Building hourly indoor temperature prediction method and system based on double driving mechanism

Abstract

The application provides a building hourly indoor temperature prediction method and system based on a double driving mechanism, and belongs to the technical field of building energy consumption management and control. The method comprises the following steps: selecting a heat balance differential equation with a building indoor temperature as an interface variable as a benchmark, and representing a heat transfer process of an envelope structure in a space state, so as to construct a guide module for a physical model; a data driving module is constructed, an indoor temperature change difference caused by a non-principal component item is taken as a target variable, and outdoor meteorological parameters are taken as inputs, so that a back propagation neural network is constructed and trained; the outputs of the physical model guide module and the data driving module are combined, and indoor temperature prediction is carried out based on the back propagation neural network. The application can simultaneously consider the physical authenticity of the hourly indoor temperature prediction and the accuracy and rapidity of the building hourly indoor temperature prediction.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of building energy consumption management and control, and particularly relates to a building hourly indoor temperature prediction method and system based on a double driving mechanism. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] The building indoor temperature is a core target variable for realizing building indoor thermal environmental control and is one of the key characteristic parameters for building energy consumption and load prediction. Therefore, predicting the building indoor temperature, especially hourly prediction, can not only enhance the stability of building indoor thermal environmental control and improve the building indoor environmental quality, but also guide the effective management of building energy consumption and load.

[0004] At present, the mainstream building indoor temperature prediction methods mainly include physical models and data-driven models. The physical model has the advantages of high result interpretability, high reliability and strong generalization ability, but the construction process is very complex and time-consuming; while the data-driven model has the advantages of fast, convenient and accurate modeling and prediction, but has the defect of non-interpretable results.

[0005] Therefore, related research has begun to focus on the physical and data double driving mechanism modeling method in the field of buildings, such as constructing a physical model for describing the heat change inside the building through thermodynamic principles, and constructing a data-driven model through machine learning algorithm, and then combining the physical model with the data-driven model. However, this method has the following defects:

[0006] (1) In the prior art, most fusion methods use highly simplified physical models, which are difficult to accurately depict the coupling effects of building envelope heat transfer, indoor and outdoor heat exchange, solar radiation, air permeation and other multi-physical processes, resulting in limited prediction accuracy under complex weather conditions or building structures.

[0007] (2) In the coupling of the two models, the existing double driving methods mostly use simple series or parallel structures, which cannot construct an explicit guide from the physical mechanism to the data compensation, resulting in shallow fusion of physical information and data information, weak interpretability, and unsatisfactory synergistic enhancement effect. SUMMARY

[0008] To overcome the shortcomings of the prior art, the present application provides a building hourly indoor temperature prediction method and system based on a double driving mechanism, which can simultaneously consider the physical reality of hourly indoor temperature prediction and the accuracy and rapidity of building hourly indoor temperature prediction.

[0009] To achieve the above object, one or more embodiments of the present application provide the following technical solutions:

[0010] The first aspect of the present application provides a building hourly indoor temperature prediction method based on a double driving mechanism.

[0011] A building hourly indoor temperature prediction method based on a double driving mechanism comprises:

[0012] A physical model of a building to be measured is constructed, and based on basic information of the measured building and outdoor meteorological parameters, an hourly building indoor temperature time series is determined through building energy consumption simulation software;

[0013] A heat balance differential equation with the building indoor temperature as an interface variable is selected as a reference, and the heat transfer process of the envelope structure is represented in a spatial state to construct a physical model guiding module, and an indoor temperature time series after physical guidance is obtained;

[0014] A data-driven module is constructed, with the difference in indoor temperature change caused by unexplained non-principal component items of the physical model guiding module as a target variable, and with outdoor meteorological parameters as input, a back propagation neural network is constructed and trained to obtain a residual prediction sequence;

[0015] The indoor temperature time series after physical guidance of the building is combined with the residual prediction sequence, and the final indoor temperature time series of the building is predicted based on the back propagation neural network, and the prediction result is error evaluated.

