Air conditioner control method and device, electronic equipment and storage medium

Abstract

The application provides an air conditioner control method and device, an electronic device, and a storage medium. The method comprises: obtaining target operating power of an air conditioner indicated by a power grid side; determining a demand response control strategy and a target frequency according to the target operating power and current operating power of the air conditioner in a demand response period, the demand response control strategy comprising a first strategy or a second strategy, wherein the first strategy is to adjust the operating frequency of a compressor to the target frequency, and the second strategy is to adjust the operating frequency of the compressor to the target frequency and adjust the air baffle of a fan to a target air baffle; wherein the target frequency is determined according to a functional relationship between operating power and frequency; and adjusting the air conditioner according to the demand response control strategy. The method of the application can solve the problem of how to respond to more refined power reduction requirements of the power grid side.

Description

Technical Field

[0001] This application relates to air conditioning control technology, and more particularly to an air conditioning control method, device, electronic equipment, and storage medium. Background Technology

[0002] With the rapid development of the social economy and the continuous improvement of people's living standards, various types of air conditioners have been widely used. This widespread use of air conditioners has created a considerable "air conditioning load." The rapid increase in air conditioning load can lead to many problems on the power grid side, such as widening the peak-to-valley difference, deteriorating load characteristics, and the emergence of significant load spikes. To ensure the economical, safe, and stable operation of the power grid, the power grid side will issue power reduction orders to air conditioning systems to regulate their load.

[0003] Currently, air conditioners respond to power reduction demands from the power grid in only two modes: conventional reduction, which involves actively reducing the compressor frequency to 70% of its rated frequency, and strict reduction, which involves actively reducing the compressor frequency to 40% of its rated frequency. However, when applied to inverter air conditioners, these modes can only reduce the compressor frequency to 70% or 40% of its rated power, failing to meet the more refined power reduction demands from the power grid. These more refined demands might include the grid's requirement for power reduction within a range of ±15% (or 10%, or 20%, etc.) of the power deficit. Furthermore, this also limits the flexibility of inverter air conditioners in adjusting their frequency reduction.

[0004] Therefore, how to respond to the grid's more refined power reduction demands remains a problem to be solved. Summary of the Invention

[0005] This application provides an air conditioning control method, apparatus, electronic device, and storage medium to address the problem of how to respond to more refined power reduction demands from the power grid side.

[0006] On the one hand, this application provides an air conditioning control method, including:

[0007] Obtain the target operating power of the air conditioner as indicated by the power grid;

[0008] Based on the target operating power and the current operating power of the air conditioner during the demand response period, a demand response control strategy and a target frequency are determined. The demand response control strategy includes a first strategy or a second strategy. The first strategy is to adjust the compressor operating frequency to the target frequency, and the second strategy is to adjust the compressor operating frequency to the target frequency and adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency.

[0009] The air conditioner is adjusted according to the demand response control strategy.

[0010] In one embodiment, determining the demand response control strategy and target frequency based on the target operating power and the current operating power of the air conditioner includes:

[0011] Based on the target operating power and the current operating power, the index values ​​of the demand response evaluation indicators are determined, wherein the demand response evaluation indicators include at least one of the target load reduction amount, the target load reduction density, and the target demand response potential;

[0012] Acquire building disturbance data and human behavior prediction data during the demand response period. The human behavior prediction data is used to characterize the predicted human behavior state, and the human behavior state corresponds to an indoor temperature limit range. The indoor temperature limit range corresponds to a compressor operating frequency limit range. The building disturbance data includes outdoor temperature and indoor temperature.

[0013] The building disturbance data, the human behavior prediction data, and the indicator values ​​are input into the black box model to obtain the demand response control strategy output by the black box model.

[0014] The black box model reflects the mapping relationship between input parameters and demand response control strategies. The input parameters include: building disturbance data, human behavior prediction data, and the index values ​​of demand response evaluation indicators.

[0015] In one embodiment, the black box model is created in the following way:

[0016] Obtain the building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values ​​of demand response evaluation indicators for each historical demand response period in N historical demand response periods, where N is a positive integer, and the demand response event refers to responding to the power grid side to adjust the air conditioning.

[0017] For each historical demand response period, create a mapping relationship between the corresponding building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and the index values ​​of demand response evaluation indicators.

[0018] Create a quantitative database based on N mapping relationships;

[0019] The black-box model is created based on the quantitative database.

[0020] In one embodiment, obtaining the index value of the demand response evaluation index corresponding to each of the N historical demand response periods includes:

[0021] For a given historical demand response period, the operating power of the air conditioner under different operating modes is obtained, including the operating mode during the demand response period and the operating mode after the demand response is completed.

[0022] Based on the operating power under the different operating modes, the index values ​​of the demand response evaluation indicators are determined.

[0023] In one embodiment, obtaining the operating power of the air conditioner in different operating modes includes:

[0024] Based on the equivalent heat capacity and equivalent impedance of the air conditioner, as well as the building disturbance data and human behavior prediction data of the air conditioner in the corresponding operating mode, the operating power in the corresponding mode is determined.

