Building indoor temperature prediction method and system based on thermal resistance-thermal capacity model

Abstract

The invention discloses a building indoor temperature prediction method and system based on a thermal resistance-thermal capacitance model, and the method comprises the steps: taking the heat flow in the heat transfer process of the building indoor temperature as the current, taking the temperature difference as the voltage, and enabling the thermal resistance and the thermal capacitance to correspond to the resistance and the capacitance respectively; simplifying a temperature transfer system of the indoor temperature of the building and constructing a thermal resistance-thermal capacity model composed of thermal resistance and thermal capacity; constructing a heat balance equation of the heat resistance-heat capacity model; performing parameter identification on the heat balance equation; and substituting the identified parameters into a heat balance equation to predict the indoor temperature of the building. The method aims at solving the problem that an existing building indoor temperature prediction grey box model is poor in interpretability, and the accuracy and interpretability of building indoor temperature prediction are improved.

Description

Technical Field

[0001] This invention belongs to the field of building indoor air conditioning technology, specifically relating to a method and system for predicting building indoor temperature based on a thermal resistance-thermal capacity model. Background Technology

[0002] Currently, building energy consumption accounts for over 33% of global energy consumption, and within this, HVAC systems account for a significant portion. Accurate prediction of building indoor temperature is crucial for optimizing HVAC system operation and reducing building energy consumption. Existing methods for predicting building indoor temperature can be broadly categorized into three types: white-box models, gray-box models, and black-box models. White-box models are built upon detailed building thermophysics principles and can accurately describe building thermal processes; however, they are complex, requiring numerous building parameters and complex calculations, making them difficult to widely apply in practical engineering. Black-box models rely heavily on extensive historical data for training and do not consider internal building physics. While simple and flexible, they suffer from poor interpretability and struggle to adapt to various complex building conditions. Gray-box models fall between the two, combining physical laws and data advantages, with moderate complexity and relatively lower requirements for data and computational power. However, existing gray-box models used in building indoor temperature prediction methods suffer from poor interpretability. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method and system for predicting building indoor temperature based on a thermal resistance-heat capacity model, addressing the aforementioned problems in the existing technology. This invention aims to solve the problem of poor interpretability of existing gray box models for predicting building indoor temperature, and improve the accuracy and interpretability of building indoor temperature prediction.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for predicting building indoor temperature based on a thermal resistance-heat capacity model includes the following steps: S101, the building indoor temperature In the heat transfer process, the heat flow is considered as current, the temperature difference as voltage, and thermal resistance and thermal capacity correspond to resistance and capacitance, respectively. The indoor temperature of the building is... The temperature transfer system is simplified and constructed to obtain a thermal resistance-thermal capacity model consisting of thermal resistance and thermal capacity; S102, construct the thermal balance equation of the thermal resistance-heat capacity model; S103, parameter identification of the heat balance equation; S104 uses the identified parameters to predict the building's indoor temperature by substituting them into the heat balance equation. .

[0005] 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 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, and the temperature of the outer surface of the exterior wall increases. 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.

[0006] Optionally, the thermal balance equations of the thermal resistance-heat capacity model constructed in step S102 include the nodal thermal balance equations of each exterior wall, the nodal thermal balance equations of the roof, and the thermal balance equations of the indoor air nodes; the functional expressions of the nodal thermal balance equations of each exterior wall are as follows: , , in, The heat capacity of the air layer on the exterior wall surface. The temperature of the outer surface of the exterior wall. For time, The area of ​​the exterior wall. Outdoor temperature Temperature of the outer surface of the exterior wall With outdoor temperature Thermal resistance between them The temperature of the inner surface of the exterior wall. The correlation coefficient of solar radiation on the exterior wall. The intensity of solar radiation received by the exterior wall. For the heat capacity of the exterior wall, For the building's indoor temperature, The thermal resistance of indoor air; The functional expression of the nodal thermal 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. 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 heat balance equation for the indoor air node is: , 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.

