A building indoor temperature prediction method and system based on a thermal resistance-thermal capacity model

Abstract

The application discloses a kind of building indoor temperature prediction method and system based on thermal resistance-thermal capacity model, the method of the present application includes the heat flow in the heat transfer process of building indoor temperature is regarded as electric current, temperature difference is regarded as voltage, thermal resistance and thermal capacity correspond to resistance and capacitance respectively, the temperature transfer system of building indoor temperature is simplified and constructed to obtain the thermal resistance-thermal capacity model consisting of thermal resistance and thermal capacity;Thermal balance equation of thermal resistance-thermal capacity model is constructed;Parameter identification is carried out on thermal balance equation;The identified parameter is substituted into thermal balance equation to predict building indoor temperature.The present application aims to solve the problem that the existing building indoor temperature prediction grey box model has poor interpretability, improve the accuracy and interpretability of building indoor temperature prediction.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of air conditioning in building indoor, and particularly relates to a building indoor temperature prediction method and system based on a thermal resistance-capacity model. BACKGROUND

[0002] At present, building energy consumption accounts for more than 33% of the total global energy consumption, and the energy consumption of the heating, ventilation and air conditioning system accounts for a significant proportion in the building energy consumption. Accurate prediction of the building indoor temperature is of key significance for optimizing the operation of the heating, ventilation and air conditioning system and reducing the building energy consumption. The existing building indoor temperature prediction methods can be mainly divided into three categories of white-box model, gray-box model and black-box model. The white-box model is constructed based on detailed building thermal physical principles, can accurately describe the building thermal process, but the model is complex, a large number of building parameters and complex calculation are required for model establishment, and it is difficult to be widely applied in actual engineering. The black-box model mainly relies on a large number of historical data for training, does not consider the physical process inside the building, although the model is simple and flexible, but it has poor interpretability and is difficult to adapt to various complex situations of the building. The gray-box model is between the two, combines the advantages of physical laws and data, has moderate model complexity, and has relatively low requirements for data and calculation capacity. However, the gray-box model used in the existing building indoor temperature prediction method has the problem of poor interpretability. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a building indoor temperature prediction method and system based on a thermal resistance-capacity model, aiming at solving the problem of poor interpretability of the existing gray-box model for building indoor temperature prediction, and improving the accuracy and interpretability of building indoor temperature prediction.

[0004] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0005] A building indoor temperature prediction method based on a thermal resistance-capacity model, comprising the following steps:

[0006] S101, regarding the heat flow in the heat transfer process of the building indoor temperature as electric current, regarding the temperature difference as voltage, regarding the thermal resistance and the thermal capacity as resistance and capacitance respectively, simplifying and constructing a temperature transfer system of the building indoor temperature to obtain a thermal resistance-capacity model composed of thermal resistance and thermal capacity;

[0007] S102, constructing a heat balance equation of the thermal resistance-capacity model;

[0008] S103, performing parameter identification on the heat balance equation;

[0009] S104, substituting the identified parameters into the heat balance equation to predict the building indoor temperature .

[0010] The thermal resistance-heat capacity model constructed in step S101 describes the dynamic heat transfer process of a building as a heat transfer process through a thermal resistance-heat capacity network, including the building's indoor temperature. As the central node, it utilizes the indoor air heat capacity It stores heat and exchanges it with the outdoor temperature through exterior windows, exterior walls, and roof. Multiple heat exchange processes occur between them, where: the first heat exchange process is indoor temperature. Thermal resistance through the exterior window With outdoor temperature Heat transfer occurs; the second heat exchange process is indoor temperature. By roof outer surface temperature Roof inner surface temperature With outdoor temperature Heat transfer occurs, and the temperature of the roof's outer surface increases. With outdoor temperature The thermal resistance between them is Roof surface temperature The corresponding heat capacity of the air layer on the roof surface is Roof surface temperature With the temperature of the inner surface of the roof The thermal resistance between them is Roof inner surface temperature The corresponding heat capacity of the roof is Roof inner surface temperature With indoor temperature The thermal resistance between them is equal to the thermal resistance of the indoor air. The third heat exchange process is indoor temperature. Temperature of the outer surface of the east, west, south, and north exterior walls Temperature of the inner surface of the exterior wall With outdoor temperature Heat transfer occurs. External wall surface temperature With outdoor temperature The thermal resistance between them is Temperature of the outer surface of the exterior wall The corresponding heat capacity of the air layer on the exterior wall surface is Temperature of the outer surface of the exterior wall With the temperature of the inner surface of the exterior wall The thermal resistance between them is Temperature of the inner surface of the exterior wall The corresponding heat capacity of the exterior wall is Temperature of the inner surface of the exterior wall With indoor temperature The heat transfer thermal resistance between the outdoor temperature and the outer surface temperature of the external wall is equal to the heat transfer thermal resistance of the indoor air wherein the external wall outer surface temperature and the external wall inner surface temperature The subscript is one of east, west, south, and north.

[0011] Optionally, the heat balance equation of the thermal resistance-thermal capacity model constructed in step S102 comprises a node heat balance equation of each external wall, a node heat balance equation of the roof, and a heat balance equation of an indoor air node; the function expression of the node heat balance equation of each external wall is:

[0012] ,

[0013] ,

[0014] wherein, is the heat capacity of the external wall surface air layer, is the external wall outer surface temperature, is time, is the area of the external wall, is the outdoor temperature, is the heat transfer thermal resistance between the external wall outer surface temperature and the outdoor temperature , is the external wall inner surface temperature, is the solar radiation correlation coefficient of the external wall, is the solar radiation intensity received by the external wall, is the heat capacity of the external wall, is the indoor temperature of the building, is the heat transfer thermal resistance of the indoor air.

[0015] The function expression of the node heat balance equation of the roof is:

[0016] ,

[0017] ,

[0018] wherein, is the heat capacity of the roof surface air layer, is the roof outer surface temperature, is the area of the roof, is the heat transfer thermal resistance between the roof outer surface temperature and the outdoor temperature , is the roof inner surface temperature, is the solar radiation correlation coefficient of the roof, is the roof outer surface temperature With the temperature of the inner surface of the roof Thermal resistance between them The intensity of solar radiation received by the roof. The heat capacity of the roof; The thermal resistance of indoor air;

[0019] The functional expression of the heat balance equation for the indoor air node is:

[0020] ,

[0021] in, The thermal resistance of the exterior window. The area of ​​the exterior window. For the correlation coefficient of the air conditioning system, For heating, ventilation, and air conditioning (HVAC) cooling or heating, The correlation coefficient of the internal heat source. This refers to the heat dissipation from internal heat sources, including indoor occupants, lighting, and equipment. The permeability correlation coefficient, This refers to the increase or decrease in indoor heat caused by infiltration ventilation.