[0025] In one embodiment, the method further includes:

[0026] For each completed demand response event, the black-box model is trained based on the input parameters and output demand response control strategy during the demand response period of the demand response event.

[0027] In one embodiment, the second strategy includes:

[0028] Adjust the compressor operating frequency to the target frequency. If the air conditioner operating power is still greater than the target operating power, then adjust the fan damper to the target damper.

[0029] Alternatively, adjust the compressor operating frequency to the target frequency while simultaneously adjusting the fan damper to the target damper.

[0030] On the other hand, this application provides an air conditioning control device, including:

[0031] The acquisition module is used to acquire the target operating power of the air conditioner indicated by the power grid side;

[0032] The processing module is used to determine a demand response control strategy and a target frequency based on the target operating power and the current operating power of the air conditioner. The demand response control strategy includes a first strategy or a second strategy, wherein the first strategy is to adjust the compressor operating frequency to the target frequency, and the second strategy is to adjust the compressor operating frequency to the target frequency and adjust the fan damper to the target damper; wherein the target frequency is determined based on the functional relationship between operating power and frequency.

[0033] An adjustment module is used to adjust the air conditioner according to the demand response control strategy.

[0034] On the other hand, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0035] The memory stores computer-executed instructions;

[0036] The processor executes computer execution instructions stored in the memory to implement the method as described in the first aspect.

[0037] On the other hand, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed, cause a computer to perform the method described in the first aspect.

[0038] In summary, embodiments of this application provide an air conditioning control method. This air conditioning control method includes: acquiring a target operating power of the air conditioner indicated by the power grid; determining a demand response control strategy and a target frequency based on the target operating power and the current operating power of the air conditioner; and adjusting the air conditioner according to the demand response control strategy. The demand response control strategy includes a first strategy or a second strategy, wherein the first strategy involves adjusting the compressor operating frequency to the target frequency, and the second strategy involves adjusting the compressor operating frequency to the target frequency and adjusting the fan damper to the target damper; wherein the target frequency is determined based on the functional relationship between operating power and frequency.

[0039] That is, upon receiving the target operating power indicated by the power grid, a demand response control strategy is generated to adjust the air conditioner's operating power to the target operating power. Specifically, this demand response control strategy adjusts the compressor's operating frequency to the target frequency, and may also adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency; that is, the target frequency is not fixed but calculated based on the target operating power and is variable. Thus, compared to the traditional solution which only has two operating power levels, this embodiment increases the compressor frequency adjustment range from 40% and 70% to [f min f max This provides a full-range solution, enhancing the flexibility of variable frequency air conditioner control and facilitating more refined demand response regulation of the power grid. Furthermore, this application can provide different demand response control strategies based on the actual operating status of the air conditioner, making the air conditioner's regulation and control more realistic and improving its performance. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0041] Figure 1 A schematic diagram illustrating an application scenario of the air conditioning control method provided in this application;

[0042] Figure 2 A schematic flowchart of an air conditioning control method provided in one embodiment of this application;

[0043] Figure 3 A schematic flowchart of an air conditioning control method provided in another embodiment of this application;

[0044] Figure 4 A schematic diagram illustrating the generation of a quantitative database in an air conditioning control method provided in one embodiment of this application;

[0045] Figure 5 A schematic diagram of an air conditioning control device provided in one embodiment of this application;

[0046] Figure 6 A schematic diagram of an electronic device provided for one embodiment of this application.

[0047] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0048] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0049] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0050] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0051] First, let me explain the terms used in this application:

[0052] Load: The power or torque required by the engine output for the driven machinery. It can be expressed as an absolute or relative value (a percentage of rated power or maximum torque) of the power or torque.

[0053] Monte Carlo method: a statistical simulation method that approximates numerical calculations by random sampling from a probability model.

[0054] Markov chains are a type of stochastic method based on the idea that "all past information is stored in the present state," meaning that the probability of a person's behavior in the next moment depends only on the current state of that behavior.

[0055] With the rapid development of the social economy and the continuous improvement of people's living standards, various types of air conditioners have been widely used. This widespread use of air conditioners has created a considerable "air conditioning load." The rapid increase in air conditioning load can lead to many problems on the power grid side, such as widening the peak-to-valley difference, deteriorating load characteristics, and the emergence of significant load spikes. To ensure the economical, safe, and stable operation of the power grid, the power grid side will issue power reduction orders to air conditioning systems to regulate their load.

[0056] Currently, air conditioners respond to power reduction demands from the power grid in only two modes: conventional reduction, which involves actively reducing the compressor frequency to 70% of its rated frequency, and strict reduction, which involves actively reducing the compressor frequency to 40% of its rated frequency. However, when applied to inverter air conditioners, these modes can only reduce the compressor frequency to 70% or 40% of its rated power, failing to meet the more refined power reduction demands from the power grid. These more refined demands might include the power grid's requirement for power reduction within a range of ±15% (or 10%, or 20%, etc.) of the power deficit. Furthermore, this also limits the flexibility of inverter air conditioners in adjusting their frequency reduction. How to respond to these more refined power reduction demands from the power grid remains a question that needs to be considered.