[0007] 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 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 nodal heat balance equations of each exterior wall, the nodal heat balance equation of the roof, and the heat balance equations of indoor air nodes, constructs the heat balance equations under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply: , , , , , S202 uses a TRNSYS model to simulate the heat balance equation under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply to obtain the temperatures at each node under these conditions, including the external 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 and outdoor temperature ; S203 is an objective function that minimizes 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 solution algorithm is used to solve the heat balance equation under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply to obtain the resistance-capacity characteristic parameters.

[0008] Optionally, in step S103, 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 wall. Correlation coefficient with roof solar radiation The identification of correlation coefficients for solar radiation includes: 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: , , , , , 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 ; S303 uses the identified parameters as known parameters for the heat balance equation under the conditions of solar radiation, no internal heat source, infiltration, and HVAC cooling / heating supply. It constructs an objective function 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. It then uses a specified optimization algorithm 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.

[0009] Optionally, in step S103, when identifying parameters for the heat balance equation, the identified parameters include the correlation coefficient of the internal heat source. And the correlation coefficient with the internal heat source The identification includes: S401, 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 and internal heat sources, without infiltration and HVAC cooling / heating supply: , , , , , S402, the heat balance equation under the conditions of solar radiation and internal heat source, no 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 and internal heat source, no 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 The intensity of solar radiation received by the roof and heat dissipation from internal heat source ; S403 uses the identified parameters as known parameters for the heat balance equation under conditions of solar radiation and internal heat sources, no infiltration, and HVAC cooling / heating supply. It constructs an objective function to minimize the absolute error between the nodal temperatures calculated using the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. A specified optimization algorithm is then used to solve the heat balance equation under these conditions to obtain the internal heat source correlation coefficient. .

[0010] Optionally, in step S103, when identifying parameters for the heat balance equation, the identified parameters include the permeability correlation coefficient. Correlation coefficient with air conditioning system And the correlation coefficient with permeability The identification includes: S501, 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 a heat balance equation under the condition of solar radiation, internal heat sources and infiltration, without HVAC cooling / heating supply: , , , , ; S502, the heat balance equation under the condition of solar radiation, internal heat source and infiltration, without HVAC cooling / heating supply, is simulated using the TRNSYS model to obtain the temperature of each node under the condition of solar radiation, internal heat source and infiltration, without HVAC cooling / heating supply, including the temperature of the outer surface 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 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 ; S503 uses the identified parameters as known parameters in the heat balance equation under conditions of solar radiation, internal heat sources, and infiltration, but without HVAC cooling / heating. It constructs an objective function to minimize the absolute error between the nodal temperatures calculated using the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. A specified optimization algorithm is then used to solve the heat balance equation under these conditions to obtain the infiltration correlation coefficient. .

[0011] Correlation coefficient of air conditioning system The identification includes: 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: , , , , , S602 uses a TRNSYS model to simulate the heat balance equation under conditions of solar radiation, internal heat sources, and HVAC cooling / heating supply to obtain the temperatures at each node under these conditions, including the outer surface temperature of the exterior walls. 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 The intensity of solar radiation received by the roof Heat dissipation from internal heat source The increase or decrease in indoor heat caused by infiltration ventilation. Heating, ventilation and air conditioning (HVAC) cooling or heating ; S603 uses the identified parameters as known parameters for the heat balance equation under conditions of solar radiation, internal heat sources, and HVAC cooling / heating. It constructs an objective function to minimize the absolute error between the nodal temperatures calculated using the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. A specified optimization algorithm is then used to solve the heat balance equation under these conditions to obtain the correlation coefficient of the air conditioning system. .

[0012] Furthermore, the present invention also provides a building indoor temperature prediction system based on a thermal resistance-heat capacity model, including a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-heat capacity model.

[0013] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-heat capacity model via a processor.

[0014] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-heat capacity model via a processor.

