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

Abstract

The invention provides an air conditioner control method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining target operation power of the air conditioner indicated by a power grid side; according to the target operating power and the current operating power of the air conditioner in the demand response time period, demand response control strategies and target frequency are determined, the demand response control strategies comprise a first strategy or a second strategy, the first strategy is used for adjusting the operating frequency of a compressor to the target frequency, and the second strategy is used for adjusting the operating frequency of the compressor to the target frequency. The second strategy is to adjust the operation frequency of the compressor to the target frequency and adjust the wind gear of the fan to the target wind gear; wherein the target frequency is determined according to a function relationship between the operation power and the frequency; and the air conditioner is adjusted according to the demand response control strategy. According to the method, the problem of how to respond to the finer power reduction demand of the power grid side can be solved.

Description

Technical Field

[0001] The present application relates to air conditioning control technology, and in particular to an air conditioning control method, device, electronic device, and storage medium. Background Art

[0002] With rapid socioeconomic development and the continuous improvement of people's living standards, various types of air conditioners have become widely used. This widespread use of air conditioners has resulted in a significant "air conditioning load." This rapid increase in air conditioning load can lead to numerous problems on the power grid, such as widening peak-to-valley differences, deteriorating load characteristics, and significant load spikes. To ensure the economical, safe, and stable operation of the power grid, the power grid will issue power reduction orders to air conditioners to regulate the load.

[0003] At present, when air conditioners respond to the power reduction requirements issued by the power grid, there are only two response modes. One of the response modes is conventional reduction, that is, the air conditioner compressor is actively reduced to 70% of the rated frequency, and the other response mode is strict reduction, that is, the air conditioner compressor is actively reduced to 40% of the rated frequency. However, when these two response modes are applied to variable-frequency air conditioners, the air conditioner compressor can only be reduced to 70% or 40% of the rated power, which cannot adapt to the more refined power reduction requirements of the power grid. Such more refined power reduction requirements, for example, the power grid hopes to be able to reduce power within the range of ±15% (or 10%, or 20%, etc.) of the power gap. In addition, it will also limit the flexibility of the variable-frequency air conditioner in frequency reduction adjustment.

[0004] Therefore, how to respond to the more refined power reduction needs on the grid side still needs to be solved. Summary of the Invention

[0005] The present application provides an air conditioning control method, device, electronic device, and storage medium to solve the problem of how to respond to more refined power reduction needs on the grid side.

[0006] In one aspect, the present application provides an air conditioning control method, comprising:

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

[0008] 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 during the demand response period, wherein 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 windshield to the target windshield; wherein the target frequency is determined based on a functional relationship between the operating power and the 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 the target frequency according to the target operating power and the current operating power of the air conditioner includes:

[0011] determining an index value of a demand response evaluation index according to the target operating power and the current operating power, the demand response evaluation index comprising at least one of a target load reduction amount, a target load reduction density, and a target demand response potential;

[0012] Obtaining building interference data and human behavior prediction data during a demand response period, wherein the human behavior prediction data is used to represent a 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, and the building interference data includes outdoor temperature and indoor temperature;

[0013] Inputting the building interference 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;

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

[0015] In one embodiment, the black box model is created by:

[0016] Obtaining building interference data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values of demand response evaluation indicators corresponding to each of N historical demand response periods, where N is a positive integer and the demand response event refers to performing air conditioning adjustment in response to the grid side;

[0017] For each historical demand response period, create a mapping relationship between the building interference data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and indicator values of demand response evaluation indicators corresponding to the historical demand response period;

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

[0019] The black box model is created according to the quantitative database.

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

[0021] For a historical demand response period, obtaining the operating power of the air conditioner in different operating modes, wherein the operating mode includes the operating mode during the demand response period and the operating mode after the demand response is completed;

[0022] Determine the index value of the demand response evaluation index according to the operating power under the different operating modes.

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

[0024] 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 interference data and human behavior prediction data of the air conditioner in the corresponding operating mode.

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

[0026] Each time a demand response event is completed, the black box model is trained according to the input parameters of the demand response period during which the demand response event occurs and the output demand response control strategy.

[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, adjust the fan windshield to the target windshield.

[0029] Alternatively, the compressor operating frequency is adjusted to the target frequency while the fan windshield is adjusted to the target windshield.

