligent control and optimized regulation of the air conditioning system, improving energy efficiency and user comfort.

[0029] Step S104: Based on the operating status parameters and environmental condition parameters, construct the load equivalent thermal parameter model of the air conditioner.

[0030] In one optional embodiment, the obtained operating status parameters and environmental condition parameters of the air conditioner can be processed and cleaned to ensure the accuracy and integrity of the data. When determining the load equivalent thermal parameter model of the air conditioner, appropriate parameters can be selected according to the actual situation, and a mathematical model can be established to describe the heat load characteristics of the air conditioning system. The model can be established using heat transfer principles and thermodynamic equations, incorporating operating status parameters and environmental condition parameters. After the model is established, it can be verified and adjusted. By comparing and analyzing with actual data, the accuracy and reliability of the model can be verified. If there are deviations or errors in the model, adjustments and optimizations can be made to improve the model's fit and prediction accuracy. In the above process, by constructing a load equivalent thermal parameter model, the heat load characteristics of the air conditioning system can be described more accurately, improving the system's control accuracy and intelligence level. An accurate load equivalent thermal parameter model can help the system more effectively regulate the air conditioning load, achieve optimized energy utilization and conservation, reduce energy costs, provide a scientific basis for determining the control parameters of the air conditioning load aggregate, realize intelligent control and energy utilization optimization of the air conditioning system, and further improve the system's performance and efficiency.

[0031] Step S106: Based on the preset comfort temperature range and the load equivalent thermal parameter model, construct the load aggregation model of the air conditioner.

[0032] The preset comfort temperature range is used to indicate the pre-set comfort temperature range for the user.

[0033] In one optional embodiment, a user comfort temperature range can be preset based on the user's comfort needs and standards. This range can be comprehensively considered based on factors such as indoor and outdoor temperature, humidity, and season to ensure that the user feels comfortable under different environmental conditions. Next, the preset comfort temperature range can be combined with an established load equivalent thermal parameter model. The load equivalent thermal parameter model describes the heat load characteristics of the air conditioning system, including parameters such as heat conduction and heat radiation, while the preset comfort temperature range describes the user's comfort requirements. Based on the preset comfort temperature range and the load equivalent thermal parameter model, a load aggregation model for the air conditioning system is constructed. The load aggregation model comprehensively considers the user's comfort needs and the heat load characteristics of the air conditioning system to achieve regulation of the air conditioning system while ensuring user comfort. In the above process, by presetting the comfort temperature range, a suitable temperature range can be set according to the user's needs, improving the user's comfort experience and making the user feel more comfortable in the air-conditioned environment. Combining the load equivalent thermal parameter model and user comfort needs to construct the load aggregation model for the air conditioning system enables precise regulation of the air conditioning system. The system can adjust the air conditioning operating mode and parameters according to user needs and heat load characteristics, improve system operating efficiency, effectively balance user comfort and air conditioning system energy utilization efficiency, and improve system performance and user experience.

[0034] Step S108: Determine the control parameters of the air conditioner based on the load aggregation model.

[0035] Among them, the control parameters are used to characterize the control potential of the load aggregation of air conditioning.

[0036] In one optional embodiment, based on the constructed load aggregation model, optimization algorithms, such as genetic algorithms and particle swarm optimization algorithms, can be used to optimize and adjust the parameters in the model. These parameters include air conditioning operating modes, fan speed, temperature settings, etc., and can be adjusted according to the model output results and user comfort requirements. The control parameters of the air conditioning system can be determined based on the optimized load aggregation model. These control parameters can include switching air conditioning operating modes, adjusting temperature setpoints, and controlling fan speed to achieve precise control of the air conditioning load. Then, the determined control parameters can be applied to the actual air conditioning system, and the control parameters can be continuously adjusted and optimized based on real-time monitoring of the air conditioning operating status and environmental parameters. Through real-time monitoring and feedback information, dynamic adjustment of the air conditioning system can be achieved, maintaining the system in a good operating state. Furthermore, the control potential of the air conditioning system can be evaluated through the optimization and real-time adjustment of the control parameters. The control potential reflects the air conditioning system's ability to achieve efficient energy utilization and conservation while meeting user comfort requirements. In the above process, the control parameters are determined based on the load aggregation model to realize intelligent control of the air conditioning system, improve the system's response speed and accuracy, achieve precise control of the air conditioning system, improve system performance and energy utilization efficiency, and ensure user comfort.