[0022] Optionally, in step S103, when identifying parameters for the heat balance equation, the identified parameters include resistance-capacitance characteristic parameters, including the outer surface temperature of the outer wall. With outdoor temperature Thermal resistance between Temperature of the outer surface of the exterior wall With the temperature of the inner surface of the exterior wall Thermal resistance between Roof inner surface temperature Temperature of the inner surface of the exterior wall With indoor temperature Thermal resistance between Roof surface temperature With outdoor temperature Thermal resistance between Roof surface temperature With the temperature of the inner surface of the roof Thermal resistance between The heat capacity of the air layer on the exterior wall surface The heat capacity of the exterior wall The heat capacity of the air layer on the roof surface The heat capacity of the roof Indoor air heat capacity and the thermal resistance of the exterior window The identification of RC characteristic parameters includes:

[0023] S201, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the heat balance equation of the indoor air node, construct the heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and HVAC cooling / heating capacity at night:

[0024] ,

[0025] ,

[0026] ,

[0027] ,

[0028] ,

[0029] S202, using TRNSYS model simulation on the heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and HVAC cooling / heating capacity at night to obtain the node temperature under the conditions of no solar radiation, no internal heat source, no penetration and HVAC cooling / heating capacity at night, including the external wall surface temperature , the internal wall surface temperature , the roof surface temperature , the roof internal surface temperature , the indoor temperature of the building and the outdoor temperature ;

[0030] S203, construct a target function with the minimum absolute error between all node temperatures calculated by the thermal resistance-capacitance model and the node temperatures output by the TRNSYS simulation, and use a specified optimization solving algorithm to solve the heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and HVAC cooling / heating capacity at night to obtain the resistance-capacitance characteristic parameters.

[0031] Optionally, in step S103, the identified parameters include solar radiation related coefficients, and the solar radiation related coefficients include the solar radiation related coefficient of the external wall and the solar radiation related coefficient of the roof The identification of the solar radiation related coefficient includes:

[0032] S301, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the heat balance equation of the indoor air node, construct the heat balance equation under the conditions of solar radiation, no internal heat source, no penetration and HVAC cooling / heating capacity:

[0033] ,

[0034] ,

[0035] ,

[0036] ,

[0037] ,

[0038] S302, using the TRNSYS model to simulate the heat balance equation under the condition of solar radiation, no internal heat source, no infiltration and no heating / cooling capacity of heating ventilation and air conditioning to obtain the temperature of each node under the condition of solar radiation, no internal heat source, no infiltration and no heating / cooling capacity of heating ventilation and air conditioning, including , the temperature of the outer surface of the outer wall , the temperature of the inner surface of the outer wall , the temperature of the outer surface of the roof , the temperature of the inner surface of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each outer wall and the solar radiation intensity received by the roof

[0039] S303, taking the identified parameters as the known parameters of the heat balance equation under the condition of solar radiation, no internal heat source, no infiltration and no heating / cooling capacity of heating ventilation and air conditioning, constructing a target function with the minimum absolute error between all node temperatures calculated by the thermal resistance-thermal capacity model and the node temperatures output by the TRNSYS simulation, and using a specified optimization algorithm to solve the heat balance equation under the condition of solar radiation, no internal heat source, no infiltration and no heating / cooling capacity of heating ventilation and air conditioning to obtain the solar radiation correlation coefficient.

[0040] Optionally, in the step S103 of identifying the parameters of the heat balance equation, the identified parameters include the internal heat source correlation coefficient , and the identification of the internal heat source correlation coefficient includes:

[0041] S401, based on the node heat balance equation of each outer wall, the node heat balance equation of the roof and the heat balance equation of the indoor air node, constructing the heat balance equation under the condition of solar radiation and internal heat source, no infiltration and no heating / cooling capacity of heating ventilation and air conditioning:

[0042] ,

[0043] ,

[0044] ,

[0045] ,

[0046] ,

[0047] S402, using the TRNSYS model to simulate the heat balance equation under the condition of solar radiation and internal heat source, no penetration and air conditioning cooling / heating capacity to obtain the temperature of each node under the condition of solar radiation and internal heat source, no penetration and air conditioning cooling / heating capacity, including the temperature of the outer surface of the external wall , the temperature of the inner surface of the external wall , the temperature of the outer surface of the roof , the temperature of the inner surface of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof and the heat dissipation of the internal heat source ;

[0048] S403, taking the identified parameters as the known parameters of the heat balance equation under the condition of solar radiation and internal heat source, no penetration and air conditioning cooling / heating capacity, constructing a target function with the minimum absolute error between the node temperature calculated by the thermal resistance-thermal capacity model and the node temperature output by the TRNSYS simulation, and using a specified optimization algorithm to solve the heat balance equation under the condition of solar radiation and internal heat source, no penetration and air conditioning cooling / heating capacity to obtain the internal heat source correlation coefficient .

[0049] Optionally, in step S103, the identified parameters include the penetration correlation coefficient and the air conditioning system correlation coefficient , and the identification of the penetration correlation coefficient includes:

[0050] S501, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the heat balance equation of the indoor air node, constructing the heat balance equation under the condition of solar radiation, internal heat source and penetration, no air conditioning cooling / heating capacity:

[0051] ,

[0052] ,

[0053] ,

[0054] ,

[0055] ;

[0056] S502, using TRNSYS model simulation on the heat balance equation under the condition of solar radiation, internal heat source and infiltration, and no HVAC cooling / heating capacity to obtain the temperature of each node under the condition of solar radiation, internal heat source and infiltration, and no HVAC cooling / heating capacity, including respectively the outside surface temperature of the external wall , the inside surface temperature of the external wall , the outside surface temperature of the roof , the inside surface temperature of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , the heat dissipation of the internal heat source , and the indoor heat increase or decrease caused by infiltration ventilation ;

[0057] S503, taking the identified parameters as the known parameters of the heat balance equation under the condition of solar radiation, internal heat source and infiltration, and no HVAC cooling / heating capacity, constructing a target function with the minimum absolute error between the node temperature calculated by the thermal resistance-capacity model and the node temperature output by the TRNSYS simulation, and using a specified optimization algorithm to solve the heat balance equation under the condition of solar radiation, internal heat source and infiltration, and no HVAC cooling / heating capacity to obtain the infiltration correlation coefficient .

[0058] The identification of the air conditioning system correlation coefficient includes:

[0059] S601, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the heat balance equation of the indoor air node, constructing the heat balance equation under the condition of solar radiation, internal heat source and HVAC cooling / heating capacity:

[0060] ,

[0061] ,

[0062] ,

[0063] ,

[0064] ,

[0065] S602, using TRNSYS model simulation on the heat balance equation under the condition of solar radiation, internal heat source and HVAC cooling / heating capacity to obtain the temperature of each node under the condition of solar radiation, internal heat source and HVAC cooling / heating capacity, including respectively the outside surface temperature of the external wall , exterior wall inner surface temperature , roof outer surface temperature , roof inner surface temperature , building indoor temperature , outdoor temperature , solar radiation intensity received by each exterior wall , solar radiation intensity received by roof , heat dissipation of internal heat source , indoor heat increase or decrease amount caused by infiltration ventilation , and cooling or heating amount of heating ventilation and air conditioning ;

[0066] S603, constructing a target function with the absolute error between all node temperatures calculated by the thermal resistance-capacitance model and the node temperatures output by the TRNSYS simulation being the minimum, using a specified optimization algorithm to solve the heat balance equation under the conditions of solar radiation, internal heat source, and heating ventilation and air conditioning cooling / heating to obtain the air conditioning system correlation coefficient .

[0067] In addition, the present application also provides a building indoor temperature prediction system based on a thermal resistance-capacitance model, comprising a microprocessor and a memory connected to each other, the microprocessor being programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-capacitance model.

[0068] In addition, the present application also provides a computer readable storage medium, wherein a computer program or instructions are stored, the computer program or instructions being programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-capacitance model by a processor.

[0069] In addition, the present application also provides a computer program product, comprising a computer program or instructions, the computer program or instructions being programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-capacitance model by a processor.

