Indoor temperature prediction method and device, air conditioning equipment and storage medium

Abstract

The invention provides an indoor temperature prediction method, an indoor temperature prediction device, air conditioning equipment and a storage medium. The indoor temperature prediction method comprises the steps that an indoor temperature sequence and an outdoor temperature sequence of a current time period, an air conditioning equipment cooling capacity output value at the current moment and the current date and time are obtained; and the target indoor temperature of the next moment is calculated through a temperature prediction model according to the indoor temperature sequence and the outdoor temperature sequence of the current time period and the cooling capacity output value of the air conditioning equipment at the current moment, and the temperature prediction model is constructed through a heat balance equation and obtained through historical temperature data training. Thus, the temperature prediction model is obtained by constructing and training the heat balance equation representing the heat change, and the heat balance equation is only established according to the basic rule of the physical heat transfer theory, so that the temperature prediction model does not depend on detailed understanding of the target building during modeling; the automation degree and reusability of the indoor temperature prediction method are improved, and the prediction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning equipment, and in particular to an indoor temperature prediction method, an indoor temperature prediction device, air conditioning equipment and a computer-readable storage medium. Background Art

[0002] Model predictive control is an efficient operation control method. When applied to the terminal control of air-conditioning systems, it can greatly release the energy-saving potential of air-conditioning systems. When applied to the terminal of air-conditioning systems, the performance of model predictive control is highly dependent on the accuracy of the indoor temperature change prediction model of the building.

[0003] At present, the indoor temperature prediction method of buildings is mainly based on three models: white box model, gray box model and black box model. Among them, the white box model is mainly based on the actual situation of the target building. By replicating its physical model in the computer, it simulates the indoor thermal process to achieve indoor temperature prediction. The gray box model is mainly based on the actual situation of the building and the abstraction and simplification of physical rules, and most of the parameters in the model are trained based on system operation data. The black box model ignores the specific heat transfer process inside the building and directly trains a pure mathematical model without physical meaning through the input and output data of the thermal system.

[0004] However, the white-box and gray-box models are highly coupled with the target building, requiring each target building to be modeled separately, and lack reusability. The black-box model has high requirements on the quality of training data and has low extrapolation prediction accuracy. Summary of the invention

[0005] In view of this, embodiments of the present invention provide an indoor temperature prediction method, an indoor temperature prediction device, an air conditioning device, and a non-volatile computer-readable storage medium.

[0006] The indoor temperature prediction method according to the embodiment of the present invention comprises:

[0007] Get the indoor temperature sequence, outdoor temperature sequence, cooling output value of the air conditioner at the current time, and the current date and time;

[0008] The target indoor temperature at the next moment is predicted by a temperature prediction model based on the indoor temperature sequence of the current period, the outdoor temperature sequence, the cooling output value of the air-conditioning equipment at the current moment, and the current date and time. The temperature prediction model is constructed by a heat balance equation and trained with historical temperature data.

[0009] In some embodiments, before obtaining the indoor temperature sequence, the outdoor temperature sequence and the cooling output value of the air-conditioning device at the current time period, the indoor temperature prediction method further includes:

[0010] Obtain indoor heat transfer data and historical temperature data;

[0011] constructing a heat balance equation for indoor air based on the heat transfer data;

[0012] constructing an initial prediction model based on the historical temperature data and the heat balance equation;

[0013] The initial prediction model is trained by a machine learning algorithm to generate the temperature prediction model.

[0014] In certain embodiments, constructing a heat balance equation for indoor air based on the heat transfer data comprises:

[0015] Constructing a first heat change equation of total indoor heat change according to the heat, heat capacity and temperature of indoor air, wherein the total heat change includes air conditioning heat change, building envelope heat change, air convection heat change, solar radiation heat change and indoor heat gain change;

[0016] The second heat change equation of building envelope heat change is constructed according to the heat transfer resistance of the envelope structure and the outdoor air temperature;

[0017] According to the change rules of the air convection heat change, the solar radiation heat change and the indoor heat gain change, a third heat change equation is constructed which is the sum of the air convection heat change, the solar radiation heat change and the indoor heat gain change;

[0018] The heat balance equation is constructed according to the first heat change equation, the second heat change equation and the third heat change equation.

[0019] In certain embodiments, constructing an initial prediction model based on the historical temperature data and the heat balance equation comprises:

[0020] The heat balance equation is solved according to a parameter estimation algorithm and the historical temperature data to generate the initial prediction model, wherein the parameter estimation algorithm includes one of a least squares estimation method, a maximum likelihood estimation method, and a Bayesian estimation method.

