Offline building energy simulation method based on heat inter-response theory

Abstract

This invention discloses an offline building energy simulation method based on thermal inductance theory, comprising: acquiring structural information of a public building by combining a temperature and humidity sensor system with building design drawings, and dividing the entire building space into zones; establishing communication with the building's local control system according to the OPC UA protocol, collecting temperature and humidity changes every minute in real time, monitoring the cooling capacity of the central air conditioning system and outdoor weather conditions; performing preliminary data analysis using natural steam heat storage analysis and thermal inductance analysis; fitting a comprehensive thermal interaction digital model of the building considering the interaction of hot and cold air and historical temperature and humidity conditions; deploying the digital model in the form of executable code on the building's local server; and using the error index R... 2 This invention evaluates the simulation effect of the thermal interaction model on temperature changes. It effectively quantifies heat transfer between zones, facilitating a rapid understanding of the impact of building central air conditioning systems on temperature.

Description

Technical Field

[0001] This invention relates to the field of building energy system simulation technology, and in particular to an offline building energy simulation method based on thermal inductance theory. Background Technology

[0002] Building air conditioning system simulation is one of the important tools in air conditioning energy conservation research. Physics-based building simulation calculations can help analyze and predict air conditioning energy consumption and operating costs. Generally speaking, building energy simulation technology considers the flow of people, lighting, electrical outlets, and thermal characteristics of the space to calculate the building's heating and cooling loads. Accurate calculation of heating and cooling loads can help to better measure the energy-saving potential of the air conditioning system.

[0003] Building energy simulation methods are generally classified into three categories: white-box models, black-box models, and gray-box models. White-box models typically consider the building's geometry, building materials, and lighting, and are expressed using intuitive mathematical and physical formulas. Black-box models use data to simulate energy. Gray-box models are a hybrid approach, combining elements of both white-box and black-box models. Black-box models, such as artificial neural networks, offer extremely high accuracy but generally lack interpretability. White-box models are purely based on physical formulas, making the model easier to understand, but often at the expense of accuracy and computational complexity. Gray-box models attempt to combine the advantages of both, balancing accuracy, interpretability, and portability. Among the various gray-box model methods, the RC model is the most representative. Also known as the thermal resistance-capacity model, the RC model abstracts the thermal properties of an object into a circuit model, where thermal resistance is represented by resistance and thermal capacity by capacitance. This model estimates the internal heat flow of an object by analyzing temperature changes over time and predicts the impact of the external environment on the object's temperature. In practical applications, RC models describe the heat transfer process through temperature nodes. Generally speaking, the more nodes there are, the finer the spatial division, and the more complex the model. However, RC models have certain limitations. They often linearize nonlinear heat transfer equations for simplification, which may lead to inaccurate results. Furthermore, estimating RC model parameters solely based on building construction details or historical data is challenging. Additionally, RC models typically rely on statistical regression methods, which often fail to provide a physical basis for parameter ranges, significantly reducing the reliability of RC models.

[0004] Current building energy simulation technologies using RC models have certain theoretical limitations: 1. Current model theories cannot explain heat transfer between spatial zones. Specifically, RC models cannot provide qualitative and quantitative analysis of heat transfer between two interconnected spaces without walls or doors. 2. RC models primarily rely on statistical fitting methods to obtain building thermal characteristics, rarely providing reference values ​​for the range of heat capacity and thermal resistance, and mainly pursuing fitting accuracy, failing to guarantee that the fitted values ​​have accurate physical meaning.

[0005] Furthermore, building energy simulation technologies similar to RC models also have application limitations: 1. Most RC models rely on temperature sensors placed on both the inside and outside of the walls to calculate the thermal resistance of the building envelope (equivalent to the thermal resistance of heat transfer from the outdoor environment to the interior). However, most buildings do not have corresponding sensors installed during construction, meaning such solutions are usually only feasible in small laboratories. 2. These technologies do not provide methods for truly deploying RC models in real-world scenarios, making it difficult to provide effective guidance on model creation processes, data communication and interaction, and model operation and maintenance, resulting in a gap between energy simulation technology and real-world scenarios. Additionally, relying solely on heat capacity and thermal resistance parameters cannot characterize heat transfer between spatial zones, leading to inaccurate temperature distribution calculations when spatial zones exist within a building.