[0070] Compared with the prior art, the present application mainly has the following beneficial effects: the method of the present application comprises regarding the heat flow in the heat transfer process of the building indoor temperature as an electric current, regarding the temperature difference as a voltage, regarding the thermal resistance and the thermal capacitance as the resistance and the capacitance respectively, simplifying the temperature transfer system of the building indoor temperature , and constructing a thermal resistance-capacitance model composed of thermal resistance and thermal capacitance; constructing a heat balance equation of the thermal resistance-capacitance model; performing parameter identification on the heat balance equation; and substituting the identified parameters into the heat balance equation to predict the building indoor temperature The building cold and heat load prediction method based on the thermal resistance-thermal capacity model belongs to a grey box model, considers indoor thermal disturbance and ventilation conditions, can consider physical characteristics of the building, has lower model complexity and better interpretability, has strong universality, can accurately and quickly predict the indoor temperature of the building, solves the problem of poor interpretability of the existing grey box model for predicting the indoor temperature of the building, improves the accuracy and interpretability of the indoor temperature prediction of the building, and provides an effective basis for the optimized operation control of the heating ventilation air conditioning system. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 It is a basic flow diagram of the method of the embodiment of the present application.

[0072] Figure 2 It is a schematic diagram of the thermal resistance-thermal capacity model in the embodiment of the present application.

[0073] Figure 3 It is a comparison of the results of the thermal resistance-thermal capacity model prediction and TRNSYS simulation in the embodiment of the present application. DETAILED DESCRIPTION

[0074] In order for those skilled in the art to better understand the technical solutions of the present application, the technical solutions of the present application will be further described in detail below in combination with the drawings in the embodiments of the present application.

[0075] As shown in the drawings, Figure 1 the building indoor temperature prediction method based on the thermal resistance-thermal capacity model in the embodiment includes the following steps:

[0076] S101, the heat flow in the heat transfer process of the indoor temperature of the building is regarded as the electric current, the temperature difference is regarded as the voltage, the thermal resistance and the thermal capacity correspond to the resistance and the capacitance respectively, and the temperature transfer system of the indoor temperature of the building is simplified and constructed to obtain the thermal resistance-thermal capacity model composed of the thermal resistance and the thermal capacity;

[0077] S102, a heat balance equation of the thermal resistance-thermal capacity model is constructed;

[0078] S103, parameter identification is performed on the heat balance equation;

[0079] S104, the identified parameters are substituted into the heat balance equation to predict the indoor temperature of the building .

[0080] The building indoor temperature prediction method based on the thermal resistance-capacity model in this embodiment is based on the thermal resistance-capacity network theory to conduct modeling analysis of building indoor temperature prediction. The thermal resistance-capacity network theory is a mathematical modeling method for analyzing and predicting heat transfer processes, which analyzes heat transfer processes by analogy with resistance and capacitance in circuit theory, and simplifies complex heat transfer systems into a network composed of thermal resistance and thermal capacity. In this theory, heat flow is regarded as electric current, temperature difference is regarded as voltage, and thermal resistance and thermal capacity correspond to resistance and capacitance, respectively. In the method of this embodiment, the following building thermal processes are considered: (1) heat conduction between indoor and outdoor air and building opaque envelope (exterior wall and roof); (2) energy transfer of solar radiation through building transparent envelope (exterior window); (3) cooling / heating of building by building heating, ventilation and air conditioning system to maintain indoor air temperature at a certain level; (4) heat storage and release of building thermal storage, mainly affected by the opaque outer envelope heat storage and release process under the influence of indoor and outdoor temperature difference and solar radiation; (5) heat dissipation of internal heat source, ventilation and air infiltration exchange process.

[0081] As shown in Figure 2 , the thermal resistance-capacity model composed of thermal resistance and thermal capacity constructed in step S101 is to describe the building dynamic heat transfer process as a heat transfer process of thermal resistance-capacity network, including the building indoor temperature as the central node, storing heat, and a plurality of heat exchange processes between the outdoor temperature through the exterior window (window connecting indoor and outdoor), the exterior wall and the roof, respectively, wherein: the first heat exchange process is the heat transfer of the indoor temperature through the heat transfer resistance of the exterior window to the outdoor temperature ; the second heat exchange process is the heat transfer of the indoor temperature through the outdoor temperature of the roof outer surface temperature , the indoor temperature of the roof inner surface temperature , and the outdoor temperature , the heat transfer resistance between the outdoor temperature of the roof outer surface temperature is , the heat capacity of the roof surface air layer corresponding to the outdoor temperature of the roof outer surface temperature is , the heat transfer resistance between the outdoor temperature of the roof inner surface temperature is , the heat capacity of the roof corresponding to the outdoor temperature of the roof inner surface temperature The thermal resistance between them is equal to the thermal resistance of the indoor air. The third heat exchange process is indoor temperature. Temperature of the outer surface of the east, west, south, and north exterior walls Temperature of the inner surface of the exterior wall With outdoor temperature Heat transfer occurs. External wall surface temperature With outdoor temperature The thermal resistance between them is Temperature of the outer surface of the exterior wall The corresponding heat capacity of the air layer on the exterior wall surface is Temperature of the outer surface of the exterior wall With the temperature of the inner surface of the exterior wall The thermal resistance between them is Temperature of the inner surface of the exterior wall The corresponding heat capacity of the exterior wall is Temperature of the inner surface of the exterior wall With indoor temperature The thermal resistance between them is equal to the thermal resistance of the indoor air. The temperature of the outer surface of the exterior wall and the temperature of the inner surface of the exterior wall subscript It is one of the four directions: east, west, south, and north.

[0082] like Figure 2 As shown, since the solar radiation intensity of the building envelope varies in each direction, this embodiment establishes a 3R2C model (3 thermal resistance 2 thermal capacity) for the four walls and roof, as well as a thermal resistance model for the exterior windows. Because the wall materials are consistent, the thermal resistance between the outer surfaces of the east, west, south, and north exterior walls and the outdoor air is the same; the thermal resistance between the exterior walls in each direction is the same; and the thermal resistance between the inner surfaces of the exterior walls in each direction and the indoor air is the same. The symbols and their units are defined as follows: Outdoor temperature, °C; The temperature of the outer surface of the exterior wall. It can be in four directions: east, west, south, and north (e, w, s, n), ℃; The temperature of the inner surface of the exterior wall. It can be in four directions: east, west, south, and north (e, w, s, n), ℃; The temperature of the roof's outer surface, in °C; The temperature of the inner surface of the roof, in °C; The indoor temperature is expressed in °C. The thermal resistance between the outer surface of the exterior wall and the outdoor air. ; For the thermal resistance of the exterior wall, ; is the heat transfer thermal resistance between the exterior wall, the inner surface of the roof and the indoor air, ; is the heat transfer thermal resistance between the outer surface of the roof and the outdoor air, ; is the heat transfer thermal resistance of the roof, ; is the heat transfer thermal resistance of the exterior window, ; is the heat capacity of the air layer on the surface of the exterior wall, ; is the heat capacity of the exterior wall, ; is the heat capacity of the air layer on the surface of the roof, ; is the heat capacity of the roof, ; is the heat capacity of the indoor air, ; is the solar radiation intensity received by each exterior wall, ; is the solar radiation intensity received by the roof, ; is the heat dissipation of the internal heat source, ; is the cooling (negative value) / heat (positive value) of the heating, ventilation and air conditioning, ; is the indoor heat increase (positive value) or heat decrease (negative value) caused by permeation ventilation, ; is the area of the exterior wall in the east, west, south and north (i can be e, w, s, n) directions, ; is the area of the roof, ; is the area of the exterior window, ; is the solar radiation correlation coefficient of the exterior wall; is the solar radiation correlation coefficient of the roof; is the internal heat source correlation coefficient; is the air conditioning system correlation coefficient; is the permeation correlation coefficient; is the sampling step, 1h.