[0021] In certain embodiments, training the initial prediction model by a machine learning algorithm to generate the temperature prediction model comprises:

[0022] Fitting the predicted values ​​and measured values ​​generated by the initial prediction model according to the historical temperature data through a machine learning algorithm to generate a residual model;

[0023] The residual model and the initial prediction model are added to generate the temperature prediction model.

[0024] In some embodiments, the indoor temperature prediction method comprises:

[0025] Predicting the cooling capacity of the air-conditioning equipment at the next moment according to the current indoor temperature and the target indoor temperature;

[0026] The air-conditioning device is controlled according to the cooling capacity of the air-conditioning device so that the indoor temperature at the next moment reaches the target indoor temperature.

[0027] In some embodiments, controlling the air-conditioning device according to the cooling capacity of the air-conditioning device so that the indoor temperature at the next moment reaches the target indoor temperature includes:

[0028] The chilled water flow and air volume of the air-conditioning equipment are adjusted according to the cooling capacity of the air-conditioning equipment through a closed-loop control method so that the indoor temperature at the next moment reaches the target indoor temperature.

[0029] The indoor temperature prediction device of the embodiment of the present application comprises:

[0030] The acquisition module is used to obtain the indoor temperature sequence, outdoor temperature sequence and cooling output value of the air-conditioning equipment at the current time period;

[0031] The prediction module is used to calculate the target indoor temperature at the next moment according to the indoor temperature sequence of the current period, the outdoor temperature sequence and the cooling output value of the air-conditioning equipment at the current moment through a temperature prediction model, wherein the temperature prediction model is constructed by a heat balance equation and obtained through training of historical temperature data.

[0032] The air-conditioning device of the implementation mode of the present application includes a processor and a memory, and the memory stores a computer program. When the computer program is executed by the processor, the processor implements any of the indoor temperature prediction methods described above.

[0033] The non-volatile computer-readable storage medium of the computer program of the embodiment of the present application enables the processor to implement any of the indoor temperature prediction methods described above when the computer program is executed by the processor.

[0034] In the indoor temperature prediction method of the present application, the temperature prediction model is constructed and trained by a heat balance equation that characterizes heat changes, and the heat balance equation is only established by the basic rules of physical heat transfer, so that the temperature prediction model does not rely on a detailed understanding of the target building when modeling, and the model accuracy can be improved. The temperature prediction model can accurately predict the indoor temperature at the next moment based on the indoor temperature sequence, outdoor temperature sequence and the cooling output value of the air-conditioning equipment at the current moment in the current period, thereby controlling the air-conditioning equipment to output the corresponding cooling, so that the indoor temperature meets the user's needs.

[0035] Additional aspects and advantages of the embodiments of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0037] Figure 1 is a schematic flow chart of an indoor temperature prediction method according to certain embodiments of the present invention;

[0038] Figure 2 is a schematic diagram of a module of an indoor temperature prediction device according to some embodiments of the present invention;

[0039] Figure 3 is a schematic flow chart of an indoor temperature prediction method according to certain embodiments of the present invention;

[0040] Figure 4 It is a schematic flow chart of an indoor temperature prediction method according to certain embodiments of the present invention. DETAILED DESCRIPTION

[0041] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0042] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0043] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or mutual communication; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0045] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0046] In the current building indoor temperature prediction methods, the white-box model needs to model each target building separately, so the model's reusability is extremely low, and there are many parameters that need to be determined during modeling. In actual operation, it often takes a lot of time and manpower to obtain these parameters. Compared with the white-box model, the gray-box model reduces the difficulty of parameter determination, but the modeling still partially relies on the characteristic information of the target building, and the reusability is poor. The black-box model does not require the use of domain knowledge when modeling, and most of the work can be completed automatically by computers, so it is more efficient and flexible. However, the black-box model has high requirements on the quality and richness of training data. When encountering new scenarios that are not covered by the training data set, the prediction accuracy of this type of model is often low, and abnormal results that violate domain knowledge may occur.

[0047] In view of this, please refer to Figure 1 , the present application provides an indoor temperature prediction method, the indoor temperature prediction method comprising:

[0048] S10: Obtaining the indoor temperature sequence, outdoor temperature sequence, cooling output value of the air conditioner at the current time, and the current date and time;

[0049] S20: Predicting the target indoor temperature at the next moment through a temperature prediction model according to the indoor temperature sequence of the current period, the outdoor temperature sequence, the cooling output value of the air-conditioning equipment at the current moment, and the current date and time. The temperature prediction model is constructed by a heat balance equation and trained through historical temperature data.