[0030] In another aspect, the present application provides an air conditioning control device, comprising:

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

[0032] a processing module, configured 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 including 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 windshield to the target windshield; wherein the target frequency is determined based on a functional relationship between the operating power and the frequency;

[0033] A regulating module is used to regulate the air conditioner according to the demand response control strategy.

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

[0035] The memory stores computer-executable instructions;

[0036] The processor executes the computer-executable instructions stored in the memory to implement the method according to the first aspect.

[0037] On the other hand, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the instructions are executed, the computer executes the method described in the first aspect.

[0038] In summary, an embodiment of the present application provides an air conditioning control method. The air conditioning control method includes: obtaining the target operating power of the air conditioner indicated by the grid side; 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 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 windshield to the target windshield; wherein the target frequency is determined based on the functional relationship between the operating power and the frequency.

[0039] That is, after receiving the target operating power indicated by the grid side, a demand response control strategy is generated to adjust the operating power of the air conditioner to the target operating power. Specifically, the demand response control strategy will adjust the compressor operating frequency to the target frequency, or will also adjust the fan windshield to the target windshield. Among them, the target frequency is determined based on the functional relationship between the operating power and the frequency, that is, the target frequency is not a fixed setting, but is calculated based on the target operating power and is variable. In this way, compared with the traditional solution with only two operating powers, this embodiment increases the compressor frequency adjustment range from 40% and 70% to [f min , f max ] full range, improving the flexibility of variable frequency air conditioner control and being more conducive to the refined regulation of power grid demand response. In addition, this application can also provide different demand response control strategies based on the actual operating status of the air conditioner, making the air conditioner regulation and control more practical and improving the use effect of the air conditioner. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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

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

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

[0047] The above drawings illustrate specific embodiments of the present disclosure, which will be described in more detail below. These drawings and textual descriptions are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of the present disclosure to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0048] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present 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 understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0051] First, let’s explain the terms involved in this application:

[0052] Load: The amount of power or torque that the driven machine requires the engine to output. It can be expressed as an absolute value or relative value (relative to the rated power or maximum torque).

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

[0054] Markov chain: A random method based on the assumption that all past information is stored in the present state, that is, the probability of human behavior at the next moment depends only on the human behavior state at the current moment.

[0055] With rapid socioeconomic development and the continuous improvement of people's living standards, various types of air conditioners have become widely used. This widespread use of air conditioners has resulted in a significant "air conditioning load." This rapid increase in air conditioning load can lead to numerous problems on the power grid, such as widening peak-to-valley differences, deteriorating load characteristics, and significant load spikes. To ensure the economical, safe, and stable operation of the power grid, the power grid will issue power reduction orders to air conditioners to regulate the load.

[0056] Currently, when air conditioners respond to power reduction demands issued by the grid, there are only two response modes. One response mode is conventional reduction, that is, the air conditioner compressor is actively reduced to 70% of the rated frequency, and the other response mode is strict reduction, that is, the air conditioner compressor is actively reduced to 40% of the rated frequency. However, when these two response modes are applied to variable-frequency air conditioners, the air conditioner compressor can only be reduced to 70% or 40% of the rated power, which cannot adapt to the more refined power reduction demands of the grid. For example, the grid hopes to reduce power within the range of ±15% (or 10%, or 20%, etc.) of the power gap. In addition, it will also limit the flexibility of variable-frequency air conditioners in frequency reduction adjustment. How to respond to the more refined power reduction demands of the grid still needs to be considered.

[0057] Based on this, the present application provides an air conditioning control method, device, electronic device, and storage medium. The air conditioning control method includes: obtaining the target operating power of the air conditioner indicated by the grid side; determining the demand response control strategy and 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 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 windshield to the target windshield; wherein the target frequency is determined based on the functional relationship between the operating power and the frequency.

[0058] That is, after receiving the target operating power indicated by the grid side, a demand response control strategy is generated to adjust the operating power of the air conditioner to the target operating power. Specifically, the demand response control strategy will adjust the compressor operating frequency to the target frequency, or will also adjust the fan windshield to the target windshield. Among them, the target frequency is determined based on the functional relationship between the operating power and the frequency, that is, the target frequency is not a fixed setting, but is calculated based on the target operating power and is variable. In this way, compared with the traditional solution with only two operating powers, the present application improves the flexibility of variable frequency air conditioner control and is more conducive to the refined regulation of demand response of the power grid. In addition, the present application can also give different demand response control strategies based on the actual operating status of the air conditioner, so that the adjustment and control of the air conditioner is more in line with reality, and improves the use effect of the air conditioner.