[0083] For the thermal resistance-capacity model shown in Figure 2 , the heat balance equation of the thermal resistance-capacity model constructed in step S102 of the embodiment includes the node heat balance equation of each exterior wall, the node heat balance equation of the roof and the node heat balance equation of the indoor air; the functional expression of the node heat balance equation of each exterior wall is:

[0084] (1)

[0085] (2)

[0086] wherein, is the heat capacity of the outer wall surface air layer, is the outer wall outer surface temperature, is time, is the area of the outer wall, is the outdoor temperature, is the heat transfer thermal resistance between the outer wall outer surface temperature and the outdoor temperature , is the outer wall inner surface temperature, is the solar radiation related coefficient of the outer wall, is the solar radiation intensity received by the outer wall, is the heat capacity of the outer wall, is the building indoor temperature, is the heat transfer thermal resistance of the indoor air;

[0087] The function expression of the node heat balance equation of the roof is:

[0088] (3)

[0089] (4)

[0090] wherein, is the heat capacity of the roof surface air layer, is the roof outer surface temperature, is the area of the roof, is the heat transfer thermal resistance between the roof outer surface temperature and the outdoor temperature , is the roof inner surface temperature, is the solar radiation related coefficient of the roof, is the heat transfer thermal resistance between the roof outer surface temperature and the roof inner surface temperature , is the solar radiation intensity received by the roof, is the heat capacity of the roof, is the heat transfer thermal resistance of the indoor air;

[0091] The function expression of the node heat balance equation of the indoor air is:

[0092] (5)

[0093] wherein, The thermal resistance of the exterior window. The area of ​​the exterior window. For the correlation coefficient of the air conditioning system, For heating, ventilation, and air conditioning (HVAC) cooling or heating, The correlation coefficient of the internal heat source. This refers to the heat dissipation from internal heat sources, including indoor occupants, lighting, and equipment. The permeability correlation coefficient, This refers to the increase or decrease in indoor heat caused by infiltration ventilation.

[0094] The parameters involved in formulas (1) to (5) include building thermal resistance. , , , and Building heat capacity , , , and Solar radiation correlation coefficient and Internal heat source correlation coefficient Permeability correlation coefficient Correlation coefficient of air conditioning system The basic principle of the building indoor temperature prediction method in this embodiment is as follows: considering ventilation and internal disturbances, the resistance-capacity characteristic parameters, solar radiation correlation coefficient, internal heat source correlation coefficient, infiltration correlation coefficient, and air conditioning system correlation coefficient of the building thermal resistance-heat capacity model are identified step by step using TRNSYS simulation data.

[0095] In step S103 of this embodiment, when identifying parameters for the heat balance equation, the identified parameters include resistance-capacitance characteristic parameters, including the outer surface temperature of the outer wall. With outdoor temperature Thermal resistance between Temperature of the outer surface of the exterior wall With the temperature of the inner surface of the exterior wall Thermal resistance between Roof inner surface temperature Temperature of the inner surface of the exterior wall With indoor temperature Thermal resistance between Roof surface temperature With outdoor temperature Thermal resistance between Roof surface temperature With the temperature of the inner surface of the roof Thermal resistance between The heat capacity of the air layer on the exterior wall surface thermal capacity of the outside wall thermal capacity of the roof surface air layer thermal capacity of the roof thermal capacity of the indoor air and heat transfer thermal resistance of the outside window The identification of the resistance-capacity characteristic parameters comprises:

[0096] S201, based on the node heat balance equation of each outside wall, the node heat balance equation of the roof and the node heat balance equation of the indoor air, a heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and no heating / cooling of the heating and air conditioning at night is constructed:

[0097] (6)

[0098] (7)

[0099] (8)

[0100] (9)

[0101] (10)

[0102] S202, the heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and no heating / cooling of the heating and air conditioning at night is simulated by using the TRNSYS model to obtain the node temperatures under the conditions of no solar radiation, no internal heat source, no penetration and no heating / cooling of the heating and air conditioning at night, including the outside wall outside surface temperature , the outside wall inside surface temperature , the roof outside surface temperature , the roof inside surface temperature , the indoor temperature of the building and the outdoor temperature ;

[0103] S203, a target function with the minimum absolute error between all node temperatures calculated by the thermal resistance-capacity model and the node temperatures output by the TRNSYS simulation is constructed, and an optimization solving algorithm is used to solve the heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and no heating / cooling of the heating and air conditioning at night to obtain the resistance-capacity characteristic parameters.

[0104] According to the formulas (6)-(10), a program is written in MATLAB, and the optimization variable is the resistance-capacity characteristic parameter (R, C) , , , , , , , , , and The objective function for identifying the thermal resistance-heat capacity model is to minimize the absolute error between all nodal temperatures calculated by the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation, which can be expressed as:

[0105] (11)

[0106] in, Let be the objective function. To identify the amount of data, For predictions using the thermal resistance-heat capacity model Temperature at each node at any given time. The output of each node temperature is obtained through simulation using the TRNSYS model. As an optional implementation, this embodiment utilizes a genetic algorithm in MATLAB to solve the heat balance equations under conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply to obtain the resistance-capacitance characteristic parameters. , , , , , , , , , and ).

[0107] In step S103 of this embodiment, when identifying parameters for the heat balance equation, the identified parameters include solar radiation correlation coefficients, which include the solar radiation correlation coefficients of the external walls. Correlation coefficient with roof solar radiation The identification of correlation coefficients for solar radiation includes:

[0108] S301, based on the nodal heat balance equations of each exterior wall, the nodal heat balance equation of the roof, and the heat balance equations of the indoor air nodes, constructs the heat balance equations under the conditions of solar radiation, no internal heat source, infiltration, and HVAC cooling / heating supply:

[0109] (12)

[0110] (13)

[0111] (14)

[0112] (15)

[0113] (16)

[0114] S302, the heat balance equation under the conditions of solar radiation, no internal heat source, infiltration, and HVAC cooling / heating supply is simulated using the TRNSYS model to obtain the temperatures at each node under the conditions of solar radiation, no internal heat source, infiltration, and HVAC cooling / heating supply, including the outer surface temperature of the exterior wall. Temperature of the inner surface of the exterior wall Roof surface temperature Roof inner surface temperature Building indoor temperature outdoor temperature The intensity of solar radiation received by each exterior wall and the intensity of solar radiation received by the roof ;

[0115] S303, the identified parameters (determined according to the actual order, for example, in this embodiment, the identified parameters refer to the RC characteristic parameters mentioned above, including...) , , , , , , , , , and As known parameters of the heat balance equation under the conditions of solar radiation, no internal heat source, infiltration, and HVAC cooling / heating supply, the objective function is to minimize the absolute error between all node temperatures calculated by the thermal resistance-heat capacity model and the node temperatures output by the TRNSYS simulation. The specified optimization algorithm is used to solve the heat balance equation under the conditions of solar radiation, no internal heat source, infiltration, and HVAC cooling / heating supply to obtain the solar radiation correlation coefficient.

[0116] Write a program in MATLAB based on formulas (12) to (16) to optimize the solar radiation correlation coefficient. and The objective function for identifying the thermal resistance-heat capacity model is to minimize the absolute error between all nodal temperatures calculated by the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. The objective function is shown in formula (11). The correlation coefficients of solar radiation between the exterior walls and roof of the thermal resistance-heat capacity model are obtained using a genetic algorithm in MATLAB. and .

