Building energy consumption and carbon emission prediction model and prediction method based on BIM technology

Abstract

The application discloses a building energy consumption and carbon emission prediction model and a prediction method based on BIM technology, and the model comprises a physical mechanics layer, a data information layer, a model layer and an actual application layer. The model establishment method comprises the following steps: constructing a room digital twin model; establishing a physical object, obtaining building entity information, constructing an entity building graph, and forming a physical mechanics layer; establishing a data information layer and adding data information; interacting the physical mechanics layer and the data layer to form a basic model layer; obtaining building room basic information from a BIM file, performing real-time mapping of the physical object and the BIM model, and perfecting the construction of the physical mechanics layer and the data information layer; constructing the corresponding relationship between different room digital twin models and main calculation parameters of building energy consumption and carbon emission, and establishing a building energy consumption and carbon emission prediction model. The application can perform prediction calculation of building energy consumption and carbon emission, and improves the accuracy of building energy consumption and carbon emission prediction.

Description

Technical Field

[0001] The present invention relates to a building energy consumption and carbon emission prediction model and prediction method based on BIM technology, and belongs to the field of information technology. Background Art

[0002] Issues such as building energy consumption and building carbon emissions have always been hot topics of concern to all countries. The establishment of a building energy consumption and building carbon emissions evaluation system aims to reduce the ecological environmental burden, improve the quality of the building environment, and provide a broader space for future development.

[0003] Currently, the most commonly used building energy consumption prediction methods include simplified engineering algorithms, physical modeling, and artificial intelligence algorithms. Simplified engineering calculation methods are prone to significant calculation errors when comprehensively considering building type and thermal inertia. Physical modeling methods often require tedious steps, such as establishing complex models and manually setting numerous calculation parameters. Artificial intelligence algorithms, which are detached from the building itself and rely on existing data for learning and prediction, have certain limitations when predicting different building room types and actual conditions, potentially leading to significant deviations between predicted results and actual conditions.

[0004] With the rapid development of science and technology today, intelligent buildings are the trend of the future. BIM, as an intelligent building technology, is an information model technology based on a huge database. It can be simulated and analyzed by computers in the planning, design, construction, operation and maintenance stages of the project. Digital twin technology links buildings and their spaces with their design goals and objectives, and regards digital twins as digital replicas of buildings. BIM and digital twin technology complement each other, and their combination in the life cycle of a building is inevitable. At present, there is no method to predict building energy consumption and building carbon emissions based on intelligent building technology. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a building energy consumption and carbon emission prediction model and prediction method based on BIM technology, which can not only perform forecast calculations on building energy consumption and carbon emissions, but also improve the accuracy of building energy consumption and carbon emission predictions.

[0006] The technical solution adopted by the present invention to solve the technical problem is:

[0007] In the first aspect, an embodiment of the present invention provides a building energy consumption and carbon emission prediction model based on BIM technology, which includes a physical mechanics layer, a data information layer, a model layer, and a practical application layer. It uses BIM and digital twin technology to achieve automated intelligent prediction of building energy consumption and carbon emissions.

[0008] The physical mechanics layer is the basic data of the building energy consumption and carbon emission prediction model. It uses reality capture and photography technology to obtain physical information of building structure, building materials, building equipment and building environment to construct a physical building map.

[0009] The data information layer includes measurement parameters, relevant material parameters, geometric size parameters and object property parameters;

[0010] The model layer is a physical and mechanical model formed by the interaction between the physical and mechanical layer and the data information layer:

[0011] S i (j) = {s1, s2, s3, s4 ... s i}

[0012] Among them, S i (j) represents the feature coding of the physical and mechanical layer and part of the data information layer of the digital twin model of different room types. It is the information matching condition and constraint condition of the digital twin model and is used to match the building room model in the BIM file uploaded by the user with the digital twin model. i∈N, N is the total number of feature information codes; si is the information parameter that determines the digital twin model of the room, including measurement parameters, related material parameters, geometric size parameters and object property parameters;

[0013] The practical application layer is used to predict building energy consumption and carbon emissions.

[0014] In a second aspect, an embodiment of the present invention provides a method for establishing a building energy consumption and carbon emission prediction model based on BIM technology, comprising the following steps:

[0015] Constructing a digital twin model of the room, the digital twin model comprising a physical mechanics layer, a data information layer, a model layer, and a practical application layer; the parameters of the digital twin model of the room include architectural construction, construction technology, measurement parameters, relevant material parameters, and object properties;

[0016] Establish physical objects, obtain information on building structure, building materials, building equipment and building environment, construct physical building maps, and form a physical mechanics layer;

[0017] Establish a data information layer and add data information through data collection, data governance, data transmission and data interaction technologies;

[0018] Interact the physical mechanics layer and the data layer to form a basic model layer;

[0019] Obtain basic building room information from BIM files, use it as the physical and mechanical layer and data information layer to match and constrain points and dimensions with the digital twin model, perform real-time mapping between physical objects and BIM models, and improve the construction of the physical and mechanical layer and data information layer;

[0020] By calculating the matrix, the correspondence between the digital twin models of different rooms and the main calculation parameters of building energy consumption and carbon emissions is constructed to realize the building description, building diagnosis, multi-faceted prediction and operation and maintenance decision-making functions, and finally establish a building energy consumption and carbon emission prediction model.

[0021] As a possible implementation of this embodiment, establishing the data information layer includes:

[0022] Establishing a correspondence between the physical and mechanical layers and calculation parameters, including the heat transfer coefficient of the building envelope, airflow rate, airflow operation time fraction, hourly usage rate of electrical equipment, hourly occupancy rate of people in the room, equipment power per unit area, lighting power and occupant heat generation, shading coefficient of external obstacles in the effective solar energy collection area, emissivity of external surface thermal radiation, and internal heat capacity of the building;

[0023] The data and attributes are stored, processed, analyzed, and calculated to obtain the calculation parameter matrix corresponding to the digital twin models of different types of rooms, and then determine the mapping relationship between the digital twin models of different rooms and the calculation parameters.

[0024] As a possible implementation of this embodiment, the calculation formula of the calculation parameter matrix is:

[0025] U′=α1U

[0026] q ve，k =α2q ve

[0027] f ve，t，k =α3f ve，t

[0028] F sh，ob，s =α4F sh，ob

[0029] ε′=α5ε

[0030] C m ′=α6C m

[0031] …

[0032]

[0033] Where: U is the heat transfer coefficient of the building envelope, q ve is the airflow velocity, f ve，t is the fraction of the airflow's operating time, F sh，ob is the shading coefficient of external obstacles in the effective solar energy collection area of ​​the surface, ε is the emissivity of thermal radiation of the external surface, C mis the internal heat capacity of a building or building area, U′ is the heat transfer coefficient of the building envelope of a certain material, and q ve，k is the airflow rate of airflow element k, f ve，t，k is the running time fraction of airflow element k, F sh，ob，s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s, ε′ is the emissivity of thermal radiation on the outer surface of a certain material, C m ′ is the internal heat capacity of a building or building area of ​​a certain material, I i is the calculation parameter matrix corresponding to the digital twin model of different types of rooms, α i is the mapping coefficient between the digital twin model and different calculation parameters.