[0006] Therefore, those skilled in the art are dedicated to providing an offline building energy simulation method based on thermal inductance theory. This method uses sensors in different spatial zones of a building to determine the thermal capacity, thermal resistance, and thermal inductance between the object space and its interconnected neighboring spaces. Furthermore, it proposes a three-stage method for energy simulation—modeling, deployment, and maintenance—to ensure the long-term effectiveness of the RLC model. Summary of the Invention

[0007] In view of the deficiencies in the existing technology, the technical problem to be solved by the present invention is how to provide an offline building energy simulation method based on the theory of thermal mutual inductance.

[0008] To achieve the above objectives, this invention provides an offline building energy simulation method based on thermal inductance theory, comprising the following steps:

[0009] Step S1: Combine the temperature and humidity sensor system with the architectural design drawings to obtain the structural information of the public building and divide the entire building space into areas;

[0010] Step S2: Establish communication with the building's local control system according to the OPC UA protocol, collect temperature and humidity changes every minute in real time, and monitor the cooling capacity of the central air conditioning system and outdoor weather conditions;

[0011] Step S3: Preliminary data analysis was performed using natural steam heat storage analysis and thermal mutual inductance analysis methods;

[0012] Step S4: Fit a comprehensive thermal interaction digital model of the building that considers the interaction between hot and cold air and historical temperature and humidity conditions, including thermal resistance, thermal capacity, and thermal inductance.

[0013] Step S5: Deploy the digital model as executable code in a lightweight manner on the building's local server;

[0014] Step S6: Using the error index R 2 Evaluate the simulation effect of the thermal interaction model on temperature changes.

[0015] Preferably, in step S1, the structural information includes the building's functional zoning, facade, orientation, lighting conditions, and thermal bridge location information.

[0016] Furthermore, step S2 specifically includes the following steps:

[0017] Step S21: Read the actual values ​​of parameters according to the OPC UA protocol, including: temperature and humidity of different zones, inlet and outlet water temperature of central air conditioning terminal, and interaction address of outdoor weather in the building.

[0018] Step S22: Store the data in the local database in real time.

[0019] Furthermore, in step S3, the heat transfer from the perspective of the partition space is quantified by multiplying the net heat gain of the previous moment by the change in relative humidity, and is defined as the thermal inductance coefficient L.

[0020] Furthermore, in step S4, the net heat gain and historical temperature and humidity are used as inputs, and the future temperature is used as the predicted output. The net heat gain comes from the difference between the output of the building's heat source and cold source.

[0021] Furthermore, in step S4, the temperature change within a single space of the digital model is calculated as follows:

[0022]

[0023] △ k T inter =T inter,i,k -T k

[0024] △ k T out =T out,k -T k

[0025] △ k I inter,i =I inter,i,k -I inter,i,k-1

[0026] △ k RH inter,i =RH inter,i,k -RH inter,i,k-1

[0027] In the formula, k represents time, C is the heat capacity of the building space, and α is the dimensionless distribution coefficient of the air conditioning cooling output in the space. R inter,i L is the thermal resistance between the object space and its nearest neighbor open space i. inter->i For the thermal inductance between an object space and its nearest neighbor open space i, T k Let I be the temperature of space at time k. net,k R represents the net heat gain obtained during the time interval between k and k-1, where τ is the length of the time interval. ext RH is the thermal resistance between the object space and the outside air, M is the number of spaces that communicate with the object space, and RH is the thermal resistance between the object space and the outside air. inter,i,k I is the relative humidity of the neighboring space i that communicates with the object space at time k. inter,i,k It is the net heat gain of the neighboring space i that communicates with the object space at time k.

[0028] Furthermore, in summer, the building heat source includes the heat dissipation of passengers and the outdoor hot air forced in through the fresh air unit, while the building cold source includes the cooling capacity of the central air conditioning system.