[0117] In step S103 of this embodiment, when identifying parameters for the heat balance equation, the identified parameters include the correlation coefficient of the internal heat source. and the identification of the internal heat source related coefficient includes:

[0118] S401, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the heat balance equation of the indoor air node, the heat balance equation under the conditions of solar radiation and internal heat source, no penetration and air conditioning cooling / heat supply is constructed:

[0119] , (17)

[0120] , (18)

[0121] , (19)

[0122] , (20)

[0123] , (21)

[0124] S402, the heat balance equation under the conditions of solar radiation and internal heat source, no penetration and air conditioning cooling / heat supply is simulated by using TRNSYS model to obtain the temperature of each node under the conditions of solar radiation and internal heat source, no penetration and air conditioning cooling / heat supply, including the temperature of the external wall , the temperature of the external wall , the temperature of the roof , the temperature of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof and the heat dissipation of internal heat source ;

[0125] S403, the identified parameters (determined according to the actual order, for example, the identified parameters in this embodiment refer to the resistance and capacitance characteristic parameters and the solar radiation related coefficient in the foregoing, including , , , , , , , , , and , and As known parameters of the heat balance equation under the conditions of solar radiation and internal heat source, no permeation and warm air conditioning cooling / heating capacity, the absolute error between all node temperatures calculated by the thermal resistance-heat capacity model and the node temperatures output by the TRNSYS simulation is minimized to obtain the objective function, and the optimization algorithm is used to solve the heat balance equation under the conditions of solar radiation and internal heat source, no permeation and warm air conditioning cooling / heating capacity to obtain the internal heat source correlation coefficient .

[0126] According to formulas (17)-(21), a program is written in MATLAB, the optimization variable is the internal heat source correlation coefficient , the objective function of the thermal resistance-heat capacity model identification is the absolute error between all node temperatures calculated by the thermal resistance-heat capacity model and the node temperatures output by the TRNSYS simulation, and the objective function is shown in formula (11). The internal heat source correlation coefficient of the thermal resistance-heat capacity model is obtained by using the genetic algorithm in MATLAB .

[0127] In the step S103 of the embodiment, the identified parameters include the permeation correlation coefficient and the air conditioning system correlation coefficient , the identification of the permeation correlation coefficient includes:

[0128] S501, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the heat balance equation of the indoor air node, the heat balance equation under the conditions of solar radiation, internal heat source and permeation, no warm air conditioning cooling / heating capacity is constructed:

[0129] , (22)

[0130] , (23)

[0131] , (24)

[0132] , (25)

[0133] ; (26)

[0134] S502, the heat balance equation under the conditions of solar radiation, internal heat source and permeation, no warm air conditioning cooling / heating capacity is simulated by using the TRNSYS model to obtain the node temperatures under the conditions of solar radiation, internal heat source and permeation, no warm air conditioning cooling / heating capacity, including the external wall external surface temperature , the external wall internal surface temperature , the roof external surface temperature Roof inner surface temperature Building indoor temperature outdoor temperature The intensity of solar radiation received by each exterior wall The intensity of solar radiation received by the roof Heat dissipation from internal heat source and the increase or decrease in indoor heat caused by infiltration ventilation ;

[0135] S503, the identified parameters (determined according to the actual order, for example, in this embodiment, the identified parameters refer to the aforementioned resistance-capacitance characteristic parameters, solar radiation correlation coefficient, and internal heat source correlation coefficient) are processed. ,include , , , , , , , , , and , and , Using known parameters of the heat balance equation under conditions of solar radiation, internal heat sources, and infiltration, but without HVAC cooling / heating supply, the objective function is to minimize the absolute error between the nodal temperatures calculated by the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. A specified optimization algorithm is used to solve the heat balance equation under these conditions to obtain the infiltration correlation coefficient. .

[0136] Write a program in MATLAB based on formulas (22) to (26) to optimize the permeability correlation coefficient as the variable. The objective function for identifying the thermal resistance-heat capacity model is to minimize the absolute error between all node temperatures calculated by the thermal resistance-heat capacity model and the node temperatures output by the TRNSYS simulation. The objective function is shown in formula (11). The permeation correlation coefficient of the thermal resistance-heat capacity model can be obtained by solving the problem using the genetic algorithm in MATLAB. .

[0137] Correlation coefficient of air conditioning system The identification includes:

[0138] S601, based on the nodal heat balance equations of each exterior wall, the nodal heat balance equation of the roof, and the heat balance equations of the indoor air nodes, constructs heat balance equations under the conditions of solar radiation, internal heat sources, and HVAC cooling / heating supply:

[0139] (27)

[0140] (28)

[0141] (29)

[0142] (30)

[0143] (31)

[0144] S602, using the TRNSYS model to simulate the heat balance equation under the condition of solar radiation, internal heat source, and air conditioning cooling / heating to obtain the temperature of each node under the condition of solar radiation, internal heat source, and air conditioning cooling / heating, including the temperature of the outer surface of the external wall the temperature of the outer surface of the roof the temperature of the inner surface of the roof the indoor temperature of the building the outdoor temperature the solar radiation intensity received by each external wall the solar radiation intensity received by the roof the heat dissipation of the internal heat source the indoor heat increase or decrease caused by permeable ventilation and the air conditioning cooling or heating ;

[0145] S603, the identified parameters (determined according to the actual order, for example, the identified parameters in this embodiment refer to the resistance-capacitance characteristic parameters, solar radiation correlation coefficient, internal heat source correlation coefficient and permeation correlation coefficient , including , , , , , , , , , and , and , , ) as the known parameters of the heat balance equation under the conditions of solar radiation, internal heat source, and heating / cooling capacity of the heating ventilation and air conditioning system, a target function with the minimum absolute error between the node temperatures calculated by the thermal resistance-capacitance model and the node temperatures output by the TRNSYS simulation is constructed, and an optimization algorithm is used to solve the heat balance equation under the conditions of solar radiation, internal heat source, and heating / cooling capacity of the heating ventilation and air conditioning system to obtain the air conditioning system correlation coefficient .

[0146] According to formulas (27)-(31), a program is written in MATLAB, the optimization variable is the air conditioning system correlation coefficient , the target function of the thermal resistance-capacitance model identification is the minimum absolute error between the node temperatures calculated by the thermal resistance-capacitance model and the node temperatures output by the TRNSYS simulation, and the target function is shown in formula (11). The air conditioning system correlation coefficient of the thermal resistance-capacitance model is obtained by using the genetic algorithm in MATLAB .

[0147] According to the above identification steps, the simulation data under different conditions are obtained by using the TRNSYS simulation software, and the resistance-capacitance characteristic parameters of the thermal resistance-capacitance model are identified , , , , , , , , , and , the solar radiation correlation coefficient and , the internal heat source correlation coefficient gamma, permeation correlation coefficient 、 and the air conditioning system correlation coefficient . Through the established building thermal resistance-capacitance model, the related expression of the heat balance equation of the future indoor temperature is obtained, the identified parameters are substituted into the heat balance equation to predict the building indoor temperature , thereby providing a basis for the optimization control of the air conditioning system.

[0148] In order to verify the building indoor temperature prediction method based on the thermal resistance-capacitance model in the embodiment, an office room with a size of 10 m (length) x 10 m (width) x 5 m (height) is established in the TRNSYS software by using the meteorological data of Changsha City, and a 12.5 m 2 external window is arranged on the south external wall of the building, and the related information of the building envelope structure is shown in Table 1. The internal heat source of the room is set to 134 W / person for indoor personnel and 9 W / m 2and the electrical equipment 15 W / m 2 . The indoor person density is 10 m 2 / person, and the indoor person occupancy rate, lighting and electrical equipment usage rate are shown in Table 2. The operation time of the HVAC is 7:00-18:00 from Monday to Friday, and the infiltration air setting is 2 (1 / h).