[0057] Based on this, this application provides an air conditioning control method, apparatus, electronic device, and storage medium. The air conditioning control method includes: acquiring the target operating power of the air conditioner indicated by the power grid; determining a demand response control strategy and a target frequency based on the target operating power and the current operating power of the air conditioner; and adjusting the air conditioner according to the demand response control strategy. The demand response control strategy includes a first strategy or a second strategy, wherein the first strategy involves adjusting the compressor operating frequency to the target frequency, and the second strategy involves adjusting the compressor operating frequency to the target frequency and adjusting the fan damper to the target damper; wherein the target frequency is determined based on the functional relationship between operating power and frequency.

[0058] That is, upon receiving the target operating power indicated by the power grid, a demand response control strategy is generated to adjust the air conditioner's operating power to the target operating power. Specifically, this demand response control strategy adjusts the compressor's operating frequency to the target frequency, and may also adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency; that is, the target frequency is not fixed but calculated based on the target operating power and is variable. Thus, compared to traditional solutions with only two operating power levels, this application improves the flexibility of variable frequency air conditioner control and is more conducive to refined demand response control of the power grid. In addition, this application can also provide different demand response control strategies based on the actual operating state of the air conditioner, making the air conditioner's adjustment and control more realistic and improving the air conditioner's performance.

[0059] The air conditioning control method provided in this application is applied to electronic devices, such as air conditioning controllers, cloud servers, etc. Figure 1 This is a schematic diagram illustrating the application of the air conditioning control method provided in this application. In the diagram, the electronic device acquires the target operating power of the air conditioner indicated by the power grid side. Based on the target operating power and the current operating power of the air conditioner, it determines a demand response control strategy and a target frequency. The air conditioner is then adjusted according to the demand response control strategy. The demand response control strategy includes a first strategy or a second strategy. The first strategy involves adjusting the compressor operating frequency to the target frequency, and the second strategy involves adjusting both the compressor operating frequency to the target frequency and the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency.

[0060] Please see Figure 2 One embodiment of this application provides an air conditioning control method, apparatus, electronic device, and storage medium. The air conditioning control method includes:

[0061] S210, obtain the target operating power of the air conditioner indicated by the grid side.

[0062] As described above, load can be expressed as an absolute or relative value of power or torque. The target operating power indicated by the grid side can be understood as the indicated air conditioning load.

[0063] The power grid can issue power reduction requirements to air conditioners, and the corresponding target operating power is lower than the normal operating power of the air conditioners.

[0064] S220, based on the target operating power and the current operating power of the air conditioner, determine the demand response control strategy and the target frequency. The demand response control strategy includes a first strategy or a second strategy, wherein the first strategy is to adjust the compressor operating frequency to the target frequency, and the second strategy is to adjust the compressor operating frequency to the target frequency and adjust the fan damper to the target damper; wherein the target frequency is determined based on the functional relationship between operating power and frequency.

[0065] The current operating power of the air conditioner refers to its operating power during the demand response period, including the operating power at multiple points in time. For example, if the demand response period is 1 minute and the time step is 20 seconds, there will be operating power at 4 points in time (second 0, second 20, second 40, and second 60). That is, the current operating power of the air conditioner has 4 points.

[0066] The operating power of an air conditioner can be calculated based on the compressor's operating frequency or based on a compressor model, such as a ten-coefficient model. When calculating the operating power based on the compressor's operating frequency, the formula P = mf + n is used, where P represents the operating power and f represents the compressor's operating frequency. This ten-coefficient model is... Where y represents the compressor's cooling capacity, energy efficiency ratio, power consumption, mass flow rate, and other performance parameters, and T... e T represents the evaporation temperature. c Representing condensation temperatures, c1, c2, ..., c9, c 10 It is the compressor's coefficient.

[0067] When determining the demand response control strategy based on the target operating power and the current operating power, the index values ​​of the air conditioning demand response evaluation indicators can be determined first based on the target operating power and the current operating power, and then the demand response control strategy and the target frequency of the air conditioning compressor can be determined based on the index values. The demand response evaluation indicators include at least one of the following: target load reduction amount, target load reduction density, and target demand response potential.

[0068] Optional, load reduction Load reduction density Demand response potential In the formula, P Base,i This represents the operating power of the air conditioner during the demand response period, i.e., the current operating power described, P. DR,i This represents the target operating power, where n represents the operating power at n times during the demand response period, and i represents the i-th time.

[0069] After determining the demand response control strategy, the target frequency of the compressor in the air conditioner is determined based on the functional relationship between operating power and frequency. Specifically, the target operating power is substituted into this functional relationship to determine the target frequency. Optionally, this functional relationship can be P = mf + n, where P represents the operating power of the air conditioner, f represents the operating frequency of the compressor, and m and n are constant coefficients. m and n can be obtained by fitting historical operating data or measured data of the air conditioner.