[0037] In this embodiment of the invention, a method for determining the control parameters of an air conditioning load aggregation is provided, comprising: acquiring the operating status parameters of the air conditioner and the environmental condition parameters of the environment in which the air conditioner is located; constructing a load equivalent thermal parameter model of the air conditioner based on the operating status parameters and the environmental condition parameters; constructing a load aggregation model of the air conditioner based on a preset comfort temperature range and the load equivalent thermal parameter model, wherein the preset comfort temperature range is used to represent a pre-set comfort temperature range for the user; and determining the control parameters of the air conditioner based on the load aggregation model, wherein the control parameters are used to characterize the control potential of the air conditioner load aggregation. It is noteworthy that by acquiring the operating status parameters and environmental condition parameters of the air conditioner, constructing a load equivalent thermal parameter model, and combining it with a preset comfort temperature range to construct a load aggregation model, this application can more accurately assess the adjustable potential of the air conditioning system. Considering that different users have different needs and preferences for temperature-controlled loads, this application, by using a preset comfort temperature range, can set a suitable temperature range according to the user's personalized needs, thereby improving the user's comfort experience. By acquiring the adjustable potential of the air conditioning load aggregation while taking into account user comfort, it is possible to better meet the personalized needs of users and increase user satisfaction. It can effectively improve the intelligent control capability of air conditioning systems, meet users' personalized needs, optimize energy utilization efficiency, improve system flexibility and response speed, and thus solve the technical problems of low accuracy and efficiency in determining the control parameters of air conditioning load aggregates in related technologies.

[0038] Optionally, based on operating status parameters and environmental condition parameters, an equivalent thermal parameter model of the air conditioner load is constructed, including: based on operating status parameters and environmental condition parameters, constructing the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation of the air conditioner; and based on the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation, constructing an equivalent thermal parameter model of the load.

[0039] In one optional embodiment, constructing an equivalent thermal parameter model of the air conditioning load based on operating status parameters and environmental condition parameters can more accurately describe the heat load characteristics of the air conditioning system, thereby achieving intelligent system control and energy efficiency optimization. Specifically, by analyzing the cooling capacity data of the air conditioning system at different room temperatures, a relationship between room temperature and cooling capacity can be established. Mathematical methods such as curve fitting and regression analysis can be used to determine the influence of room temperature on cooling capacity. Frequency is a key parameter for the operation of the air conditioning compressor and is closely related to cooling capacity. Through experimental data or simulation calculations, a relationship between cooling capacity and frequency can be established to represent the influence of frequency on cooling capacity. There is a certain relationship between the power and frequency consumption of the air conditioning system. Power consumption is an important reference indicator for cooling capacity. Based on experimental data or model analysis, a relationship between power and frequency can be established to represent the influence of power on cooling capacity. Considering that frequency regulation affects the temperature control effect of the air conditioning system, the impact of frequency regulation on temperature deviation can be determined, and a relationship between frequency and temperature deviation can be established to represent the influence of frequency regulation on temperature control. Based on the above relationships, an equivalent thermal parameter model of the load can be constructed, comprehensively considering the operating status of the air conditioning system and environmental conditions, to achieve accurate prediction and control of the air conditioning load. In the above process, by establishing a load equivalent thermal parameter model, the heat load characteristics of the air conditioning system can be described more accurately, enabling intelligent control and optimized operation. Based on the established relationships and models, the cooling capacity and power consumption of the air conditioning system can be accurately predicted, achieving effective energy utilization and conservation.

[0040] Optionally, based on operating status parameters and environmental condition parameters, a relationship between room temperature and cooling capacity of the air conditioner is constructed, including: determining the differential equation of the equivalent thermal parameter model of the air conditioner based on the cooling capacity parameter in the operating status parameters, the indoor parameter in the environmental condition parameters, and the outdoor parameter in the environmental condition parameters; and discretizing the differential equation of the equivalent thermal parameter model to obtain the relationship between room temperature and cooling capacity.

[0041] In one optional embodiment, the relationship between room temperature and cooling capacity of an air conditioner can be constructed based on operating state parameters and environmental condition parameters to achieve precise control and energy efficiency optimization of the air conditioning system. Specifically, firstly, the equivalent thermal parameter model differential equation of the air conditioning system can be determined based on the cooling capacity parameter in the operating state parameters and the indoor and outdoor parameters in the environmental condition parameters. Next, the determined differential equation can be discretized, converting the continuous time variable into discrete time steps to facilitate numerical calculation and simulation. This transforms the differential equation into a difference equation, making it more suitable for simulation and analysis on a computer. Finally, based on the discretized equivalent thermal parameter model, the relationship between room temperature and cooling capacity is obtained through numerical calculation or simulation. This relationship describes the dynamic change between room temperature and cooling capacity, thus representing the heat load characteristics of the air conditioning system. Through these steps, the relationship between room temperature and cooling capacity of the air conditioning system can be established, enabling modeling and analysis of the heat load characteristics of the air conditioning system. By establishing the relationship between room temperature and cooling capacity, the relationship between the cooling capacity and room temperature of the air conditioning system can be predicted more accurately, achieving precise control of the air conditioning system and improving system operating efficiency. Establishing a relationship between room temperature and cooling capacity allows for better control of air conditioning systems, improves user comfort in air-conditioned environments, meets user comfort needs, and provides important technical support for intelligent system control and energy efficiency optimization.