[0034] As a possible implementation of this embodiment, the basic model layer is a physical mechanics model formed by the interaction of the physical mechanics layer and the data information layer.

[0035] As a possible implementation of this embodiment, the digital twin model information matching condition is:

[0036] S(j)=S i (j)

[0037] The user uploads the basic information of the BIM file room, that is, the code S(j) of the physical and mechanical layer and the data information layer of the BIM model and the code S(j) of the digital twin model of a certain type of room i (j) match.

[0038] As a possible implementation of this embodiment, the basic data of the physical mechanics layer and the parameter information of the data information layer can be improved with the help of BIM technology.

[0039] In a third aspect, an embodiment of the present invention provides a method for predicting building energy consumption and carbon emissions based on BIM technology, comprising the following steps:

[0040] The user uploads the BIM file of the target building, and the model obtains the basic information of the building room. It then matches and constrains the points and dimensions of the digital twin model as the physical mechanics layer and data information layer, achieving real-time mapping between the physical object and the user-side BIM model.

[0041] Use building energy consumption and carbon emission prediction models to predict building energy consumption and carbon emissions;

[0042] The method of using the building energy consumption and carbon emission prediction model to predict building energy consumption and carbon emissions includes the following steps:

[0043] The user uploads the BIM file that needs to be predicted, and the model completes the matching between the BIM model and the room digital twin model;

[0044] Calculate the building envelope heat transfer and infiltration heat transfer data as well as the heat gain of equipment and personnel and solar heat gain;

[0045] Calculate building heating and cooling dynamics, energy requirements, and total electricity consumption;

[0046] Calculate the carbon emissions of buildings.

[0047] As a possible implementation of this embodiment, the calculation of the building envelope heat transfer and infiltration heat transfer data as well as the heat gain of equipment and personnel and solar heat includes:

[0048] Calculate heat transfer of building envelope:

[0049]

[0050] Where A i is the area of ​​building envelope element i; U i is the heat transfer coefficient of building envelope element i; A Z is the area of ​​the total enclosure structure, i.e. A i The sum of d is the area of ​​the floor; U d is the heat transfer coefficient of the floor; T int，set is the set temperature of the zone; T e is the monthly average outdoor temperature; t is the duration of the calculation step;

[0051] Calculate the penetration heat transfer:

[0052]

[0053] Where k represents each relevant airflow element, including mechanical ventilation, natural ventilation, and infiltration; q ve，k is the airflow rate of airflow element k; f ve，t，k is the running time fraction of airflow element k, T int，set is the set temperature of the zone; T e The monthly average outside temperature; t is the duration of the calculation step;

[0054] Calculation equipment personnel heat gain:

[0055]

[0056] Where, f i is the hourly usage rate of electrical equipment, the hourly presence rate of room personnel, and the hourly usage rate of lighting; P is the power of equipment per unit area, lighting power, and heat generated by personnel; A c is the total area of ​​the room; t is the duration of the calculation step;

[0057] Calculate solar heat gain:

[0058]

[0059] Where, F sh，ob，s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s; A sol，s is the effective collection area; I sol，s is the solar radiation intensity; F r，k is the shape factor value of the radiation between the element and the sky. For horizontal roofs without sunshades, F r =1, vertical wall uses F r =0.5, ΔT er is the average difference between the outside air temperature and the sky temperature, which should be taken as 9 K in the subpolar regions, 13 K in the tropical regions, and 11 K in the central regions, t is the duration of the calculation step, and ε is the emissivity of the external surface thermal radiation.

[0060] As a possible implementation of this embodiment, the calculation of dynamic parameters, energy demand, and total power consumption of building heating and cooling includes:

[0061] Calculate heating dynamic parameters:

[0062]

[0063] Q H，ht =Q tr +Q ve

[0064] Q H，gn =Q int +Q sol

[0065] Where, χ H is the dimensionless heat balance ratio in heating mode; Q H，ht is the total heat transfer of the determined heating mode, the heat transfer of the building envelope Q tr and penetration heat transfer Q ve ;Q H，gn is the total heat gain in heating mode, and the heat gain of equipment and personnel is Q int and the sun's heat sol ;

[0066] If χ H >0 and χ H ≠1, then:

[0067]

[0068] If χ H =1, then:

[0069]

[0070] If χ H <0, then:

[0071]

[0072] Where η H，gn refers to the heating gain utilization efficiency; τ is the time constant of the building area;

[0073]

[0074] Where H tr，adj =∑ i A i ·U i +0.1A z +A·U;H ve，adj =1200(∑ k f ve，t，k ·q ve，k );C m is the internal heat capacity of a building or building area; where A f is the building area;

[0075] Calculate cooling dynamic parameters:

[0076]

[0077] Where, χ C is the dimensionless heat balance ratio in cooling mode; Q C，ht is the total heat transfer in cooling mode; Q C，gn is the total heat gain in cooling mode;

[0078] If χ C >0 and χ C ≠1, then:

[0079]

[0080] If χ C =1, then:

[0081]

[0082] If χ C <0, then:

[0083] η C，ls =1

[0084] Where η C，ls is the cooling loss utilization coefficient, τ is the time constant of the building area;

[0085] Calculate energy requirements for heating:

[0086] Q H =QH，ht -η H，gn Q H，gn

[0087] Where Q H The cumulative heat consumption per month / year; Q H，ht is the total heat transfer in heating mode; Q H，gn is the total heat gain in heating mode; η H，gn is the dimensionless heating gain utilization coefficient;

[0088] Calculate cooling energy requirements:

[0089] Q C =Q C，gn -η C，is Q C，ht

[0090] Where Q C Q is the monthly / annual cumulative cooling consumption; C，ht is the total heat transfer in cooling mode; Q C，gn is the total heat gain in cooling mode; η C，is is the dimensionless utilization coefficient of heat loss;

[0091] Calculate the total annual heating and cooling electricity consumption:

[0092] E=E H +E C

[0093] Where, E is the monthly / annual heating and cooling power consumption; E C E is the monthly / annual cooling power consumption; H Monthly / annual heating electricity consumption;

[0094] Total cooling power consumption:

[0095]

[0096] Where, COP is the comprehensive performance coefficient of the cooling system of public buildings;

[0097] Annual heating power consumption in severe cold regions and cold regions:

[0098]

[0099] Where η1 is the comprehensive efficiency of the heating system with the heat source being a coal-fired boiler; q1 is the calorific value of standard coal; q2 is the comprehensive coal consumption for power generation;

[0100] Annual heating power consumption of public buildings in hot summer and warm winter zone A, hot summer and cold winter, hot summer and warm winter, and mild areas:

[0101]

[0102] Where, η2 is the comprehensive efficiency of the heating system with the heat source being a gas boiler; q3 is the standard calorific value of natural gas; is the conversion coefficient of natural gas to standard coal;

[0103] Annual heating power consumption of residential buildings in hot summer and warm winter zone A, hot summer and cold winter and mild areas:

[0104]

[0105] Where, COP H is the comprehensive performance coefficient of the heating system.