[0070] In an optional embodiment, a black-box model is obtained, and the demand response control strategy and the target frequency are derived based on the black-box model. This black-box model includes, but is not limited to, linear regression models, support vector machine models, logistic regression models, decision tree models, random forest models, Naive Bayes models, neural network models, and deep learning models. Black-box models are computationally fast, typically outputting results in no more than 1 to 3 seconds.

[0071] Specifically, this black-box model reflects the mapping relationship between input parameters and demand response control strategies. This mapping relationship also includes the functional relationship between operating power and frequency. The input parameters of the black-box model include: building disturbance data, human behavior prediction data, and the index values ​​of the demand response evaluation indicators. The output of the black-box model is the demand response control strategy and the target frequency. Therefore, based on the target operating power and the current operating power, the index values ​​of the demand response evaluation indicators are determined. These indicators include at least one of the target load reduction amount, target load reduction density, and target demand response potential. Building disturbance data and human behavior prediction data for the demand response period are then acquired. This building disturbance data, human behavior prediction data, and the index values ​​are input into the black-box model to obtain the demand response control strategy output by the black-box model.

[0072] The building interference data includes outdoor and indoor temperatures, and may also include solar radiation, humidity, wind speed, etc.

[0073] The human behavior prediction data is used to characterize the predicted human behavior state, which corresponds to an indoor temperature limit range. For example, if the human behavior state is setting the air conditioner temperature, then based on historical experience, the corresponding indoor temperature limit range can be determined, for example, from 22°C to 28°C. This indoor temperature limit range corresponds to a compressor operating frequency limit range, with the highest temperature within this range corresponding to the lowest compressor operating frequency f. min The lowest temperature in this indoor temperature limit range corresponds to the compressor's highest operating frequency f. max It should be noted that demand response events do not change the indoor temperature limit range corresponding to human behavior.

[0074] Optionally, behavioral prediction data can be obtained based on the user's schedule settings. Taking an air conditioner remote controller with a schedule setting function as an example, the controller can automatically control the operating mode and corresponding parameters of the air conditioning system according to the user's pre-set time plan. The operating mode and corresponding parameters of the air conditioning system include the adjusted indoor temperature. Optionally, behavioral state can be predicted based on the Monte Carlo Markov chain prediction method. Other methods can also be used to obtain behavioral prediction data; this embodiment is not limited to any particular method.

[0075] S230, adjust the air conditioner according to the demand response control strategy.

[0076] As described above, the demand response control strategy includes a first strategy or a second strategy. The first strategy involves adjusting the compressor operating frequency to the target frequency. The second strategy involves adjusting both the compressor operating frequency to the target frequency and the fan damper to the target damper.

[0077] The first strategy is implemented when simply reducing the compressor's operating frequency can meet the power reduction demands on the grid side. This strategy is applied when the compressor's operating power has already been reduced to the minimum operating frequency f. min If the power output still cannot meet the power reduction demand from the grid, the target windshield for the wind turbine is determined based on the unmet power, and the windshield is adjusted to the target windshield. The unmet power is equal to the target operating power and the compressor reaching the target frequency (i.e., the minimum operating frequency f). min The difference between ).

[0078] When the demand response control strategy is the first strategy, adjusting the compressor operating frequency to the target frequency allows the air conditioner to respond to the grid demand. When the demand response control strategy is the second strategy, adjusting the compressor operating frequency to the target frequency (i.e., the minimum operating frequency f) min ) and adjust the windshield to the target windshield.

[0079] The second strategy includes: adjusting the compressor operating frequency to the target frequency, and if the air conditioner operating power is still greater than the target operating power, then adjusting the fan damper to the target damper; or, adjusting the compressor operating frequency to the target frequency while simultaneously adjusting the fan damper to the target damper.

[0080] It should be noted that when the power reduction demand on the grid side is small, for example, when the target power is less than the first power but greater than the second power, the first strategy applies. When the power reduction demand on the grid side is moderate, for example, when the target power is less than the third power but greater than the fourth power, the second strategy applies, adjusting the compressor operating frequency to the target frequency. If the air conditioner's operating power is still greater than the target operating power, the fan speed is then adjusted to the target speed. When the power reduction demand on the grid side is large, for example, when the target power is less than the fifth power but greater than the sixth power, the second strategy applies, simultaneously adjusting the compressor operating frequency to the target frequency and the fan speed to the target speed. Here, the first power is greater than the second power, the second power is greater than the third power, the third power is greater than the fourth power, the fourth power is greater than the fifth power, and the fifth power is greater than the sixth power. Optionally, when the target power is less than the seventh power and the power reduction demand on the grid side cannot be met, the second strategy applies, simultaneously adjusting the compressor operating frequency to the target frequency and the fan speed to the target speed, thus regulating the air conditioner.

