Air conditioner control method oriented to power grid requirements and user willingness and electronic equipment

Abstract

The invention provides a power grid demand and user will-oriented air conditioner control method and electronic equipment, and the method comprises the steps: carrying out the fuzzy processing of an obtained power grid demand value to obtain a target power grid demand subset, carrying out the fuzzy processing of an obtained user will value to obtain a target user will subset, and carrying out the fuzzy processing of the target user will subset; carrying out fuzzy processing on the obtained predicted average value to obtain a target predicted average value subset, carrying out fuzzy reasoning according to the target power grid demand subset, the target user will subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function, and calculating the target compressor frequency function according to the target compressor frequency function. And determining a target compressor frequency to control the air conditioner to operate according to the target compressor frequency. The power grid demand, the user will and the predicted average value of the thermal comfort are comprehensively considered from the power supply side and the user side, so that air conditioner operation is controlled, flexibility of a power system is improved, and stable operation of the power system is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of air conditioning control technology, and more specifically, to an air conditioning control method and electronic equipment oriented to power grid demand and user wishes. Background Art

[0002] With the increase of renewable energy in power supply, the problem of power supply and demand balance has attracted more and more attention. Demand response can solve the problem of power supply and demand balance by changing the load state on the demand side. Air conditioning accounts for a large proportion of demand-side resources and has great regulation potential. Therefore, it is urgent to tap the potential of air conditioning to provide power auxiliary regulation services for the power system.

[0003] In the prior art, the demand response of the air conditioner is controlled by two control methods. The first control method is temperature control, specifically adjusting the air conditioner operating temperature according to the grid demand. The second control method is compressor state control, specifically adjusting the air conditioner compressor state according to the grid demand.

[0004] However, the temperature control in the existing technology has a lag and cannot provide power support for the power system, and adjusting the operating temperature of the air conditioner limits the flexibility of power adjustment. Although the compressor state control responds to the grid demand, it does not take into account the user's comfort, and the user experience is poor. Summary of the invention

[0005] The purpose of this application is to provide an air conditioning control method and electronic equipment oriented to grid demand and user wishes in view of the deficiencies in the above-mentioned prior art, so as to solve the problems of low power regulation flexibility and poor user experience in the prior art.

[0006] To achieve the above objectives, the technical solutions adopted in this application are as follows:

[0007] In a first aspect, the present application provides an air conditioning control method oriented to grid demand and user wishes, the method comprising:

[0008] Performing fuzzy processing on the acquired power grid demand value to obtain a target power grid demand subset, wherein the target power grid demand subset includes a plurality of first parameters, each first parameter corresponds to a power adjustment strategy, and the value of each first parameter is used to characterize the membership of the power grid demand value in the corresponding power adjustment strategy dimension;

[0009] Performing fuzzy processing on the acquired user willingness value to obtain a target user willingness subset, wherein the target user willingness subset includes a plurality of second parameters, each second parameter corresponds to a user willingness type, and the value of each second parameter is used to characterize the membership of the user willingness value on the corresponding user willingness type dimension;

[0010] The obtained predicted average value is fuzzified to obtain a target predicted average value subset, wherein the target predicted average value subset includes a plurality of third parameters, each third parameter corresponds to a thermal reaction type, and the value of each third parameter is used to characterize the membership of the predicted average value on the corresponding thermal reaction type dimension;

[0011] Performing fuzzy reasoning according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function;

[0012] According to the target compressor frequency function, a target compressor frequency is determined to control the operation of the air conditioner according to the target compressor frequency.

[0013] Optionally, the fuzzy processing is performed on the acquired power grid demand value to obtain a target power grid demand subset, including:

[0014] Based on the power grid demand function and according to the acquired power grid demand value, the target power grid demand subset is calculated.

[0015] Optionally, the fuzzy processing is performed on the acquired user willingness value to obtain a target user willingness subset, including:

[0016] Based on the user willingness function and according to the acquired user willingness value, the target user willingness subset is calculated.

[0017] Optionally, the obtained predicted average value is fuzzy processed to obtain a target predicted average value subset, including:

[0018] Based on the prediction average function, the target prediction average subset is calculated according to the acquired prediction average.

[0019] Optionally, the fuzzy rule base includes multiple power adjustment strategies, multiple user intention types, and multiple thermal reaction types. The fuzzy rule base also includes: multiple compressor frequency functions constructed by combining three of each power adjustment strategy, each user intention type, and each thermal reaction type.

[0020] Optionally, performing fuzzy reasoning according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function includes:

[0021] Determining the membership degree corresponding to each compressor frequency function in the fuzzy rule base according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base;

[0022] The target compressor frequency function is determined according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base.

[0023] Optionally, determining the membership degree corresponding to each compressor frequency function in the fuzzy rule base according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base includes:

[0024] According to the value of the target first parameter in the target grid demand subset, the value of the target second parameter in the target user willingness subset and the value of the target third parameter in the target predicted average value subset, the membership degree corresponding to the compressor frequency function corresponding to the three-three combination of the target first parameter, the target second parameter and the target third parameter in the fuzzy rule base is determined, wherein the target first parameter is any first parameter in the target grid demand subset, the target second parameter is any second parameter in the target user willingness subset, and the target third parameter is any third parameter in the target predicted average value subset.