[0106] As a possible implementation of this embodiment, calculating the carbon emissions of a building includes:

[0107] Carbon emissions per unit area of ​​buildings during operation (C M ) is calculated as follows:

[0108]

[0109]

[0110] Where C M E is the carbon emission per unit building area during the operation phase; I is the annual energy consumption of the building, I represents different types of energy; EF I is the carbon emission factor of energy; E I,J is the energy consumption of Category I of Category J system; ER I,J The amount of Class I energy consumed by the renewable energy system for the Class J system; C p is the annual carbon reduction of the building green space carbon sequestration system; y is the building design life; A is the building area.

[0111] The technical solution of the embodiment of the present invention can have the following beneficial effects:

[0112] The present invention provides a prediction model and method for building energy consumption and carbon emissions based on BIM digital twin technology, which rationally considers the thermal inertia of building components and multiple indoor and outdoor influencing factors. The model matches the basic room information obtained through BIM files with the constructed room digital twin model, thereby forming a corresponding relationship. While comprehensively considering multiple influencing factors of building energy consumption, the model completes the prediction and calculation of building energy consumption and carbon emissions, improving the accuracy of the prediction. With the help of BIM and digital twin technology, the model realizes automated intelligent prediction of building energy consumption and carbon emissions. The present invention not only enables the prediction and calculation of building energy consumption and carbon emissions, but also improves the accuracy of building energy consumption and carbon emissions prediction, and has the advantages of precision, standardization, lightweight, visualization, and interactivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] Figure 1 This is a flow chart of a method for establishing a building energy consumption and carbon emission prediction model based on BIM technology according to an exemplary embodiment;

[0114] Figure 2 This is a flowchart of a method for predicting building energy consumption and carbon emissions based on BIM technology according to an exemplary embodiment;

[0115] Figure 3 This is a diagram illustrating a digital twin model technology and its application principle according to an exemplary embodiment;

[0116] Figure 4 The present invention is a workflow diagram for predicting building energy consumption and carbon emissions according to an exemplary embodiment. DETAILED DESCRIPTION

[0117] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0118] In order to clearly illustrate the technical features of this solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings. The disclosure below provides many different embodiments or examples for realizing different structures of the present invention. In order to simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplicity and clarity and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the accompanying drawings are not necessarily drawn to scale. The present invention omits descriptions of well-known components and processing technologies and processes to avoid unnecessary limitations on the present invention.

[0119] The embodiment of the present invention provides a building energy consumption and carbon emission prediction model based on BIM technology, which includes a physical mechanics layer, a data information layer, a model layer, and a practical application layer. It uses BIM and digital twin technology to achieve automated intelligent prediction of building energy consumption and carbon emissions.

[0120] The physical mechanics layer is the basic data of the building energy consumption and carbon emission prediction model. It uses reality capture and photography technology to obtain physical information of building structure, building materials, building equipment and building environment to construct a physical building map.

[0121] The data information layer includes measurement parameters, relevant material parameters, geometric size parameters and object property parameters;

[0122] The model layer is a physical and mechanical model formed by the interaction between the physical and mechanical layer and the data information layer:

[0123] S i(j) = {s1, s2, s3, s4...s i}

[0124] Among them, S i (j) represents the feature coding of the physical and mechanical layer and part of the data information layer of the digital twin model of different room types. It is the information matching condition and constraint condition of the digital twin model and is used to match the building room model in the BIM file uploaded by the user with the digital twin model. i∈N, N is the total number of feature information coding; s i To determine the information parameters of the room digital twin model, including measurement parameters, relevant material parameters, geometric size parameters and object property parameters;

[0125] The practical application layer is used to predict building energy consumption and carbon emissions.

[0126] like Figure 1 As shown, an embodiment of the present invention provides a method for establishing a building energy consumption and carbon emission prediction model based on BIM technology, comprising the following steps:

[0127] Constructing a digital twin model of the room, the digital twin model comprising a physical mechanics layer, a data information layer, a model layer, and a practical application layer; the parameters of the digital twin model of the room include architectural construction, construction technology, measurement parameters, relevant material parameters, and object properties;

[0128] Establish physical objects, obtain entity information such as building structure, building materials, building equipment and building environment, construct physical building maps, and form a physical mechanics layer;

[0129] Establish a data information layer and add data information through data collection, data governance, data transmission and data interaction technologies;

[0130] Interact the physical mechanics layer and the data layer to form a basic model layer;

[0131] Obtain basic building room information from BIM files, use it as the physical and mechanical layer and data information layer to match and constrain points and dimensions with the digital twin model, perform real-time mapping between physical objects and BIM models, and improve the construction of the physical and mechanical layer and data information layer;

[0132] By calculating the matrix, the correspondence between the digital twin models of different rooms and the main calculation parameters of building energy consumption and carbon emissions is constructed to realize the building description, building diagnosis, multi-faceted prediction and operation and maintenance decision-making functions, and finally establish a building energy consumption and carbon emission prediction model.

[0133] As a possible implementation of this embodiment, establishing the data information layer includes:

[0134] Establishing a correspondence between the physical and mechanical layers and calculation parameters, including the heat transfer coefficient of the building envelope, airflow rate, airflow operation time fraction, hourly usage rate of electrical equipment, hourly occupancy rate of people in the room, equipment power per unit area, lighting power and occupant heat generation, shading coefficient of external obstacles in the effective solar energy collection area, emissivity of external surface thermal radiation, and internal heat capacity of the building;

[0135] The data and attributes are stored, processed, analyzed, and calculated to obtain the calculation parameter matrix corresponding to the digital twin models of different types of rooms, and then determine the mapping relationship between the digital twin models of different rooms and the calculation parameters.