[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 of the application of the air conditioning control method provided in this application. In the figure, the electronic device obtains the target operating power of the air conditioner indicated by the grid side, and determines the demand response control strategy and target frequency based on the target operating power and the current operating power of the air conditioner. The air conditioner is adjusted according to the demand response control strategy. 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 windshield to the target windshield; wherein the target frequency is determined based on the functional relationship between the operating power and the frequency.

[0060] See Figure 2 One embodiment of the present application provides an air conditioning control method, device, 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, the load can be represented by an absolute value or a 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 grid side can issue a power reduction request to the air conditioner. Correspondingly, the target operating power is lower than the normal operating power of the air conditioner.

[0064] S220, 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, the demand response control strategy including 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 windshield to the target windshield; wherein the target frequency is determined based on a functional relationship between the operating power and the frequency.

[0065] The current operating power of the air conditioner refers to the operating power of the air conditioner during the demand response period, including the operating power at multiple moments. For example, if the demand response period is 1 minute and the time step is 20 seconds, there will be four operating powers at the 0th second, the 20th second, the 40th second, and the 60th second. In other words, the current operating power of the air conditioner has four.

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

[0067] When determining a demand response control strategy based on the target operating power and the current operating power, an indicator value of a demand response evaluation index for the air conditioner may be first determined 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 conditioner compressor may be determined based on the indicator value. The demand response evaluation index includes at least one of a target load reduction amount, a target load reduction density, and a target demand response potential.

[0068] Optional, load reduction Load reduction density Demand response potential Where, P Base,i Represents the operating power of the air conditioner during the demand response period, that is, the current operating power described, P DR,i represents the target operating power, n represents the operating power at n moments in the demand response period, and i represents the i-th moment.

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

[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. Such black box models include, but are not limited to, linear regression models, support vector machine models, logistic regression models, decision tree models, random forest models, naive Bayesian models, neural network models, and deep learning models. Black box models have a fast computational speed, typically outputting results in no more than 1 to 3 seconds.

[0071] Specifically, the black box model reflects the mapping relationship between input parameters and demand response control strategy. The mapping relationship also includes the functional relationship between operating power and frequency. The input parameters of the black box model include: building interference data, human behavior prediction data and the index value of the demand response evaluation index. 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 value of the demand response evaluation index is determined. The demand response evaluation index includes at least one of the target load reduction amount, the target load reduction density and the target demand response potential. Then, the building interference data and human behavior prediction data for the demand response period are obtained. The building interference data, the human behavior prediction data and the index value 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 temperature and indoor temperature, 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, and the human behavior state corresponds to an indoor temperature limit range. For example, if the human behavior state is to set the air conditioning temperature, then based on historical experience, the indoor temperature limit range corresponding to the set air conditioning temperature can be determined, for example, 22°C to 28°C. The indoor temperature limit range corresponds to the compressor operating frequency limit range, and the highest temperature in the indoor temperature limit range corresponds to the minimum compressor operating frequency f min The lowest temperature in the indoor temperature limit range corresponds to the maximum operating frequency of the compressor f max It should be noted that demand response events will not change the indoor temperature limit range corresponding to the human behavior state.

[0074] Optionally, the predicted human behavior data can be obtained based on the user's schedule settings. For example, an air conditioner wired controller with a schedule setting function can automatically control the air conditioning system's operating mode and corresponding parameters according to the user's pre-set schedule. The air conditioning system's operating mode and corresponding parameters include the adjusted indoor temperature. Optionally, human behavior status can be predicted based on Monte Carlo-Markov chain prediction methods. Other methods can also be used to obtain predicted human behavior data, and this embodiment does not limit them.

[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 is to adjust the compressor operating frequency to the target frequency. The second strategy is to adjust the compressor operating frequency to the target frequency and adjust the fan windage to the target windage.