[0042] Optionally, based on a preset comfort temperature range and a load equivalent thermal parameter model, a load aggregation model for the air conditioner is constructed, including: determining the state of charge parameters based on the preset comfort temperature range and the load equivalent thermal parameter model; determining the relationship between the power and state of charge of the air conditioner based on the state of charge parameters and the operating state parameters; and constructing the load aggregation model based on the relationship between the power and state of charge.

[0043] In one optional embodiment, constructing a load aggregation model for the air conditioner based on a preset comfort temperature range and a load equivalent thermal parameter model can comprehensively consider user comfort needs and the thermal load characteristics of the air conditioning system, achieving intelligent control and energy efficiency optimization of the air conditioning system. Specifically, the state of charge (SOC) parameters can be determined based on the preset comfort temperature range and the load equivalent thermal parameter model. Next, the relationship between the air conditioner's power and SOC can be determined based on the SOC and operating state parameters. By analyzing the power consumption of the air conditioning system under different SOC states, a relationship between power and SOC is established to represent the influence of power on SOC. Finally, a load aggregation model for the air conditioner can be constructed based on the determined power-SOC relationship. This load aggregation model comprehensively considers user comfort needs, the thermal load characteristics of the air conditioning system, and power consumption, achieving comprehensive control of the air conditioning system. In the above steps, by constructing a load aggregation model, user comfort needs and the thermal load characteristics of the air conditioning system can be comprehensively considered, achieving intelligent control of the system and improving the system's operating efficiency and stability. By considering user comfort needs and load characteristics, constructing a load aggregation model can better control the air conditioning system, improve user comfort in the air-conditioned environment, and enhance user satisfaction.

[0044] Optionally, based on a preset comfort temperature range and a load equivalent thermal parameter model, the state of charge parameters are determined, including: determining the control quantity parameters and control capacity parameters of the air conditioner based on the preset comfort temperature range and the load equivalent thermal parameter model; and determining the state of charge parameters based on the ratio of the control quantity parameters and the control capacity parameters.

[0045] In one optional embodiment, firstly, the control quantity parameters and control capacity parameters of the air conditioner can be determined based on a preset comfort temperature range and a load equivalent thermal parameter model. The control quantity parameters can refer to the controllable quantities of the air conditioning system, such as temperature setpoint and fan speed; the control capacity parameters can refer to the control capacity of the air conditioning system, i.e., the actual controllability the system can provide. Next, the state of charge (SOC) parameters can be determined based on the ratio of the control quantity parameters to the control capacity parameters. This ratio reflects the relationship between the air conditioning system's controllability and actual demand, thereby determining the system's current SOC. By analyzing the ratio between the control quantity parameters and the control capacity parameters, it can be understood whether the system's current controllability meets the user's comfort requirements. Based on the analysis results, the SOC parameters are adjusted. If it is found that the system's controllability is insufficient to meet user needs, the control quantity parameters can be adjusted or the control capacity parameters increased to improve the system's control performance and user comfort. In the above process, by analyzing the ratio of the control quantity parameter and the control capacity parameter, the system's control capability can be evaluated, the system's energy utilization efficiency can be optimized, energy consumption costs can be reduced, and the state of charge parameter can be adjusted to ensure that the system's control capability matches the user's comfort needs, thereby improving the user's comfort experience in the air-conditioned environment and enhancing user satisfaction.

[0046] Optionally, the control parameters of the air conditioner are determined based on the load aggregation model, including: constructing an aggregate parameter identification objective function for the air conditioner based on the load aggregation model, wherein the aggregate parameter identification objective function is used to represent the objective function constructed based on the error between the actual data and the predicted data of the control parameters, and the predicted data is used to represent the data obtained by predicting the control parameters based on the load aggregation model; and searching the aggregate parameter identification objective function based on a preset biomimetic optimization algorithm to obtain the control parameters.