[0025] Optionally, determining the target compressor frequency function according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base includes:

[0026] According to the membership degree corresponding to each compressor frequency function in the fuzzy rule base, each compressor frequency function is fused to obtain the target compressor frequency function.

[0027] Optionally, determining a target compressor frequency according to the target compressor frequency function comprises:

[0028] Based on the area center method, the target compressor frequency is determined according to the target compressor frequency function.

[0029] In a second aspect, the present application provides an air conditioning control device, the device comprising:

[0030] A first processing module is used to perform fuzzy processing on the acquired power grid demand value to obtain a target power grid demand subset, wherein the target power grid demand subset includes a plurality of first parameters, each of which corresponds to a power adjustment strategy, and the value of each first parameter is used to characterize the membership of the power grid demand value in the corresponding power adjustment strategy dimension;

[0031] A second processing module is used to perform fuzzy processing on the acquired user willingness value to obtain a target user willingness subset, wherein the target user willingness subset includes a plurality of second parameters, each second parameter corresponds to a user willingness type, and the value of each second parameter is used to characterize the degree of membership of the user willingness value on the corresponding user willingness type dimension;

[0032] A third processing module is used to perform fuzzy processing on the obtained predicted average value to obtain a target predicted average value subset, wherein the target predicted average value subset includes a plurality of third parameters, each third parameter corresponds to a thermal reaction type, and the value of each third parameter is used to characterize the membership of the predicted average value on the corresponding thermal reaction type dimension;

[0033] A fuzzy reasoning module, used for performing fuzzy reasoning according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function;

[0034] A determination module is used to determine a target compressor frequency according to the target compressor frequency function, so as to control the operation of the air conditioner according to the target compressor frequency.

[0035] Optionally, the first processing module is specifically used for:

[0036] Based on the power grid demand function and according to the acquired power grid demand value, the target power grid demand subset is calculated.

[0037] Optionally, the second processing module is specifically used for:

[0038] Based on the user willingness function and according to the acquired user willingness value, the target user willingness subset is calculated.

[0039] Optionally, the third processing module is specifically used for:

[0040] Based on the prediction average function, the target prediction average subset is calculated according to the acquired prediction average.

[0041] Optionally, the fuzzy rule base includes multiple power adjustment strategies, multiple user intention types, and multiple thermal reaction types. The fuzzy rule base also includes: multiple compressor frequency functions constructed by combining three of each power adjustment strategy, each user intention type, and each thermal reaction type.

[0042] Optionally, the fuzzy reasoning module is specifically used for:

[0043] Determining the membership degree corresponding to each compressor frequency function in the fuzzy rule base according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base;

[0044] The target compressor frequency function is determined according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base.

[0045] Optionally, the fuzzy reasoning module is specifically used for:

[0046] According to the value of the target first parameter in the target grid demand subset, the value of the target second parameter in the target user willingness subset and the value of the target third parameter in the target predicted average value subset, the membership degree corresponding to the compressor frequency function corresponding to the three-three combination of the target first parameter, the target second parameter and the target third parameter in the fuzzy rule base is determined, wherein the target first parameter is any first parameter in the target grid demand subset, the target second parameter is any second parameter in the target user willingness subset, and the target third parameter is any third parameter in the target predicted average value subset.

[0047] Optionally, the fuzzy reasoning module is specifically used for:

[0048] According to the membership degree corresponding to each compressor frequency function in the fuzzy rule base, each compressor frequency function is fused to obtain the target compressor frequency function.

[0049] Optionally, the determination module is specifically used for:

[0050] Based on the area center method, the target compressor frequency is determined according to the target compressor frequency function.

[0051] In a third aspect, the present application provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the above-mentioned air-conditioning control method oriented to grid demand and user wishes.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the air-conditioning control method oriented to grid demand and user wishes as described above are executed.

[0053] The beneficial effects of the present application are as follows: by fuzzifying the grid demand value, the user willingness value and the predicted average value, the target grid demand subset, the target user willingness subset and the target predicted average value subset are determined, and then fuzzy reasoning is performed based on the target grid demand subset, the target user willingness subset, the target predicted average value subset and the preset fuzzy rule base to obtain the target compressor frequency function, and the target compressor frequency is determined based on the target compressor frequency function, and the air conditioner operation is controlled according to the target compressor frequency. This embodiment controls the air conditioner operation by comprehensively considering the grid demand, user willingness and the predicted average value of thermal comfort from the power supply side and the user side, which is conducive to improving the flexibility of the power system, ensuring the stable operation of the power system and the user's electricity demand, and more reasonably regulating the grid load supply. The grid power supply side can adjust different demand response time periods according to the target compressor frequency, and can effectively deal with short-term power supply and demand tensions, renewable energy consumption difficulties and other situations, and has good economy and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 It is a flowchart of an air conditioning control method oriented to grid demand and user wishes provided in an embodiment of the present application;