[0077] When the power reduction demand on the grid side can be met by only reducing the compressor operating frequency, the first strategy is output. min , but still cannot meet the power reduction demand on the grid side, the target windshield of the fan is further determined based on the unsatisfied power, and the fan windshield is adjusted to the target windshield. The unsatisfied power is equal to the target operating power and the compressor reaches the target frequency (i.e. the minimum operating frequency f min ) between .

[0078] When the demand response control strategy is the first strategy, the compressor operating frequency is adjusted to the target frequency, that is, the air conditioner can be adjusted to complete the demand response on the grid side. When the demand response control strategy is the second strategy, the compressor operating frequency is adjusted to the target frequency (that is, 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, adjusting the fan windshield to the target windshield; or, adjusting the compressor operating frequency to the target frequency while adjusting the fan windshield to the target windshield.

[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 and greater than the second power, the first strategy corresponds. When the power reduction demand on the grid side is medium, for example, when the target power is less than the third power and greater than the fourth power, the second strategy corresponds to adjusting the compressor operating frequency to the target frequency. If the air conditioner operating power is still greater than the target operating power, the fan damper is adjusted to the target damper. When the power reduction demand on the grid side is large, for example, when the target power is less than the fifth power and greater than the sixth power, the second strategy corresponds to adjusting the compressor operating frequency to the target frequency while adjusting the fan damper to the target damper. Among them, 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 air conditioner is adjusted according to the second strategy while adjusting the compressor operating frequency to the target frequency.

[0081] In summary, this embodiment provides an air conditioning control method. The air conditioning control method includes: obtaining 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 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 windshield to the target windshield; wherein the target frequency is determined based on a functional relationship between operating power and frequency.

[0082] That is, after receiving the target operating power indicated by the grid side, a demand response control strategy is generated to adjust the operating power of the air conditioner to the target operating power. Specifically, the demand response control strategy will adjust the compressor operating frequency to the target frequency, or will also adjust the fan windshield to the target windshield. Among them, the target frequency is determined based on the functional relationship between the operating power and the frequency, that is, the target frequency is not a fixed setting, but is calculated based on the target operating power and is variable. In this way, compared with the traditional solution with only two operating powers, this embodiment increases the compressor frequency adjustment range from 40% and 70% to [f min , f max ] full range, improving the flexibility of variable frequency air conditioner control and being more conducive to the refined regulation of power grid demand response. In addition, this application can also provide different demand response control strategies based on the actual operating status of the air conditioner, making the air conditioner regulation and control more practical and improving the use effect of the air conditioner.

[0083] See Figure 3Another embodiment of the present application provides an air conditioning control method, which describes in detail the process of creating a black box model, including:

[0084] S310, obtaining building interference data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and indicator values of demand response evaluation indicators corresponding to 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 grid side.

[0085] A demand response event refers to air conditioning adjustments in response to the grid. Before establishing a black box model, multiple demand response events occurred. Based on data from these demand response event periods, a black box model can be built. This black box model outputs demand response strategies to improve the speed of grid-side demand response. Data from these demand response event periods can be stored in the cloud and accessed when needed.

[0086] The data during the period when a demand response event occurs include: building interference 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] Among them, building interference data includes indoor temperature and outdoor temperature, and can also include solar radiation, humidity, wind speed, etc.

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

[0089] The indicator value of the demand response evaluation index includes at least one of load reduction amount, load reduction density, and demand response potential. The load reduction amount, load reduction density, and demand response potential all need to be calculated.

[0090] Optionally, for a historical demand response period, the operating power of the air conditioner in different operating modes is obtained, and the operating mode includes 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 the different operating modes, the index value of the demand response evaluation index is determined. Specifically, the load reduction amount Load reduction density Demand response potential Where, P Base,iRepresents the operating power of the air conditioner in the demand response period, P DR,i represents the operating power of the air conditioner in the operation mode after demand response is completed, n represents the operating power at n moments in the demand response period, and i represents the i-th moment.

