Air conditioner control method and electronic device facing power grid demand and user willingness

Abstract

The application provides an air conditioner control method and electronic equipment facing power grid demand and user willingness, the method comprises the following steps: performing fuzzy processing on the obtained power grid demand value to obtain a target power grid demand subset, performing fuzzy processing on the obtained user willingness value to obtain a target user willingness subset, performing fuzzy processing on the obtained predicted average value to obtain a target predicted average value subset, performing fuzzy reasoning according to the target power 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, and determining a target compressor frequency according to the target compressor frequency function to control the air conditioner to operate according to the target compressor frequency. The power grid demand, the user willingness and the predicted average value of thermal comfort are comprehensively considered from the power supply side and the user side, so that the air conditioner is controlled to operate, and the flexibility of the power system is improved, and stable operation of the power system is ensured.

Description

Technical Field

[0001] This application relates to the field of air conditioning control technology, and more specifically, to an air conditioning control method and electronic device that is oriented towards power grid requirements and user preferences. Background Technology

[0002] With the increasing use of renewable energy in the electricity supply, the issue of electricity supply and demand balance has received growing attention. Demand response can address this balance by altering load conditions on the demand side. Air conditioning accounts for a significant portion of demand-side resources and has substantial regulation potential. Therefore, it is urgent to tap into the potential of air conditioning to provide power auxiliary regulation services to the power system.

[0003] In existing technologies, air conditioning demand response is controlled through two methods. The first method is temperature control, which adjusts the air conditioner's operating temperature according to grid demand. The second method is compressor status control, which adjusts the air conditioner compressor's status according to grid demand.

[0004] However, existing temperature control technologies suffer from lag, failing to provide power support to the power system, and adjusting the air conditioner's operating temperature limits the flexibility of power regulation. While compressor status control responds to grid demands, it does not consider user comfort, resulting in a poor user experience. Summary of the Invention

[0005] The purpose of this application is to address the shortcomings of the prior art by providing an air conditioning control method and electronic device that meets the needs of the power grid and the preferences of users, thereby solving the problems of low power regulation flexibility and poor user experience in the prior art.

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

[0007] Firstly, this application provides an air conditioning control method oriented towards grid demand and user preferences, the method comprising:

[0008] The obtained grid demand value is fuzzified to obtain a target grid demand subset. The target grid demand subset includes multiple first parameters, each of which corresponds to a power adjustment strategy. The value of each first parameter is used to characterize the membership degree of the grid demand value in the corresponding power adjustment strategy dimension.

[0009] The obtained user intention values ​​are fuzzed to obtain a subset of target user intentions. The subset of target user intentions includes multiple second parameters, each of which corresponds to a user intention type. The value of each second parameter is used to characterize the membership degree of the user intention value in the corresponding user intention type dimension.

[0010] The obtained predicted average value is fuzzed to obtain a subset of target predicted average values. The subset of target predicted average values ​​includes multiple third parameters, each of which corresponds to a type of thermal reaction. The value of each third parameter is used to characterize the membership degree of the predicted average value in the corresponding thermal reaction type dimension.

[0011] Based on the target power grid demand subset, the target user willingness subset, the target predicted average value subset, and the preset fuzzy rule base, fuzzy inference is performed to obtain the target compressor frequency function;

[0012] The target compressor frequency is determined based on the target compressor frequency function, and the air conditioner is controlled to operate according to the target compressor frequency.

[0013] Optionally, the step of fuzzifying the obtained power grid demand value to obtain a subset of the target power grid demand includes:

[0014] Based on the power grid demand function, the target power grid demand subset is calculated according to the obtained power grid demand values.

[0015] Optionally, the step of blurring the obtained user intention values ​​to obtain a subset of target user intentions includes:

[0016] Based on the user willingness function, the target user willingness subset is calculated according to the obtained user willingness values.

[0017] Optionally, the step of blurring the obtained predicted average value to obtain a subset of the target predicted average value includes:

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

[0019] Optionally, 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 each of the power adjustment strategies, user intention types, and thermal response types in three-by-three combinations.

[0020] Optionally, the step of performing fuzzy inference based on the target power grid demand subset, the target user willingness subset, the target predicted average subset, and a preset fuzzy rule base to obtain the target compressor frequency function includes:

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

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

[0023] Optionally, determining the membership degree of each compressor frequency function in the fuzzy rule base based on the target power grid demand subset, the target user willingness subset, the target predicted average subset, and the fuzzy rule base includes:

[0024] Based on the values ​​of the target first parameter in the target power grid demand subset, the target second parameter in the target user willingness subset, and the target third parameter in the target predicted average value subset, the membership degree of 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. Here, 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.