[0015] Compared with existing technologies, the present invention mainly has the following beneficial effects: The method of the present invention includes controlling the indoor temperature of a building. In the heat transfer process, the heat flow is considered as current, the temperature difference as voltage, and thermal resistance and thermal capacity correspond to resistance and capacitance, respectively. The indoor temperature of the building is... The temperature transfer system is simplified and a thermal resistance-heat capacity model is constructed, consisting of thermal resistance and heat capacity. The heat balance equation of the thermal resistance-heat capacity model is then constructed. Parameters of the heat balance equation are identified. The identified parameters are then substituted into the heat balance equation to predict the indoor temperature of the building. This invention presents a building heating and cooling load prediction method based on a thermal resistance-heat capacity model, which belongs to the gray box model category. This method considers indoor thermal disturbance and ventilation conditions, taking into account both the physical characteristics of the building and possessing low model complexity and good interpretability. It has strong versatility and can accurately and quickly predict building indoor temperatures, solving the problem of poor interpretability in existing gray box models for building indoor temperature prediction. This improves the accuracy and interpretability of building indoor temperature prediction and provides an effective basis for the optimized operation and control of HVAC systems. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the thermal resistance-thermal capacity model in an embodiment of the present invention.

[0018] Figure 3 This is a comparison of the results of thermal resistance-thermal capacity model prediction and TRNSYS simulation in the embodiments of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0020] like Figure 1 As shown, the building indoor temperature prediction method based on the thermal resistance-heat capacity model in this embodiment includes the following steps: S101, the building indoor temperature In the heat transfer process, the heat flow is considered as current, the temperature difference as voltage, and thermal resistance and thermal capacity correspond to resistance and capacitance, respectively. The indoor temperature of the building is... The temperature transfer system is simplified and constructed to obtain a thermal resistance-thermal capacity model consisting of thermal resistance and thermal capacity; S102, construct the thermal balance equation of the thermal resistance-heat capacity model; S103, parameter identification of the heat balance equation; S104 uses the identified parameters to predict the building's indoor temperature by substituting them into the heat balance equation. .

[0021] This embodiment of the building indoor temperature prediction method based on the thermal resistance-capacity model is based on the thermal resistance-capacity network theory to perform modeling analysis for building indoor temperature prediction. The thermal resistance-capacity network theory is a mathematical modeling method for analyzing and predicting heat transfer processes. It analyzes the heat transfer process by analogy with resistance and capacitance in circuit theory, simplifying the complex heat transfer system into a network composed of thermal resistance and capacitance. In this theory, heat flow is regarded as current, temperature difference is regarded as voltage, and thermal resistance and capacitance correspond to resistance and capacitance, respectively. In this embodiment of the method, the following building thermal processes are considered: (1) heat conduction between indoor and outdoor air and the building's opaque envelope (exterior walls and roof); (2) energy transfer of solar radiation through the building's transparent envelope (exterior windows); (3) cooling / heating supply to the building by the building's HVAC system to maintain the indoor air temperature at a certain level; (4) heat storage and release of the building's heat storage body, mainly affected by the indoor and outdoor temperature difference and solar radiation, etc., the heat storage and release process of the opaque outer envelope; (5) heat dissipation of internal heat sources, ventilation and air infiltration exchange process.

[0022] like Figure 2 As shown, 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 outside temperature through external windows (windows connecting the interior and exterior), 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, and the temperature of the outer surface of the exterior wall increases. 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.

[0023] like Figure 2As 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 the 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, ; The thermal resistance between the interior surfaces of the exterior walls and roof and the indoor air. ; The thermal resistance between the outer surface of the roof and the outdoor air. ; The thermal resistance of the roof. ; The thermal resistance of the exterior window. ; The heat capacity of the air layer on the exterior wall surface. ; For the heat capacity of the exterior wall, ; The heat capacity of the air layer on the roof surface. ; For the heat capacity of the roof, ; For indoor air heat capacity, ; The intensity of solar radiation received by each exterior wall. ; The intensity of solar radiation received by the roof. ; This refers to the heat dissipation of the internal heat source. ; This represents the cooling (negative value) / heating (positive value) of the HVAC system. ; This represents the increase (positive value) or decrease (negative value) in indoor heat caused by infiltration ventilation. ; Let i be the area of ​​the outer walls in the four directions of east, west, south, and north (i can be e, w, s, n). ; The area of ​​the roof. ; The area of ​​the exterior window. ; The correlation coefficient of solar radiation on the exterior wall; The correlation coefficient of solar radiation on the roof; The correlation coefficient of the internal heat source; The correlation coefficient for the air conditioning system; This is the permeability correlation coefficient; The sampling step size is 1 hour.