[0029] How air conditioner output is calculated:

[0030] Q = Tw diff G total Cp w

[0031] Among them, Tw diff G represents the temperature difference between the main supply water pipe and the return water pipe. total Cp represents the flow rate of the air conditioning water system. w Q represents the specific heat capacity of water at constant pressure, and Q represents the air conditioning output.

[0032] The method for calculating heat dissipation from the human body is as follows:

[0033] I people =n people ηc load

[0034] Where, n people The number of people in the current spatial partition represents η, which is the heat dissipation index of the human body during light exercise, and c load It is the cooling load factor, I people This is the amount of heat dissipated by the human body;

[0035] The method for calculating the heat brought in by fresh air is as follows:

[0036] I air =G air H ext ρ air

[0037] Among them, G air It's the fresh air volume, H ext It is the enthalpy of outdoor air, ρ air It is air density, I air The heat brought in by the fresh air.

[0038] Preferably, in step S5, the parameters of the RLC model fitted to the building are retrained every six months using historical data.

[0039] Preferably, the fitting method in step S5 includes ordinary least squares, ridge regression, and Lasso regression.

[0040] Furthermore, in step S6, R 2 The calculation method is as follows:

[0041]

[0042] Among them, SS res SS represents the sum of squared residuals. tot This represents the total sum of squares.

[0043] The present invention has at least the following beneficial technical effects:

[0044] 1. The offline building energy simulation method based on thermal inductance theory provided by this invention establishes an RLC model that considers thermal inductance phenomena to predict temperature changes in building zones. It can effectively quantify the heat transfer between zones. After the model parameters are fitted, it can quickly provide building temperature simulation and quickly understand the impact of the building's central air conditioning system on temperature.

[0045] 2. The offline building energy simulation method based on thermal inductance theory provided by this invention features lightweight deployment technology and generates RLC models for each space, which helps to promote the simulation architecture.

[0046] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0047] Figure 1 This is a flowchart of an offline building energy simulation method based on thermal inductance theory according to an embodiment of the present invention;

[0048] Figure 2This is a diagram showing the relationship between offline building thermal characteristic identification and temperature simulation in the offline building energy simulation method based on thermal mutual inductance theory according to an embodiment of the present invention. Detailed Implementation

[0049] The preferred embodiments of the present invention are described below to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0050] In the accompanying drawings, components with the same structure are indicated by the same numerical designation, and components with similar structures or functions are indicated by similar numerical designations. The dimensions and thicknesses of each component shown in the drawings are arbitrary, and the present invention does not limit the dimensions and thicknesses of each component. To make the illustrations clearer, the thickness of some components has been appropriately exaggerated in the drawings.

[0051] This invention provides an offline building energy simulation method based on thermal inductance theory. It addresses the problem that the RC model cannot provide qualitative and quantitative analysis of the heat transfer between two interconnected spaces that are not connected by walls or doors. It analyzes the natural steam heat storage phenomenon and thermal inductance, and then integrates the thermal inductance coefficient into the thermal capacity-thermal resistance model to form a brand-new thermal resistance-thermal capacity-thermal inductance model (RLC model).

[0052] Natural steam heat storage analysis is primarily based on the inverse relationship between temperature and relative humidity (absolute humidity / saturated humidity). If the temperature decreases due to air conditioning cooling capacity or outdoor weather, the saturated humidity will also decrease, and the upper limit of heat absorption by moisture in the air will decrease. Conversely, when the saturated humidity increases, the upper limit of heat absorption by the air will increase. The value of natural steam heat storage analysis lies in treating the moisture in the air as a natural heat storage reservoir, which can help maintain the temperature within a small range for a short period. Thermal inductance refers to the phenomenon where, for two relatively independent building spaces, if there are vertical passages such as stairwells, escalators, or atriums, then due to the natural convection effect, hot air from the lower space will enter the upper space through the vertical passages, and cold air from the upper space will also enter the lower space through the vertical passages. This interaction of the thermal environments of the upper and lower spaces caused by natural convection is the phenomenon of thermal inductance. Based on this, this invention uses the net heat gain of the previous moment multiplied by the change in relative humidity to quantify heat transfer from the perspective of spatial partitioning, and creates a thermal inductance coefficient (L) to qualitatively characterize the spatial energy transfer relationship.