[0081] In summary, this embodiment provides an air conditioning control method. The air conditioning control method includes: acquiring the target operating power of the air conditioner indicated by the power grid; determining a demand response control strategy and a target frequency based on the target operating power and the current operating power of the air conditioner; and adjusting the air conditioner according to the demand response control strategy. The demand response control strategy includes a first strategy or a second strategy, wherein the first strategy involves adjusting the compressor operating frequency to the target frequency, and the second strategy involves adjusting the compressor operating frequency to the target frequency and adjusting the fan damper to the target damper; wherein the target frequency is determined based on the functional relationship between operating power and frequency.

[0082] That is, upon receiving the target operating power indicated by the power grid, a demand response control strategy is generated to adjust the air conditioner's operating power to the target operating power. Specifically, this demand response control strategy adjusts the compressor's operating frequency to the target frequency, and may also adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency; that is, the target frequency is not fixed but calculated based on the target operating power and is variable. Thus, compared to the traditional solution which only has two operating power levels, this embodiment increases the compressor frequency adjustment range from 40% and 70% to [f min f max This provides a full-range solution, enhancing the flexibility of variable frequency air conditioner control and facilitating more refined demand response regulation of the power grid. Furthermore, this application can provide different demand response control strategies based on the actual operating status of the air conditioner, making the air conditioner's regulation and control more realistic and improving its performance.

[0083] Please see Figure 3Another embodiment of this application provides an air conditioning control method, which details the creation process of a black box model, including:

[0084] S310, obtain the building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values ​​of demand response evaluation indicators for each historical demand response period in N historical demand response periods, where N is a positive integer, and the demand response event refers to the air conditioning adjustment in response to the power grid side.

[0085] Demand response events refer to air conditioning adjustments made in response to grid-side demands. Before establishing a black-box model, multiple demand response events occur. A black-box model can be built based on data from the periods these events occur, and the model can then output demand response strategies to improve the speed of response to grid-side demands. This data can be stored in the cloud and accessed when needed.

[0086] The data for the period in which demand response events occur includes: building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and indicator values ​​of demand response evaluation indicators.

[0087] The building disturbance data includes indoor and outdoor temperatures, and may also include solar radiation, humidity, wind speed, etc.

[0088] Among them, human behavior prediction data is used to characterize the predicted human behavior state. The human behavior state corresponds to an indoor temperature limit range, and the indoor temperature limit range corresponds to a compressor operating frequency limit range. The highest temperature within this indoor temperature limit range corresponds to the lowest compressor operating frequency f. min The lowest temperature in this indoor temperature limit range corresponds to the compressor's highest operating frequency f. max It should be noted that demand response events do not change the indoor temperature limit range corresponding to human behavior.

[0089] The indicators for demand response evaluation include at least one of load shedding amount, load shedding density, and demand response potential. All three indicators—load shedding amount, load shedding density, and demand response potential—need to be calculated.

[0090] Optionally, for a historical demand response period, the operating power of the air conditioners under different operating modes is obtained. These operating modes include the operating mode during the demand response period and the operating mode after the demand response is completed. Then, based on the operating power under these different operating modes, the index values ​​of the demand response evaluation indicators are determined. Specifically, the load reduction amount... Load reduction density Demand response potential In the formula, P Base,iP represents the operating power of the air conditioner in the demand response period's operating mode. DR,i This represents the operating power of the air conditioner in the demand response completion operation mode, where n represents the operating power at n times during the demand response period, and i represents the i-th time.

[0091] Optionally, when obtaining the operating power of the air conditioner under different operating modes, a building cooling and heating load model is introduced to determine the operating power of the air conditioner under different operating modes. This building cooling and heating load model is, for example, the first-order equivalent thermal parameter model (RC model) of the air conditioner. From the perspective of simplifying calculations, assuming that the indoor temperature is equal to the solid temperature, the RC model can be simplified to: Among them, C a Where Q is the equivalent heat capacity, T is the cooling and heating load, and Q is the equivalent heat capacity. env For the outside temperature, T i Let R1 be the indoor gas temperature, R1 be the equivalent impedance, and t be time. a R1 needs to be obtained by fitting historical operating data of the air conditioner. When using the RC model to determine the operating power of the air conditioner in different operating modes, the operating power in the corresponding mode is determined based on the equivalent heat capacity and equivalent impedance of the air conditioner, as well as the building disturbance data and human behavior prediction data of the air conditioner in the corresponding operating mode.

[0092] Optionally, the RC model can be replaced with simplified 2R2C, 3R2C, or 3R3C models, or other higher-dimensional thermal network models. Taking the 3R2C thermal network model as an example, the three Rs in this model represent the combined convective and radiative heat transfer resistance of the outer surface, the wall thermal resistance, and the internal region convective and radiative heat transfer resistance, respectively. The two Cs represent the wall capacitance and the region node capacitance, respectively. A total of 11 nodes are used for thermal modeling of the entire building envelope. Due to the extreme difficulty of directly solving the differential equations, high-dimensional RC models are generally solved using Laplace transforms. The transfer matrix of the 3R2C model after Laplace transform is:

[0093] The simplified transfer equation is After the calculation is complete, the final result can be obtained through the inverse Laplace transform. Because the power calculation based on the 3R2C model is quite complex, it is not recommended for use on conventional air conditioners.