[0047] In one optional embodiment, determining the air conditioning control parameters based on a load aggregate model can achieve precise control and optimization of the air conditioning system. Specifically, firstly, an aggregate parameter identification objective function for the air conditioning system can be constructed based on the load aggregate model. This objective function represents the error between actual and predicted data based on the control parameters, i.e., the difference between actual data and model-predicted data. Optimizing the aggregate parameter identification objective function helps adjust the air conditioning system parameters to reduce errors and achieve better system performance. The predicted data is obtained by predicting the control parameters based on the load aggregate model. Through model prediction, predicted data generated according to changes in the control parameters can be obtained, which is used to compare with actual data and thus optimize the control parameters. Next, a pre-defined biomimetic optimization algorithm, such as a genetic algorithm or particle swarm optimization algorithm, can be used to search the aggregate parameter identification objective function to optimize the control parameters. The pre-defined biomimetic optimization algorithm simulates the evolutionary mechanism in nature, finding the optimal solution through iteration. By searching the aggregate parameter identification objective function, the optimized control parameters are finally obtained. These parameters will be used to adjust the operating status of the air conditioning system to minimize the error between actual and predicted data, thereby improving system performance and efficiency. In the above process, by searching for the objective function of aggregate parameter identification based on a preset biomimetic optimization algorithm, intelligent control of the air conditioning system is achieved, improving the system's intelligence level, enhancing its adaptability and response speed. This enables intelligent control and energy efficiency optimization of the air conditioning system, improving system performance and user experience.

[0048] The technical solution proposed in this application is described below with reference to an optional embodiment. This application proposes a method for evaluating the adjustable potential of central air conditioning load aggregation considering user comfort. User comfort is taken as an important constraint in the air conditioning load aggregation model, and the Black-winged Kite optimization algorithm is proposed to identify the parameters of the air conditioning load aggregation model. This solution adopts a bottom-up approach to air conditioning load aggregation modeling, starting from the individual air conditioning load effects, establishing an air conditioning load aggregation model that takes into account both air conditioning load characteristics and user comfort, and using a novel intelligent algorithm to optimize and identify the model parameters. This solution effectively taps into the adjustable potential of air conditioning load aggregation, providing technical support for the effective utilization of temperature-controlled load resources such as air conditioning.

[0049] The technical solution of this application is as follows: First, an equivalent thermal parameter model of the air conditioning load is established. The actual power consumption of the central air conditioning system is mainly related to the cooling and heating capacity. The cooling capacity required indoors is mainly related to factors such as room area and room heat dissipation coefficient. Generally, it is described by actual models and equivalent thermal parameter models. Using an equivalent thermal parameter model to describe the temperature change of the air-conditioned room under the influence of various factors such as outdoor temperature and indoor equipment heat dissipation can more accurately simulate the relevant working conditions of the air conditioning load. The differential equation of the equivalent thermal parameter model of the central air conditioning system can be obtained, as shown in the following equation:

[0050]

[0051] In the formula, C represents the building's equivalent heat capacity, in units of F and T. i (t) represents the indoor temperature at time t, in °C; T o (t) represents the outdoor temperature at time t, in °C; R represents the building's equivalent thermal resistance, in Ω; Q(t) represents the cooling capacity of the air conditioner at time t, in kW.

[0052] Discretizing the differential equation of the above equivalent thermal parameter model yields the relationship between room temperature and air conditioning cooling capacity, i.e., the relationship between room temperature and cooling capacity, as follows:

[0053]

[0054] The actual operating power and cooling capacity of a variable frequency central air conditioner are approximately linearly related to its frequency. As the frequency increases, both the power and cooling capacity of the variable frequency central air conditioner will increase. The relationship between frequency and the cooling capacity and actual power of a variable frequency central air conditioner, i.e., the relationship between cooling capacity and frequency and the relationship between power and frequency, can be expressed as follows:

[0055] Q(t) = a·f(t) + b;

[0056] P(t) = m·f(t) + n;

[0057] In the formula, Q(t) represents the cooling capacity of the central air conditioning unit at time t, in kW; P(t) represents the actual electrical power of the central air conditioning unit at time t, in kW; f represents the frequency of the compressor, in Hz; a and b represent constant coefficients for cooling capacity; and m and n represent constant coefficients for electrical power.

[0058] Variable frequency central air conditioning system adjusts according to the set temperature T s and indoor temperature T i The compressor frequency is adjusted by changing the air conditioner frequency. Assume the range of frequency variation, i.e., the preset comfort temperature range, is [f]. min ,f max At an indoor temperature T iThe deviation T greater than the set temperature s +ΔT + At that time, the variable frequency central air conditioner operates at its highest frequency f max Run until the room temperature reaches the set temperature range; when T i The deviation below the set temperature limit T s -ΔT - At that time, the lowest frequency f min run.