[0056] Figure 2 It is a technical architecture diagram of another air conditioning control method oriented to grid demand and user wishes provided in an embodiment of the present application;

[0057] Figure 3 This is a schematic diagram of the effect of an air conditioning control method oriented to grid demand and user wishes provided in an embodiment of the present application;

[0058] Figure 4 is a structural schematic diagram of an air conditioning control device provided in an embodiment of the present application;

[0059] Figure 5 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0061] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0062] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0063] In the prior art, the air conditioner operating temperature is adjusted according to the grid demand, but this method may have hysteresis and cannot provide power support for the power system, and adjusting the air conditioner operating temperature limits the flexibility of power adjustment. Although adjusting the air conditioner compressor state according to the grid demand responds to the grid demand, it does not take into account the user comfort and the user experience is poor.

[0064] Based on this, the present application provides an air conditioning control method oriented to grid demand and user intention. The method fuzzifies the grid demand value, the user intention value and the predicted average value respectively to obtain a target grid demand subset, a target user intention subset and a target predicted average value subset, and then performs fuzzy reasoning based on the target grid demand subset, the target user intention subset and the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function, and then determines the target compressor frequency based on the target compressor frequency function, thereby controlling the air conditioning operation according to the target compressor frequency. The present application obtains the target compressor frequency by performing fuzzy control based on the grid demand value, the user intention value and the predicted average value, so that the air conditioning output result takes into account the operation of the grid power generation side and the needs of the user side, thereby ensuring the balance of power supply and demand while providing users with a better user experience.

[0065] Next, refer to Figure 1 The specific steps of the air conditioning control method proposed in this application based on grid demand and user wishes are introduced. Figure 1 1 is a flow chart of an air conditioning control method oriented to grid demand and user intention provided in an embodiment of the present application. Optionally, the air conditioning control method oriented to grid demand and user intention can be applied to electronic devices with computing capabilities.

[0066] S101. Fuzzy process the acquired grid demand value to obtain a target grid demand subset, wherein the target grid demand subset includes multiple first parameters, each first parameter corresponds to a power adjustment strategy, and the value of each first parameter is used to characterize the membership of the grid demand value in the corresponding power adjustment strategy dimension.

[0067] The grid demand value may be a regulation signal on the power generation side of the power grid, which is used to adjust the power on the power consumption side according to factors such as the current power consumption status and time. The grid demand value may be in numerical form or text form. Taking the grid demand value in numerical form as an example, the grid demand value may be a value between 0 and 1. When the power consumption is at peak, the grid demand value issued by the power generation side is 0.1.

[0068] Optionally, a grid demand fuzzy set may be pre-set, and the grid demand fuzzy set includes multiple power adjustment strategies, each strategy may correspond to a function, and the function corresponding to each strategy may be called a grid demand function. Based on this, the process of fuzzifying the grid demand value may be to determine its membership in the dimensions of each power adjustment strategy in the grid demand fuzzy set according to the grid demand value, that is, to determine the set of values ​​of the grid demand value in the function corresponding to each power adjustment strategy in the grid demand fuzzy set. Among them, the membership in the dimension of each power adjustment strategy is the value of each first parameter.

[0069] Optionally, the power adjustment strategy may include, for example, a power down-regulation strategy, a power non-adjustment strategy, and a power up-regulation strategy. Based on this, the power grid demand fuzzy set may be expressed as the following formula (1):

[0070] ΔP Grid ∈[p1,p2,p3](1)

[0071] Among them, ΔP Grid Represents the fuzzy set of power grid demand, p1 is the power reduction strategy, p2 is the power non-adjustment strategy, and p3 is the power increase strategy.

[0072] S102. Fuzzy process the acquired user willingness value to obtain a target user willingness subset, wherein the target user willingness subset includes a plurality of second parameters, each second parameter corresponds to a user willingness type, and the value of each second parameter is used to characterize the degree of membership of the user willingness value on the corresponding user willingness type dimension.

[0073] Optionally, the user willingness value may be the user's willingness to participate in demand response, that is, the user's willingness to participate in air conditioning control. The user willingness value may be in numerical form or text form. Taking the user willingness value in numerical form as an example, the user willingness value may be a value between 0 and 1. When the user's willingness to participate in demand response is extremely low, the user willingness value may be 0 or 0.1.

[0074] Optionally, a user willingness fuzzy set may be pre-set, and the user willingness fuzzy set includes multiple user willingness types, each type may correspond to a function, and the function corresponding to each type may be called a user willingness function. Based on this, the process of fuzzifying the user willingness value may be to determine its membership degree on the dimension of each user willingness type in the user willingness fuzzy set according to the user willingness value, that is, to determine the set of values ​​of the user willingness value in the function corresponding to each user willingness type in the user willingness fuzzy set. Among them, the membership degree on the dimension of each user willingness type is the value of each second parameter.