[0024] against Figure 2 The thermal resistance-heat capacity model shown in this embodiment, the thermal balance equations constructed in step S102 include the nodal thermal balance equations for each exterior wall, the nodal thermal balance equation for the roof, and the thermal balance equations for the indoor air nodes; the functional expressions for the nodal thermal balance equations for each exterior wall are as follows: (1) (2) in, The heat capacity of the air layer on the exterior wall surface. The temperature of the outer surface of the exterior wall. For time, The area of ​​the exterior wall. Outdoor temperature Temperature of the outer surface of the exterior wall With outdoor temperature Thermal resistance between them The temperature of the inner surface of the exterior wall. The correlation coefficient of solar radiation on the exterior wall. The intensity of solar radiation received by the exterior wall. For the heat capacity of the exterior wall, For the building's indoor temperature, The thermal resistance of indoor air; The functional expression of the nodal thermal balance equation of the roof is: (3) (4) in, The heat capacity of the air layer on the roof surface. The temperature of the outer surface of the roof. 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. For the heat capacity of the roof, The thermal resistance of indoor air; The functional expression of the heat balance equation for the indoor air node is: (5) 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.

[0025] 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 of the building thermal resistance-capacity model, the correlation coefficient of solar radiation, the correlation coefficient of internal heat sources, the correlation coefficient of infiltration, and the correlation coefficient of the air conditioning system are identified step by step using TRNSYS simulation data.

[0026] 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 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 nodal heat balance equations of each exterior wall, the nodal heat balance equation of the roof, and the heat balance equations of indoor air nodes, constructs the heat balance equations under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply: (6) (7) (8) , (9) , (10) S202 uses a TRNSYS model to simulate the heat balance equation under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply to obtain the temperatures at each node under these conditions, including the external 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 and outdoor temperature ; S203 is an objective function that minimizes 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 solution algorithm is used to solve the heat balance equation under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply to obtain the resistance-capacity characteristic parameters.

[0027] Write a program in MATLAB based on formulas (6) to (10), and optimize the variable as the resistance-capacitance characteristic parameter ( , , , , , , , , , 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: (11) 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 ).

[0028] 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 wall. Correlation coefficient with roof solar radiation The identification of correlation coefficients for solar radiation includes: 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: (12) (13) (14) (15) (16) 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 ; 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.

[0029] Write a program in MATLAB based on formulas (12) to (16) to optimize the solar radiation correlation coefficient as the variable. 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 .

[0030] 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 correlation coefficient with the internal heat source The identification includes: S401, 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 and internal heat sources, without infiltration and HVAC cooling / heating supply: (17) (18) (19) (20) ,(twenty one) S402, the heat balance equation under the conditions of solar radiation and internal heat source, no 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 and internal heat source, no 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 The intensity of solar radiation received by the roof and heat dissipation from internal heat source ; S403, the identified parameters (determined according to the actual order, for example, in this embodiment, the identified parameters refer to the aforementioned RC characteristic parameters and solar radiation correlation coefficient, including...) , , , , , , , , , and , and Using known parameters of the heat balance equation under conditions of solar radiation and internal heat sources, without infiltration and HVAC cooling / heating supply, the objective function 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. A specified optimization algorithm is used to solve the heat balance equation under these conditions to obtain the internal heat source correlation coefficient. .

[0031] Write a program in MATLAB based on formulas (17) to (21) to optimize the internal heat source 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 correlation coefficient of the internal heat source in the thermal resistance-heat capacity model can be obtained by solving the problem using the genetic algorithm in MATLAB. .

[0032] In step S103 of this embodiment, when identifying parameters for the heat balance equation, the identified parameters include the permeability correlation coefficient. Correlation coefficient with air conditioning system Correlation coefficient with permeability The identification includes: S501, 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 a heat balance equation under the condition of solar radiation, internal heat sources and infiltration, without HVAC cooling / heating supply: ,(twenty two) ,(twenty three) ,(twenty four) (25) (26) S502, the heat balance equation under the condition of solar radiation, internal heat source and infiltration, without HVAC cooling / heating supply, is simulated using the TRNSYS model to obtain the temperature of each node under the condition of solar radiation, internal heat source and infiltration, without HVAC cooling / heating supply, including the temperature of the outer surface 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 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 ; 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. .