[0149] Table 1 Building envelope parameters

[0150]

[0151] Table 2 Indoor person occupancy rate, lighting and electrical equipment usage rate

[0152]

[0153] Examples of parameter identification in this embodiment include:

[0154] (1) Identification of resistance-capacitance characteristic parameters: using the TRNSYS simulation data of indoor temperature , outdoor temperature , and the internal and external surface temperatures of walls and roofs , , and under the conditions of no solar radiation, internal heat source, infiltration and HVAC cooling / heating at night to identify the resistance-capacitance characteristic parameters , , , , , , , , , and . The input data are the indoor temperature , outdoor temperature , external surface temperature of each external wall , internal surface temperature of each external wall , external surface temperature of the roof , and internal surface temperature of the roof from 9:00 on the evening of June 30 to 7:00 on the morning of July 1 for 10 hours. The output data are the resistance-capacitance characteristic parameters , , , , , , , , , and The resistance and capacitance characteristic parameters satisfying equation (11) are solved by using the genetic algorithm in MATLAB, as shown in Table 3.

[0155] Table 3 Resistance and capacitance characteristic parameters obtained by the first step of identification

[0156]

[0157] (2) Solar radiation correlation coefficient identification: taking the resistance and capacitance characteristic parameters obtained by the first step of identification as known parameters, the indoor temperature , , , , , , , , , and are output from the TRNSYS simulation platform under the conditions of no internal heat source, no infiltration and no cooling / heating capacity of a heating and air conditioning system on a weekend with solar radiation. , , , , and , the solar radiation intensity received by each external wall , and the solar radiation intensity received by the roof . The solar radiation correlation coefficients of each external wall ( may be e, w, s and n) and the solar radiation correlation coefficient of the roof are identified. The input data are the indoor temperature , the outdoor temperature , the external wall surface temperature , the internal wall surface temperature , the roof surface temperature , the internal wall surface temperature , the solar radiation intensity received by each external wall , and the solar radiation intensity received by the roof for 48 hours from June 30 to July 1. The output data are the solar radiation correlation coefficients of each external wall ( may be e, w, s and n) and the solar radiation correlation coefficient of the roof . The solar radiation correlation coefficients satisfying equation (11) are solved by using the genetic algorithm in MATLAB, as shown in Table 4.

[0158] Table 4 Solar radiation correlation coefficients obtained by the second step of identification

[0159]

[0160] Table 4, , , and are the solar radiation related coefficients of the external walls in i e, w, s, n (east, west, south, north) respectively.

[0161] (3) Identification of internal heat source related coefficient: using the resistance and capacitance characteristic parameters , , , , , , , , , and identified in the first step and the solar radiation related coefficients (γ may be e, w, s, n) and identified in the second step as known parameters, the indoor temperature , outdoor temperature , and the internal and external surface temperatures of the walls and roof , , and , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , and the internal heat source are output from the TRNSYS simulation platform under the conditions of having solar radiation and internal heat source and without cooling / heating provided by HVAC. The internal heat source related coefficient γ is identified. The input data are the indoor temperature , outdoor temperature , external surface temperature of each external wall , internal surface temperature of each external wall , external surface temperature of the roof , internal surface temperature of the roof , solar radiation intensity received by each external wall , solar radiation intensity received by the roof , and heat dissipation of the internal heat source from July 1 to July 7, 168 hours. The output data are the internal heat source related coefficient . The genetic algorithm in MATLAB is used to solve the internal heat source related coefficient = 0.337 that satisfies formula (11).

[0162] (4) Permeation correlation coefficient identification: the resistance and capacitance characteristic parameters identified in the first step , , , , , , , , , and , the solar radiation correlation coefficient ( e, w, s, n) and identified in the second step, and the internal heat source correlation coefficient γ identified in the third step are taken as known parameters, and the indoor temperature , the outdoor temperature , and the internal and external surface temperatures of the walls and roof , , and , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , the heat dissipation of the internal heat source , the heat change caused by permeation , and the identified permeation correlation coefficient are output from the TRNSYS simulation platform under the conditions of solar radiation, internal heat source, and permeation, without the cooling / heating capacity of the heating, ventilation, and air conditioning system. The input data are the indoor temperature , the outdoor temperature , the external surface temperature of each external wall , the internal surface temperature of each external wall , the external surface temperature of the roof , the internal surface temperature of the roof , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , the heat dissipation of the internal heat source , and the heat caused by permeation from July 1 to July 7, 168 hours. The output data are the permeation correlation coefficient . The internal heat source correlation coefficient = 0.825 satisfying formula (11) is solved by using the genetic algorithm in MATLAB.

[0163] (5) Air conditioning system correlation coefficient identification: the resistance and capacitance characteristic parameters identified in the first step , , , , , , , , , and , the solar radiation correlation coefficient identified in the second step , may be e, w, s, n , the internal heat source correlation coefficient identified in the third step and the permeation correlation coefficient identified in the fourth step As known parameters, the outdoor temperature , the indoor temperature , and the inner and outer surface temperatures of the wall and the roof , , and , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , the heat dissipation of the internal heat source , the heat caused by the permeation and the air conditioning cooling capacity are output from the TRNSYS simulation platform under the conditions of solar radiation, internal heat source, permeation and air conditioning cooling / heating capacity β The air conditioning system correlation coefficient is identified The input data are the indoor temperature , the outdoor temperature , the inner and outer surface temperatures of each external wall , the inner and outer surface temperatures of the roof , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , the heat dissipation of the internal heat source , the heat caused by the permeation and the air conditioning cooling capacity from July 1 to July 7 The output data are the air conditioning system correlation coefficient The air conditioning system correlation coefficient satisfying equation (11) is solved by using the genetic algorithm in MATLAB = 0.120

[0164] The resistance and capacity characteristic parameters of the building resistance-capacity model, the solar radiation correlation coefficient, the internal heat source correlation coefficient, the permeation correlation coefficient and the air conditioning system correlation coefficient are identified through the above five steps. In this embodiment, the outdoor temperature , the indoor temperature and the inner and outer surface temperatures of the wall and the roof , are output from the TRNSYS simulation platform from July 8 to July 10. and intensity of solar radiation received by each exterior wall intensity of solar radiation received by the roof amount of heat dissipated by internal heat sources amount of heat caused by infiltration and amount of cooling provided by air conditioning to verify the identified thermal resistance-capacity model, the indoor temperature predicted by the thermal resistance-capacity model (thermal resistance-capacity model prediction) and the TRNSYS output indoor temperature (TRNSYS simulation) are compared as shown in Figure 3 It can be seen from Figure 3 that the building cooling and heating load prediction method based on the thermal resistance-capacity model can accurately and quickly predict the indoor temperature of the building.

[0165] To sum up, the building cooling and heating load prediction method based on the thermal resistance-capacity model considers indoor thermal disturbance and ventilation conditions, can consider the physical characteristics of the building, has lower model complexity and better interpretability, has strong universality, can accurately and quickly predict the indoor temperature of the building, solves the problem of poor interpretability of the existing building indoor temperature prediction gray box model, and the building cooling and heating load prediction method based on the thermal resistance-capacity model can effectively improve the accuracy and interpretability of building indoor temperature prediction, and provides an effective basis for the optimized operation control of the heating, ventilation and air conditioning system.

[0166] In addition, the embodiment also provides a building indoor temperature prediction system based on a thermal resistance-capacity model, which comprises a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-capacity model.