[0094] It should be noted that when creating the black box model, the mapping relationship between the equivalent heat capacity and equivalent impedance of the air conditioner, as well as the building disturbance data and human behavior prediction data of the air conditioner in the corresponding operating mode, and the operating power of the air conditioner is also created in the black box model.

[0095] The demand response control strategy for demand response events includes either the first strategy or the second strategy as described above.

[0096] S320 creates a mapping relationship between the corresponding building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and the index values ​​of demand response evaluation indicators for each historical demand response period.

[0097] A mapping relationship is created for each historical demand response period. This mapping relationship refers to the relationship between the building disturbance data, human behavior prediction data, demand response events, demand response control strategies for the corresponding historical demand response period, and the indicator values ​​of demand response evaluation metrics.

[0098] N historical demand response periods will correspond to N mapping relationships.

[0099] The established mapping relationships include the functional relationship between operating power and frequency in the compressor model, the mapping relationship between building disturbance data and human behavior prediction data in the building heating and cooling load model, and the mapping relationship between the index values ​​of demand response evaluation indicators and demand response control strategies.

[0100] S330 creates a quantization database based on N mapping relationships.

[0101] The mapping relationship of this quantitative database is as follows: f(internal & external disturbance, human behavior, DR events, control strategy).

[0102] →y(ΔP or DSI or DR flex In this context, internal & external disturbance represents building disturbance data, human behavior represents human behavior prediction data, DR events represent demand response events, and ControlStrategy represents demand response strategy. ΔP, DSI, or DR are also relevant. flex These represent the values ​​of demand response indicators. ΔP represents the load reduction amount, DSI represents the load reduction density, and DR represents the load reduction rate. flex This represents the potential for demand response.

[0103] Optionally, a human behavior prediction module (module 1), a building load prediction module (module 2), and a demand response control strategy selection module (module 3) are provided. Module 1 is used to acquire human behavior prediction data, module 2, such as the RC model mentioned above, is used to acquire operating power and specify the compressor operating frequency limit range, and module 3 is used to select a demand response control strategy.

[0104] Please see Figure 4 For the variable input parameters of Modules 1, 2, and 3, such as outdoor temperature, indoor temperature, solar radiation intensity, wind speed, air volume, wind direction, outdoor humidity, indoor occupancy rate, air conditioning utilization rate, air conditioning and lighting utilization rate, energy consumption behavior, compressor frequency adjustment value, fan speed adjustment value, and duration, a massive sample is generated through sampling. Sampling methods can include random sampling, stratified sampling, and Latin hypercube sampling. This massive sample is then input into Modules 1, 2, and 3 for calculation, yielding the operating power of the air conditioner under different operating modes. Finally, the demand response index values ​​output by Modules 1, 2, and 3 are obtained. A quantitative database of demand response is established based on the mapping relationships between Modules 1, 2, and 3.

[0105] S340, Based on this quantitative database, create the black box model.

[0106] The acquired quantitative database is transformed into a demand response black-box model through an inverse process. The input parameters of this black-box model include building disturbance data, human behavior prediction data, and the index values ​​of demand response evaluation indicators. The output of the black-box model is the demand response control strategy.

[0107] This black-box model includes, but is not limited to, linear regression models, support vector machine models, logistic regression models, decision tree models, random forest models, Naive Bayes models, neural network models, and deep learning models. Black-box models are computationally fast, typically outputting results in no more than 1 to 3 seconds. Therefore, using black-box models to output demand response control strategies is suitable for the macro-control of large-scale air conditioning system clusters and numerous pieces of equipment.

[0108] In an optional embodiment, after each demand response event, the black-box model is trained based on the input parameters and output demand response control strategy during the demand response period of the event. That is, it is updated in real time based on measured demand response data, so as to gradually obtain a more accurate black-box model in each demand response period. Online learning and real-time updating of the black-box model can make the model lightweight, and from a long-term perspective, it can gradually improve the computational accuracy of the black-box model.

[0109] S350: Obtain the target operating power of the air conditioner indicated by the grid side; based on the black box model, the target operating power, and the current operating power of the air conditioner during the demand response period, determine the demand response control strategy and target frequency, and adjust the air conditioner according to the demand response control strategy.

[0110] Step S350 can be referred to the relevant descriptions of steps S210-S230 in the above embodiments, and will not be repeated here.

[0111] In summary, this embodiment provides an air conditioning control method, including: acquiring building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values ​​of demand response evaluation indicators for each of N historical demand response periods, where N is a positive integer, and the demand response event refers to air conditioning adjustment in response to the power grid. For each historical demand response period, a mapping relationship is created between the building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values ​​of demand response evaluation indicators. A quantitative database is created based on the N mapping relationships. A black box model is created based on the quantitative database. The target operating power of the air conditioner indicated by the power grid is obtained. Based on the black box model, the target operating power, and the current operating power of the air conditioner during the demand response period, a demand response control strategy and a target frequency are determined, and the air conditioner is adjusted according to the demand response control strategy.