[0091] Optionally, when obtaining the operating power of the air conditioner in different operating modes, a building cooling and heating load model is introduced to determine the operating power of the air conditioner in different operating modes. The building cooling and heating load model is, for example, a first-order equivalent thermal parameter model (RC model) of the air conditioner. From the perspective of simplified calculation, assuming that the indoor temperature is equal to the solid temperature, the RC model can be simplified to: Among them, C a is the equivalent heat capacity, Q is the cooling and heating load, T env is the outside temperature, T i is the indoor gas temperature, R1 is the equivalent impedance, and t is the time. a R and R1 need to be fitted based on the air conditioner's historical operating data. When using the RC model to determine the air conditioner's operating power in different operating modes, the air conditioner's equivalent heat capacity and equivalent impedance, as well as the air conditioner's building interference data and predicted human behavior data under the corresponding operating mode, are used to determine the operating power in the corresponding mode.

[0092] Optionally, the RC model can also be replaced by a simplified 2R2C model, 3R2C model, 3R3C model or other higher-dimensional thermal network models. Taking the 3R2C thermal network model as an example, the three Rs of the model are the combined heat transfer resistance of external convection and radiation, the wall thermal resistance and the internal area convection and radiation heat transfer resistance, and the two Cs are the wall capacitance and the regional node capacitance. A total of 11 nodes are used for thermal modeling of the entire building envelope. Due to the great difficulty of directly solving differential equations, high-dimensional RC models are generally solved by Laplace transformation. The transfer matrix of the 3R2C model after Laplace transformation is: The simplified transfer equation is After the calculation is completed, the final result can be obtained through the inverse Lapland transformation. Since the calculation work of obtaining power based on the 3R2C model is relatively complicated, it is not recommended to use it on conventional air conditioners.

[0093] It should be noted that when creating the black box model, the mapping relationship between the air conditioner's equivalent heat capacity and equivalent impedance, as well as the building interference data and human behavior prediction data of the air conditioner in the corresponding operating mode, and the air conditioner's operating power is also created in the black box model.

[0094] The demand response control strategy for a demand response event includes the first strategy or the second strategy described above.

[0095] S320, for each historical demand response period, creating a mapping relationship between the building interference data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and indicator values of demand response evaluation indicators corresponding to the historical demand response period.

[0096] A mapping relationship is created for each historical demand response period. This mapping relationship refers to the mapping relationship between the building interference data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and the indicator values of demand response evaluation indicators corresponding to the historical demand response period.

[0097] N historical demand response periods will create corresponding N mapping relationships.

[0098] The mapping relationships created include the functional relationship between operating power and frequency in the compressor model, the mapping relationship between building interference data and human behavior prediction data in the building cooling and heating load model and operating power, and the mapping relationship between the indicator values of the demand response evaluation index and the demand response control strategy.

[0099] S330: Create a quantitative database based on the N mapping relationships.

[0100] The mapping relationship of the quantitative database is as follows: f(internal & external disturbance, human behavior, DR events, Control Strategy)

[0101] →y(ΔP or DSI or DR flex ), where internal & external disturbance represent building disturbance data, human behavior represents human behavior prediction data, DR events represents demand response events, ControlStrategy represents demand response strategy, ΔP or DSI or DR flex Represents the index value of the demand response index. ΔP represents the load reduction amount, DSI represents the load reduction density, DR flex Represents demand response potential.

[0102] 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 obtain human behavior prediction data, Module 2, such as the aforementioned RC model, is used to obtain operating power and set a compressor operating frequency limit range, and Module 3 is used to select a demand response control strategy.

[0103] 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 humidity, building occupancy rate, air conditioning usage rate, air conditioning lighting usage rate, energy consumption behavior, compressor frequency adjustment value, fan gear adjustment value, duration, etc., a large number of samples are sampled and generated. The sampling method can be random sampling, stratified sampling, Latin hypercube sampling, etc. The large number of samples are input into Modules 1, 2, and 3 for calculation, and the operating power of the air conditioner under different operating modes is obtained respectively. Finally, the indicator values of the demand response indicators output by Modules 1, 2, and 3 are obtained. A quantitative database for demand response is established based on the mapping relationships in Modules 1, 2, and 3.

[0104] S340: Create the black box model based on the quantitative database.

[0105] The obtained quantitative database is converted into a demand response black box model through an inversion process. The input parameters of the black box model include building interference data, human behavior prediction data, and the index value of the demand response evaluation index. The output of the black box model is the demand response control strategy.