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

[0026] Based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base, the compressor frequency functions are fused to obtain the target compressor frequency function.

[0027] Optionally, determining the target compressor frequency based on the target compressor frequency function includes:

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

[0029] Secondly, this application provides an air conditioning control device, the device comprising:

[0030] The first processing module is used to perform fuzzification processing on the obtained power grid demand value to obtain a target power grid demand subset. The target power grid demand subset includes multiple first parameters, each of which corresponds to a power adjustment strategy. The value of each first parameter is used to characterize the membership degree of the power grid demand value in the corresponding power adjustment strategy dimension.

[0031] The second processing module is used to perform fuzzing processing on the obtained user intention values ​​to obtain a target user intention subset. The target user intention subset includes multiple second parameters, each of which corresponds to a user intention type. The value of each second parameter is used to characterize the membership degree of the user intention value in the corresponding user intention type dimension.

[0032] The third processing module is used to perform fuzzing processing on the obtained predicted average value to obtain a subset of target predicted average values. The subset of target predicted average values ​​includes multiple third parameters, each of which corresponds to a type of thermal reaction. The value of each third parameter is used to characterize the membership degree of the predicted average value in the corresponding thermal reaction type dimension.

[0033] The fuzzy inference module is used to perform fuzzy inference based on the target power grid demand subset, the target user intention subset, the target predicted average value subset, and a preset fuzzy rule base to obtain the target compressor frequency function;

[0034] The determining module is used to determine the target compressor frequency based on the target compressor frequency function, so as to control the air conditioner operation according to the target compressor frequency.

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

[0036] Based on the power grid demand function, the target power grid demand subset is calculated according to the obtained power grid demand values.

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

[0038] Based on the user willingness function, the target user willingness subset is calculated according to the obtained user willingness values.

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

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

[0041] Optionally, 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 each of the power adjustment strategies, user intention types, and thermal response types in three-by-three combinations.

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

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

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

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

[0046] Based on the values ​​of the target first parameter in the target power grid demand subset, the target second parameter in the target user willingness subset, and the target third parameter in the target predicted average value subset, the membership degree of 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. Here, 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.

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

[0048] Based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base, the compressor frequency functions are fused to obtain the target compressor frequency function.

[0049] Optionally, the 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] Thirdly, this application provides an electronic device, including: 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 communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the air conditioning control method described above, which is oriented towards power grid requirements and user preferences.

[0052] Fourthly, this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the air conditioning control method described above, which is oriented towards power grid requirements and user preferences.

[0053] The beneficial effects of this application are as follows: By fuzzifying the grid demand value, user willingness value, and predicted average value, a subset of target grid demand, a subset of target user willingness, and a subset of target predicted average value are determined. Then, based on the subset of target grid demand, subset of target user willingness, subset of target predicted average value, and a preset fuzzy rule base, fuzzy inference is performed to obtain the target compressor frequency function. Based on 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 predicted average value of thermal comfort from both the power supply side and the user side. This is beneficial to improving the flexibility of the power system, ensuring the stable operation of the power system and meeting user electricity demand, and more rationally regulating the grid load supply. The power supply side can regulate different demand response periods according to the target compressor frequency, and can effectively cope with short-term power supply and demand tensions, difficulties in renewable energy consumption, etc., exhibiting good economic efficiency and flexibility. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating an air conditioning control method that caters to both grid demands and user preferences, as provided in an embodiment of this application.

[0056] Figure 2 This is a technical architecture diagram of another air conditioning control method oriented towards power grid requirements and user preferences provided in the embodiments of this application;

[0057] Figure 3 This is a schematic diagram illustrating the effect of an air conditioning control method that caters to both grid demands and user preferences, as provided in an embodiment of this application.

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

[0059] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0061] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

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

[0063] In existing technologies, air conditioner operating temperature is adjusted according to grid demand. However, this method may have a lag, failing to provide power support to the power system, and adjusting the air conditioner's operating temperature limits the flexibility of power regulation. While adjusting the air conditioner compressor's status according to grid demand responds to grid needs, it does not consider user comfort, resulting in a poor user experience.