[0016] Further, the basic information of the measured building includes the geographical position, appearance size, orientation, internal structure, envelope structure type, thermal parameter and room function of the building.

[0017] Further, the heat balance differential equation with the building indoor temperature as an interface variable is represented as:

[0018] ;

[0019] Wherein, represents the air sensible heat capacity, , , and represent the indoor, outdoor air, wall inner surface and air conditioning coil surface temperatures respectively; represents the sum of all internal heat source convective heat transfer, represents the sum of all wall inner surface convective heat transfer, represents the heat brought in by air permeation.

[0020] Further, the heat transfer process of the envelope is represented in a space state, that is, a space state equation with two internal nodes is used to represent the heat transfer process of the envelope.

[0021] Further, the construction of the physical model guiding module comprises: time-discretizing the benchmark formula corresponding to the physical model and the space state equation used to represent the heat transfer process of the envelope in a differential manner, and screening a plurality of physical variables, so that the remaining physical variables after screening are used as principal components to construct the physical model guiding module.

[0022] Further, the outdoor meteorological parameters comprise an outdoor air temperature, a direct solar radiation intensity, a scattered radiation intensity, a sky temperature and a wind speed.

[0023] Further, the prediction result is error-evaluated by simultaneously using MSE error, normalized mean deviation error and root mean square error variation coefficient.

[0024] The second aspect of the present application provides a building hourly indoor temperature prediction system based on a double driving mechanism.

[0025] A building hourly indoor temperature prediction system based on a double driving mechanism, comprising:

[0026] A physical model construction unit configured to: construct a physical model of a building to be measured, and determine an hourly building indoor temperature time sequence by using building energy consumption simulation software according to basic information of the measured building and outdoor meteorological parameters;

[0027] A physical guiding calculation unit configured to: select a heat balance differential equation with building indoor temperature as an interface variable as a benchmark, represent a heat transfer process of an envelope in a space state, construct a physical model guiding module, and obtain a building indoor temperature time sequence after physical guidance;

[0028] A data-driven calculation unit configured to: construct a data-driven module, use a difference value of indoor temperature change caused by a non-principal component term that is not explained by the physical model guiding module as a target variable, use outdoor meteorological parameters as input, construct and train a back propagation neural network, and obtain a residual prediction sequence;

[0029] A temperature prediction evaluation unit configured to: combine the building indoor temperature time sequence after physical guidance and the residual prediction sequence, obtain a final building indoor temperature time sequence based on the back propagation neural network prediction, and error-evaluate the prediction result.

[0030] The third aspect of the present application provides a computer readable storage medium, which stores a program, and the program is executed by a processor to implement the steps of the building hourly indoor temperature prediction method based on a double driving mechanism according to the first aspect of the present application.

[0031] The fourth aspect of the present application provides an electronic device, which comprises a memory, a processor and a program stored in the memory and executable on the processor, and the processor implements the steps of the building hourly indoor temperature prediction method based on a double driving mechanism according to the first aspect of the present application when executing the program.

[0032] The above one or more technical solutions have the following beneficial effects:

[0033] (1) According to the basic information of the measured building and the outdoor meteorological parameters, the physical model of the building to be measured is constructed by the building energy consumption simulation software EnergyPlus, and the hourly building indoor temperature time series is determined. Compared with the prior art, multiple factors such as building geographical location, building envelope thermal characteristics, indoor load and complex time-varying meteorological parameters can be integrated, and the coupling physical processes such as building envelope heat transfer, indoor and outdoor air heat exchange, solar radiation heat gain, air permeation and internal heat source influence can be simulated completely and accurately. Therefore, the present application fundamentally overcomes the defect that the simplified physical model is difficult to describe the complex thermal process, significantly improves the model-based precision and adaptability under variable weather conditions or special-shaped building structures, and can provide more reliable and more physically realistic benchmark and data basis for subsequent fusion prediction.