[0167] In addition, the embodiment also provides a computer readable storage medium, which stores a computer program or instructions, and the computer program or instructions are programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-capacity model by a processor.

[0168] In addition, the embodiment also provides a computer program product, which comprises a computer program or instructions, and the computer program or instructions are programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-capacity model by a processor.

[0169] Those skilled in the art will appreciate that the technology provided herein is not limited to any particular form of implementation. The technology provided herein can be implemented in hardware, software, or a combination thereof. Those skilled in the art will appreciate that the technology provided herein can be implemented in a number of different embodiments, including method embodiments, system embodiments, and computer program product embodiments. The technology provided herein can be implemented in any combination of hardware, software, or a combination thereof. The technology provided herein can be implemented in a number of different ways, including as a computer program product stored on a computer readable storage medium, as a system on chips (SOCs), as an application specific integrated circuit (ASIC), or as a combination of the above. The technology provided herein can be implemented using any suitable hardware, software, firmware, or combination thereof. The technology provided herein can be implemented in one or more computer programs or one or more articles of manufacture that contain computer readable program code. The technology provided herein can be implemented using any suitable computer readable storage medium, including storage devices that are external or internal to a computer. Suitable computer readable storage mediums can include, but are not limited to, volatile memory, non-volatile memory, removable storage, and non-removable storage. Suitable computer readable storage mediums can include, but are not limited to, RAM, ROM, EEPROM, flash memory, or any other memory technology. Suitable computer readable storage mediums can include, but are not limited to, magnetic cassettes, magnetic tapes, magnetic disks, memory cards or sticks, optical storage media, or any other storage medium suitable for storing computer readable program code. The computer readable program code can be executed using any suitable computer processor, including a general purpose computer, a special purpose computer, an embedded computer, or any other computer. The computer readable program code can be executed using any suitable operating system, including a UNIX operating system, a LINUX operating system, a WINDOWS operating system, a MAC OS operating system, or any other operating system. The computer readable program code can be executed using any suitable computer programming language, including a high level programming language, a low level programming language, an object oriented programming language, a visual programming language, or any other computer programming language. Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks

[0170] The above description is only preferred embodiments of the application. The protection scope of the application is not limited to the above-mentioned embodiments. Any technical scheme falling within the concept of the application is within the protection scope of the application. It should be noted that some improvements and refinements made by those skilled in the art without departing from the principles of the application are also considered to be within the protection scope of the application.