[0050] See also Figure 2 The present application also provides an indoor temperature prediction device 100, and the above-mentioned indoor temperature prediction method can be applied to the indoor temperature prediction device 100.

[0051] The indoor temperature prediction device 100 includes an acquisition module 10 and a prediction module 20. S10 can be implemented by the acquisition module 10, and S20 can be implemented by the prediction module 20. That is, the acquisition module 10 is used to obtain the indoor temperature sequence, the outdoor temperature sequence, the cooling output value of the air-conditioning equipment at the current moment, and the current date and time of the current period. The prediction module 20 is used to calculate the target indoor temperature at the next moment according to the indoor temperature sequence, the outdoor temperature sequence, the cooling output value of the air-conditioning equipment at the current moment, and the current date and time of the current period through a temperature prediction model. The temperature prediction model is constructed by a heat balance equation and obtained through training with historical temperature data.

[0052] The present application also provides an air-conditioning device, which includes a processor and a memory, wherein the memory stores a computer program. When the computer program is executed by the processor, the processor is used to implement the above-mentioned indoor temperature prediction method, that is, the processor is used to obtain the indoor temperature sequence, outdoor temperature sequence, cooling output value of the air-conditioning device at the current moment and the current date and time of the current period, and is used to calculate the target indoor temperature at the next moment according to the indoor temperature sequence, outdoor temperature sequence, cooling output value of the air-conditioning device at the current moment and the current date and time of the current period through a temperature prediction model, wherein the temperature prediction model is constructed by a heat balance equation and obtained through training with historical temperature data.

[0053] In the indoor temperature prediction method of the present application, the temperature prediction model is constructed and trained by a heat balance equation that characterizes heat changes, and the heat balance equation is only established by the basic rules of physical heat transfer, so that the temperature prediction model does not rely on a detailed understanding of the target building when modeling, and the model accuracy can be improved. The temperature prediction model can accurately predict the indoor temperature at the next moment based on the indoor temperature sequence, outdoor temperature sequence and the cooling output value of the air-conditioning equipment at the current moment in the current period, thereby controlling the air-conditioning equipment to output the corresponding cooling, so that the indoor temperature meets the user's needs.

[0054] The indoor temperature sequence refers to multiple indoor temperatures detected within a period of time. For example, the indoor temperature sequence may be three indoor temperatures detected within ten minutes. The outdoor temperature sequence refers to multiple outdoor temperatures detected within a period of time.

[0055] The air conditioning device may be a commercial air conditioner or a household air conditioner. For example, the air conditioning device may be a central air conditioner. The air conditioning device includes a temperature prediction device. The temperature prediction device may control the cooling capacity output by the air conditioner, thereby adjusting the indoor temperature.

[0056] The temperature prediction device 100 may be a discrete component assembled in a certain manner to have the aforementioned functions, or a chip in the form of an integrated circuit having the aforementioned functions, or a computer software code segment that enables the computer to have the aforementioned functions when running on the computer.

[0057] The temperature prediction model can be a machine learning model. The temperature prediction model can be constructed based on the heat balance equation and trained through historical temperature data. That is, the historical temperature data is divided into a training set and a test set. The initial prediction model can be generated through machine learning based on the training set data. The initial prediction model is applied to the test set for prediction. The best performance is selected as the temperature prediction model based on the performance of the initial prediction model in the test set. Among them, the machine learning model is an expression of an algorithm. The machine learning model can find patterns or make predictions by combing through massive data. The historical temperature data can be the historical operation and environmental data of the building, such as indoor and outdoor temperature data, the cooling capacity of the air conditioning equipment input to the building, and the measurement time and date.

[0058] Specifically, the indoor temperature prediction method is used to predict the indoor temperature of a building. By setting a temperature sensor indoors, the indoor temperature sensor can measure the indoor temperature once every period of time and transmit it to the temperature prediction model, so that the temperature prediction model can obtain the indoor temperature sequence at any time. By setting a temperature sensor outdoors, the outdoor temperature sensor can measure the outdoor temperature once every period of time and transmit it to the temperature prediction model, so that the temperature prediction model obtains the outdoor temperature sequence at any time. By setting a heat meter at the end of the air-conditioning equipment in the building, the cooling value input by the air-conditioning equipment to the indoor of the building can be measured, that is, the cooling output value of the air-conditioning equipment at any time can be obtained through the heat meter and transmitted to the temperature prediction model.