[0075] Among them, the user willingness type may include, for example, a user with high willingness to participate in demand response, a user unwilling to participate in demand response, and a user with low willingness to participate in demand response. Based on this, the user willingness fuzzy set can be expressed as the following formula (2):

[0076]

[0077] in, Represents the user willingness fuzzy set, l1 means the user has a high willingness to participate in demand response, l2 means the user is unwilling to participate in demand response, and l3 means the user has a low willingness to participate in demand response.

[0078] S103, fuzzy processing is performed on the obtained predicted average value to obtain a target predicted average value subset, wherein the target predicted average value subset includes multiple third parameters, each third parameter corresponds to a thermal reaction type, and the value of each third parameter is used to characterize the membership of the predicted average value on the corresponding thermal reaction type dimension.

[0079] Among them, the predicted mean vote (PMV) is a comprehensive evaluation index based on the basic equation of human thermal balance and the level of psychophysiological subjective thermal sensation. It takes into account many relevant factors of human thermal comfort and is usually used to indicate the user's comfort.

[0080] Optionally, a higher predicted average value indicates a higher thermal comfort level for the human body.

[0081] Optionally, a predicted average fuzzy set may be pre-set, and the predicted average fuzzy set includes multiple thermal reaction types, each type may correspond to a function, and the function corresponding to each type may be called a predicted average function. Based on this, the process of fuzzifying the predicted average value may be to determine its membership in the dimensions of each thermal reaction type in the predicted average fuzzy set according to the predicted average value, that is, to determine the set of values ​​of the predicted average value in the function corresponding to each thermal reaction type in the predicted average fuzzy set. Among them, the membership in the dimensions of each predicted average type is the value of each third parameter.

[0082] Among them, the thermal reaction types may include, for example, cold, cool, comfortable, warm and hot. Based on this, the predicted average fuzzy set can be expressed as the following formula (3):

[0083] PMV∈[r1,r2,r3,r4,r5](3)

[0084] Among them, PMV represents the predicted mean fuzzy set, r1 represents cold, r2 represents cool, r3 represents comfortable, r4 represents warm, and r5 represents hot.

[0085] S104, performing fuzzy reasoning according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function.

[0086] The fuzzy rule base includes multiple power adjustment strategies in the power grid demand fuzzy set, multiple user willingness types in the user willingness fuzzy set, and multiple thermal response types in the estimated average fuzzy set, as well as compressor frequency functions corresponding to each power adjustment strategy, each user willingness type, and each thermal response type. The compressor frequency function is the fuzzy output corresponding to each power adjustment strategy, each user willingness type, and each thermal response type combination. The set containing multiple compressor frequency functions can be used as a compressor frequency fuzzy set.

[0087] Exemplarily, the compressor frequency fuzzy set may include three compressor frequency functions, expressed as the following equation (4):

[0088] f VRFS ∈[f1,f2,f3](4)

[0089] Among them, f VRFS is the compressor frequency fuzzy set, f1 represents the frequency down-regulation strategy, f2 represents the frequency non-adjustment strategy, and f3 represents the frequency up-regulation strategy.

[0090] Specifically, the target compressor frequency function is determined according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the compressor frequency functions corresponding to the target grid demand subset, the target user willingness subset and the target predicted average value subset in the fuzzy rule base.

[0091] S105 . Determine a target compressor frequency according to the target compressor frequency function, so as to control the operation of the air conditioner according to the target compressor frequency.

[0092] Specifically, the target compressor frequency function is defuzzified to determine the target compressor frequency.

[0093] Optionally, after determining the target compressor frequency, the air conditioner is controlled to operate at the target compressor frequency. The target compressor frequency is obtained by comprehensively considering the grid demand value, the user willingness value and the predicted average value, which meets the needs of power system and user comfort regulation, and can also provide reference guidance for demand response on the grid power supply side.

[0094] In this embodiment, by fuzzifying the grid demand value, the user willingness value and the predicted average value, the target grid demand subset, the target user willingness subset and the target predicted average value subset are determined, and then fuzzy reasoning is performed based on the target grid demand subset, the target user willingness subset, the target predicted average value subset and the preset fuzzy rule base to obtain the target compressor frequency function. According to the target compressor frequency function, the target compressor frequency is determined and the air conditioner operation is controlled according to the target compressor frequency. This embodiment controls the air conditioner operation by comprehensively considering the grid demand, user willingness and the predicted average value of thermal comfort from the power supply side and the user side, which is conducive to improving the flexibility of the power system, ensuring the stable operation of the power system and the user's electricity demand, and more reasonably regulating the grid load supply. The grid power supply side can adjust different demand response time periods according to the target compressor frequency, and can effectively deal with short-term power supply and demand tensions, renewable energy consumption difficulties and other situations, with good economy and flexibility.