[0033] 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. .

[0034] Correlation coefficient of air conditioning system The identification includes: 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: (27) (28) (29) (30) (31) S602 uses a TRNSYS model to simulate the heat balance equation under conditions of solar radiation, internal heat sources, and HVAC cooling / heating supply to obtain the temperatures at each node under these conditions, including the outer surface temperature of the exterior walls. 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 The intensity of solar radiation received by the roof Heat dissipation from internal heat source The increase or decrease in indoor heat caused by infiltration ventilation. Heating, ventilation and air conditioning (HVAC) cooling or heating ; S603, 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. Correlation coefficient with permeability ,include , , , , , , , , , and , and , , Using known parameters of the heat balance equation under conditions of solar radiation, internal heat sources, and HVAC cooling / heating supply, the objective function 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. A specified optimization algorithm is then used to solve the heat balance equation under these conditions to obtain the correlation coefficient of the air conditioning system. .

[0035] Based on formulas (27) to (31), write a program in MATLAB to optimize the correlation coefficient of the air conditioning system 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 correlation coefficient of the air conditioning system based on the thermal resistance-heat capacity model can be obtained by solving the problem using the genetic algorithm in MATLAB. .

[0036] Based on the above identification steps, the resistance-capacitance characteristic parameters of the thermal resistance-capacitance model were identified using simulation data from TRNSYS software under different conditions. , , , , , , , , , and solar radiation correlation coefficient and Correlation coefficient of internal heat source γ、 Permeability correlation coefficient 、 Correlation coefficient with air conditioning system By establishing a building thermal resistance-heat capacity model, future indoor temperatures can be obtained. The relevant expressions of the heat balance equation are used to predict building indoor temperature by substituting the identified parameters into the heat balance equation. This provides a basis for optimizing the control of the air conditioning system.

[0037] To verify the building indoor temperature prediction method based on the thermal resistance-heat capacity model in this embodiment, meteorological data from Changsha City was used to create an office room measuring 10m (length) × 10m (width) × 5m (height) in TRNSYS software. The south-facing exterior wall has a 12.5m... 2 The relevant information regarding the exterior windows and building envelope is shown in Table 1. The internal heat source for the rooms is set at 134 W / person for indoor occupants and 9 W / m² for lighting. 2 and electrical equipment 15 W / m 2 The indoor population density is 10 m³. 2 The occupancy rate of indoor personnel, and the utilization rate of lighting and electrical equipment are shown in Table 2. The operating hours of the HVAC system are from 7:00 to 18:00, Monday to Friday, with an infiltration air setting of 2 (1 / h).

[0038] Table 1 Parameters of Building Envelope

[0039] Table 2 Indoor Occupancy Rate, Utilization Rate of Lighting and Electrical Equipment

[0040] Examples of parameter identification in this embodiment include: (1) Identification of resistance and capacitance characteristic parameters: using the indoor temperature under conditions of no solar radiation at night, internal heat source, infiltration and HVAC cooling / heating supply. Outdoor temperature and the internal and external surface temperatures of the walls and roof , , and Identifying RC characteristic parameters using TRNSYS simulation data , , , , , , , , , and The input data is the indoor temperature over a 10-hour period from 9:00 PM on June 30th to 7:00 AM on July 1st. outdoor temperature Temperature of each exterior wall surface Temperature of the inner surface of each exterior wall Roof surface temperature Roof inner surface temperature The output data consists of resistance-capacitance characteristic parameters. , , , , , , , , , and Using the genetic algorithm in MATLAB, the RC characteristic parameters that satisfy formula (11) are obtained, as shown in Table 3.

[0041] Table 3. Resistance-capacitance characteristic parameters obtained from the first step.