[0059] The relationship between the frequency and temperature deviation of a variable frequency central air conditioning load, i.e., the relationship between frequency and temperature deviation, can be expressed as:

[0060]

[0061] In the formula, ΔT is the deviation between the indoor temperature and the set temperature, ΔT = T i -T s K is a constant.

[0062] The load equivalent thermal parameter model of a variable frequency central air conditioner can be composed of the above-mentioned relationships between room temperature and cooling capacity, cooling capacity and frequency, power and frequency, and frequency and temperature deviation.

[0063] Next, a control potential model for a single air conditioner can be established. From the power-frequency relationship, it can be seen that the electrical power of a variable frequency central air conditioner is directly proportional to the compressor's operating frequency. This section proposes a control method for the central air conditioning load based on its operating characteristics, and calculates its control potential accordingly. Based on the central air conditioning set temperature T... s and outdoor temperature T o Calculate the operating frequency f corresponding to the central air conditioning load. Assume a short time T... o To remain unchanged, that is:

[0064] T o (t+1)=T o (t) = T;

[0065] When the central air conditioning is running stably, the room temperature equals the set temperature, that is:

[0066] T i (t+1)=T i (t)=T s ;

[0067] Combining the above formulas for the relationship between room temperature and cooling capacity, we can obtain:

[0068]

[0069] Substituting the above formula into the relationship between cooling capacity and frequency, we get:

[0070]

[0071] Combining the above formula and the power-frequency relationship, the electrical power of the central air conditioner can be calculated as follows:

[0072]

[0073] As can be seen from the above, the control potential of a single central air conditioning unit can be expressed by the following formula.

[0074] ΔP=P(T s ,T o )-P min =P(T) s ,T o )-(m·f min +n);

[0075] Maximum controllable duration t of air conditioner control for:

[0076]

[0077] In the formula, T0 is the room temperature at the start of the control, in °C; T1 is the room temperature at the end of the control, in °C; Q min This represents the cooling capacity corresponding to the lowest operating frequency, in kW.

[0078] Next, an air conditioning load aggregation model that considers user comfort can be established, i.e., a load aggregation model. Based on the relationships between cooling capacity and frequency, and power and frequency, the relationship between the central air conditioning cooling capacity Q and electrical power P can be obtained as follows:

[0079]

[0080] Considering user comfort, the temperature range is [T min ,T max The control potential of a central air conditioning system is linearly related to its set temperature. When the indoor temperature is T... max If the adjustable capacity of the central air conditioning is 0, then when the indoor temperature is T... min The maximum adjustable capacity of the central air conditioning system is S. max Therefore, the indoor temperature can be calculated as T. i Time-controlled quantity S in That is, the control parameter, can be expressed as:

[0081] S in =λ(T) max -T i );

[0082] In the formula, λ is the control coefficient, with the unit being kW / ℃.

[0083] The maximum adjustable quantity S can be obtained.max That is, the control capacity parameter is:

[0084] S max =λ(T) max -T min );

[0085] The state of charge (SOC) can be defined, that is, the state of charge parameter is the control quantity S. in With regulation capacity S max The ratio:

[0086]

[0087] When room temperature T i When the temperature remains constant, the cooling capacity Q can be obtained from the differential equation of the equivalent thermal parameter model as follows:

[0088]

[0089] Substituting the control parameters into the above formula, we can obtain the central air conditioning power P as follows:

[0090]

[0091] Will Substituting into the above equation, we obtain the relationship between P and SOC as follows:

[0092] P = αSOC + βT o +γ;

[0093] The formulas for calculating α, β, and γ are as follows:

[0094]

[0095] According to the definition of State of Charge (SOC), the range of SOC is [0, 1]. The central air conditioning units are divided into N groups based on their SOC, with m1, m2, ..., m... i , ..., m N When m i When the value is large, the state of charge (SOC) of the central air conditioning units in the i-th group can be unified as SOC. i :

[0096]

[0097] The central air conditioning load of the i-th group is aggregated as follows:

[0098] P i =α i SOC i +β i T o +γ i ;

[0099]

[0100] In the formula, P i α represents the total polymerization power of the i-th group; i_k β i_k and γ i_k They are α, β, and γ of the k-th central air conditioner in the i-th group, respectively.

[0101] Next, a model for the control potential of air conditioning load aggregation can be established.

[0102] Assuming that relevant data on central air conditioning are known, the overall control potential of the central air conditioning load aggregation can be calculated based on the above model. The total polymerization power can be obtained as follows:

[0103]

[0104] In the formula, P i Let be the total aggregation power of the i-th load group.