[0064] Based on this, this application provides an air conditioning control method oriented towards grid demand and user preferences. This method fuzzifies the grid demand value, user preference value, and predicted average value to obtain a target grid demand subset, a target user preference subset, and a target predicted average value subset. Then, fuzzy inference is performed based on these subsets and a pre-defined fuzzy rule base to obtain a target compressor frequency function. Finally, the target compressor frequency is determined based on this function, thereby controlling the air conditioner operation according to the target compressor frequency. This application, by using fuzzy control based on grid demand value, user preference value, and predicted average value to obtain the target compressor frequency, ensures that the air conditioner output considers both the grid generation side's operation and the user side's needs, thus guaranteeing a balance between power supply and demand while providing a better user experience.

[0065] Next, refer to Figure 1 This paper describes the specific steps of the air conditioning control method proposed in this application, which is oriented towards both grid demand and user preferences. Among them, Figure 1 This is a schematic flowchart illustrating an air conditioning control method based on grid demand and user preferences, provided in an embodiment of this application. Optionally, the air conditioning control method based on grid demand and user preferences can be applied to electronic devices with computing capabilities.

[0066] S101. The obtained grid demand value is fuzzified to obtain a target grid demand subset. The target grid demand subset includes multiple first parameters, each of which corresponds to a power adjustment strategy. The value of each first parameter is used to characterize the membership degree of the grid demand value in the corresponding power adjustment strategy dimension.

[0067] The grid demand value can be an adjustment signal from the grid generation side, used to adjust the power output of the consumption side based on factors such as the current electricity consumption status and time. The grid demand value can be in numerical or textual form. For example, taking the grid demand value as a numerical value, it can be a value between 0 and 1. During peak electricity consumption periods, the grid generation side sends a grid demand value of 0.1.

[0068] Optionally, a fuzzy set of grid demand can be pre-defined. This fuzzy set includes multiple power adjustment strategies, each corresponding to a function, which can be called the grid demand function. Based on this, the process of fuzzifying the grid demand value can involve determining the membership degree of the grid demand value in each power adjustment strategy dimension of the fuzzy set. That is, determining the set of values ​​of the grid demand value in the function corresponding to each power adjustment strategy in the fuzzy set. Here, the membership degree in each power adjustment strategy dimension is the value of each first parameter.

[0069] Optionally, the power adjustment strategy may include, for example, a power reduction strategy, a no-adjustment strategy, and a power increase strategy. Based on this, the fuzzy set of grid demand can be expressed as follows (1):

[0070] ΔP Grid ∈[p1,p2,p3](1)

[0071] Wherein, ΔP Grid Let p1 represent the fuzzy set of grid demand, p2 represent the power reduction strategy, p3 represent the power no-adjustment strategy, and p3 represent the power increase strategy.

[0072] S102. The obtained user intention values ​​are fuzzed to obtain a subset of target user intentions. The subset of target user intentions includes multiple second parameters, each of which corresponds to a user intention type. The value of each second parameter is used to characterize the membership degree of the user intention value in the corresponding user intention type dimension.

[0073] Optionally, the user willingness value can 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 can be in numerical form or textual form. Taking the user willingness value as a numerical form as an example, the user willingness value can be a value between 0 and 1. When the user's willingness to participate in demand response is extremely low, the user willingness value can be 0 or 0.1.

[0074] Optionally, a fuzzy set of user intentions can be pre-defined. This set includes multiple user intention types, each corresponding to a function, which can be called a user intention function. Based on this, the process of fuzzifying user intention values ​​can involve determining the membership degree of each user intention value across the dimensions of each user intention type in the fuzzy set. That is, determining the set of values ​​for each user intention value within the function corresponding to each user intention type in the fuzzy set. Here, the membership degree across each user intention type dimension is the value of each second parameter.

[0075] Among these, user willingness types can include, for example, high willingness to participate in demand response, unwillingness to participate in demand response, and low willingness to participate in demand response. Based on this, the fuzzy set of user willingness can be represented as follows (2):

[0076]

[0077] in, Let l1 represent a fuzzy set of user intentions, where l1 represents users with a high willingness to participate in demand response, l2 represents users who are unwilling to participate in demand response, and l3 represents users with a low willingness to participate in demand response.

[0078] S103. The obtained predicted average value is fuzzed to obtain a subset of the target predicted average value. The subset of the target predicted average value includes multiple third parameters, each of which corresponds to a type of thermal reaction. The value of each third parameter is used to characterize the membership degree of the predicted average value in the corresponding thermal reaction type dimension.