Claims

1. A method of predicting the temperature in a building based on a thermal resistance-capacity model, characterized by, comprising the following steps: S101, the building indoor temperature The heat flow in the heat transfer process is regarded as the electric current, the temperature difference is regarded as the voltage, and the thermal resistance and the thermal capacity correspond to the resistance and the capacitance, respectively. The temperature transfer system of the building indoor temperature is simplified and constructed to obtain a thermal resistance-capacitance model composed of thermal resistance and thermal capacity; S102, constructing a heat balance equation of a thermal resistance-capacitance model; S103, parameter identification of the heat balance equation; S104, substituting the recognized parameter into a heat balance equation to predict the indoor temperature of the building ; The thermal resistance-heat capacity model constructed in step S101 describes the building's dynamic heat transfer process as a heat transfer process through a thermal resistance-heat capacity network, including the building's indoor temperature. As the central node, it utilizes the indoor air heat capacity It stores heat and exchanges it with the outdoor temperature through exterior windows, exterior walls, and roof. Multiple heat exchange processes occur between them, where: the first heat exchange process is indoor temperature. Thermal resistance through the exterior window With outdoor temperature Heat transfer occurs; the second heat exchange process is indoor temperature. By roof outer surface temperature Roof inner surface temperature With outdoor temperature Heat transfer occurs, and the temperature of the roof's outer surface increases. With outdoor temperature The thermal resistance between them is Roof surface temperature The corresponding heat capacity of the air layer on the roof surface is Roof surface temperature With roof inner surface temperature The thermal resistance between them is Roof inner surface temperature The corresponding heat capacity of the roof is Roof inner surface temperature With indoor temperature The thermal resistance between them is equal to the thermal resistance of the indoor air. The third heat exchange process is indoor temperature. Temperature of the outer surface of the east, west, south, and north exterior walls Temperature of the inner surface of the exterior wall With outdoor temperature Heat transfer occurs. External wall surface temperature With outdoor temperature The thermal resistance between them is Temperature of the outer surface of the exterior wall The corresponding heat capacity of the air layer on the exterior wall surface is Temperature of the outer surface of the exterior wall With the temperature of the inner surface of the exterior wall The thermal resistance between them is Temperature of the inner surface of the exterior wall The corresponding heat capacity of the exterior wall is Temperature of the inner surface of the exterior wall With indoor temperature The thermal resistance between them is equal to the thermal resistance of the indoor air. wherein the outside wall outer surface temperature and the outside wall inner surface temperature the subscript is one of east, west, south, and north; the heat balance equation of the thermal resistance-thermal capacity model constructed in step S102 includes a node heat balance equation of each outside wall, a node heat balance equation of a roof, and a heat balance equation of an indoor air node; a functional expression of the node heat balance equation of each outside wall is: ， ， wherein, is the heat capacity of the air layer of the outer wall surface, is the temperature of the outer surface of the outer wall, is time, is the area of the outer wall, is the outdoor temperature, is the temperature of the outer surface of the outer wall is the heat transfer resistance between the temperature of the outer surface of the outer wall and the outdoor temperature, is the temperature of the inner surface of the outer wall, is the solar radiation-related coefficient of the outer wall, is the intensity of the solar radiation received by the outer wall, is the heat capacity of the outer wall, is the indoor temperature of the building, is the heat transfer resistance of the indoor air; The functional expression of the node heat balance equation of the roof is: ， ， in, The heat capacity of the air layer on the roof surface. The temperature of the outer surface of the roof. For the area of ​​the roof, The temperature of the outer surface of the roof With outdoor temperature Thermal resistance between them The temperature of the inner surface of the roof. The correlation coefficient for solar radiation on the roof. The temperature of the outer surface of the roof With roof inner surface temperature Thermal resistance between them The intensity of solar radiation received by the roof. The heat capacity of the roof; The thermal resistance of indoor air; The functional expression of the node heat balance equation of the indoor air is: ， wherein, Rth is the heat transfer resistance of the outer window, A is the area of the outer window, C is the air conditioning system related coefficient, Q is the cooling or heating of the heating, ventilation, and air conditioning, Cin is the internal heat source related coefficient, Qin is the internal heat source heat dissipation amount, the internal heat source including indoor personnel, lighting, and equipment, Cp is the penetration related coefficient, Qp is the indoor heat increase or heat decrease amount caused by the penetration ventilation. 2.The building indoor temperature prediction method based on thermal resistance-capacity model according to claim 1, wherein, In step S103, when identifying parameters for the heat balance equation, the identified parameters include resistance-capacitance characteristic parameters, including the outer surface temperature of the outer wall. With outdoor temperature Thermal resistance between Temperature of the outer surface of the exterior wall With the temperature of the inner surface of the exterior wall Thermal resistance between Roof inner surface temperature Temperature of the inner surface of the exterior wall With indoor temperature Thermal resistance between Roof surface temperature With outdoor temperature Thermal resistance between Roof surface temperature With roof inner surface temperature Thermal resistance between The heat capacity of the air layer on the exterior wall surface The heat capacity of the exterior wall The heat capacity of the air layer on the roof surface The heat capacity of the roof Indoor air heat capacity and the thermal resistance of the exterior window The identification of RC characteristic parameters includes: S201, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the node heat balance equation of the indoor air, constructing a heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and no heating / cooling of HVAC at night: ， ， ， ， ， S202, using the TRNSYS model to simulate the heat balance equation under the condition of no solar radiation, internal heat source, permeation and cooling / heating capacity of heating ventilation and air conditioning at night to obtain the temperature of each node under the condition of no solar radiation, internal heat source, permeation and cooling / heating capacity of heating ventilation and air conditioning at night, including the temperature of the outer surface of the external wall , the temperature of the inner surface of the external wall , the temperature of the outer surface of the roof , the temperature of the inner surface of the roof , the indoor temperature of the building and the outdoor temperature ; S203, constructing a target function of minimum absolute error between all node temperatures calculated by the thermal resistance-capacitance model and the node temperatures output by TRNSYS simulation, and solving the heat balance equation under the conditions of no solar radiation, no internal heat source, no penetration and no heating / cooling of HVAC at night by using a specified optimization algorithm to obtain the resistance-capacitance characteristic parameters. 3.The building indoor temperature prediction method based on thermal resistance-capacity model according to claim 2, characterized in that, The identified parameters in the parameter identification of the heat balance equation in step S103 include a solar radiation correlation coefficient, the solar radiation correlation coefficient including a solar radiation correlation coefficient of the external wall and a solar radiation correlation coefficient of the roof The identification of the solar radiation correlation coefficient includes: S301, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the node heat balance equation of the indoor air, constructing a heat balance equation under the conditions of solar radiation, no internal heat source, no penetration and no heating / cooling of HVAC: ， ， ， ， ， S302, using the TRNSYS model to simulate the heat balance equation under the condition of having solar radiation, no internal heat source, infiltration and air conditioning cooling / heating capacity to obtain the temperature of each node under the condition of having solar radiation, no internal heat source, infiltration and air conditioning cooling / heating capacity, including the temperature of the outer surface of the external wall , the temperature of the inner surface of the external wall , the temperature of the outer surface of the roof , the temperature of the inner surface of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each external wall and the solar radiation intensity received by the roof ; S303, taking the identified parameters as known parameters of the heat balance equation under the conditions of solar radiation, no internal heat source, no penetration and no heating / cooling of HVAC, constructing a target function of minimum absolute error between all node temperatures calculated by the thermal resistance-capacitance model and the node temperatures output by TRNSYS simulation, and solving the heat balance equation under the conditions of solar radiation, no internal heat source, no penetration and no heating / cooling of HVAC by using a specified optimization algorithm to obtain the solar radiation correlation coefficient. 4.The building indoor temperature prediction method based on thermal resistance-capacity model according to claim 2, wherein, In the parameter identification of the heat balance equation in step S103, the identified parameters include the internal heat source correlation coefficient , and the identification of the internal heat source correlation coefficient includes: S401, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the node heat balance equation of the indoor air, constructing a heat balance equation under the conditions of solar radiation and internal heat source, no penetration and no heating / cooling of HVAC: ， ， ， ， ， S402, for the solar radiation and internal heat source, no penetration and warm air conditioning cooling / heating capacity under the condition of heat balance equation using TRNSYS model simulation to obtain the solar radiation and internal heat source, no penetration and warm air conditioning cooling / heating capacity under the condition of each node temperature, including the outer surface temperature of the outer wall , the inner surface temperature of the outer wall , the outer surface temperature of the roof , the inner surface temperature of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each outer wall , the solar radiation intensity received by the roof and the internal heat source heat dissipation ; S403, the identified parameters are used as known parameters of the heat balance equation under the conditions of solar radiation and internal heat source, no infiltration and air conditioning cooling / heating capacity, and a target function of minimizing the absolute error between all node temperatures calculated by the thermal resistance-thermal capacity model and the node temperatures output by the TRNSYS simulation is constructed, and an optimization algorithm is used to solve the heat balance equation under the conditions of solar radiation and internal heat source, no infiltration and air conditioning cooling / heating capacity to obtain the internal heat source correlation coefficient . 5.The building indoor temperature prediction method based on thermal resistance-capacity model according to claim 2, wherein, In the parameter identification of the heat balance equation in step S103, the identified parameters include a permeation correlation coefficient and an air conditioning system correlation coefficient , and the identification of the permeation correlation coefficient includes: S501, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the node heat balance equation of the indoor air, constructing a heat balance equation under the conditions of solar radiation, internal heat source and penetration, no heating / cooling of HVAC: ， ， ， ， ； S502, using the TRNSYS model to simulate the heat balance equation under the condition of having solar radiation, internal heat source and infiltration, and no heating / cooling by HVAC to obtain the temperature of each node under the condition of having solar radiation, internal heat source and infiltration, and no heating / cooling by HVAC, including the temperature of the outer surface of the external wall , the temperature of the inner surface of the external wall , the temperature of the outer surface of the roof , the temperature of the inner surface of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , the heat dissipation of the internal heat source , and the indoor heat increase or decrease caused by infiltration ventilation ; S503, the identified parameters as a solar radiation, internal heat source and permeation, no warm air conditioning cooling / heating capacity under the condition of the heat balance equation of the known parameters, the absolute error between the minimum objective function of all node temperature calculated by thermal resistance-heat capacity model and the node temperature output by TRNSYS simulation, and the specified optimization algorithm is used to solve the heat balance equation under the condition of solar radiation, internal heat source and permeation, no warm air conditioning cooling / heating to obtain the permeation correlation coefficient ; Correlation coefficient of air conditioning system The identification includes: S601, based on the node heat balance equation of each external wall, the node heat balance equation of the roof and the node heat balance equation of the indoor air, constructing a heat balance equation under the conditions of solar radiation, internal heat source, penetration and heating / cooling of HVAC: ， ， ， ， ， S602, using the TRNSYS model to simulate the heat balance equation under the condition of solar radiation, internal heat source, permeation and air conditioning cooling / heating capacity to obtain the temperature of each node under the condition of solar radiation, internal heat source, permeation and air conditioning cooling / heating capacity, including the temperature of the outer surface of the external wall , the temperature of the inner surface of the external wall , the temperature of the outer surface of the roof , the temperature of the inner surface of the roof , the indoor temperature of the building , the outdoor temperature , the solar radiation intensity received by each external wall , the solar radiation intensity received by the roof , the heat dissipation of the internal heat source , the indoor heat increase or decrease caused by permeation ventilation , and the air conditioning cooling or heating capacity ; S603, the identified parameters are used as known parameters of the heat balance equation under the conditions of solar radiation, internal heat source, infiltration and heating / cooling capacity of air conditioning, a target function with the minimum absolute error between all node temperatures calculated by the thermal resistance-thermal capacity model and the node temperatures output by the TRNSYS simulation is constructed, and an optimization algorithm is used to solve the heat balance equation under the conditions of solar radiation, internal heat source, infiltration and heating / cooling capacity of air conditioning to obtain the related coefficients of the air conditioning system .

6. A building indoor temperature prediction system based on thermal resistance-capacity model, comprising a microprocessor and a memory connected to each other, characterized in that, The microprocessor is programmed or configured to perform the building indoor temperature prediction method based on the thermal resistance-capacitance model according to any one of claims 1-5.

7. A computer-readable storage medium having stored therein a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to perform the building indoor temperature prediction method based on the thermal resistance-capacitance model according to any one of claims 1-5 by the processor.

8. A computer program product comprising computer programs or instructions, characterized in that, The computer program or instructions are programmed or configured to perform the building indoor temperature prediction method based on the thermal resistance-capacitance model according to any one of claims 1-5 by the processor.

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