[0136] As a possible implementation of this embodiment, the calculation formula of the calculation parameter matrix is:

[0137] U′=α1U

[0138] q ve，k =α2q ve

[0139] f ve，t，k =α3f ve，t

[0140] F sh，ob，s =α4F sh，ob

[0141] ε′=α5ε

[0142] C m ′=α6C m

[0143] …

[0144]

[0145] Where: U is the heat transfer coefficient of the building envelope, q ve is the airflow velocity, f ve，t is the fraction of the airflow's operating time, F sh，ob is the shading coefficient of external obstacles in the effective solar energy collection area of ​​the surface, ε is the emissivity of thermal radiation of the external surface, C m is the internal heat capacity of a building or building area, U′ is the heat transfer coefficient of the building envelope of a certain material, and q ve，k is the airflow rate of airflow element k, f ve，t，k is the running time fraction of airflow element k, F sh，ob，s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s, ε′ is the emissivity of thermal radiation on the outer surface of a certain material, C m ′ is the internal heat capacity of a building or building area of ​​a certain material, I iis the calculation parameter matrix corresponding to the digital twin model of different types of rooms, α i is the mapping coefficient between the digital twin model and different calculation parameters.

[0146] As a possible implementation of this embodiment, the basic model layer is a physical and mechanical model formed by the interaction between the physical and mechanical layer and the data information layer, that is:

[0147] S i (j) = {s1, s2, s3, s4...s i}

[0148] Where S i (j) represents the feature coding of the physical and mechanical layer and part of the data information layer of the digital twin model of different room types. It is the matching condition and constraint condition of the digital twin model information and is used to match the building room model in the BIM file uploaded by the user with the digital twin model. i∈N is the number of feature information codes; s i To determine the information parameters of the digital twin model of the room, including measurement parameters, related material parameters, geometric size parameters, object property parameters, etc.

[0149] As a possible implementation of this embodiment, the digital twin model information matching condition is:

[0150] S(j)=S i (j)

[0151] The user uploads the basic information of the BIM file room, that is, the code S(j) of the physical and mechanical layer and the data information layer of the BIM model and the code S(j) of the digital twin model of a certain type of room i (j) match.

[0152] As a possible implementation of this embodiment, the basic data of the physical mechanics layer and the parameter information of the data information layer can be improved with the help of BIM technology.

[0153] like Figure 2 As shown, an embodiment of the present invention provides a method for predicting building energy consumption and carbon emissions based on BIM technology, comprising the following steps:

[0154] The user uploads the BIM file of the target building, and the model obtains the basic information of the building room. It then matches and constrains the points and dimensions of the digital twin model as the physical mechanics layer and data information layer, achieving real-time mapping between the physical object and the user-side BIM model.

[0155] Use building energy consumption and carbon emission prediction models to predict building energy consumption and carbon emissions;

[0156] The method of using the building energy consumption and carbon emission prediction model to predict building energy consumption and carbon emissions includes the following steps:

[0157] The user uploads the BIM file that needs to be predicted, and the model completes the matching between the BIM model and the room digital twin model;

[0158] Calculate the building envelope heat transfer and infiltration heat transfer data as well as the heat gain of equipment and personnel and solar heat gain;

[0159] Calculate building heating and cooling dynamics, energy requirements, and total electricity consumption;

[0160] Calculate the carbon emissions of buildings.

[0161] As a possible implementation of this embodiment, the calculation of the building envelope heat transfer and infiltration heat transfer data as well as the heat gain of equipment and personnel and solar heat includes:

[0162] Calculate heat transfer of building envelope:

[0163]

[0164] Where A i is the area of ​​building envelope element i; U i is the heat transfer coefficient of building envelope element i; A Z is the area of ​​the total enclosure structure, i.e. A i The sum of d is the area of ​​the floor; U d is the heat transfer coefficient of the floor; T int，set is the set temperature of the zone; T e is the monthly average outdoor temperature; t is the duration of the calculation step;

[0165] Calculate the penetration heat transfer:

[0166]

[0167] Where k represents each relevant airflow element, including mechanical ventilation, natural ventilation, and infiltration; q ve，k is the airflow rate of airflow element k; f ve，t，k is the running time fraction of airflow element k, T int，set is the set temperature of the zone; T e The monthly average outside temperature; t is the duration of the calculation step;

[0168] Calculation equipment personnel heat gain:

[0169]

[0170] Where, f iis the hourly usage rate of electrical equipment, the hourly presence rate of room personnel, and the hourly usage rate of lighting; P is the power of equipment per unit area, lighting power, and heat generated by personnel; A c is the total area of ​​the room; t is the duration of the calculation step;

[0171] Calculate solar heat gain:

[0172]

[0173] Where, F sh，ob，s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s; A sol，s is the effective collection area; I sol，s is the solar radiation intensity; F r，k is the shape factor value of the radiation between the element and the sky. For horizontal roofs without sunshades, F r =1, vertical wall uses F r =0.5, ΔT er is the average difference between the outside air temperature and the sky temperature, which should be taken as 9 K in the subpolar regions, 13 K in the tropical regions, and 11 K in the central regions, t is the duration of the calculation step, and ε is the emissivity of the external surface thermal radiation.

[0174] As a possible implementation of this embodiment, the calculation of dynamic parameters, energy demand, and total power consumption of building heating and cooling includes:

[0175] Calculate heating dynamic parameters:

[0176]

[0177] Q H，ht =Q tr +Q ve

[0178] Q H，gn =Q int +Q sol

[0179] Where, χ H is the dimensionless heat balance ratio in heating mode; Q H，ht is the total heat transfer of the determined heating mode, the heat transfer of the building envelope Q tr and penetration heat transfer Q ve ;Q H，gn is the total heat gain in heating mode, and the heat gain of equipment and personnel is Q int and the sun's heat sol ;

[0180] If χ H >0 and χ H ≠1, then:

[0181]

[0182] If χ H =1, then:

[0183]

[0184] If χ H <0, then:

[0185]

[0186] Where η H，gn refers to the heating gain utilization efficiency; τ is the time constant of the building area;

[0187]

[0188] Where H tr，adj =∑ i A i ·U i +0.1A z +A d ·U d ;H ve，adj =1200(∑ k f ve，t，k ·q ve，k );C m is the internal heat capacity of a building or building area; where A f is the building area;

[0189] Calculate cooling dynamic parameters:

[0190]

[0191] Where, χ C is the dimensionless heat balance ratio in cooling mode; Q C，ht is the total heat transfer in cooling mode; Q C，gn is the total heat gain in cooling mode;

[0192] If χ C >0 and χ C ≠1, then:

[0193]

[0194] If χ C =1, then:

[0195]