[0059] Furthermore, the temperature prediction model can make predictions based on the current indoor temperature sequence, the outdoor temperature sequence, the cooling output value of the air-conditioning equipment at the current moment, and the current date and time to obtain the target indoor temperature at the next moment, and then can calculate based on the target indoor temperature at the next moment and the indoor temperature at the current moment, so that the temperature prediction device 100 can control the cooling output of the air-conditioning equipment so that the indoor temperature at the next moment reaches the target indoor temperature.

[0060] It should be noted that the historical temperature data can be obtained based on the current date and time, for example, obtaining the historical operation and environmental data of the building at the current date and time in previous years.

[0061] In this way, a temperature prediction model is constructed based on the heat balance equation, and historical temperature data is used for training and learning, thereby improving the automation and reusability of the indoor temperature prediction method. The temperature prediction device 100 performs calculations based on the target indoor temperature and the indoor temperature, thereby controlling the cooling output of the air-conditioning equipment, so that the indoor temperature reaches the target indoor temperature, thereby meeting the user's demand for indoor temperature.

[0062] See also Figure 3 In some embodiments, before S10, the indoor temperature prediction method further includes:

[0063] S01: Obtain heat transfer data and historical indoor temperature data;

[0064] S02: Construct the heat balance equation of indoor air based on heat transfer data;

[0065] S03: Construct an initial prediction model based on historical temperature data and heat balance equations;

[0066] S04: Train the initial prediction model through a machine learning algorithm to generate a temperature prediction model.

[0067] In some embodiments, the indoor temperature prediction device 100 also includes a first building module 30, a second building module 40 and a training module 50, wherein S01 can be implemented by the acquisition module 10, S02 can be implemented by the first building module 30, S03 can be implemented by the second building module 40, and S04 can be implemented by the training module 50, that is, the acquisition module 10 is used to acquire heat transfer data and historical indoor temperature data, the first building module 30 is used to construct a thermal balance equation for indoor air based on the heat transfer data, the second building module 40 is used to construct an initial prediction model based on the historical temperature data and the thermal balance equation, and the training module 50 is used to train the initial prediction model through a machine learning algorithm to generate a temperature prediction model.

[0068] In certain embodiments, the processor is used to obtain heat transfer data and historical temperature data of the room, and to construct a heat balance equation for the indoor air based on the heat transfer data, and to construct an initial prediction model based on the historical temperature data and the heat balance equation, and to train the initial prediction model through a machine learning algorithm to generate a temperature prediction model.

[0069] Specifically, the heat transfer data can be data such as the cooling output of the air-conditioning equipment at a certain moment, the heat conduction of the enclosing structure, the convective heat exchange of indoor and outdoor air, solar radiation, and indoor heat changes caused by indoor personnel activities and equipment operation. Based on the heat transfer data, a thermal balance equation for indoor air can be constructed.

[0070] First, the indoor temperature prediction device 100 can construct a first heat change equation of the total indoor heat change according to the heat, heat capacity and temperature of the indoor air. The total heat change includes air conditioning heat change, building envelope heat change, air convection heat change, solar radiation heat change and indoor heat gain change:

[0071]

[0072] Among them, Q IA is the heat of the indoor air, Q 空调 is the cooling capacity output of the air conditioning equipment at that moment, Q 围护结构 is the heat conduction of the enclosure structure at that moment, Q 空气对流 is the indoor and outdoor air convection heat exchange at that moment, Q 太阳辐射 is the solar radiation at that moment, Q 室内热增 is the indoor heat change caused by indoor personnel activities and equipment operation at that moment, C IA is the heat capacity of the indoor air, T IA is the temperature of the indoor air.

[0073] According to Fourier's law, there is a delay in the process of outdoor heat being transferred to the indoor through the enclosure structure. The indoor temperature prediction device 100 constructs a second heat change equation of the building enclosure heat according to the heat transfer resistance of the enclosure structure and the outdoor air temperature:

[0074]

[0075] Among them, a i is the weight of heat transfer at different times, R is the heat transfer resistance of the enclosure structure, T OA is the outdoor air temperature.

[0076] The indoor temperature prediction device 100 constructs a third heat change equation of the sum of air convection heat change, solar radiation heat change and indoor heat gain change according to the change rules of air convection heat change, solar radiation heat change and indoor heat gain change:

[0077]

[0078] Among them, b j 、c j and d are both unknown constants. is the unit period length, and J is the number of terms in the Fourier expansion.