[0095] As an optional implementation, Figure 2is a technical architecture diagram of another air conditioning control method oriented to grid demand and user wishes provided in an embodiment of the present application. Figure 2 As shown, the electronic device includes a fuzzifier, a fuzzy inference device and a defuzzifier. The grid demand value, the user willingness value and the predicted average value are respectively input into the fuzzifier for fuzzification processing, and the result of the fuzzification processing output is input into the fuzzy inference device. The result of the fuzzification processing output includes a target grid demand subset, a target user willingness subset and a target predicted average value subset. The fuzzy inference device performs fuzzy inference based on the result of the fuzzification processing output and the fuzzy rule base to obtain a target compressor frequency function. The fuzzy inference device inputs the target compressor frequency function into the defuzzifier to obtain and output the target compressor frequency. So that the air conditioner operates according to the target compressor frequency.

[0096] Furthermore, the specific process of performing fuzzy processing on the acquired power grid demand value in the above step S101 to obtain the target power grid demand subset is as follows:

[0097] Optionally, based on the grid demand function, a target grid demand subset is calculated according to the acquired grid demand value.

[0098] The grid demand function may include functions of various strategies in the power adjustment strategy. Specifically, the grid demand function may include a power down-regulation function, a power non-adjustment function, and a power up-regulation function.

[0099] As an optional implementation, the grid demand function may be a triangle membership function.

[0100] As an optional implementation, the membership corresponding to the power down-regulation element is determined according to the power down-regulation function and the grid demand value, the membership corresponding to the power non-adjustment element is determined according to the power non-adjustment function and the grid demand value, and the membership corresponding to the power up-regulation element is determined according to the power up-regulation function and the grid demand value. The membership corresponding to the power down-regulation element, the membership corresponding to the power non-adjustment element, and the membership corresponding to the power up-regulation element are respectively used as the value of a first parameter in the target grid demand subset.

[0101] In this embodiment, the target grid demand subset is calculated based on the grid demand function and the grid demand value is obtained, so that the grid demand value is fuzzified, thereby avoiding the use of a precise mathematical model and being suitable for complex control systems.

[0102] Furthermore, the process of performing fuzzy processing on the obtained user willingness value in the above step S102 to obtain the target user willingness subset is introduced as follows:

[0103] Optionally, based on the user willingness function, a target user willingness subset is calculated according to the acquired user willingness value.

[0104] The user willingness function may include functions of various types in the user willingness type. Specifically, the user willingness function may include a user high willingness function for participating in demand response, a user non-participation demand response willingness function, and a user low willingness function for participating in demand response.

[0105] As an optional implementation, the user willingness function may be a triangle membership function.

[0106] As an optional implementation, the degree of membership corresponding to the type of user with high willingness to participate in demand response is determined according to the user's high willingness function to participate in demand response and the user's willingness value, the degree of membership corresponding to the type of user unwilling to participate in demand response is determined according to the user's non-participation willingness function to participate in demand response and the user's willingness value, and the degree of membership corresponding to the type of user with low willingness to participate in demand response is determined according to the user's low willingness function to participate in demand response and the user's willingness value. The degree of membership corresponding to the type of user with high willingness to participate in demand response, the degree of membership corresponding to the type of user unwilling to participate in demand response, and the degree of membership corresponding to the type of user with low willingness to participate in demand response are respectively used as the value of a second parameter in the target user willingness subset.

[0107] In this embodiment, based on the user willingness function, according to the acquired user willingness value, the target user willingness subset is calculated, so that the user willingness value is fuzzified, thereby avoiding the use of a precise mathematical model, and being applicable to complex control systems.

[0108] Furthermore, the specific process of performing fuzzy processing on the obtained predicted average value in the above step S103 to obtain the target predicted average value subset is introduced as follows:

[0109] Optionally, based on the prediction average function, a target prediction average subset is calculated according to the acquired prediction average.

[0110] The predicted average function may include functions of various types in the thermal response type. Specifically, the predicted average function may include a cold type function, a cool type function, a comfortable type function, a warm type function, and a hot type function.

[0111] As an optional implementation, the prediction average function may be a triangle membership function.

[0112] As an optional implementation, the membership corresponding to the cold type is determined according to the cold type function and the predicted average value, the membership corresponding to the cool type is determined according to the cool type function and the predicted average value, the membership corresponding to the comfort type is determined according to the comfort type function and the predicted average value, the membership corresponding to the warm type is determined according to the warm type function and the predicted average value, and the membership corresponding to the hot type is determined according to the hot type function and the predicted average value. The membership corresponding to the cold type, the membership corresponding to the cool type, the membership corresponding to the comfort type, the membership corresponding to the warm type, and the membership corresponding to the hot type are respectively used as the value of a third parameter in the target predicted average value subset.

[0113] In this embodiment, a target predicted average value subset is calculated based on the obtained predicted average value based on the predicted average function, thereby fuzzifying the predicted average value, thereby avoiding the use of a precise mathematical model, and is suitable for complex control systems.

[0114] Optionally, the fuzzy rule base includes multiple power adjustment strategies, multiple user willingness types, and multiple thermal response types. The fuzzy rule base also includes: multiple compressor frequency functions constructed by combining three of each power adjustment strategy, each user willingness type, and each thermal response type.