[0079] The Predicted Mean Vote (PMV) is a comprehensive evaluation index that takes into account many factors related to human thermal comfort, based on the basic equation of human thermal balance and the level of subjective thermal sensation in psychophysiology. It is usually used to represent the user's comfort level.

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

[0081] Optionally, a pre-defined predictive average fuzzy set can be established, which includes multiple thermal reaction types. Each type can correspond to a function, and the function corresponding to each type can be called the predictive average function. Based on this, the process of fuzzifying the predicted average can involve determining the membership degree of the predicted average in each thermal reaction type dimension of the predictive average fuzzy set; that is, determining the set of values ​​of the predicted average in the function corresponding to each thermal reaction type in the predictive average fuzzy set. Here, the membership degree in each predicted average type dimension is the value of each third parameter.

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

[0083] PMV∈[r1,r2,r3,r4,r5](3)

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

[0085] S104. Based on the target power grid demand subset, the target user willingness subset, the target predicted average value subset, and the preset fuzzy rule base, perform fuzzy inference to obtain the target compressor frequency function.

[0086] The fuzzy rule base includes multiple power adjustment strategies from the fuzzy set of grid demand, multiple user intention types from the fuzzy set of user intentions, and multiple thermal response types from the fuzzy set of predicted averages, as well as compressor frequency functions corresponding to each power adjustment strategy, user intention type, and thermal response type. The compressor frequency function is the fuzzy output corresponding to the combination of each power adjustment strategy, user intention type, and thermal response type. A set containing multiple compressor frequency functions can be considered as a compressor frequency fuzzy set.

[0087] For example, the compressor frequency fuzzy set may include three compressor frequency functions, represented by the following equation (4):

[0088] f VRFS ∈[f1,f2,f3](4)

[0089] Among them, f VRFS Let f1 be the fuzzy set of compressor frequencies, f2 be the frequency down-adjustment strategy, f3 be the frequency no-adjustment strategy, and f3 be the frequency up-adjustment strategy.

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

[0091] S105. Determine the target compressor frequency based on the target compressor frequency function, so as to control the air conditioner operation 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 grid demand, user preferences, and predicted average values, satisfying the needs of power system and user comfort regulation, while also providing reference guidance for demand response regulation on the grid supply side.

[0094] In this embodiment, by fuzzifying the grid demand value, user willingness value, and predicted average value, a target grid demand subset, a target user willingness subset, and a target predicted average value subset are determined. Then, based on the target grid demand subset, target user willingness subset, target predicted average value subset, and a preset fuzzy rule base, fuzzy inference is performed to obtain the target compressor frequency function. Based on 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 air conditioner operation by comprehensively considering grid demand, user willingness, and predicted average values ​​of thermal comfort from both the power supply side and the user side. This is beneficial for improving the flexibility of the power system, ensuring the stable operation of the power system and meeting user electricity demand, and more rationally regulating grid load supply. The power supply side can adjust different demand response periods according to the target compressor frequency, and can effectively cope with short-term power supply and demand tensions and difficulties in renewable energy consumption, exhibiting good economic efficiency and flexibility.

[0095] As an alternative implementation method, Figure 2This is a technical architecture diagram of another air conditioning control method based on power grid requirements and user preferences, provided in an embodiment of this application. (For example...) Figure 2 As shown, the electronic device includes a fuzzifier, a fuzzy inferencer, and a defuzzifier. The grid demand value, user willingness value, and predicted average value are input into the fuzzifier for fuzzification processing, and the output of the fuzzification processing is input into the fuzzy inferencer. The output of the fuzzification processing includes a subset of the target grid demand, a subset of the target user willingness, and a subset of the target predicted average value. The fuzzy inferencer performs fuzzy inference based on the output of the fuzzification processing and the fuzzy rule base to obtain the target compressor frequency function. The fuzzy inferencer inputs the target compressor frequency function into the defuzzifier to obtain and output the target compressor frequency, so that the air conditioner operates at the target compressor frequency.

[0096] Furthermore, the specific process of fuzzifying the obtained power grid demand value in step S101 above to obtain the target power grid demand subset is as follows:

[0097] Optionally, based on the power grid demand function, a subset of the target power grid demand is calculated according to the obtained power grid demand values.

[0098] The grid demand function can include functions of various power adjustment strategies. Specifically, the grid demand function can include a power down-adjustment function, a power no-adjustment function, and a power up-adjustment function.