[0112] In other words, a quantitative database of demand response is created based on a large number of samples, and a black-box model of demand response is then created based on this database. This black-box model reflects the mapping relationship between input parameters and demand response control strategies. The input parameters include building disturbance data, human behavior prediction data, and the index values ​​of demand response evaluation indicators. The output of the black-box model is the demand response control strategy. The black-box model is computationally fast, typically outputting results in no more than 1 to 3 seconds. Therefore, using the black-box model to output demand response control strategies is suitable for the macro-control of large-scale air conditioning system clusters and numerous pieces of equipment.

[0113] In addition, the air conditioning control method provided in this embodiment, upon receiving the target operating power indicated by the power grid side, generates a demand response control strategy to adjust the air conditioner's operating power to the target operating power. Specifically, this demand response control strategy adjusts the compressor operating frequency to the target frequency, and may also adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency; that is, the target frequency is not fixed but calculated based on the target operating power and is variable. Thus, compared to the traditional solution which only has two operating power levels, this embodiment increases the compressor frequency adjustment range from 40% and 70% to [f min f max This provides a full-range solution, enhancing the flexibility of variable frequency air conditioner control and facilitating more refined demand response regulation of the power grid. Furthermore, this application can provide different demand response control strategies based on the actual operating status of the air conditioner, making the air conditioner's regulation and control more realistic and improving its performance.

[0114] Please see Figure 5 An embodiment of this application also provides an air conditioning control device 10, comprising:

[0115] The acquisition module 11 is used to acquire the target operating power of the air conditioner indicated by the power grid side.

[0116] The processing module 12 is used to determine a demand response control strategy and a target frequency based on the target operating power and the current operating power of the air conditioner. The demand response control strategy includes a first strategy or a second strategy. The first strategy is to adjust the compressor operating frequency to the target frequency, and the second strategy is to adjust the compressor operating frequency to the target frequency and adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency.

[0117] The adjustment module 13 is used to adjust the air conditioner according to the demand response control strategy.

[0118] Optionally, the processing module 12 is specifically used for: determining the index value of the demand response evaluation index based on the target operating power and the current operating power, wherein the demand response evaluation index includes at least one of the target load reduction amount, target load reduction density, and target demand response potential; acquiring building disturbance data and human behavior prediction data for the demand response period, wherein the human behavior prediction data is used to characterize the predicted human behavior state, the human behavior state corresponds to an indoor temperature limit range, the indoor temperature limit range corresponds to a compressor operating frequency limit range, wherein the building disturbance data includes outdoor temperature and indoor temperature; inputting the building disturbance data, the human behavior prediction data, and the index value into a black box model to obtain the demand response control strategy output by the black box model; wherein the black box model reflects the mapping relationship between the input parameters and the demand response control strategy, and the input parameters include: building disturbance data, human behavior prediction data, and the index value of the demand response evaluation index.

[0119] Optionally, the black box model is created as follows: Obtain building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and indicator values ​​of demand response evaluation indicators for each of N historical demand response periods, where N is a positive integer, and the demand response event refers to responding to air conditioning adjustments on the grid side; for each historical demand response period, create a mapping relationship between the building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and indicator values ​​of demand response evaluation indicators; create a quantitative database based on the N mapping relationships; and create the black box model based on this quantitative database.

[0120] Specifically, for a given historical demand response period, the operating power of the air conditioner under different operating modes is obtained. These operating modes include the operating mode during the demand response period and the operating mode after the demand response is completed. Based on the operating power under these different operating modes, the index values ​​of the demand response evaluation indicators are determined.

[0121] Specifically, the operating power in the corresponding mode is determined based on the equivalent heat capacity and equivalent impedance of the air conditioner, as well as building disturbance data and human behavior prediction data of the air conditioner in the corresponding operating mode.

[0122] Optionally, the processing module 12 is also used to train the black box model based on the input parameters and output demand response control strategy during the demand response period when the demand response event occurs, after each demand response event is completed.

[0123] Optionally, the second strategy includes: adjusting the compressor operating frequency to the target frequency, and if the air conditioner operating power is still greater than the target operating power, adjusting the fan damper to the target damper; or, adjusting the compressor operating frequency to the target frequency while simultaneously adjusting the fan damper to the target damper.

[0124] The air conditioning control device provided in this application embodiment can execute the backlight compensation method in the above method embodiment. Its implementation principle and technical effects are similar, and will not be repeated here. It should be noted that the above... Figure 5 The division of modules shown is merely illustrative. This application does not limit the division of modules or the naming of modules.

[0125] Please see Figure 6 This application also provides an electronic device 20, including a processor 21 and a memory 22 communicatively connected to the processor 21. The memory 22 stores computer-executable instructions, and the processor 21 executes the computer-executable instructions stored in the memory to implement the air conditioning control method provided in any of the preceding embodiments.

[0126] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, cause the computer-executable instructions to be executed by a processor to implement the air conditioning control method provided in any of the preceding embodiments.