[0053] Based on the above principles, the RLC model can provide qualitative and quantitative analysis of heat transfer between building zones on the basis of the RC model, thus enabling a more accurate simulation of heat transfer changes within building zones. The RLC model does not require temperature sensors on the building envelope surface, but only temperature and humidity sensors for each spatial zone and the outdoor air. Its heat capacity can be calculated based on the temperature and humidity changes and net heat gain of each spatial zone. By comparing the temperature and humidity changes and net heat gain between spatial zones, thermal resistance and thermal inductance can be calculated, thus obtaining more accurate and reliable results with fewer sensors and less computation.

[0054] This invention proposes a method for determining the thermal capacity, thermal resistance, and thermal inductance between an object space and its interconnected neighboring spaces based on sensors within different spatial zones of a building. Furthermore, it proposes a three-stage method for energy simulation—modeling, deployment, and maintenance—to ensure the long-term effectiveness of the RLC model. The specific processes of the three stages are as follows.

[0055] Modeling Phase: Based on sensors within the actual building space zones, Relative Thermal Lithography (RLC) models are performed. The temperature and humidity within each zone are determined by the average of all temperature and humidity values ​​within that zone. Each zone possesses a heat capacity and exhibits thermal resistance and thermal inductance with adjacent zones. The thermal resistance between zones represents the overall impact of the corresponding internal structure on heat transfer, while the thermal inductance from the object space to its interconnected neighboring spaces quantitatively describes the heat transfer process between the object space and its interconnected neighboring spaces caused by changes in humidity and temperature.

[0056] Deployment Phase: Develop scripts that read and write sensor data and control central air conditioning equipment, following the OPC UA (Open Platform Communications Unified Architecture) protocol. OPC UA is an open, cross-platform, and scalable communication protocol used for data communication and information exchange between industrial automation devices. It offers advantages such as flexibility, scalability, security, and cross-platform compatibility. Applying RLC models to buildings using OPC UA is a convenient and cost-effective approach.

[0057] Maintenance Phase: Due to material aging, equipment depreciation, and interior renovations during building operation, the building's thermal characteristics will inevitably change periodically. This invention proposes updating the RLC model every six months. Because the RLC model has few parameters and low computational load, its parameter fitting process can be completed in less than 10 seconds on a computer with 4GB of memory, making this maintenance process very convenient. Furthermore, updating the model at a shorter interval of six months helps identify problems with sensors and other equipment. If the building has not undergone major renovations in the short term, but the parameters described by a specific sensor show significant changes, it indicates a sensor malfunction, prompting maintenance personnel to perform maintenance. This allows for maintenance of the energy simulation calculation framework solely based on data.

[0058] The integrated application process covering modeling, deployment, and maintenance phases enables rapid deployment of RLC models and effectively reduces manual operations during maintenance, thus facilitating the promotion and application of RLC models and promoting the intelligent operation and maintenance of buildings.

[0059] The present invention also provides the following specific embodiments, which, based on historical data and codes of a certain subway station, provide detailed implementation methods and specific operation processes, as follows.

[0060] Step 1: Combine the temperature and humidity sensor system with the architectural design drawings to obtain the structural information of the public building, and divide the entire building space into reasonable areas, mainly into the public area concourse level, the public area platform level, and the relatively independent central air conditioning system equipment level.

[0061] Specifically, the building structure information used in this step includes the location of the central air conditioning system terminals within the building, the location of sensors in the subway station, and the area of ​​the station hall and platform; this information is primarily derived from architectural design drawings. Essentially, a representative temperature and humidity sensor characterizes a building zone, and a dedicated RLC model is built around this zone. Each building zone contains a heat capacity and has a thermal resistance and thermal inductance with each adjacent zone. The thermal resistance between zones represents the overall impact of the corresponding internal structure on heat transfer, while the thermal inductance from one zone to another quantitatively represents the heat transfer process caused by changes in humidity and temperature.