[0099] As an alternative implementation, the power grid demand function can be a triangular membership function.

[0100] As an optional implementation, the membership degree corresponding to the power reduction element is determined based on the power reduction function and the grid demand value; the membership degree corresponding to the power non-adjustment element is determined based on the power non-adjustment function and the grid demand value; and the membership degree corresponding to the power increase element is determined based on the power increase function and the grid demand value. The membership degrees corresponding to the power reduction element, the power non-adjustment element, and the power increase element are each used as the value of a first parameter in the target grid demand subset.

[0101] In this embodiment, a subset of target grid demand is calculated based on the obtained grid demand value using the grid demand function, thereby fuzzifying the grid demand value and avoiding the use of precise mathematical models, making it suitable for complex control systems.

[0102] Furthermore, the process of blurring the obtained user intention values ​​in step S102 to obtain a subset of target user intentions is described below:

[0103] Optionally, based on the user willingness function, a subset of target user willingness is calculated according to the obtained user willingness values.

[0104] The user willingness function can include functions of various types within the user willingness category. Specifically, the user willingness function can include functions representing high willingness to participate in responding to user needs, functions representing willingness to not participate in responding to user needs, and functions representing low willingness to participate in responding to user needs.

[0105] As an optional implementation, the user preference function can be a triangular membership function.

[0106] As an optional implementation, the membership degree corresponding to the type with high user willingness to participate in demand response is determined based on the user participation demand response high willingness function and user willingness value; the membership degree corresponding to the type with no user willingness to participate in demand response is determined based on the user no willingness function and user willingness value; and the membership degree corresponding to the type with low user willingness to participate in demand response is determined based on the user participation demand response low willingness function and user willingness value. The membership degrees corresponding to the types with high user willingness to participate in demand response, the types with no user willingness to participate in demand response, and the types with low user willingness to participate in demand response are each used as a value of a second parameter in the target user willingness subset.

[0107] In this embodiment, a subset of target user intentions is calculated based on the obtained user intention values ​​using a user intention function, thereby fuzzifying the user intention values ​​and avoiding the use of precise mathematical models, making it suitable for complex control systems.

[0108] Furthermore, the specific process of fuzzing the obtained predicted average value in step S103 above to obtain a subset of the target predicted average value is described below:

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

[0110] The predictive averaging function can include functions of various types within the thermal response type. Specifically, the predictive averaging function can include cold type function, cool type function, comfortable type function, warm type function, and hot type function.

[0111] As an alternative implementation, the prediction average function can be a triangular membership function.

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

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

[0114] Optionally, 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 each power adjustment strategy, each user intention type, and each thermal response type in three-by-three combinations.

[0115] For example, a fuzzy rule base can be shown in Table 1 below:

[0116] Table 1

[0117]

[0118]

[0119] Taking the heat response type as cool type r2, the user willingness type as low user participation demand response type l3, and the power adjustment strategy as power down adjustment strategy p1 as an example, the corresponding compressor frequency function can be determined to be frequency up adjustment strategy f3 through the above fuzzy rule base.

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

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

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

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

[0124] Optionally, the target compressor frequency function can be determined based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base.

[0125] As an optional implementation, the membership degrees corresponding to the frequency functions of each compressor can be combined, and the function consisting of the maximum membership degree value corresponding to each frequency in the combined compressor frequency function values ​​can be used as the target compressor frequency function. Here, the input to the compressor frequency function is each frequency value, and the output is the membership degree value corresponding to each 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 based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base. Thus, the fuzzy set is determined based on the fuzzy rule base, avoiding the use of precise mathematical models and making it 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 based on the target power grid demand subset, the target user willingness subset, the target predicted average value subset, and the fuzzy rule base is described in the above steps.

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

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

[0130] As an optional implementation method, the process of determining the compressor frequency function corresponding to the three-three combination of the first parameter, the second parameter, and the third parameter of the target in the fuzzy rule base can be as follows: (5)

[0131]

[0132] in, Let R represent the compressor frequency function corresponding to the k-th 3x3 combination. k This represents the k-th fuzzy rule. ° represents the fuzzy synthesis operation. Taking R1 and R2 as examples, their specific representations are 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-three combination of the first target parameter, the second target parameter, and the third target parameter in the fuzzy rule base, the minimum value among the first target parameter value in the target power grid demand subset, the second target parameter value in the target user intention subset, the third target parameter value in the target predicted average subset, and the corresponding compressor frequency function value is taken as the membership degree corresponding to the compressor frequency function.