[0034] (2) From the output of the high-fidelity physical model, the principal component physical variables and their relationships that dominate the indoor temperature change are explicitly identified and extracted by methods such as partial derivative analysis, forming an interpretable physical guided calculation core; and the remaining part that the physical model cannot completely explain is defined as the target residual, which is learned and predicted by the data-driven model. This architecture makes the learning goal of the data-driven module very clear, and realizes the deep fusion and clear division of physical information and data information at different levels. This not only enhances the physical interpretability of the final prediction result, but also accurately compensates for the complex residual through data-driven.

[0035] The advantages of the additional aspects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0036] The drawings accompanying the specification of the present application form a part thereof and serve to provide further understanding of the present application, the illustrative embodiments of the present application and its description serve to explain the present application, and do not constitute improper limitations on the present application.

[0037] Figure 1 FIG. 1 is a flowchart of a building hourly indoor temperature prediction method based on a double driving mechanism according to an embodiment of the present application.

[0038] Figure 2 FIG. 2 is a space state diagram with two internal nodes according to an embodiment of the present application.

[0039] Figure 3 FIG. 3 is a diagram of a building indoor temperature real value time series simulated by EnergyPlus software according to an embodiment of the present application.

[0040] Figure 4 FIG. 4 is a diagram of a building indoor temperature prediction value time series calculated by a physical model guiding module according to an embodiment of the present application.

[0041] Figure 5 FIG. 5 is a comparison diagram of building indoor temperature prediction results according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] It should be noted that the following detailed description is merely exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the application belongs.

[0043] It is to be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application.

[0044] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0045] Embodiment One

[0046] The present embodiment discloses a building hourly indoor temperature prediction method based on a double driving mechanism.

[0047] As shown in FIG. 1, a building hourly indoor temperature prediction method based on a double driving mechanism comprises the following steps: Figure 1

[0048] Step S1, constructing a physical model of a building to be measured, determining a building hourly indoor temperature time series by building energy consumption simulation software according to basic information and outdoor meteorological parameters of the measured building;

[0049] Step S2, selecting a heat balance differential equation with building indoor temperature as an interface variable as a reference, and representing a building envelope heat transfer process in a space state to construct a physical model guiding module, and obtaining a building indoor temperature time series after physical guidance;

[0050] ​Step S3, constructing a data-driven module, taking the difference of indoor temperature variation caused by the non-principal component item which is not explained by the physical model guiding module as a target variable, taking outdoor meteorological parameters as input, constructing and training a back propagation neural network to obtain a residual prediction sequence;

[0051] Step S4, combining the building indoor temperature time sequence guided by the physical model with the residual prediction sequence, obtaining a final building indoor temperature time sequence based on the back propagation neural network prediction, and performing error evaluation on the prediction result.

[0052] The first objective of the present application is to fully exert the role of the physical model, form simultaneous efficient utilization of its internal physical principles and output data, to ensure the physical authenticity and interpretability of the building hourly indoor temperature prediction; the second objective is to exert the ability of the data-driven model in dealing with nonlinear problems, to fit the nonlinear part in the physical model output data which is difficult to explain by simple and explicit physical principles, to ensure the accuracy and rapidity of the building hourly indoor temperature prediction. Based on the above process, the present application can simultaneously consider the physical authenticity of the hourly indoor temperature prediction and the accuracy and rapidity of the building hourly indoor temperature prediction. For the convenience of understanding the technical scheme of the present application, the specific implementation method in the technical scheme of the present application will be further explained and described below.

[0053] In step S1, the physical model of the building to be tested is constructed, and the hourly building indoor temperature time sequence is determined by the building energy consumption simulation software according to the basic information of the tested building and the outdoor meteorological parameters.