[0062] Step 2: Establish communication with the building's local control system equipment according to the OPC UA protocol, collect temperature and humidity changes every minute in real time, and monitor the cooling capacity of the central air conditioning system and outdoor weather conditions.

[0063] Specifically, in this step, communication is established with the building's local control system equipment according to the OPC UA protocol to collect real-time temperature and humidity changes every minute, monitor the cooling capacity of the central air conditioning system's air outlets, and outdoor weather conditions. This data can be read from the building's local control system via the OPC UA protocol.

[0064] Step 3: Use natural steam heat storage analysis and thermal inductance analysis to conduct preliminary data analysis on the problem of hot and cold air interaction.

[0065] In practical implementation scenarios, subway stations are mainly divided into two spaces: the concourse level and the platform level. Because there are connecting escalators between them, thermal inductance occurs. Specifically, the platform level is below, and its hot air eventually rises to the upper ceiling, while the concourse level is above, and its cool air flows downwards due to the airflow from the fans. Simultaneously, during station operation, passengers moving up and down the escalators between the concourse and platform also contribute to heat transfer as their bodies dissipate heat. Based on this phenomenon, the heat transfer based on the spatial division angle is quantified by multiplying the net heat gain from the previous moment by the change in relative humidity, and a thermal inductance coefficient (L) is created to represent the spatial energy transfer relationship.

[0066] Step 4: Fit an RLC model that takes into account the interaction of hot and cold air and historical temperature and humidity conditions.

[0067] The model takes net heat gain and historical temperature and humidity as inputs, and future temperature as the predicted output. Net heat gain comes from the difference between the building's heat and cold sources. In a specific implementation scenario, during summer, the building's heat sources are mainly the heat dissipation from passengers and the outdoor hot air forced in by the fresh air system, while the building's cold sources are mainly the cooling capacity of the central air conditioning system. According to the RLC model, the temperature change within a single space is calculated as follows:

[0068]

[0069] △ k T inter =T inter,i,k -T k

[0070] △ k T out =T out,k -T k

[0071] △ k I inter,i =I inter,i,k -I inter,i,k-1

[0072] △ k RH inter,i =RH inter,i,k -RH inter,i,k-1

[0073] Where k represents time; C is the heat capacity of the building space, kJ / K. In the specific embodiment, its maximum value is calculated under the assumption that the two floors of the subway station are filled with water vapor; the minimum value is calculated under the assumption that the two floors of the subway station are filled with dry air.

[0074] α is the dimensionless distribution coefficient of the cooling output of the air conditioner in the object space: Thermal diffusivity is measured in meters (m). 2 / s, cooling time is in seconds, space area (m²) 2 ) is the sum of the areas of all surfaces in the object space; where the minimum value of α is calculated based on the thermal diffusivity of air at 24°C, and the maximum value is calculated based on the thermal diffusivity of air at 28°C.

[0075] R inter,i It is the thermal resistance between the object space and its nearest interconnected space i, in K / kW, which mainly represents the heat transfer inertia caused by the spatial structure and materials; in specific implementation scenarios, the station hall and the platform are connected by multiple escalator entrances.

[0076] L inter->i Let K / kJ represent the thermal inductance between the object space and its nearest interconnected space i. The main reason for heat transfer between the station hall and the platform is that the passenger flow itself, as a heat source, moves from the station hall to the platform via escalators, generating heat transfer during this movement. Similarly, in addition to the natural rise of hot air, disembarking passengers move from the platform towards the station hall and eventually leave the station, thus transferring heat from the platform to the station hall.

[0077] L inter->i Let K be the temperature of the space at time k.

[0078] I net,k Let be the net heat gain of the object space between time k and time k-1, expressed in kJ. This net heat gain is the difference between the heat infiltration into the building space and the cooling capacity of the central air conditioning system. During the cooling season, the building's heat source is mainly heat dissipation from the human body and outdoor hot air, while the building's cold source is only the cooling capacity of the central air conditioning system. During the heating season, the building's heat source is mainly the heating capacity of the central air conditioning system and heat dissipation from the human body, while the building's cold source is outdoor cold air.