[0115] Exemplarily, the fuzzy rule base may be as shown in Table 1 below:

[0116] Table 1

[0117]

[0118]

[0119] Taking the thermal response type as cool type r2, the user willingness type as type l3 with low user willingness to participate in demand response, and the power adjustment strategy as power reduction strategy p1 as an example, the corresponding compressor frequency function can be determined as frequency increase strategy f3 through the above fuzzy rule base.

[0120] Based on the content of the fuzzy rule base, in the above step S104, fuzzy reasoning is performed according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the preset fuzzy rule base to obtain the specific process of determining the target compressor frequency function as follows:

[0121] Optionally, the membership degree corresponding to each compressor frequency function in the fuzzy rule base is determined according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base.

[0122] Specifically, the membership corresponding to each compressor frequency function in the rule base is determined according to the membership on each power adjustment strategy dimension in the target grid demand subset, the membership on each user willingness type dimension in the target user willingness subset, and the membership on each thermal response type dimension in the target predicted average value subset.

[0123] Optionally, if there is a value of 0 in the membership on each power adjustment strategy dimension in the target grid demand subset, the membership on each user willingness type dimension in the target user willingness subset, and the membership on each thermal response type dimension in the target predicted average value subset, then the membership corresponding to the compressor frequency function in the fuzzy rule base corresponding to the membership of 0 is 0.

[0124] Optionally, the target compressor frequency function is determined according to the degree of membership corresponding to each compressor frequency function in the fuzzy rule base.

[0125] As an optional implementation, the memberships corresponding to the compressor frequency functions can be combined, and the function composed of the maximum membership values ​​corresponding to the frequencies in the combined compressor frequency function values ​​is used as the target compressor frequency function. The input of the compressor frequency function is the frequency values, and the output is the membership value corresponding to the frequency.

[0126] In this embodiment, after determining the membership degree corresponding to each compressor frequency function in the fuzzy rule base, the target compressor frequency function is determined according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base, thereby determining the fuzzy set based on the fuzzy rule base, avoiding the use of precise mathematical models, and being suitable for complex control systems.

[0127] Furthermore, the specific process of determining the membership degree corresponding to each compressor frequency function in the fuzzy rule base according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base in the above steps is introduced.

[0128] Optionally, according to the value of the target first parameter in the target grid demand subset, the value of the target second parameter in the target user willingness subset and the value of the target third parameter in the target predicted average value subset, the membership degree corresponding to the compressor frequency function corresponding to the three-by-three combination of the target first parameter, the target second parameter and the target third parameter in the fuzzy rule base is determined.

[0129] The target first parameter is any first parameter in the target power grid demand subset, the target second parameter is any second parameter in the target user willingness subset, and the target third parameter is any third parameter in the target predicted average value subset.

[0130] As an optional implementation, the process of determining the compressor frequency function corresponding to the three-by-three combination of the target first parameter, the target second parameter, and the target third parameter in the fuzzy rule base can be as follows:

[0131]

[0132] in, represents the compressor frequency function corresponding to the kth three-three combination. k represents the kth fuzzy rule, ° is the fuzzy synthesis operation. Taking R1 and R2 as an example, R1 and R2 are specifically expressed as follows:

[0133] R1: IF( is l1)and(PMV is r1)and(ΔP Grid is p1)

[0134] THEN(f VRFS is f1)

[0135] R2: IF( is l3)and(PMV is r5)and(ΔP Grid is p3)

[0136] THEN(f VRFS is f1)

[0137] Optionally, after determining the compressor frequency function corresponding to the three-by-three combination of the target first parameter, the target second parameter and the target third parameter in the fuzzy rule base, the minimum value among the value of the target first parameter in the target grid demand subset, the value of the target second parameter in the target user willingness subset, the value of the target third parameter in the target predicted average value subset, and the corresponding compressor frequency function value is taken as the membership corresponding to the corresponding compressor frequency function.

[0138] In this embodiment, by determining the membership of the compressor frequency function corresponding to the three-by-three combination of the target first parameter, the target second parameter and the target third parameter in the fuzzy rule base, the target compressor frequency function is determined according to the membership of the function, thereby obtaining an accurate target compressor frequency.

[0139] As an optional implementation, in the above steps, according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base, the specific steps of determining the target compressor frequency function are as follows:

[0140] Optionally, the compressor frequency functions are fused according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base to obtain a target compressor frequency function.

[0141] As an optional implementation, each compressor frequency function is fused, and the function formed by the maximum value in the fused function is used as the target compressor frequency function. Specifically, the maximum value of the membership degree corresponding to each frequency in each fused function is used as the value corresponding to each frequency in the target compressor frequency function.

[0142] As an optional implementation, in the above steps, the specific steps of determining the target compressor frequency according to the target compressor frequency function are as follows:

[0143] Optionally, the target compressor frequency is determined according to a target compressor frequency function based on an area center method.

[0144] Specifically, the value of the center point of the area surrounded by the target compressor frequency function and the horizontal coordinate is used as the target compressor frequency.