[0054] The physical model of the building is established using the EnergyPlus simulation software. The inputs of the modeling include the basic information of the building such as geographical location, appearance size, orientation, internal structure, envelope type, thermal parameters, room function, and the hourly time sequence data of outdoor meteorological parameters; the output is set as the hourly building indoor temperature time sequence ; wherein, Tout represents the indoor temperature at the th hour, Tout represents the total number of output indoor temperatures.

[0055] In this embodiment, an EnergyPlus software is used to establish a building model, a typical meteorological year is selected for the meteorological parameter file, the simulation time is set from July 1st to 31st, the simulation time step is 1 hour, and other specific settings are shown in Table 1. It should be noted that the parameters in the envelope structure setting and the indoor setting in Table 1 are set from the built-in parameter setting options of the EnergyPlus simulation software, therefore, the parameters in Table 1 will not be explained in detail.

[0056] Table 1 EnergyPlus simulation software modeling settings

[0057]

[0058] According to the above modeling settings, the simulated building indoor temperature true value time series is as shown in Figure 3 Figure 3 Each value in the indoor temperature time series shown in

[0059] is calculated according to a specific physical principle and formula, has complete physical interpretability, and thus serves as the indoor temperature true value.

[0060] In step S2, a heat balance differential equation with the building indoor temperature as an interface variable is selected as a benchmark, and a spatial state is used to represent the building envelope heat transfer process to construct a physical model guiding module, and a building indoor temperature time series after physical guidance is obtained.

[0061]

[0062]

[0063] Subsequently, for the most critical building envelope heat transfer process, a spatial state equation with two internal nodes as shown in Figure 2 is used to represent it:

[0064]

[0065]

[0066] ​​​​​​​​​​​​​​​​​​​​respectively represent the convective heat transfer coefficient and the heat transfer area of the building envelope surface.

[0067] Then, the reference formula and the state space equation are written in time discrete form using the difference method and a certain derivation is performed, and the expression of the indoor temperature at time t is obtained as The physical variable values at time t and time t+1 are represented by The expression of the indoor temperature at time t is obtained, and the partial derivatives of each physical variable with respect to t are calculated, and the physical variables whose partial derivatives are not zero are screened out, and the terms containing only these physical variables are used as the main components to form the calculation formula of the physical module:

[0068]

[0069] wherein, cp represents the air sensible heat capacity.

[0070] Finally, the output of the EnergyPlus software at each time t is substituted into the above formula, and the indoor temperature at time t can be calculated. The time series of the indoor temperature can be obtained by calculating all the time t, which is the output of the physical module. The time series of the predicted indoor temperature obtained by the physical model guided module is shown in FIG. 3, and the calculation result of the physical model guided module is consistent with the true value in trend, which indicates that the calculation result of the physical model guided module contains the main physical principle in the change process of the indoor temperature, and this part has physical interpretability; however, it can also be seen that there is a large error between the calculation result of the physical model guided module and the true value, and these errors are mainly caused by the nonlinear non-main component terms, and therefore the data driven module needs to be constructed to fit the non-main components to reduce the final prediction error.

[0071] In step S3, the data driven module is constructed, the difference value of the indoor temperature caused by the non-main component terms not explained by the physical model guided module is taken as the target variable, and the outdoor meteorological parameters are taken as the input, and the back propagation neural network is constructed and trained to obtain the residual prediction sequence. Figure 4 Figure 3

[0072] In step S3, the data driven module is constructed, the difference value of the indoor temperature caused by the non-main component terms not explained by the physical model guided module is taken as the target variable, and the outdoor meteorological parameters are taken as the input, and the back propagation neural network is constructed and trained to obtain the residual prediction sequence.

[0073] ​​​​​​​​​​​​​​​First, the non-principal component terms other than the principal components will cause... Time and Difference in indoor temperature over time As the target variable of the data-driven module Output using EnergyPlus software Indoor temperature at all times With the output of the physics model module Subtracting the two gives us:

[0074] ;

[0075] in, express Chinese correspondence The elements of time, namely Difference in indoor temperature at any given time .