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

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

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

[0141] As an optional implementation, the frequency functions of each compressor are fused, and the function formed by the maximum value of 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, the specific steps for determining the target compressor frequency based on the target compressor frequency function in the above steps are as follows:

[0143] Optionally, the target compressor frequency can be determined based on the area center method and the target compressor frequency function.

[0144] Specifically, the value of the center point of the area enclosed by the pre-axiom coordinate of the target compressor frequency function is taken as the target compressor frequency.

[0145] Alternatively, in addition to the area center method, the target compressor frequency can also be determined based on the area centroid method, the area equal division method, the maximum membership degree average method, the maximum membership degree minimization method, and the large membership degree maximization method, according to the target compressor frequency function.

[0146] In this embodiment, the target compressor frequency is determined by the area center method, and then the fuzziness is resolved based on the target compressor frequency function to determine the accurate compressor frequency.

[0147] Figure 3 This is a schematic diagram illustrating the effect of an air conditioning control method tailored to both grid demands and user preferences, as provided in an embodiment of this application. The following refers to... Figure 3 The results of verifying the air conditioning control method oriented towards grid demand and user preferences are explained. Three experimental groups were set up, with user preference values ​​of 0.0, 0.5, and 1.0, respectively. In the experiment, users were scheduled to participate in demand response at 8:30 AM. Figure 3 It can be seen that the changes in PMV differ depending on the user's willingness value. When the user's willingness value is 0.0, the indoor PMV remains within the comfort zone. When the user's willingness value is 0.5, the PMV exceeds the comfort zone 70 minutes after the user responds. When the user's willingness value is 1.0, the PMV exceeds the comfort zone 50 minutes after the user responds. In this situation, using an air conditioning control method that considers both grid demand and user willingness can prevent the PMV from increasing within 30 minutes and restore the PMV value to the comfort zone after 35 minutes. Based on this, a demand response within 50 minutes can be provided for users with a willingness value of 1.0, and a demand response within 70 minutes can be provided for users with a willingness value of 0.5, thereby meeting the needs of the power system and user comfort regulation, while also providing regulatory guidance for the power grid supply side.

[0148] Based on the same inventive concept, this application also provides an air conditioning control device corresponding to the air conditioning control method oriented towards power grid demand and user wishes. Since the principle of the device in this application is similar to the air conditioning control method oriented towards power grid demand and user wishes described above in this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0149] Reference Figure 4 The diagram shown is a structural schematic of an air conditioning control device provided in an embodiment of this application. The device includes:

[0150] The first processing module 401 is used to perform fuzzification processing on the obtained power grid demand value to obtain a target power grid demand subset. The target power 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 degree of the power grid demand value in the corresponding power adjustment strategy dimension.

[0151] The second processing module 402 is used to perform fuzzing processing on the obtained user intention value to obtain a target user intention subset. The target user intention subset includes multiple second parameters, each of which corresponds to a user intention type. The value of each second parameter is used to characterize the membership degree of the user intention value in the corresponding user intention type dimension.

[0152] The third processing module 403 is used to perform fuzzing processing on the obtained predicted average value to obtain a subset of target predicted average values. The subset of target predicted average values ​​includes multiple third parameters, each of which corresponds to a type of thermal reaction. The value of each third parameter is used to characterize the membership degree of the predicted average value in the corresponding thermal reaction type dimension.

[0153] The fuzzy inference module 404 is used to perform fuzzy inference based on the target power grid demand subset, the target user intention subset, the target predicted average value subset, and a preset fuzzy rule base to obtain the target compressor frequency function;

[0154] The determining module 405 is used to determine the target compressor frequency according to the target compressor frequency function, so as to control the air conditioner operation 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, the target power grid demand subset is calculated according to the obtained power grid demand values.

[0157] Optionally, the second processing module 402 is specifically used for:

[0158] Based on the user willingness function, the target user willingness subset is calculated according to the obtained user willingness values.

[0159] Optionally, the third processing module 403 is specifically used for:

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

[0161] Optionally, 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 each of the power adjustment strategies, user intention types, and thermal response types in three-by-three combinations.

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

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

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

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

[0166] Based on the values ​​of the target first parameter in the target power grid demand subset, the target second parameter in the target user willingness subset, and the target third parameter in the target predicted average value subset, the membership degree of 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. Here, 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.