[0106] Black box models include, but are not limited to, linear regression models, support vector machine models, logistic regression models, decision tree models, random forest models, naive Bayesian models, neural network models, and deep learning models. Black box models offer fast computational speeds, typically outputting results in less than 1 to 3 seconds. Therefore, using black box models to generate demand response control strategies is suitable for macro-control of large-scale air conditioning system clusters and large numbers of devices.

[0107] In one optional embodiment, each time a demand response event is completed, the black box model is trained based on the input parameters and output demand response control strategy for the demand response period during which the demand response event occurred. In other words, real-time updates are performed based on measured demand response data, allowing for a progressively more accurate black box model to be acquired with each successive demand response period. This online learning and real-time updating of the black box model can make the model lightweight and, from a long-term perspective, gradually improve the computational accuracy of the black box model.

[0108] S350, obtaining the target operating power of the air conditioner indicated by the grid side, determining the demand response control strategy and target frequency based on the black box model, the target operating power and the current operating power of the air conditioner during the demand response period, and adjusting the air conditioner according to the demand response control strategy.

[0109] For step S350, reference may be made to the relevant descriptions of steps S210 to S230 in the above embodiment, which will not be repeated here.

[0110] In summary, this embodiment provides an air conditioning control method, comprising: obtaining building interference data, human behavior prediction data, demand response events, demand response control strategies for these demand response events, and demand response evaluation index values corresponding to each of N historical demand response periods, where N is a positive integer and the demand response event refers to air conditioning regulation in response to the power grid. For each historical demand response period, a mapping relationship is created between the building interference data, human behavior prediction data, demand response events, demand response control strategies for these demand response events, and the demand response evaluation index values corresponding to these historical demand response periods. A quantitative database is created based on the N mapping relationships. A black box model is created based on the quantitative database. A target operating power for 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 regulated according to the demand response control strategy.

[0111] Specifically, a quantitative database of demand response is created based on a large number of samples, and a black-box model of demand response is created based on this database. This black-box model maps the relationship between input parameters and demand response control strategies. The input parameters include building interference data, predicted human behavior data, and the values of demand response evaluation indicators. The output of this black-box model is the demand response control strategy. The black-box model has a fast computational speed, typically outputting results in no more than 1 to 3 seconds. Therefore, using this black-box model to output demand response control strategies is suitable for macro-control of large-scale air conditioning system clusters and large numbers of devices.

[0112] In addition, the air conditioning control method provided in this embodiment generates a demand response control strategy to adjust the operating power of the air conditioner to the target operating power after receiving the target operating power indicated by the grid side. Specifically, the demand response control strategy will adjust the compressor operating frequency to the target frequency, or will also adjust the fan windshield to the target windshield. The target frequency is determined based on the functional relationship between the operating power and the frequency, that is, the target frequency is not a fixed setting, but is calculated based on the target operating power and is variable. In this way, compared with the traditional solution with only two operating powers, this embodiment increases the compressor frequency adjustment range from 40% and 70% to [f min , f max ] full range, improving the flexibility of variable frequency air conditioner control and being more conducive to the refined regulation of power grid demand response. In addition, this application can also provide different demand response control strategies based on the actual operating status of the air conditioner, making the air conditioner regulation and control more practical and improving the use effect of the air conditioner.

[0113] See Figure 5 One embodiment of the present application further provides an air conditioning control device 10, comprising:

[0114] The acquisition module 11 is configured to acquire the target operating power of the air conditioner indicated by the grid side.

[0115] 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, 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 windshield to the target windshield; wherein the target frequency is determined based on the functional relationship between the operating power and the frequency.

[0116] The regulating module 13 is configured to regulate the air conditioner according to the demand response control strategy.

[0117] Optionally, the processing module 12 is specifically used to: determine an index value of a demand response evaluation index based on the target operating power and the current operating power, the demand response evaluation index including at least one of a target load reduction amount, a target load reduction density, and a target demand response potential; obtain building interference data and human behavior prediction data during 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, and the indoor temperature limit range corresponds to a compressor operating frequency limit range, wherein the building interference data includes outdoor temperature and indoor temperature; input the building interference 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 input parameters and the demand response control strategy, and the input parameters include: building interference data, human behavior prediction data, and the index value of the demand response evaluation index.