[0196] If χ C <0, then:

[0197] ηC，ls = 1

[0198] where η C，ls is the cooling loss utilization factor, and τ is the time constant of the building area;

[0199] Calculate the energy demand for heating:

[0200] Q H = Q H，ht - η H，gn Q H，gn

[0201] where Q H is the monthly / annual cumulative heat consumption; Q H，ht is the total heat transfer in heating mode; Q H，gn is the total heat gain in heating mode; η H，gn is the dimensionless heating gain utilization factor;

[0202] Calculate the energy demand for cooling:

[0203] Q C = Q C，cont = Q C，gn - η C，is Q C，ht

[0204] where Q C is the monthly / annual cumulative cooling consumption; Q C，ht is the total heat transfer in cooling mode; Q C，gn is the total heat gain in cooling mode; η C，is is the dimensionless utilization factor of heat loss;

[0205] Calculate the total annual heating and cooling power consumption:

[0206] E = E H + E C

[0207] where E is the monthly / annual heating and cooling power consumption; E C is the monthly / annual cooling power consumption; E H is the monthly / annual heating power consumption;

[0208] Total cooling power consumption:

[0209]

[0210] where COP is the comprehensive performance coefficient of the public building cooling system;

[0211] Annual heating power consumption in severe cold and cold regions:

[0212]

[0213] Where η1 is the comprehensive efficiency of the heating system with the heat source being a coal-fired boiler; q1 is the calorific value of standard coal; q2 is the comprehensive coal consumption for power generation;

[0214] Annual heating power consumption of public buildings in hot summer and warm winter zone A, hot summer and cold winter, hot summer and warm winter, and mild areas:

[0215]

[0216] Where, η2 is the comprehensive efficiency of the heating system with the heat source being a gas boiler; q3 is the standard calorific value of natural gas; is the conversion coefficient of natural gas to standard coal;

[0217] Annual heating power consumption of residential buildings in hot summer and warm winter zone A, hot summer and cold winter and mild areas:

[0218]

[0219] Where, COP H is the comprehensive performance coefficient of the heating system.

[0220] As a possible implementation of this embodiment, calculating the carbon emissions of a building includes:

[0221] Carbon emissions per unit area of ​​buildings during operation (C M ) is calculated as follows:

[0222]

[0223]

[0224] Where C M E is the carbon emission per unit building area during the operation phase; I is the annual energy consumption of the building, I represents different types of energy; EF I is the carbon emission factor of energy; E I,J is the energy consumption of Category I of Category J system; ER I,J The amount of Class I energy consumed by the renewable energy system for the Class J system; C p is the annual carbon reduction of the building green space carbon sequestration system; y is the building design life; A is the building area.

[0225] The following combination Figure 3 and Figure 4 , the technology involved in the present invention is described in detail.

[0226] 1 Introduction to Digital Twin Model:

[0227] A digital twin building room refers to an information-physical system that uses building rooms as carriers by comprehensively applying technologies such as BIM, Internet of Things, GIS, artificial intelligence, big data, blockchain, intelligent control, system modeling and simulation, and engineering management. It mainly includes the physical mechanics layer, data information layer, model layer, and practical application layer to realize intelligent buildings. It can be understood as a digital twin model in digital form.

[0228] 2. Construction of the room digital twin model:

[0229] A room digital twin model should include a physical mechanics layer, a data and information layer, a model layer, and a practical application layer. To achieve the mapping between BIM buildings and digital twin models, physical objects must first be established. The building physical mechanics layer serves as the foundational data for the room digital twin model. Using reality capture and photography technologies, human intervention captures physical information such as building structure, building materials, building equipment, and the building environment, thereby constructing a physical building atlas. The data and information layer is established by incorporating data and information through technologies such as data collection, data governance, data transmission, and data exchange. The room digital twin model primarily includes parameters such as building construction, construction processes, measurement parameters, relevant material parameters, and object properties. The physical mechanics and data layers interact to form the foundational model layer. A computational matrix is ​​used to establish a correspondence between different room digital twin models and key calculation parameters for building energy consumption and carbon emissions. With the help of other computer software, automatic analysis and updates can be performed, enabling functions such as building description, building diagnosis, multi-faceted prediction, and operation and maintenance decision-making. Ultimately, a complete building room digital twin model is established. This can be used for building prediction and calculation, construction planning, and smart buildings. Among them, BIM can provide basic data of the building physical and mechanical layer and parameter information of the data information layer for the construction of the digital twin model, making the digital twin model more standardized.

[0230] Model prediction and application: After the BIM file is uploaded on the user side, the system terminal obtains the basic information of the building room, and uses it as the physical mechanics layer and data information layer to match and constrain the points and dimensions with the digital twin model, completing the real-time mapping of the physical object and the user-side BIM model. Preliminary multiplication calculations are performed with the help of the basic analyzable physical mechanics model. With the input and update of data in the data information layer, the matching digital twin model can realize the intelligent description, diagnosis and prediction of the model. In the present invention, the energy consumption and carbon emissions of the building can be further predicted and calculated, and the operation and maintenance decisions of the building room construction can be proposed to provide assistance for subsequent design. The software terminal of the present invention can realize the process of automatic analysis, upgrading and updating with the help of other computer software and a new generation of artificial intelligence algorithms, store, process, analyze and calculate data and attributes, obtain the calculation parameter matrix corresponding to the digital twin model of different types of rooms, and update the established digital twin model.

[0231] 2.1 Establishment of the digital twin model data information layer:

[0232] The data information layer of digital twin models of different types of rooms is established. This step is to establish the correspondence between the room digital twin model and meteorological data, as well as the correspondence between the room digital twin model and the calculation parameters, such as the heat transfer coefficient of the building envelope structure, airflow rate, airflow operation time fraction, hourly usage rate of electrical equipment, hourly occupancy rate of room occupants, equipment power per unit area, lighting power and occupant heat generation, shading coefficient of external obstacles in the effective solar energy collection area, emissivity of external surface thermal radiation, internal heat capacity of the building and other parameters and the numerical correspondence between the physical and mechanical layers of the digital twin of different rooms. With the help of computer software, automatic analysis is realized, and data and attributes are stored, processed, analyzed and calculated to obtain the calculation parameter matrix corresponding to the digital twin model of different types of rooms, and then determine the mapping relationship between the digital twin model of different rooms and the calculation parameters for the calculation process in the following steps. The calculation parameters are determined by preliminary multiplication calculations of the physical mechanics model. As the data information resource layer and the big database are updated, with the help of computer software tools and a new generation of artificial intelligence algorithms, dynamic interaction between the digital twin model and the calculation parameters is realized. The following formula is the data information calculation parameter matrix of the digital twin model of a certain type of room, which is used to determine the real-time parameters of this type of room.