[0105] Assuming the outdoor temperature remains constant, when the operating status of all central air conditioning units in the i-th interval is adjusted to that in the j-th interval, the combined load power change ΔP of the i-th group of central air conditioning units is... i→j for:

[0106] ΔPi→j=αi(SOCj-SOCi);

[0107] When the temperature adjustment range required for human comfort is fixed, the state of charge (SOC) of the central air conditioning load depends on the indoor temperature. Since the indoor temperature does not change abruptly but continuously over time, the SOC of the central air conditioning load is also time-varying. Therefore, there is a transient process during the adjustment of the operating state from the i-th interval to the j-th interval, and the duration of the transient is:

[0108]

[0109] In the formula, T i (i) and T i (j) represent the indoor temperatures corresponding to the i-th and j-th intervals, respectively, according to P i =α i SOC i +β i T o +γ i We can obtain:

[0110] T i (i)=T max -SOC i (T max -T min );

[0111] T i (j)=T max -SOC j (T max -T min );

[0112] Because the peak-shaving time of the system is relatively long, the regulation time of the aggregated central air conditioning is much longer than the transient process time when the central air conditioning load is regulated. Therefore, this paper does not consider the transient process generated when the state of charge of the aggregated central air conditioning load changes, but only considers the state of charge values ​​of the two intervals before and after the change. Then the maximum regulation potential is:

[0113]

[0114] Finally, the adjustable potential parameters of the air conditioning load aggregation can be identified based on the Black-winged Kitealgorithm (BKA). The aforementioned adjustable potential assessment is derived under the assumption that relevant parameters are known. Aggregate parameters are key to influencing the control potential. The aggregate parameters to be identified include the total number of air conditioning units, the optimal temperature range for users, the building's equivalent heat capacity, and the building's equivalent thermal resistance. This application can employ a pre-defined biomimetic optimization algorithm, such as the Black-winged Kitealgorithm (BKA), to identify the relevant parameters.

[0115] First, construct the target function for aggregate parameter identification, as shown in the following equation:

[0116]

[0117] In the formula: N is the total number of air conditioners participating in the aggregation; T max ,T min The maximum and minimum values ​​of the user's comfort temperature; C is the room's equivalent heat capacity; R is the room's equivalent thermal resistance; P(t) is the actual operating power of the air conditioning load aggregate; T represents the power calculated during the iteration of the Black-winged Kite Optimization (BKA) algorithm; T is the length of the time window.

[0118] The specific steps for parameter identification are as follows: First, determine the parameters to be optimized and their range, and initialize the parameters. Then, calculate the fitness value and execute the attack phase of the BKA algorithm. Next, execute the migration phase of the BKA algorithm. Finally, determine if the maximum number of iterations has been reached; if so, output the optimal parameter result; otherwise, jump to calculate the fitness value and execute the attack phase of the BKA algorithm. Finally, output the optimal parameter combination, which allows for the calculation and analysis of the adjustable potential of the air conditioning load aggregation. Based on the aggregation control potential model and the model parameter identification results, the adjustable potential of the regional load aggregation is calculated.

[0119] The technical solution proposed in this application can improve the accuracy of central air conditioning load aggregation models: by taking user comfort as an important constraint and using novel intelligent algorithms to optimize and identify model parameters, the accuracy and reliability of the model can be significantly improved. This helps to more accurately predict and control the behavior of air conditioning load aggregations, providing strong support for the effective utilization of air conditioning resources. It can effectively assess the adjustable potential of central air conditioning load aggregations; through precise modeling and parameter identification, the adjustable potential of air conditioning load aggregations can be more effectively explored and utilized. This helps to maximize energy efficiency and improve resource utilization efficiency while meeting user comfort requirements. It can provide technical support for the effective response of temperature-controlled load resources such as air conditioning. The proposal of this application provides new ideas and methods for the effective response and utilization of temperature-controlled load resources such as air conditioning. Through precise modeling and optimized control, these resources can be better utilized, achieving efficient energy utilization and sustainable development. In summary, the technical means of this application produce certain technical effects in improving model accuracy, exploring adjustable potential, and providing technical support for the effective utilization of temperature-controlled load resources.

[0120] Figure 2 This is a schematic diagram illustrating a search step for an aggregate parameter identification target function according to an embodiment of the present invention, such as... Figure 2 As shown, the process begins by determining the parameters to be optimized and initializing the parameter range; calculating the fitness value and executing the attack behavior phase; executing the migration behavior phase; determining whether the maximum number of iterations has been reached; if the maximum number of iterations has not been reached, jumping to the step of calculating the fitness value and executing the attack behavior phase; if the maximum number of iterations has been reached, outputting the load aggregate parameter identification results, including the total number of aggregated air conditioners N, comfort temperature Tmax / min, building equivalent heat capacity C, and building equivalent thermal resistance R.