[0118] Optionally, the black box model is created in the following manner: obtaining building interference data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values of demand response evaluation indicators corresponding to 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 grid side; for each historical demand response period, creating a mapping relationship between the building interference data, human behavior prediction data, demand response events, demand response control strategies for demand response events, and index values of demand response evaluation indicators corresponding to the historical demand response period; creating a quantitative database based on the N mapping relationships; and creating the black box model based on the quantitative database.

[0119] Specifically, for a historical demand response period, the operating power of the air conditioner in different operating modes is obtained, and the operating mode includes the operating mode during the demand response period and the operating mode after the demand response is completed; according to the operating power under the different operating modes, the index value of the demand response evaluation index is determined.

[0120] 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 the building interference data and human behavior prediction data of the air conditioner in the corresponding operating mode.

[0121] Optionally, the processing module 12 is further configured to train the black box model each time a demand response event is completed, based on the input parameters of the demand response period during which the demand response event occurs and the output demand response control strategy.

[0122] 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 windshield to the target windshield; or, adjusting the compressor operating frequency to the target frequency while adjusting the fan windshield to the target windshield.

[0123] The air conditioning control device provided in the embodiment of the present application can execute the backlight compensation method in the above method embodiment, and its implementation principle and technical effect are similar, which will not be described in detail here. Figure 5 The division of the modules shown is only a schematic illustration, and this application does not limit the division of the modules and the naming of the modules.

[0124] See Figure 6 The present application further provides an electronic device 20, comprising a processor 21 and a memory 22 in communication with 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 above embodiments.

[0125] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the instructions are executed, the computer-executable instructions are executed by a processor to implement the air-conditioning control method provided in any of the above embodiments.

[0126] The present application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the air conditioning control method provided in any of the above embodiments.

[0127] It should be noted that the computer-readable storage medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface mount storage device, an optical disc, or a compact disc read-only memory (CD-ROM). It may also be various electronic devices that include one or any combination of the above memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0128] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0129] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course 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 the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An air conditioning control method, characterized in that: include: Obtain the target operating power of the air conditioner indicated by the grid side; 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 during the demand response period, wherein 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 windshield to the target windshield; wherein the target frequency is determined based on a functional relationship between the operating power and the frequency; The air conditioner is adjusted according to the demand response control strategy.

2. The method according to claim 1, characterized in that The determining of a demand response control strategy and a target frequency according to the target operating power and the current operating power of the air conditioner includes: determining an index value of a demand response evaluation index according to the target operating power and the current operating power, the demand response evaluation index comprising at least one of a target load reduction amount, a target load reduction density, and a target demand response potential; Obtaining building interference data and human behavior prediction data during a demand response period, wherein the human behavior prediction data is used to represent a 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, and the building interference data includes outdoor temperature and indoor temperature; Inputting the building interference 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; The black box model reflects the mapping relationship between input parameters and demand response control strategies, and the input parameters include: building interference data, human behavior prediction data, and index values of demand response evaluation indicators.

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

4. The method according to claim 3, characterized in that The step of obtaining the index value of the demand response evaluation index corresponding to each of the N historical demand response periods includes: For a historical demand response period, obtaining the operating power of the air conditioner in different operating modes, wherein the operating mode includes the operating mode during the demand response period and the operating mode after the demand response is completed; Determine the index value of the demand response evaluation index according to the operating power under the different operating modes.

5. The method according to claim 4, characterized in that The obtaining of the operating power of the air conditioner in different operating modes includes: 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 interference data and human behavior prediction data of the air conditioner in the corresponding operating mode.

6. The method according to any one of claims 2 to 5, characterized in that The method further comprises: Each time a demand response event is completed, the black box model is trained according to the input parameters of the demand response period during which the demand response event occurs and the output demand response control strategy.

7. The method according to any one of claims 1 to 5, 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, adjust the fan windshield to the target windshield. Alternatively, the compressor operating frequency is adjusted to the target frequency while the fan windshield is adjusted to the target windshield.

8. An air conditioning control device, characterized in that: include: an acquisition module, used to acquire the target operating power of the air conditioner indicated by the grid side; a processing module, configured 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 including 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 windshield to the target windshield; wherein the target frequency is determined based on a functional relationship between the operating power and the frequency; A regulating module is used to regulate the air conditioner according to the demand response control strategy.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which, when executed, enable the computer to execute the method according to any one of claims 1 to 7.

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