[0233] U′=α1U

[0234] q ve,k =α2q ve

[0235] f ve,t,k =α3f ve,t

[0236] F sh,ob,s =α4F sh,ob

[0237] ε′=α5ε

[0238] C m ′=α6C m

[0239] …

[0240]

[0241] Where: U is the heat transfer coefficient of the building envelope, q ve is the airflow velocity, f ve,t is the fraction of the airflow's operating time, F sh,ob is the shading coefficient of external obstacles in the effective solar energy collection area of ​​the surface, ε is the emissivity of thermal radiation of the external surface, C m is the internal heat capacity of a building or building area, U' is the heat transfer coefficient of the building envelope of a certain material, and q ve,kis the airflow rate of airflow element k, f ve,t,k is the running time fraction of airflow element k, F sh,ob,s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s, ε' is the emissivity of thermal radiation on the outer surface of a certain material, C m ' is the internal heat capacity of a building or building area of ​​a certain material, I i is the calculation parameter matrix corresponding to the digital twin model of different types of rooms, α i is the mapping coefficient between the digital twin model and different calculation parameters.

[0242] 2.2 Digital Twin Model Layer Construction:

[0243] The data information resource layer of the building room is the basic data of the digital twin model of the room. Reality capture and photography technology are used to build a digital twin physical and mechanical model, and to construct the physical and mechanical layer of building structure, building materials, building equipment, building environment and other entity information; the data information layer includes measurement parameters, related material parameters, geometric dimension parameters, object property parameters and other parameters. The physical and mechanical layer interacts with part of the data information layer to form a basic and analyzable physical and mechanical model.

[0244] S i (j) = {s1, s2, s3, s4 ... s i}

[0245] S i (j) represents the feature coding of the physical and mechanical layer and part of the data information layer of the digital twin model of different room types. It is the matching condition and constraint condition of the digital twin model information and is used to match the building room model in the BIM file uploaded by the user with the digital twin model. i∈N is the number of feature information codes; s i To determine the information parameters of the digital twin model of the room, including measurement parameters, related material parameters, geometric size parameters, object property parameters, etc.

[0246] 2.3 Digital Twin Model and User Interaction:

[0247] The room digital twin model interacts with the user, the user uploads the BIM file, and the system terminal obtains the basic room parameters of the target building room, such as measurement data, auxiliary data, geometric data, object properties, and object properties. This part of information parameters is matched with different types of room digital twin models as the physical mechanics layer and part of the data information layer, realizing the interaction between the system terminal and the user, establishing the corresponding relationship between the target room and the room digital twin model, which can be used to improve the physical mechanics layer and the data information layer through BIM technology when the model is established, and the user's prediction application after the model is established. When the user inputs the prediction calculation instruction, the system terminal further matches the obtained information to the digital twin model, retrieves the calculation parameters under the building room type, and completes the prediction of building energy consumption and carbon emissions by means of system algorithm. The matching condition is:

[0248] S(j) = S i (j)

[0249] The user uploads the BIM file room basic information, that is, the encoding S(j) of the BIM model physical mechanics layer and data information layer matches the encoding S i (j) of a certain type of room digital twin model.

[0250] 3. Building energy consumption simplified prediction algorithm:

[0251] 3.1 Building envelope heat transfer:

[0252]

[0253] In the formula, A i is the area of building envelope element i, obtained from the BIM model; U i is the heat transfer coefficient of building envelope element i, retrieved from the digital twin model database; A Z is the total area of the envelope, that is, the sum of Ai; A d is the area of the floor, obtained from the BIM model; U d is the heat transfer coefficient of the floor, obtained from the digital twin model database; T int,set is the set temperature of the region, obtained from the BIM model; T e is the monthly average outdoor air temperature, obtained from the digital twin model database; t is the duration of the calculation step, selected according to Table 1, unit: Ms.

[0254] Table 1: Duration of calculation step

[0255]

[0256]

[0257] 3.2 Permeation heat transfer:

[0258]

[0259] Where k represents each relevant airflow element, including mechanical ventilation, natural ventilation, and infiltration; q ve,k is the airflow rate of airflow element k, obtained from the digital twin model database; f ve,t,k is the running time fraction of airflow element k, calculated as a fraction of hours per day, obtained from the digital twin model database; T inr,set is the set temperature of the zone; T e Monthly average outdoor temperature; t is the duration of the calculation step.

[0260] 3.3 Heat gain of equipment and personnel:

[0261]

[0262] Where, f i is the hourly usage rate of electrical equipment, the hourly occupancy rate of room personnel, and the hourly usage rate of lighting. The specific data can be obtained from the digital twin model database. P is the power of equipment per unit area, lighting power, and heat generated by personnel, which can be obtained from the digital twin model database. A c is the total area of ​​the room, obtained from the BIM model; t is the duration of the calculation step.

[0263] 3.4 Solar heat gain:

[0264]

[0265] Where, F sh,ob,s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s, obtained from the digital twin model database; A sol,s is the effective collection area, obtained from the BIM model; I sol,s is the solar radiation irradiance, that is, the average solar radiation energy per square meter of the collection area of ​​surface s at a given orientation and tilt angle within the calculation time step, obtained from the digital twin model database; F r,k is the shape factor value of the radiation between the element and the sky. For horizontal roofs without sunshades, F r =1, vertical wall uses F r =0.5, ΔT er is the average difference between the outside air temperature and the sky temperature, which should be taken as 9 K in the subpolar regions, 13 K in the tropical regions, and 11 K in the central regions. t is the duration of the calculation step. ε is the emissivity of the external surface thermal radiation, which is obtained from the digital twin model database.

[0266] 3.5 Dynamic parameters:

[0267] 3.5.1 Heating:

[0268]

[0269] Q H,ht = Q tr + Q ve

[0270] Q H,gn = Q int + Q sol

[0271] where χ H is the dimensionless heat balance ratio for the heating mode; Q H,ht is the total heat gain for the heating mode, consisting of the building envelope heat gain Q tr and the infiltration heat gain Q ve , obtained from steps 3.1 and 3.2; Q H,gn is the total heat gain for the heating mode, consisting of the equipment heat gain Q int and the solar heat gain Q sol , obtained from steps 3.3 and 3.4, respectively.

[0272] If χ H > 0 and χ H ≠ 1:

[0273]

[0274] If χ H = 1:

[0275]

[0276] If χ H < 0:

[0277]

[0278] where η H,gn is the heating gain utilization efficiency; τ is the time constant of the building zone, in hours, obtained from the following equation.