[0079] The specific parameter values ​​for the RLC model application in this embodiment are shown in Table 1.

[0080] Table 1. Parameter values ​​of the RLC model in this embodiment.

[0081]

[0082] τ is the time interval, in seconds; R ext It is the thermal resistance between the object space and the outside air, in K / kW; M is the number of spaces communicating with the object space; RHinter,i,k It is the relative humidity (%) of the nearest neighbor space i that communicates with the object space at time k; I inter,i,k It is the net heat gain of the nearest neighbor space i, which is interconnected with the object space, at time k, in kJ.

[0083] The heat output of an air conditioner can be calculated using the following formula:

[0084] Q = Tw diff G total Cp w

[0085] Among them, Tw diff The temperature difference between the main supply water pipe and the return water pipe is represented by K; G. total Represents the flow rate of the air conditioning water system, m 3 / s;Cp w Q represents the specific heat capacity of water at constant pressure, kJ / (kg·K); Q represents the air conditioning output, kW.

[0086] The formula for calculating human body heat dissipation is:

[0087] I people =n people ηc load

[0088] Where, n people The number of people in the current spatial partition is represented by η; η is the human body heat dissipation index during light exercise, kW / person; c load It is the cooling load factor; I people It is the amount of heat dissipated by the human body, measured in kW.

[0089] The formula for calculating the heat brought in by the incoming fresh air is:

[0090] I air =G air H ext ρ air

[0091] Among them, G air It's the fresh air volume, m 3 / s;H ext It is the enthalpy of outdoor air, kJ / kg; ρ air It is the density of air, kg / m³ 3 ;I air Heat brought in by fresh air, kW.

[0092] Step 5: Deploy the digital model in the form of executable code on the building’s local server. The operation first includes using historical data to fit the key parameters of the model, so that the technical framework can predict future temperatures based on central air conditioning cooling capacity, outdoor weather, and historical temperature conditions.

[0093] Specifically, a lightweight, building-specific RLC model is deployed on a local building server. Deployment simply requires the model itself to directly read data from the local control system using the OPC UA protocol. Because the model is accurate yet lightweight, it requires minimal computing power and has low configuration requirements for the local server. Furthermore, since this technology builds the model based on the building's existing sensor layout, no additional maintenance or modification processes are needed. This embodiment employs offline deployment technology, thus eliminating security issues such as data leakage. It can operate efficiently without external network connectivity, effectively ensuring local data security. To address equipment depreciation, routine maintenance of the RLC model should include retraining and fitting the building-specific RLC model's parameters every six months using recent historical data to ensure the sustainability of simulation accuracy.

[0094] The model parameter fitting part uses the statistical algorithm least squares, and the technical framework includes various least squares code packages available for runtime use:

[0095] 1. Ordinary Least Squares (OLS):

[0096] OLS is the most basic least squares method, which determines the coefficients of the best-fit line or curve by minimizing the sum of squared residuals between observed and predicted values. Its formula is:

[0097]

[0098] Where y is the dependent variable vector, X is the independent variable matrix, and β is the regression coefficient vector.

[0099] 2. Ridge Regression:

[0100] Ridge regression is a least-squares method with L2 regularization used to solve multicollinearity problems. Its formula is:

[0101]

[0102] Here, α is the regularization strength parameter, used to balance the fitting effect and the regularization term.

[0103] 3. Lasso Regression:

[0104] Lasso regression is a least-squares method with L1 regularization, used for feature selection and sparsity modeling. Its formula is:

[0105]

[0106] Here, α is the regularization strength parameter, used to balance the fitting effect and the regularization term.

[0107] Step 6, using the error index R 2 Evaluate the simulation effect of the thermal interaction model on temperature changes.

[0108] Specifically, its expression is as follows:

[0109]

[0110] Among them, SS res SS represents the sum of squared residuals. tot Represents the total sum of squares. When R... 2 When R approaches 1, the model can explain the variation in the data well; when R... 2 When the value is close to 0, the model has poor explanatory power and cannot fit the data well.

[0111] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, those skilled in the art can obtain the following results based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology.