[0121] According to another aspect of the embodiments of this application, an apparatus for determining the control parameters of air conditioning load aggregation is also provided. This apparatus can execute the method for determining the control parameters of air conditioning load aggregation described in the above embodiments. The specific implementation method and preferred application scenarios are the same as those described in the above embodiments, and will not be repeated here.

[0122] Figure 3 This is a schematic diagram of a device for determining air conditioning load aggregation control parameters according to an embodiment of this application, as shown below. Figure 3 As shown, the device includes the following: an acquisition module 302, a first construction module 304, a second construction module 306, and a determination module 308.

[0123] The system comprises the following modules: an acquisition module for acquiring the operating status parameters of the air conditioner and the environmental condition parameters of the environment in which the air conditioner is located; a first construction module for constructing a load equivalent thermal parameter model of the air conditioner based on the operating status parameters and environmental condition parameters; a second construction module for constructing a load aggregation model of the air conditioner based on a preset comfort temperature range and the load equivalent thermal parameter model, wherein the preset comfort temperature range represents the pre-set comfort temperature range for the user; and a determination module for determining the control parameters of the air conditioner based on the load aggregation model, wherein the control parameters characterize the control potential of the load aggregation of the air conditioner.

[0124] The first construction module is also used to construct the relationship between room temperature and cooling capacity, cooling capacity and frequency, power and frequency and frequency and temperature deviation of the air conditioner based on the operating status parameters and environmental condition parameters; and to construct the load equivalent thermal parameter model based on the relationship between room temperature and cooling capacity, cooling capacity and frequency, power and frequency and frequency and temperature deviation.

[0125] The first construction module is also used to determine the differential equation of the equivalent thermal parameter model of the air conditioner based on the cooling capacity parameter in the operating status parameters, the indoor parameter in the environmental condition parameters, and the outdoor parameter in the environmental condition parameters; and to discretize the differential equation of the equivalent thermal parameter model to obtain the relationship between room temperature and cooling capacity.

[0126] The second construction module is also used to determine the state of charge parameters based on the preset comfort temperature range and the load equivalent thermal parameter model; to determine the relationship between the power and state of charge of the air conditioner based on the state of charge parameters and the operating state parameters; and to construct a load aggregation model based on the relationship between the power and state of charge.

[0127] The second construction module is also used to determine the control quantity parameters and control capacity parameters of the air conditioner based on the preset comfort temperature range and the load equivalent thermal parameter model; and to determine the state of charge parameters based on the ratio of the control quantity parameters and the control capacity parameters.

[0128] The determination module is also used to construct an aggregate parameter identification objective function for air conditioning based on the load aggregate model. The aggregate parameter identification objective function is used to represent the objective function constructed based on the error between the actual data and the predicted data of the control parameters. The predicted data is used to represent the data obtained by predicting the control parameters based on the load aggregate model. Based on the preset bionic optimization algorithm, the aggregate parameter identification objective function is searched to obtain the control parameters.

[0129] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0130] The aforementioned memory can refer to devices inside a computer used to store data and programs, including RAM, hard disks, etc. RAM can be used to temporarily store running programs and data, while hard disks can be used to store programs and data long-term. Memory enables the computer to read and write data and execute programs. The aforementioned processor is responsible for executing instructions in computer programs and performing data processing. It can also be responsible for controlling and executing various operations, including arithmetic operations, logical operations, and data transmission.

[0131] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0132] The aforementioned computer storage media can refer to the media used in computer memory to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser discs, etc. Computer-readable storage media include stored programs, which can be a set of instructions that a computer can recognize and execute, running on an electronic computer to meet certain information needs.

[0133] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0134] The aforementioned computer program products can refer to software programs that have been written, tested, and released, and can run on computers or other devices. Computer program products can include application programs, operating systems, utility software, etc., used to achieve specific functions or solve specific problems.

[0135] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0136] The aforementioned non-volatile computer-readable storage medium can refer to a medium for storing data. Non-volatile computer-readable storage media can retain data without loss when power is off and can be used to store long-term data, such as operating systems, applications, and user files. Non-volatile storage media can include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0137] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0138] The aforementioned computer program can refer to a set of instructions used to tell the computer to perform specific tasks or operations. Computer programs can be written by programmers using specific programming languages ​​and can include algorithms, data structures, logic, and control flow. Computer programs can be used for a variety of purposes, including application software, operating systems, etc.