[0279]

[0280] where H tr,adj = ∑ i A i · U i + 0.1 A z + A d · U d , obtained from step 3.1; H ve,adj = 1200 (∑ k f ve,t,k · q ve,k ), obtained from step 3.2; Cm is the internal heat capacity of the building or building area, in J / K, obtained from the digital twin model database; where A f is the building area, obtained from the BIM model.

[0281] 3.5.2 Cooling (parameter acquisition method is the same as heating):

[0282]

[0283] Where, χ C is the dimensionless heat balance ratio in cooling mode; Q C,ht is the total heat transfer in cooling mode; Q C,gn is the total heat gain in cooling mode;

[0284] If χ C >0 and χ C ≠1:

[0285]

[0286] If χ C =1:

[0287]

[0288] If χ C <0:

[0289] η C,ls =1

[0290] Where η C,ls is the cooling loss utilization coefficient, τ is the time constant of the building area, h.

[0291] 3.6 Energy demand for cooling and heating:

[0292] 3.6.1 Heating:

[0293] Q H =Q H,ht -η H,gn Q H,gn

[0294] Where Q H The monthly / annual cumulative heat consumption, kWh; Q H,ht is the total heat transfer in heating mode, in MJ; Q H,gn is the total heat gain in heating mode, in MJ; η H,gn is the dimensionless heating gain utilization coefficient; the data are obtained from step 3.5.1. 3.6.2 Cooling:

[0295] Q C =Q C,gn -ηC,is Q C,ht

[0296] Where Q C is the monthly / annual cumulative cooling consumption, kWh; Q C,ht is the total heat transfer in cooling mode, MJ; Q C,gn is the total heat gain in cooling mode, MJ; η C,is is the dimensionless utilization coefficient of heat loss; the data are obtained from step 3.5.2.

[0297] 3.7 Total power consumption for cooling and heating:

[0298] Total annual heating and cooling electricity consumption:

[0299] E=E H +E C

[0300] Where, E is the monthly / annual heating and cooling power consumption, kWh; E C is the monthly / annual cooling power consumption, kWh; E H The monthly / annual heating electricity consumption, kWh.

[0301] 3.7.1 Total cooling power consumption:

[0302]

[0303] Where COP is the comprehensive performance coefficient of the public building cooling system, which is obtained from the digital twin model database.

[0304] 3.7.2 Total power consumption for heating:

[0305] ① Annual heating power consumption in severe cold areas and cold areas:

[0306]

[0307] Where η1 is the comprehensive efficiency of the heating system with the heat source being a coal-fired boiler, which is taken as 0.81; q1 is the calorific value of standard coal, which is taken as 8.14 kWh / kgce; and q2 is the comprehensive coal consumption for power generation, which is taken as 0.330 kgce / kWh.

[0308] ② Annual heating power consumption of public buildings in hot summer and warm winter zone A, hot summer and cold winter, hot summer and warm winter, and mild zones:

[0309]

[0310] Where η2 is the comprehensive efficiency of the heating system with the heat source being a gas boiler, which is taken as 0.85; q3 is the standard calorific value of natural gas, which is taken as 9.87 kWh / m 3 ; 1.21 kgce / m 3 .

[0311] ③ The annual heating electricity consumption of residential buildings in hot summer and warm winter A zone, hot summer and cold winter zone, and moderate zone:

[0312]

[0313] COP H is the comprehensive performance coefficient of the heating system, which is obtained from the digital twin mode database.

[0314] 3.8 Carbon emissions:

[0315] The calculation of carbon emissions of buildings in the operation stage is determined according to the consumption of various types of energy and the carbon emission factor of the energy. The carbon emissions per unit area of buildings in the operation stage (C M ) are calculated according to the following formula:

[0316]

[0317]

[0318] C M is the carbon emissions per unit area of buildings in the operation stage, kgCO2 / m 2 ; E I is the annual energy consumption of the building, I represents different types of energy, and is calculated, unit / a; EF I is the carbon emission factor of the energy, i represents different types of energy, and is selected according to the location according to Table 2;

[0319] Table 2: Average carbon emission factor of regional power grid:

[0320]

[0321]

[0322] E I,J is the Ith type of energy consumption of the Jth type of system, kWh / a; ER I,J is the amount of Ith type of energy consumed by the Jth type of system provided by the renewable energy system, kWh / a; obtained from step 3.7, C p is the annual carbon reduction amount of the building green land carbon sink system, kgCO2 / a; y is the design life of the building, unit: year; A is the building area, which is read from the BIM model.

[0323] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A building energy consumption and carbon emission prediction model based on BIM technology, characterized by: It includes the physical mechanics layer, data information layer, model layer and practical application layer, and uses BIM and digital twin technology to achieve automated intelligent prediction of building energy consumption and carbon emissions; The physical mechanics layer is the basic data of the building energy consumption and carbon emission prediction model. It uses reality capture and photography technology to obtain physical information of building structure, building materials, building equipment and building environment to construct a physical building map. The data information layer includes measurement parameters, relevant material parameters, geometric size parameters and object property parameters; The model layer is a physical and mechanical model formed by the interaction between the physical and mechanical layer and the data information layer: Among them, S i (j) represents the feature coding of the physical and mechanical layer and part of the data information layer of the digital twin model of different room types, which is the matching condition and constraint condition of the digital twin model information. It is used to match the building room model in the BIM file uploaded by the user with the digital twin model. i∈N, N is the total number of feature information codes; s i To determine the information parameters of the room digital twin model, including measurement parameters, relevant material parameters, geometric size parameters and object property parameters; The practical application layer is used to predict building energy consumption and carbon emissions; The method for establishing the building energy consumption and carbon emission prediction model comprises the following steps: Constructing a digital twin model of the room, the digital twin model comprising a physical mechanics layer, a data information layer, a model layer, and a practical application layer; the parameters of the digital twin model of the room include architectural construction, construction technology, measurement parameters, relevant material parameters, and object properties; Establish physical objects, obtain information on building structure, building materials, building equipment and building environment, construct physical building maps, and form a physical mechanics layer; Establish a data information layer and add data information through data collection, data governance, data transmission and data interaction technologies; Interact the physical mechanics layer and the data layer to form a basic model layer; Obtain basic building room information from BIM files, perform real-time mapping between physical objects and BIM models, and improve the construction of the physical mechanics layer and the data information layer; By calculating the matrix, the corresponding relationship between the digital twin models of different rooms and the main calculation parameters of building energy consumption and carbon emissions is constructed to establish a building energy consumption and carbon emission prediction model; The establishing of the data information layer includes: Establishing a correspondence between the physical and mechanical layers and calculation parameters, including the heat transfer coefficient of the building envelope, airflow rate, airflow operation time fraction, hourly usage rate of electrical equipment, hourly occupancy rate of people in the room, equipment power per unit area, lighting power and occupant heat generation, shading coefficient of external obstacles in the effective solar energy collection area, emissivity of external surface thermal radiation, and internal heat capacity of the building; Store, process, analyze, and calculate data and attributes to obtain the calculation parameter matrix corresponding to the digital twin models of different types of rooms, and then determine the mapping relationship between the digital twin models of different rooms and the calculation parameters; The calculation formula of the calculation parameter matrix is: … Where: U is the heat transfer coefficient of the building envelope, q ve is the airflow velocity, f ve,t is the fraction of the airflow's operating time, F sh,ob is the shading coefficient of external obstacles in the effective solar energy collection area of ​​the surface, ε is the emissivity of thermal radiation of the external surface, C m is the internal heat capacity of a building or building area, U' is the heat transfer coefficient of the building envelope of a certain material, and q ve,k is the airflow rate of airflow element k, f ve,t,k is the running time fraction of airflow element k, F sh,ob,s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s, ε' is the emissivity of thermal radiation on the outer surface of a certain material, C m ' is the internal heat capacity of a building or building area of ​​a certain material, I i is the calculation parameter matrix corresponding to the digital twin model of different types of rooms, α i It is the mapping coefficient between the digital twin models of different rooms and their corresponding calculation parameters.