Claims

1. An off-line building energy simulation method based on the theory of heat interdependence, characterized in that, Includes the following steps: Step S1: Combine the temperature and humidity sensor system with the architectural design drawings to obtain the structural information of the public building and divide the entire building space into areas; Step S2: Establish communication with the building's local control system according to the OPC UA protocol, collect temperature and humidity changes every minute in real time, and monitor the cooling capacity of the central air conditioning system and outdoor weather conditions; Step S3: Preliminary data analysis was performed using natural steam heat storage analysis and thermal mutual inductance analysis methods; Step S4: Fit a comprehensive thermal interaction digital model of the building, considering the interaction of hot and cold air and historical temperature and humidity conditions, based on thermal resistance, thermal capacity, and thermal inductance; wherein, the temperature change within a single space of the digital model is calculated as follows: In the formula, Indicates time, For the heat capacity of the building space, It is the dimensionless distribution coefficient of the air conditioning cooling output in the space: , It is the space of an object and the space of its nearest neighbors that are mutually open. Thermal resistance between To open space from object space to its nearest neighbor space thermal induction between them For space in Temperature at any moment In order to be in and The net heat gained during the time interval. It is the length of the time interval. It is the thermal resistance between the object space and the outdoor air. It refers to the amount of space that communicates with the object space. It is the adjacent space that communicates with the object space. exist Relative humidity at any given time It is the adjacent space that communicates with the object space. exist Net heat gain at any given moment; Step S5: Deploy the digital model as executable code in a lightweight manner on the building's local server; Step S6: Using error indicators Evaluate the simulation effect of the thermal interaction model on temperature changes.

2. The offline building energy simulation method based on thermal inductance theory as described in claim 1, characterized in that, In step S1, the structural information includes the building's functional zoning, facade, orientation, lighting conditions, and thermal bridge location information.

3. The offline building energy simulation method based on thermal inductance theory as described in claim 1, characterized in that, Step S2 specifically includes the following steps: Step S21: Read the actual values ​​of parameters according to the OPC UA protocol, including: temperature and humidity of different zones, inlet and outlet water temperature of central air conditioning terminal, and interaction address of outdoor weather in the building. Step S22: Store the data in the local database in real time.

4. The offline building energy simulation method based on thermal inductance theory as described in claim 1, characterized in that, In step S3, the heat transfer from the perspective of the partitioned space is quantified by multiplying the net heat gain from the previous moment by the change in relative humidity, and is defined as the thermal inductance coefficient. .

5. The offline building energy simulation method based on thermal inductance theory as described in claim 1, characterized in that, In step S4, the net heat gain and historical temperature and humidity are used as inputs, and the future temperature is used as the predicted output. The net heat gain comes from the difference between the output of the building's heat source and cold source.

6. The offline building energy simulation method based on thermal inductance theory as described in claim 5, characterized in that, In summer, building heat sources include heat dissipation from passengers and hot outdoor air forced in by fresh air units, while building cold sources include the cooling capacity of central air conditioning. How air conditioner output is calculated: in, This represents the temperature difference between the main supply water pipe and the return water pipe. Represents the flow rate of the air conditioning water system. The specific heat capacity at constant pressure of water Represents air conditioning output; The method for calculating heat dissipation from the human body is as follows: in, This represents the number of people in the current spatial partition. The heat dissipation index of the human body during light exercise. It is the cooling load factor. This is the amount of heat dissipated by the human body; The method for calculating the heat brought in by fresh air is as follows: in, It's the fresh air volume. It is the enthalpy value of outdoor air. It is air density. The heat brought in by the fresh air.

7. The offline building energy simulation method based on thermal inductance theory as described in claim 1, characterized in that, In step S5, the parameters of the RLC model fitted to the building are retrained every six months using historical data.

8. The offline building energy simulation method based on thermal inductance theory as described in claim 7, characterized in that, The fitting methods in step S5 include ordinary least squares, ridge regression, and Lasso regression.

9. The offline building energy simulation method based on thermal inductance theory as described in claim 1, characterized in that, In step S6, The calculation method is as follows: in, Represents the sum of squared residuals. This represents the total sum of squares.

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