[0079] Secondly, the indoor temperature prediction device 100 can construct a heat balance equation by combining the first heat change equation, the second heat change equation and the third heat change equation:

[0080]

[0081] In practical applications, the indoor air heat capacity C IA and the heat transfer resistance R of the enclosure structure are considered as constants, and the undetermined constants in each item are combined to obtain:

[0082]

[0083] Among them, T IA , T oA ,t,Q 空调 are all known quantities and can be obtained from historical temperature data. N and J can be determined by adjusting parameters. A i , B j , C j , D, and E are unknown constants to be identified.

[0084] Again, the indoor temperature prediction device 100 can calculate the unknown constant A by solving the heat balance equation according to the parameter estimation method and the historical temperature data. i , B j , C j, D, E, thereby generating an initial prediction model. The specific values ​​of the unknown constants can be calculated based on the historical temperature data of the building and are not limited here.

[0085] It should be noted that parameter estimation is a method of calculating system model parameters using system input and output data when the system model structure is known. Parameter estimation can be one of the least squares estimation method, maximum likelihood estimation method and Bayesian estimation method. For example, in order to select parameter estimates that make the model output and the system output as close as possible, the least squares method can use the sum of the squares of the errors between the model and the system output to measure the degree of closeness. The parameter value that minimizes the sum of the squares of the errors is the desired estimate.

[0086] Finally, the indoor temperature prediction device 100 uses a machine learning algorithm to fit the initial prediction model based on the prediction value and the measured value generated by the historical temperature data, and can generate a residual model, thereby reducing the error between the prediction value and the measured value. The residual model can predict the prediction error at a certain moment based on the cooling output of the air-conditioning equipment, indoor and outdoor temperature data, etc. The temperature prediction model can be generated by combining the residual model with the initial prediction model.

[0087] In this way, the thermal balance equation of indoor air is constructed according to the heat transfer data, the thermal balance equation is combined with the historical temperature data to construct the initial prediction model, and the initial prediction model is trained by a machine learning algorithm to generate a temperature prediction model, which in turn uses the temperature prediction model to calculate the indoor temperature at the next moment, thereby improving the automation and reusability of the indoor temperature prediction method and improving the accuracy of the prediction.

[0088] See also Figure 4 In some embodiments, the indoor temperature prediction method further includes:

[0089] S30: Calculating the cooling capacity of the air-conditioning equipment at the next moment according to the current indoor temperature and the target indoor temperature;

[0090] S40: Control the air-conditioning device according to the cooling capacity of the air-conditioning device so that the indoor temperature at the next moment reaches the target indoor temperature.

[0091] In some embodiments, the processor is used to calculate the cooling capacity of the air-conditioning equipment at the next moment based on the indoor temperature at the current moment and the target indoor temperature, and to control the air-conditioning equipment according to the cooling capacity of the air-conditioning equipment so that the indoor temperature at the next moment reaches the target indoor temperature.

[0092] Specifically, the indoor temperature at the current moment is obtained through the indoor temperature sensor, and the indoor temperature at the current moment is output to the temperature prediction model to calculate the indoor temperature at the next moment. The cooling capacity of the air-conditioning equipment at the next moment can be calculated based on the indoor temperature at the next moment and the target indoor temperature. Then the air-conditioning equipment can control the cooling capacity of the air-conditioning equipment output according to the cooling capacity of the air-conditioning equipment at the next moment, so that the indoor temperature at the next moment reaches the target indoor temperature.

[0093] It should be noted that when the air-conditioning device obtains the cooling capacity signal of the air-conditioning device at the next moment, the air-conditioning device can adjust the chilled water flow rate and air volume of the air-conditioning device according to the cooling capacity of the air-conditioning device through a closed-loop control method, so that the indoor temperature at the next moment reaches the target indoor temperature. The closed-loop control method is to control according to the information of the system output change, that is, by comparing the deviation between the system behavior and the expected behavior, and eliminating the deviation to obtain the expected system performance. For example, the air-conditioning device detects the indoor temperature in real time through the indoor temperature sensor. When the indoor temperature is close to the target indoor temperature, the air-conditioning device will reduce the output cooling capacity. When the indoor temperature reaches the target indoor temperature, the air-conditioning device will stop outputting cooling capacity.

[0094] In this way, the indoor temperature prediction method calculates the cooling capacity of the air-conditioning equipment according to the current indoor temperature and the target indoor temperature, and controls the cooling capacity output of the air-conditioning equipment according to the cooling capacity of the air-conditioning equipment, so that the indoor temperature at the next moment reaches the target indoor temperature, meeting the user's demand for the target indoor temperature.