[0042] (2) Solar radiation correlation coefficient identification: The resistive and capacitive characteristic parameters obtained in the first step are used to identify the solar radiation correlation coefficient. , , , , , , , , , and As known parameters, under weekend solar radiation conditions, without internal heat sources, infiltration, or HVAC cooling / heating supply, output the indoor temperature from the TRNSYS simulation platform. Outdoor temperature and the internal and external surface temperatures of the walls and roof , , and The intensity of solar radiation received by each exterior wall The intensity of solar radiation received by the roof Identify the correlation coefficients of solar radiation on each exterior wall. ( (e, w, s, n) and the correlation coefficient of rooftop solar radiation The input data is the indoor temperature over a 48-hour period from June 30th to July 1st. outdoor temperature Temperature of each exterior wall surface Temperature of the inner surface of each exterior wall Roof surface temperature Roof inner surface temperature The intensity of solar radiation received by each exterior wall The intensity of solar radiation received by the roof The output data consists of the correlation coefficients of solar radiation for each exterior wall. ( (e, w, s, n) and the correlation coefficient of rooftop solar radiation Using the genetic algorithm in MATLAB, the solar radiation correlation coefficients that satisfy formula (11) are obtained, as shown in Table 4.

[0043] Table 4. Solar radiation correlation coefficients obtained from the second step of identification.

[0044] In Table 4, , , and Correlation coefficients of solar radiation on exterior walls middle i The value when e, w, s, n (east, west, south, north).

[0045] (3) Identification of internal heat source correlation coefficient: The resistance-capacitance characteristic parameters obtained in the first step are used to identify the internal heat source correlation coefficient. , , , , , , , , , and And the solar radiation correlation coefficient obtained in the second step. ( (can be e, w, s, n) and As known parameters, under conditions of solar radiation and internal heat sources, but without HVAC cooling / heating supply, the indoor temperature is output from the TRNSYS simulation platform. Outdoor temperature and the internal and external surface temperatures of the walls and roof , , and The intensity of solar radiation received by each exterior wall The intensity of solar radiation received by the roof Internal heat source Identify the correlation coefficient γ of the internal heat source. Input data consists of indoor temperatures over 168 hours from July 1st to July 7th. outdoor temperature Temperature of each exterior wall surface Temperature of the inner surface of each exterior wall Roof surface temperature Roof inner surface 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 The output data is the correlation coefficient of the internal heat source. Using the genetic algorithm in MATLAB, the correlation coefficient of the internal heat source satisfying formula (11) is obtained. =0.337.

[0046] (4) Identification of permeation correlation coefficient: The resistance and capacitance characteristic parameters obtained in the first step are used to identify the permeation correlation coefficient. , , , , , , , , , and The solar radiation correlation coefficient obtained in the second step ( (can be e, w, s, n) and Using the correlation coefficient γ of the internal heat source identified in the third step as a known parameter, and under conditions of solar radiation, internal heat sources, and infiltration, but without HVAC cooling / heating supply, the indoor temperature is output from the TRNSYS simulation platform. Outdoor temperature and the internal and external surface temperatures of the walls and roof , , and 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 Heat changes caused by osmosis Identify the penetration correlation coefficient The input data is the indoor temperature over a 168-hour period from July 1st to July 7th. outdoor temperature Temperature of each exterior wall surface Temperature of the inner surface of each exterior wall Roof surface temperature Roof inner surface 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 Heat caused by osmosis The output data is the permeability correlation coefficient. Using the genetic algorithm in MATLAB, the correlation coefficient of the internal heat source satisfying formula (11) is obtained. =0.825.

[0047] (5) Identification of correlation coefficients of air conditioning system: The resistance and capacitance characteristic parameters obtained in the first step are used to identify the correlation coefficients of the air conditioning system. , , , , , , , , , and The solar radiation correlation coefficient obtained in the second step ( (can be e, w, s, n) and The correlation coefficient γ of the internal heat source identified in the third step and the permeability correlation coefficient identified in the fourth step As known parameters, under conditions of solar radiation, internal heat sources, infiltration, and HVAC cooling / heating supply, the outdoor temperature is output from the TRNSYS simulation platform. Indoor temperature and the internal and external surface temperatures of the walls and roof , , and 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 Heat caused by osmosis and air conditioning cooling capacity Identify the correlation coefficients of the air conditioning system. β The input data is the indoor temperature over a 168-hour period from July 1st to July 7th. outdoor temperature Temperature of each exterior wall surface Temperature of the inner surface of each exterior wall Roof surface temperature Roof inner surface 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 Heat caused by osmosis and air conditioning cooling capacity The output data is the correlation coefficient of the air conditioning system. Using the genetic algorithm in MATLAB, the correlation coefficient of the air conditioning system satisfying formula (11) is obtained. =0.120.