[0076] Subsequently, the input variables for the data-driven module are determined, namely: Theoretically, it is a function of non-principal component terms, and the physical variables involved in the non-principal component terms are used as input variables for the data-driven module. Specifically includes outdoor air temperature at any time Intensity of direct solar radiation Scattered radiation intensity Sky temperature and wind speed The time series, and the variables mentioned above A time series composed of the values ​​at each moment, i.e. .

[0077] Then, Dividing the dataset according to a 70%:30% ratio, the first 70% is used as the training set, denoted as... ,in Round to the nearest integer; the last 30% is used as the test set, denoted as... . Similarly, it is divided into training sets. and test set ;in, Indicates training set Chinese correspondence Elements of time Indicates the test set Chinese correspondence The element of time.

[0078] Based on this, a backpropagation neural network (BPNN) is built using the "newff" function built into Matlab software. The input of BPNN is a vector. The output is a vector. The predicted value is denoted as .use and The BPNN is trained using the following loss function: and Mean square error (MSE):

[0079] ;

[0080] in, Indicates the training set Chinese correspondence Elements of time The predicted value.

[0081] In step S4, the physical-guided building indoor temperature time series is combined with the residual prediction sequence, and the final building indoor temperature time series is predicted based on the backpropagation neural network. The prediction results are then evaluated for error.

[0082] A well-trained BPNN can be used based on the input test set variable values. The corresponding predicted value is obtained. This is the output of the data-driven module. Moment In fact The predicted value is compared with the output of the physics model guidance module. By adding them together, we can obtain the result. Predicted indoor temperature at any time ,Right now Therefore, within the test set coverage From the moment to the end At any given moment, through hourly calculations, a time series of the final predicted indoor building temperature values ​​can be generated. .

[0083] To ensure the accuracy of the prediction, three error evaluation indicators were used simultaneously to analyze the time series of the predicted indoor temperature values. and the corresponding EnergyPlus output of the building's indoor temperature time series for that time period. The errors between the values ​​are evaluated. In addition to MSE, the three error evaluation indicators include the Normalized Mean Bias Error (NMBE) and the Root Mean Square Error Coefficient of Variation (CVRMSE). Generally, the smaller the absolute values ​​of MSE, NMBE, and CVRMSE, the higher the prediction accuracy. Furthermore, if the absolute value of NMBE is less than 10% or the absolute value of CVRMSE is less than 30%, the prediction can be considered reliable. The three error evaluation indicators are expressed as follows:

[0084] ;

[0085] ;

[0086] ;

[0087] In the embodiment, the BPNN is established based on a "newff" function built in MATLAB software, the training of the model is realized by using a "train" function, and the prediction simulation of the model is realized by using a "sim" function. The hyperparameters and configuration parameters are set as shown in Table 2.

[0088] Table 2 Main hyperparameters of a back propagation neural network and corresponding settings

[0089]

[0090] The final building indoor temperature prediction value results obtained after coupling of the physical model guiding module and the data driven module, and the results obtained by the simplified physical and data double driving mechanism model 2R2C and the pure data driven BPNN are compared as shown in Figure 5 It can be seen from Figure 5 that, in general, the methods have relatively consistent fluctuation trends with the real results, and the prediction results are reasonable. However, the methods and the 2R2C model have physical interpretability which the pure data driven BPNN does not have. In addition, although the results of the methods and the pure data driven BPNN model both appear a certain degree of shock, they are more consistent with the real results in the embodiment, and the results of the 2R2C model appear a certain degree of deviation. Therefore, in view of the embodiment, the method has certain advantages in practicability, that is, the method is more consistent with the real results, and the results can be physically interpreted.

[0091] Further, the prediction result error evaluation indexes of the method and the existing method are shown in Table 3.