[0145] Optionally, in addition to the area center method, the target compressor frequency may be determined based on the target compressor frequency function based on the area centroid method, the area equal division method, the maximum membership average method, the maximum membership minimum method, and the maximum membership maximum method.

[0146] In this embodiment, the target compressor frequency is determined by the area center method, so that defuzzification is performed based on the target compressor frequency function to determine the accurate compressor frequency.

[0147] Figure 3 This is a schematic diagram of the effect of an air conditioning control method oriented to grid demand and user wishes provided by an embodiment of the present application. Figure 3 The results of verifying the air conditioning control method for grid demand and user willingness are explained. Three experimental groups are set, and the user willingness values ​​are 0.0, 0.5 and 1.0 respectively. In the experiment, it is assumed that users participate in demand response at 8:30. Figure 3 It can be seen that the changes in PMV corresponding to different user willingness values ​​are also different. When the user willingness value is 0.0, the indoor PMV value remains in the comfort zone. When the user willingness value is 0.5, the PMV exceeds the comfort zone 70 minutes after the user participates in the response. When the user willingness value is 1.0, the PMV exceeds the comfort zone 50 minutes after the user participates in the response. In this case, the use of air-conditioning control methods oriented to grid demand and user willingness can prevent PMV from increasing within 30 minutes, and restore the PMV value to the comfort zone after 35 minutes. Based on this, demand response within 50 minutes can be provided for users with a user willingness value of 1.0, and demand response within 70 minutes can be provided for users with a user willingness value of 0.5, thereby meeting the needs of power system and user comfort regulation, and providing reference guidance for regulation on the power supply side of the grid.

[0148] Based on the same inventive concept, an air-conditioning control device corresponding to the air-conditioning control method oriented to grid demand and user intention is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned air-conditioning control method oriented to grid demand and user intention in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0149] Reference Figure 4 FIG. 1 is a schematic diagram of the structure of an air conditioning control device provided in an embodiment of the present application, wherein the device comprises:

[0150] The first processing module 401 is used to perform fuzzy processing on the acquired power grid demand value to obtain a target power grid demand subset, wherein the target power grid demand subset includes a plurality of first parameters, each of which corresponds to a power adjustment strategy, and the value of each first parameter is used to characterize the membership of the power grid demand value in the corresponding power adjustment strategy dimension;

[0151] The second processing module 402 is used to perform fuzzy processing on the obtained user willingness value to obtain a target user willingness subset, wherein the target user willingness subset includes a plurality of second parameters, each second parameter corresponds to a user willingness type, and the value of each second parameter is used to characterize the membership of the user willingness value on the corresponding user willingness type dimension;

[0152] The third processing module 403 is used to perform fuzzy processing on the obtained predicted average value to obtain a target predicted average value subset, wherein the target predicted average value subset includes a plurality of third parameters, each of which corresponds to a thermal reaction type, and the value of each third parameter is used to characterize the membership of the predicted average value on the corresponding thermal reaction type dimension;

[0153] A fuzzy reasoning module 404 is used to perform fuzzy reasoning according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function;

[0154] The determination module 405 is used to determine a target compressor frequency according to the target compressor frequency function, so as to control the operation of the air conditioner according to the target compressor frequency.

[0155] Optionally, the first processing module 401 is specifically used for:

[0156] Based on the power grid demand function and according to the acquired power grid demand value, the target power grid demand subset is calculated.

[0157] Optionally, the second processing module 402 is specifically configured to:

[0158] Based on the user willingness function and according to the acquired user willingness value, the target user willingness subset is calculated.

[0159] Optionally, the third processing module 403 is specifically configured to:

[0160] Based on the prediction average function, the target prediction average subset is calculated according to the acquired prediction average.

[0161] Optionally, the fuzzy rule base includes multiple power adjustment strategies, multiple user intention types, and multiple thermal reaction types. The fuzzy rule base also includes: multiple compressor frequency functions constructed by combining three of each power adjustment strategy, each user intention type, and each thermal reaction type.

[0162] Optionally, the fuzzy reasoning module 404 is specifically used for:

[0163] Determining the membership degree corresponding to each compressor frequency function in the fuzzy rule base according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base;

[0164] The target compressor frequency function is determined according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base.

[0165] Optionally, the fuzzy reasoning module 404 is specifically used for:

[0166] According to the value of the target first parameter in the target grid demand subset, the value of the target second parameter in the target user willingness subset and the value of the target third parameter in the target predicted average value subset, the membership degree corresponding to the compressor frequency function corresponding to the three-three combination of the target first parameter, the target second parameter and the target third parameter in the fuzzy rule base is determined, wherein the target first parameter is any first parameter in the target grid demand subset, the target second parameter is any second parameter in the target user willingness subset, and the target third parameter is any third parameter in the target predicted average value subset.

[0167] Optionally, the fuzzy reasoning module 404 is specifically used for:

[0168] According to the membership degree corresponding to each compressor frequency function in the fuzzy rule base, each compressor frequency function is fused to obtain the target compressor frequency function.