[0127] This application also provides a computer program product, including a computer program that, when executed by a processor, is used to implement the air conditioning control method as provided in any of the preceding embodiments.

[0128] It should be noted that the aforementioned computer-readable storage media can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc. It can also be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0129] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0130] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0131] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0132] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0133] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0135] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An air conditioning control method, characterized in that, include: Obtain the target operating power of the air conditioner as indicated by the power grid; Based on the target operating power and the current operating power of the air conditioner during the demand response period, a demand response control strategy and a target frequency are determined. The demand response control strategy includes a first strategy and a second strategy. The first strategy is to adjust the compressor operating frequency to the target frequency, and the second strategy is to adjust the compressor operating frequency to the target frequency and adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency. The air conditioner is adjusted according to the demand response control strategy. The step of determining the demand response control strategy and target frequency based on the target operating power and the current operating power of the air conditioner includes: Based on the target operating power and the current operating power, the index values ​​of the demand response evaluation indicators are determined, wherein the demand response evaluation indicators include at least one of the target load reduction amount, the target load reduction density, and the target demand response potential; Acquire building disturbance data and human behavior prediction data during the demand response period. The human behavior prediction data is used to characterize the predicted human behavior state, and the human behavior state corresponds to an indoor temperature limit range. The indoor temperature limit range corresponds to a compressor operating frequency limit range. The building disturbance data includes outdoor temperature and indoor temperature. The building disturbance data, the human behavior prediction data, and the indicator values ​​are input into the black box model to obtain the demand response control strategy output by the black box model. The black box model reflects the mapping relationship between input parameters and demand response control strategies. The input parameters include: building disturbance data, human behavior prediction data, and the index values ​​of demand response evaluation indicators.

2. The method according to claim 1, characterized in that, The black-box model was created in the following way: Obtain the building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values ​​of demand response evaluation indicators for each historical demand response period in N historical demand response periods, where N is a positive integer, and the demand response event refers to responding to the power grid side to adjust the air conditioning. For each historical demand response period, create a mapping relationship between the corresponding building disturbance data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and the index values ​​of demand response evaluation indicators. Create a quantitative database based on N mapping relationships; The black-box model is created based on the quantitative database.

3. The method according to claim 2, characterized in that, The method of obtaining the indicator values ​​of the demand response evaluation index corresponding to each of the N historical demand response periods includes: For a given historical demand response period, the operating power of the air conditioner under different operating modes is obtained, including the operating mode during the demand response period and the operating mode after the demand response is completed. Based on the operating power under the different operating modes, the index values ​​of the demand response evaluation indicators are determined.

4. The method according to claim 3, characterized in that, The method of obtaining the operating power of the air conditioner in different operating modes includes: Based on the equivalent heat capacity and equivalent impedance of the air conditioner, as well as the building disturbance data and human behavior prediction data of the air conditioner in the corresponding operating mode, the operating power in the corresponding mode is determined.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: For each completed demand response event, the black-box model is trained based on the input parameters and output demand response control strategy during the demand response period of the demand response event.

6. The method according to any one of claims 1 to 4, characterized in that, The second strategy includes: Adjust the compressor operating frequency to the target frequency. If the air conditioner operating power is still greater than the target operating power, then adjust the fan damper to the target damper. Alternatively, adjust the compressor operating frequency to the target frequency while simultaneously adjusting the fan damper to the target damper.

7. An air conditioning control device, characterized in that, include: The acquisition module is used to acquire the target operating power of the air conditioner indicated by the power grid side; The processing module is used to determine a demand response control strategy and a target frequency based on the target operating power and the current operating power of the air conditioner. The demand response control strategy includes a first strategy and a second strategy. The first strategy is to adjust the compressor operating frequency to the target frequency, and the second strategy is to adjust the compressor operating frequency to the target frequency and adjust the fan damper to the target damper. The target frequency is determined based on the functional relationship between operating power and frequency. The adjustment module is used to adjust the air conditioner according to the demand response control strategy; The processing module determines the demand response control strategy and target frequency based on the target operating power and the current operating power of the air conditioner, including: Based on the target operating power and the current operating power, the index values ​​of the demand response evaluation indicators are determined, wherein the demand response evaluation indicators include at least one of the target load reduction amount, the target load reduction density, and the target demand response potential; Acquire building disturbance data and human behavior prediction data during the demand response period. The human behavior prediction data is used to characterize the predicted human behavior state, and the human behavior state corresponds to an indoor temperature limit range. The indoor temperature limit range corresponds to a compressor operating frequency limit range. The building disturbance data includes outdoor temperature and indoor temperature. The building disturbance data, the human behavior prediction data, and the indicator values ​​are input into the black box model to obtain the demand response control strategy output by the black box model. The black box model reflects the mapping relationship between input parameters and demand response control strategies. The input parameters include: building disturbance data, human behavior prediction data, and the index values ​​of demand response evaluation indicators.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed, cause the computer to perform the method as described in any one of claims 1-5.

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