[0092] Table 3 Comparison of prediction result error evaluation indexes

[0093]

[0094] It can be seen from the content in Table 3 that the errors of the method and the pure data driven BPNN model are basically at the same level and are relatively low, and the error level of the 2R2C model is relatively high. This result further confirms that the prediction result of the method for the building hourly indoor temperature is relatively accurate.

[0095] Based on the building hourly indoor temperature prediction method based on the double driving mechanism provided by the application, the following technical effects can be achieved: 1) The application provides a new idea and a new technical route for the building indoor temperature prediction research direction, which has certain significance for improving the building indoor environment quality and helping to achieve the building energy saving and carbon reduction goal; 2) The proposed building indoor temperature prediction method simultaneously applies the principles and processes of the physical model and high-quality data, can relatively accurately predict the building indoor temperature, and makes the prediction result have certain explainability.

[0096] Embodiment two

[0097] The embodiment discloses a building hourly indoor temperature prediction system based on a double driving mechanism.

[0098] A building hourly indoor temperature prediction system based on a double driving mechanism comprises:

[0099] A physical model construction unit is configured to: construct a physical model of a building to be measured, determine an hourly building indoor temperature time sequence through building energy consumption simulation software according to basic information and outdoor meteorological parameters of the measured building;

[0100] A physical guidance calculation unit is configured to: select a heat balance differential equation with building indoor temperature as an interface variable as a reference, and represent a heat transfer process of an envelope structure in a spatial state to construct a physical model guidance module, and obtain a building indoor temperature time sequence after physical guidance;

[0101] A data-driven calculation unit is configured to: construct a data-driven module, take a difference value of indoor temperature change caused by a non-principal component item that is not explained by the physical model guidance module as a target variable, take outdoor meteorological parameters as input, construct and train a back propagation neural network to obtain a residual prediction sequence;

[0102] A temperature prediction evaluation unit is configured to: combine the building indoor temperature time sequence after physical guidance and the residual prediction sequence, obtain a final building indoor temperature time sequence based on the back propagation neural network prediction, and evaluate the prediction result.

[0103] Embodiment three

[0104] The purpose of the embodiment is to provide a computer readable storage medium.

[0105] The computer readable storage medium stores a computer program, and the program is executed by a processor to realize the steps in the building hourly indoor temperature prediction method based on the double driving mechanism according to the embodiment one of the application.

[0106] Embodiment four

[0107] An electronic device is provided.

[0108] The electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, and the processor implements the steps in the building hourly indoor temperature prediction method based on a double driving mechanism according to the embodiment one of the present disclosure when executing the program.

[0109] The steps involved in the devices of the above embodiments two, three, and four correspond to the method embodiment one, and the specific implementation can be referred to the related description part of the embodiment one. The term "computer readable storage medium" should be understood as including a single medium or multiple media of one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying the instruction set for execution by the processor and causing the processor to perform any of the methods in the present disclosure.

[0110] Those skilled in the art should understand that each module or each step of the present disclosure described above can be realized by a general computer device, alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device for execution by a computing device, or they can be respectively manufactured into each integrated circuit module, or a plurality of modules or steps among them can be manufactured into a single integrated circuit module to realize. The present disclosure is not limited to any specific combination of hardware and software.

[0111] Although the specific embodiments of the present disclosure are described above in combination with the accompanying drawings, it is not a limitation on the protection scope of the present disclosure, and those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions of the present disclosure without creative labor are still within the protection scope of the present disclosure.