2. The building energy consumption and carbon emission prediction model based on BIM technology according to claim 1 is characterized in that: The basic model layer is a physical and mechanical model formed by the interaction between the physical and mechanical layer and the data information layer.

3. The building energy consumption and carbon emission prediction model based on BIM technology according to claim 2 is characterized in that: The digital twin model information matching conditions are: The user uploads the basic information of the BIM file room, that is, the code S(j) of the physical and mechanical layer and the data information layer of the BIM model and the code S(j) of the digital twin model of a certain type of room i (j) match.

4. A method for predicting building energy consumption and carbon emissions based on BIM technology, characterized in that: The following steps are involved: The user uploads the BIM file of the target building, and the model obtains the basic information of the building room. It then matches and constrains the points and dimensions of the digital twin model as the physical mechanics layer and data information layer, completing the real-time mapping between the physical object and the user-side BIM model. Using the building energy consumption and carbon emission prediction model according to any one of claims 1 to 3 to predict building energy consumption and carbon emissions; The method of predicting building energy consumption and carbon emissions includes the following steps: The user uploads the BIM file to be predicted, and the model matches the BIM model with the room digital twin model; calculates the building envelope heat transfer and infiltration heat transfer data, as well as the heat gain of equipment and personnel and solar heat gain; Calculate building heating and cooling dynamics, energy requirements, and total electricity consumption; Calculate the carbon emissions of buildings.

5. The method for predicting building energy consumption and carbon emissions based on BIM technology according to claim 4 is characterized in that: The calculation of building envelope heat transfer and infiltration heat transfer data as well as equipment and personnel heat gain and solar heat gain includes: Calculate heat transfer of building envelope: Where A i is the area of ​​building envelope element i; U i is the heat transfer coefficient of building envelope element i; A Z is the area of ​​the total enclosure structure, i.e. A i The sum of d is the area of ​​the floor; U d is the heat transfer coefficient of the floor; is the set temperature of the zone; is the monthly average outdoor temperature; t is the duration of the calculation step; Calculate the penetration heat transfer: Where k represents each relevant airflow element, including mechanical ventilation, natural ventilation, and infiltration; q ve,k is the airflow rate of airflow element k; f ve,t,k is the fraction of running time of airflow element k, is the set indoor temperature of the zone; The monthly average outdoor temperature; t is the duration of the calculation step; Calculation equipment personnel heat gain: Where, f i is the hourly usage rate of electrical equipment, the hourly presence rate of room personnel, and the hourly usage rate of lighting; P is the power of equipment per unit area, lighting power, and heat generated by personnel; A c is the total area of ​​the room; t is the duration of the calculation step; Calculate solar heat gain: Where, F sh,ob,s is the shading coefficient of external obstacles in the effective solar energy collection area of ​​surface s; A sol,k is the effective collection area; I sol,s is the solar radiation intensity; F r,k is the shape factor value of the radiation between the element and the sky. For horizontal roofs without sunshades, F r =1, vertical wall select F r =0.5, ΔT er is the average difference between the outdoor air temperature and the sky temperature, which should be taken as 9 K in the subpolar regions, 13 K in the tropical regions, and 11 K in the central regions. t is the duration of the calculation step, and ε is the emissivity of the thermal radiation from the external surface.

6. The method for predicting building energy consumption and carbon emissions based on BIM technology according to claim 4 is characterized in that: The calculation of dynamic parameters, energy demand and total electricity consumption of building heating and cooling includes: Calculate heating dynamic parameters: Where, is the dimensionless heat balance ratio in heating mode; Q H,ht is the total heat transfer of the determined heating mode, the heat transfer of the building envelope Q tr and penetration heat transfer Q ve ;Q H,gn is the total heat gain in heating mode, and the heat gain of equipment and personnel is Q int and the sun's heat sol ; Calculate cooling dynamic parameters: Where, χ C is the dimensionless heat balance ratio in cooling mode; Q C,ht is the total heat transfer in cooling mode; Q C,gn is the total heat gain in cooling mode; Calculate energy requirements for heating: Where Q H The cumulative heat consumption per month / year; Q H,ht is the total heat transfer in heating mode; Q H,gn is the total heat gain in heating mode; is the dimensionless heating gain utilization coefficient; Calculate cooling energy requirements: Where Q C Q is the monthly / annual cumulative cooling consumption; C,ht is the total heat transfer in cooling mode; Q C,gn is the total heat gain in cooling mode; is the dimensionless utilization coefficient of heat loss; Calculate the total annual heating and cooling electricity consumption: Where, E is the monthly / annual heating and cooling power consumption; E C E is the monthly / annual cooling power consumption; H Monthly / annual heating electricity consumption.

7. The method for predicting building energy consumption and carbon emissions based on BIM technology according to claim 4 is characterized in that: The calculation of building carbon emissions includes: Carbon emissions per unit area of ​​the building during the operation phase C M Calculate as follows: Where C M E is the carbon emission per unit building area during the operation phase; I is the annual energy consumption of the building, I represents different types of energy; EF I is the carbon emission factor of energy; E I,J is the energy consumption of Category I of Category J system; ER I,J The amount of Class I energy consumed by the renewable energy system for the Class J system; C p is the annual carbon reduction of the building green space carbon sequestration system; y is the building design life; A is the building area.

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