[0048] The above five steps identify 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. In this embodiment, TRNSYS output data from July 8th to July 10th is used to output the outdoor temperature from the TRNSYS simulation platform. Indoor temperature and the internal and external surface temperatures of the walls and roof , , and 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 Heat caused by osmosis and air conditioning cooling capacity To verify the identified thermal resistance-heat capacity model, the indoor temperature predicted by the thermal resistance-heat capacity model is... (Thermal resistance-heat capacity model prediction) and TRNSYS output indoor temperature (TRNSYS simulation) Comparison Figure 3 As shown. See also Figure 3 As can be seen, the building heating and cooling load prediction method based on the thermal resistance-heat capacity model in this embodiment can accurately and quickly predict the indoor temperature of a building.

[0049] In summary, the building cooling and heating load prediction method based on the thermal resistance-heat capacity model in this embodiment considers indoor thermal disturbance and ventilation conditions. It takes into account the physical characteristics of the building, has low model complexity and good interpretability, and has strong versatility. It can accurately and quickly predict the indoor temperature of the building, solving the problem of poor interpretability of existing gray box models for predicting indoor temperature. The building cooling and heating load prediction method based on the thermal resistance-heat capacity model in this embodiment can effectively improve the accuracy and interpretability of building indoor temperature prediction, and provide a valid basis for the optimized operation and control of HVAC systems.

[0050] Furthermore, this embodiment also provides a building indoor temperature prediction system based on a thermal resistance-heat capacity model, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-heat capacity model.

[0051] Furthermore, this embodiment also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-heat capacity model via a processor.

[0052] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-heat capacity model via a processor.

[0053] Those skilled in the art will understand that the technical solutions provided by this invention may take the form of a method, system, or computer program product. Therefore, this invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention may take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce an implementation of the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0054] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting building indoor temperature based on a thermal resistance-heat capacity model, characterized in that, Includes the following steps: S101, the building indoor temperature In the heat transfer process, the heat flow is considered as current, the temperature difference as voltage, and thermal resistance and thermal capacity correspond to resistance and capacitance, respectively. The indoor temperature of the building is... The temperature transfer system is simplified and constructed to obtain a thermal resistance-thermal capacity model consisting of thermal resistance and thermal capacity; S102, construct the thermal balance equation of the thermal resistance-heat capacity model; S103, parameter identification of the heat balance equation; S104 uses the identified parameters to predict the building's indoor temperature by substituting them into the heat balance equation. ; 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, and the temperature of the outer surface of the exterior wall increases. 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.

2. The method for predicting building indoor temperature based on the thermal resistance-heat capacity model according to claim 1, characterized in that, The thermal balance equations of the thermal resistance-heat capacity model constructed in step S102 include the nodal thermal balance equations for each exterior wall, the nodal thermal balance equation for the roof, and the thermal balance equations for the indoor air nodes; the functional expressions for the nodal thermal balance equations for each exterior wall are as follows: ， ， in, The heat capacity of the air layer on the exterior wall surface. The temperature of the outer surface of the exterior wall. For time, The area of ​​the exterior wall. Outdoor temperature Temperature of the outer surface of the exterior wall With outdoor temperature Thermal resistance between them The temperature of the inner surface of the exterior wall. The correlation coefficient of solar radiation on the exterior wall. The intensity of solar radiation received by the exterior wall. For the heat capacity of the exterior wall, For the building's indoor temperature, The thermal resistance of indoor air; The functional expression of the nodal thermal 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. 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 heat balance equation for the indoor air node is: ， 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.

3. The method for predicting building indoor temperature based on the thermal resistance-heat capacity model according to claim 2, characterized in that, 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 nodal heat balance equations of each exterior wall, the nodal heat balance equation of the roof, and the heat balance equations of indoor air nodes, constructs the heat balance equations under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply: ， ， ， ， ， S202 uses a TRNSYS model to simulate the heat balance equation under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply to obtain the temperatures at each node under these conditions, including the external 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 and outdoor temperature ; S203 is an objective function that minimizes 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 solution algorithm is used to solve the heat balance equation under the conditions of no solar radiation at night, internal heat sources, infiltration, and HVAC cooling / heating supply to obtain the resistance-capacity characteristic parameters.