Claims

1. A method for predicting hourly indoor temperature in buildings based on a dual-drive mechanism, characterized in that, include: A physical model of the building to be tested is constructed. Based on the basic information of the building and outdoor meteorological parameters, the hourly indoor temperature time series is determined using building energy consumption simulation software. A physical model guidance module is constructed by selecting the thermal balance differential equation with the building's indoor temperature as the interface variable as the benchmark and representing the heat transfer process of the building envelope in a spatial state. The physical model guidance module is then obtained by constructing a physical model guidance module. The construction of the physical model guidance module includes: discretizing the benchmark formula corresponding to the physical model and the spatial state equation used to represent the heat transfer process of the building envelope in a time-dependent manner using a difference method; filtering multiple physical variables; and using the remaining physical variables after filtering as principal components to construct the physical model guidance module. A data-driven module is constructed, with the difference in indoor temperature change caused by non-principal component terms not explained by the physical model guidance module as the target variable and outdoor meteorological parameters as input. A backpropagation neural network is constructed and trained to obtain the residual prediction sequence. The physical-guided building indoor temperature time series is combined with the residual prediction sequence, and the final building indoor temperature time series is obtained based on the backpropagation neural network. The prediction results are then evaluated for error.

2. The hourly indoor temperature prediction method for buildings based on a dual-drive mechanism as described in claim 1, characterized in that, The basic information of the building being measured includes its geographical location, external dimensions, orientation, internal structure, building envelope type, thermal parameters, and room functions.

3. The hourly indoor temperature prediction method for buildings based on a dual-drive mechanism as described in claim 1, characterized in that, The heat balance differential equation with the building's indoor temperature as the interface variable is expressed as: ； in, This indicates the sensible heat capacity of air. , and These represent the indoor and outdoor air temperatures and the surface temperature of the inner wall, respectively. This represents the sum of convective heat transfer from all internal heat sources. This represents the sum of all convective heat transfers within the walls. and These represent the convective heat transfer coefficient and heat transfer area of ​​the building envelope, respectively. This indicates the heat brought in by air infiltration.

4. The hourly indoor temperature prediction method for buildings based on a dual-drive mechanism as described in claim 1, characterized in that, The heat transfer process of the building envelope is represented in a spatial state, that is, the heat transfer process of the building envelope is represented by a spatial state equation with two internal nodes.

5. The hourly indoor temperature prediction method for buildings based on a dual-drive mechanism as described in claim 1, characterized in that, The outdoor meteorological parameters include: outdoor air temperature, direct solar radiation intensity, diffuse radiation intensity, sky temperature, and wind speed.

6. The hourly indoor temperature prediction method for buildings based on a dual-drive mechanism as described in claim 1, characterized in that, Meanwhile, the MSE error, normalized average deviation error, and root mean square error coefficient of variation are used to evaluate the prediction results.

7. A building hourly indoor temperature prediction system based on a dual-drive mechanism, characterized in that, include: The physical model building unit is configured to: build a physical model of the building to be measured, and determine the hourly indoor temperature time series of the building based on the basic information of the building and outdoor meteorological parameters using building energy consumption simulation software; The physical guidance calculation unit is configured to: select the thermal balance differential equation with the building's indoor temperature as the interface variable as the benchmark, and simultaneously represent the heat transfer process of the building envelope in a spatial state to construct a physical model guidance module and obtain the time series of the building's indoor temperature after physical guidance; wherein, the construction of the physical model guidance module includes: using a difference method to discretize the benchmark formula corresponding to the physical model and the spatial state equation used to represent the heat transfer process of the building envelope in time, and filtering multiple physical variables, using the remaining physical variables after filtering as principal components to construct the physical model guidance module; The data-driven computing unit is configured to: construct a data-driven module, using the difference in indoor temperature change caused by non-principal component terms not explained by the physical model guidance module as the target variable and outdoor meteorological parameters as input, construct and train a backpropagation neural network to obtain a residual prediction sequence; The temperature prediction and evaluation unit is configured to combine the physically guided building indoor temperature time series with the residual prediction sequence, predict the final building indoor temperature time series based on the backpropagation neural network, and evaluate the error of the prediction results.

8. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the hourly indoor temperature prediction method for buildings based on a dual-drive mechanism as described in any one of claims 1-6.

9. An electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the hourly indoor temperature prediction method for buildings based on a dual-drive mechanism as described in any one of claims 1-6.

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