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

[0168] Based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base, the compressor frequency functions are fused to obtain the target compressor frequency function.

[0169] Optionally, module 405 is specifically used for:

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

[0171] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0172] This application also provides an electronic device, such as... Figure 5 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application, including: a processor 501, a memory 502, and a bus. The memory 502 stores machine-readable instructions executable by the processor 501 (e.g., ...). Figure 4 The device includes the execution instructions corresponding to the first processing module 401, the second processing module 402, the third processing module 403, the fuzzy inference module 404, and the determination module 405. When the computer device is running, the processor 501 communicates with the memory 502 via a bus. When the machine-readable instructions are executed by the processor 501, the above-mentioned air conditioning control method oriented towards power grid requirements and user preferences is performed.

[0173] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described air conditioning control method oriented towards power grid requirements and user preferences.

[0174] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, 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 displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0175] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0176] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An air conditioning control method oriented towards power grid demand and user preferences, characterized in that, The method includes: The obtained grid demand value is fuzzified to obtain a target grid demand subset. The target grid demand subset includes multiple first parameters, each of which corresponds to a power adjustment strategy. The value of each first parameter is used to characterize the membership degree of the grid demand value in the corresponding power adjustment strategy dimension. The obtained user intention values ​​are fuzzed to obtain a subset of target user intentions. The subset of target user intentions includes multiple second parameters, each of which corresponds to a user intention type. The value of each second parameter is used to characterize the membership degree of the user intention value in the corresponding user intention type dimension. The obtained predicted average value is fuzzed to obtain a subset of target predicted average values. The subset of target predicted average values ​​includes multiple third parameters, each of which corresponds to a type of thermal reaction. The value of each third parameter is used to characterize the membership degree of the predicted average value in the corresponding thermal reaction type dimension. Based on the target power grid demand subset, the target user willingness subset, the target predicted average value subset, and the preset fuzzy rule base, fuzzy inference is performed to obtain the target compressor frequency function; Based on the target compressor frequency function, the target compressor frequency is determined so as to control the air conditioner operation according to the target compressor frequency; 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 each of the power adjustment strategies, user intention types, and thermal response types in three-by-three combinations. The step of performing fuzzy inference based on the target power grid demand subset, the target user willingness subset, the target predicted average value subset, and a preset fuzzy rule base to obtain the target compressor frequency function includes: Based on the target power grid demand subset, the target user willingness subset, the target predicted average value subset, and the fuzzy rule base, determine the membership degree corresponding to each compressor frequency function in the fuzzy rule base; The target compressor frequency function is determined based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base. The step of determining the membership degree of each compressor frequency function in the fuzzy rule base based on the target power grid demand subset, the target user willingness subset, the target predicted average value subset, and the fuzzy rule base includes: Based on the values ​​of the target first parameter in the target power grid demand subset, the target second parameter in the target user willingness subset, and the target third parameter in the target predicted average value subset, the membership degree of 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 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; Determining the target compressor frequency based on 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.

2. The air conditioning control method based on power grid demand and user preferences according to claim 1, characterized in that, The process of fuzzifying the obtained power grid demand values ​​to obtain a subset of target power grid demand includes: Based on the power grid demand function, the target power grid demand subset is calculated according to the obtained power grid demand values.

3. The air conditioning control method based on power grid demand and user preferences according to claim 1, characterized in that, The process of blurring the obtained user intention values ​​to obtain a subset of target user intentions includes: Based on the user willingness function, the target user willingness subset is calculated according to the obtained user willingness values.

4. The air conditioning control method based on power grid demand and user preferences according to claim 1, characterized in that, The process of blurring the obtained predicted average values ​​to obtain a subset of the target predicted average values ​​includes: Based on the prediction averaging function, the target prediction average subset is calculated according to the obtained prediction average.

5. The air conditioning control method based on power grid demand and user preferences according to claim 1, characterized in that, The step of determining the target compressor frequency function based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base includes: Based on the membership degree corresponding to each compressor frequency function in the fuzzy rule base, the compressor frequency functions are fused to obtain the target compressor frequency function.

6. An electronic device, characterized in that, include: The device includes a processor and a memory, the memory storing machine-readable instructions executable by the processor, which, when the electronic device is running, are executed by the processor to perform the steps of the air conditioning control method according to any one of claims 1 to 5, which is oriented towards grid demand and user preferences.

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