4. The method for predicting building indoor temperature based on the thermal resistance-heat capacity model according to claim 3, characterized in that, In step S103, 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: 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: ， ， ， ， ， 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 ; S303 uses the identified parameters as known parameters for the heat balance equation under the conditions of solar radiation, no internal heat source, infiltration, and HVAC cooling / heating supply. It constructs an objective function 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. It then uses a specified optimization algorithm 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.

5. The method for predicting building indoor temperature based on the thermal resistance-heat capacity model according to claim 3, characterized in that, In step S103, when identifying parameters for the heat balance equation, the identified parameters include the correlation coefficient of the internal heat source. And the correlation coefficient with the internal heat source The identification includes: S401, 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 and internal heat sources, without infiltration and HVAC cooling / heating supply: ， ， ， ， ， S402, the heat balance equation under the conditions of solar radiation and internal heat source, no 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 and internal heat source, no 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 The intensity of solar radiation received by the roof and heat dissipation from internal heat source ; S403 uses the identified parameters as known parameters for the heat balance equation under conditions of solar radiation and internal heat sources, no infiltration, and HVAC cooling / heating supply. It constructs an objective function to minimize the absolute error between the nodal temperatures calculated using the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. A specified optimization algorithm is then used to solve the heat balance equation under these conditions to obtain the internal heat source correlation coefficient. .

6. The method for predicting building indoor temperature based on the thermal resistance-heat capacity model according to claim 3, characterized in that, In step S103, when identifying parameters for the heat balance equation, the identified parameters include the permeability correlation coefficient. Correlation coefficient with air conditioning system And the correlation coefficient with permeability The identification includes: S501, 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 a heat balance equation under the condition of solar radiation, internal heat sources and infiltration, without HVAC cooling / heating supply: ， ， ， ， ； S502, the heat balance equation under the condition of solar radiation, internal heat source and infiltration, without HVAC cooling / heating supply, is simulated using the TRNSYS model to obtain the temperature of each node under the condition of solar radiation, internal heat source and infiltration, without HVAC cooling / heating supply, including the temperature of the outer surface 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 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 ; S503 uses the identified parameters as known parameters in the heat balance equation under conditions of solar radiation, internal heat sources, and infiltration, but without HVAC cooling / heating. It constructs an objective function to minimize the absolute error between the nodal temperatures calculated using the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. A specified optimization algorithm is then used to solve the heat balance equation under these conditions to obtain the infiltration correlation coefficient. ; Correlation coefficient of air conditioning system The identification includes: 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: ， ， ， ， ， S602 uses a TRNSYS model to simulate the heat balance equation under conditions of solar radiation, internal heat sources, and HVAC cooling / heating supply to obtain the temperatures at each node under these conditions, including the outer surface temperature of the exterior walls. 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 The intensity of solar radiation received by the roof Heat dissipation from internal heat source The increase or decrease in indoor heat caused by infiltration ventilation. Heating, ventilation and air conditioning (HVAC) cooling or heating ; S603 uses the identified parameters as known parameters for the heat balance equation under conditions of solar radiation, internal heat sources, and HVAC cooling / heating. It constructs an objective function to minimize the absolute error between the nodal temperatures calculated using the thermal resistance-heat capacity model and the nodal temperatures output by the TRNSYS simulation. A specified optimization algorithm is then used to solve the heat balance equation under these conditions to obtain the correlation coefficient of the air conditioning system. .

7. A building indoor temperature prediction system based on a thermal resistance-thermal capacity model, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to execute the building indoor temperature prediction method based on the thermal resistance-heat capacity model as described in any one of claims 1 to 6.

8. A computer-readable storage medium storing a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the building indoor temperature prediction method based on the thermal resistance-heat capacity model as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the building indoor temperature prediction method based on the thermal resistance-heat capacity model as described in any one of claims 1 to 6.

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