[0139] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] 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.

[0143] 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 described in 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.

[0144] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the control parameters of an air conditioning load aggregation system, characterized in that, include: Obtain the operating status parameters of the air conditioner and the environmental condition parameters of the environment in which the air conditioner is located; Based on the operating status parameters and the environmental condition parameters, a load equivalent thermal parameter model for the air conditioner is constructed. Based on the preset comfort temperature range and the load equivalent thermal parameter model, a load aggregation model of the air conditioner is constructed, wherein the preset comfort temperature range is used to represent the pre-set comfort temperature range for the user. The control parameters of the air conditioner are determined based on the load aggregation model, wherein the control parameters are used to characterize the control potential of the load aggregation of the air conditioner. Specifically, based on the operating status parameters and the environmental condition parameters, the load equivalent thermal parameter model of the air conditioner is constructed, including: based on the operating status parameters and the environmental condition parameters, constructing the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation of the air conditioner; and based on the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation, constructing the load equivalent thermal parameter model. Based on the preset comfort temperature range and the load equivalent thermal parameter model, a load aggregation model of the air conditioner is constructed, including: determining the state of charge parameters based on the preset comfort temperature range and the load equivalent thermal parameter model; determining the power-state of charge relationship of the air conditioner based on the state of charge parameters and the operating state parameters; and constructing the load aggregation model based on the power-state of charge relationship.

2. The method for determining the control parameters of the air conditioning load aggregation system according to claim 1, characterized in that, Based on the operating status parameters and the environmental condition parameters, a formula is constructed to express the relationship between the room temperature and the cooling capacity of the air conditioner, including: Based on the cooling capacity parameter in the operating status parameters, the indoor parameter in the environmental condition parameters, and the outdoor parameter in the environmental condition parameters, the differential equation of the equivalent thermal parameter model of the air conditioner is determined. The differential equation of the equivalent thermal parameter model is discretized to obtain the relationship between room temperature and cooling capacity.

3. The method for determining the control parameters of the air conditioning load aggregation system according to claim 1, characterized in that, Based on the preset comfort temperature range and the load equivalent thermal parameter model, the state of charge parameters are determined, including: Based on the preset comfort temperature range and the load equivalent thermal parameter model, the control quantity parameters and control capacity parameters of the air conditioner are determined. The state of charge parameter is determined based on the ratio of the control quantity parameter to the control capacity parameter.

4. The method for determining the control parameters of the air conditioning load aggregation system according to claim 1, characterized in that, The control parameters of the air conditioner are determined based on the load aggregation model, including: Based on the load aggregation model, an aggregate parameter identification objective function for the air conditioner is constructed. The aggregate parameter identification objective function is used to represent the objective function constructed based on the error between the actual data and the predicted data of the control parameters. The predicted data is used to represent the data obtained by predicting the control parameters based on the load aggregation model. Based on a preset biomimetic optimization algorithm, the target function for identifying the aggregate parameters is searched to obtain the control parameters.

5. A device for determining the control parameters of an air conditioning load aggregate, characterized in that, include: The acquisition module is used to acquire the operating status parameters of the air conditioner and the environmental condition parameters of the environment in which the air conditioner is located; The first construction module is used to construct the load equivalent thermal parameter model of the air conditioner based on the operating status parameters and the environmental condition parameters. The second construction module is used to construct the load aggregation model of the air conditioner based on the preset comfort temperature range and the load equivalent thermal parameter model, wherein the preset comfort temperature range is used to represent the pre-set comfort temperature range for the user. A determination module is used to determine the control parameters of the air conditioner based on the load aggregation model, wherein the control parameters are used to characterize the control potential of the load aggregation of the air conditioner; The first construction module is further configured to construct, based on the operating status parameters and the environmental condition parameters, the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation of the air conditioner; and to construct the load equivalent thermal parameter model based on the relationship between room temperature and cooling capacity, the relationship between cooling capacity and frequency, the relationship between power and frequency, and the relationship between frequency and temperature deviation. The second construction module is further configured to determine the state of charge parameters based on the preset comfort temperature range and the load equivalent thermal parameter model; determine the power-state of charge relationship of the air conditioner based on the state of charge parameters and the operating state parameters; and construct the load aggregation model based on the power-state of charge relationship.

6. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the method for determining the air conditioning load aggregate control parameters according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device where the storage medium is located to perform the method for determining the air conditioning load aggregation control parameters as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The system includes a computer program that, when executed by a processor, implements the method for determining the control parameters of the air conditioning load aggregate according to any one of claims 1 to 4.

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