[0169] Optionally, the determination module 405 is specifically configured to:

[0170] Based on the area center method, the target compressor frequency is determined according to the target compressor frequency function.

[0171] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0172] The present application also provides an electronic device, such as Figure 5 , which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, includes: a processor 501, a memory 502 and a bus. The memory 502 stores machine-readable instructions executable by the processor 501 (for example, Figure 4 The device includes the first processing module 401, the second processing module 402, the third processing module 403, the fuzzy reasoning module 404 and the determination module 405 corresponding to the execution instructions, etc.), when the computer device is running, the processor 501 communicates with the memory 502 through a bus, and when the machine-readable instructions are executed by the processor 501, the above-mentioned air conditioning control method oriented to the grid demand and the user's intention is executed.

[0173] The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the air-conditioning control method based on grid demand and user wishes are executed.

[0174] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0175] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or part of the technical solution that contributes to the prior art or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program code.

[0176] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. An air conditioning control method oriented to grid demand and user wishes, characterized in that: The method comprises: Performing fuzzy processing on the acquired power grid demand value to obtain a target power grid demand subset, wherein the target power grid demand subset includes a plurality of first parameters, each first parameter corresponds to a power adjustment strategy, and the value of each first parameter is used to characterize the membership of the power grid demand value in the corresponding power adjustment strategy dimension; Performing fuzzy processing on the acquired user willingness value to obtain a target user willingness subset, wherein the target user willingness subset includes a plurality of second parameters, each second parameter corresponds to a user willingness type, and the value of each second parameter is used to characterize the membership of the user willingness value on the corresponding user willingness type dimension; The obtained predicted average value is fuzzified to obtain a target predicted average value subset, wherein the target predicted average value subset includes a plurality of third parameters, each third parameter corresponds to a thermal reaction type, and the value of each third parameter is used to characterize the membership of the predicted average value on the corresponding thermal reaction type dimension; Performing fuzzy reasoning according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function; According to the target compressor frequency function, a target compressor frequency is determined to control the operation of the air conditioner according to the target compressor frequency.

2. The air conditioning control method oriented to grid demand and user wishes according to claim 1, characterized in that: The obtained power grid demand value is fuzzy processed to obtain a target power grid demand subset, including: Based on the power grid demand function and according to the acquired power grid demand value, the target power grid demand subset is calculated.

3. The air conditioning control method oriented to grid demand and user intention according to claim 1, characterized in that: The obtained user willingness value is fuzzified to obtain a target user willingness subset, including: Based on the user willingness function and according to the acquired user willingness value, the target user willingness subset is calculated.

4. The air conditioning control method oriented to grid demand and user wishes according to claim 1, characterized in that: The obtained predicted average value is subjected to fuzzy processing to obtain a target predicted average value subset, including: Based on the prediction average function, the target prediction average subset is calculated according to the acquired prediction average.

5. The air conditioning control method oriented to grid demand and user intention according to claim 1, characterized in that: The fuzzy rule base includes multiple power adjustment strategies, multiple user intention types, and multiple thermal response types. The fuzzy rule base also includes: multiple compressor frequency functions constructed by combining the power adjustment strategies, the user intention types, and the thermal response types in threes.

6. The air conditioning control method oriented to grid demand and user wishes according to claim 5, characterized in that: The method of performing fuzzy reasoning according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and a preset fuzzy rule base to obtain a target compressor frequency function includes: Determining the membership degree corresponding to each compressor frequency function in the fuzzy rule base according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base; The target compressor frequency function is determined according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base.

7. The air conditioning control method oriented to grid demand and user intention according to claim 6, characterized in that: The determining, according to the target grid demand subset, the target user willingness subset, the target predicted average value subset and the fuzzy rule base, the membership degree corresponding to each compressor frequency function in the fuzzy rule base comprises: According to the value of the target first parameter in the target grid demand subset, the value of the target second parameter in the target user willingness subset and the value of the target third parameter in the target predicted average value subset, the membership degree corresponding to the compressor frequency function corresponding to the three-three combination of the target first parameter, the target second parameter and the target third parameter in the fuzzy rule base is determined, wherein the target first parameter is any first parameter in the target grid demand subset, the target second parameter is any second parameter in the target user willingness subset, and the target third parameter is any third parameter in the target predicted average value subset.

8. The air conditioning control method oriented to grid demand and user intention according to claim 6, characterized in that: The step of determining the target compressor frequency function according to the membership degree corresponding to each compressor frequency function in the fuzzy rule base includes: According to the membership degree corresponding to each compressor frequency function in the fuzzy rule base, each compressor frequency function is fused to obtain the target compressor frequency function.

9. The air conditioning control method oriented to grid demand and user intention according to claim 1, characterized in that: Determining the target compressor frequency according to the target compressor frequency function includes: Based on the area center method, the target compressor frequency is determined according to the target compressor frequency function.

10. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor executes the machine-readable instructions to perform the steps of the air conditioning control method oriented to grid demand and user intention as described in any one of claims 1 to 9.

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