[0095] The present application also provides a non-volatile computer-readable storage medium including a computer program, and when the computer program is executed by a processor, the processor executes any one of the indoor temperature prediction methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), etc.

[0096] In the description of this specification, the description with reference to the terms "one embodiment", "certain embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0097] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for predicting indoor temperature, characterized in that: The indoor temperature prediction method comprises: Get the indoor temperature sequence, outdoor temperature sequence, cooling output value of the air conditioner at the current time, and the current date and time; The target indoor temperature at the next moment is predicted by a temperature prediction model based on the indoor temperature sequence of the current period, the outdoor temperature sequence, the cooling output value of the air-conditioning equipment at the current moment, and the current date and time. The temperature prediction model is constructed by a heat balance equation and trained with historical temperature data.

2. The indoor temperature prediction method according to claim 1, characterized in that: Before obtaining the indoor temperature sequence, the outdoor temperature sequence and the cooling output value of the air-conditioning equipment at the current time period, the indoor temperature prediction method further includes: Obtain indoor heat transfer data and historical temperature data; constructing a heat balance equation for indoor air based on the heat transfer data; constructing an initial prediction model based on the historical temperature data and the heat balance equation; The initial prediction model is trained by a machine learning algorithm to generate the temperature prediction model.

3. The indoor temperature prediction method according to claim 2, characterized in that: A heat balance equation for indoor air is constructed based on the heat transfer data, including: Constructing a first heat change equation of total indoor heat change according to the heat, heat capacity and temperature of indoor air, wherein the total heat change includes air conditioning heat change, building envelope heat change, air convection heat change, solar radiation heat change and indoor heat gain change; The second heat change equation of building envelope heat change is constructed according to the heat transfer resistance of the envelope structure and the outdoor air temperature; According to the change rules of the air convection heat change, the solar radiation heat change and the indoor heat gain change, a third heat change equation is constructed which is the sum of the air convection heat change, the solar radiation heat change and the indoor heat gain change; The heat balance equation is constructed according to the first heat change equation, the second heat change equation and the third heat change equation.

4. The indoor temperature prediction method according to claim 2, characterized in that: An initial prediction model is constructed based on the historical temperature data and the heat balance equation, including: The heat balance equation is solved according to a parameter estimation algorithm and the historical temperature data to generate the initial prediction model, wherein the parameter estimation algorithm includes one of a least squares estimation method, a maximum likelihood estimation method, and a Bayesian estimation method.

5. The indoor temperature prediction method according to claim 2, characterized in that: Training the initial prediction model by a machine learning algorithm to generate the temperature prediction model includes: Fitting the predicted values ​​and measured values ​​generated by the initial prediction model according to the historical temperature data through a machine learning algorithm to generate a residual model; The residual model and the initial prediction model are added to generate the temperature prediction model.

6. The indoor temperature prediction method according to claim 1, characterized in that: The indoor temperature prediction method comprises: Predicting the cooling capacity of the air-conditioning equipment at the next moment according to the current indoor temperature and the target indoor temperature; The air-conditioning device is controlled according to the cooling capacity of the air-conditioning device so that the indoor temperature at the next moment reaches the target indoor temperature.

7. The indoor temperature prediction method according to claim 6, characterized in that: The controlling the air-conditioning device according to the cooling capacity of the air-conditioning device so that the indoor temperature at the next moment reaches the target indoor temperature comprises: The chilled water flow and air volume of the air-conditioning equipment are adjusted according to the cooling capacity of the air-conditioning equipment through a closed-loop control method so that the indoor temperature at the next moment reaches the target indoor temperature.

8. An indoor temperature prediction device, characterized in that: include: The acquisition module is used to obtain the indoor temperature sequence, outdoor temperature sequence and cooling output value of the air-conditioning equipment at the current time period; The prediction module is used to predict the target indoor temperature at the next moment according to the indoor temperature sequence of the current period, the outdoor temperature sequence and the cooling output value of the air-conditioning equipment at the current moment through a temperature prediction model, wherein the temperature prediction model is constructed by a heat balance equation and obtained through training of historical temperature data.

9. An air conditioning device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor implements the indoor temperature prediction method according to any one of claims 1 to 7.

10. A non-volatile computer-readable storage medium containing a computer program, characterized in that: When the computer program is executed by a processor, the processor implements the indoor temperature prediction method according to any one of claims 1 to 7.

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