Green building energy consumption whole-process analysis optimization method and system based on BIM

Abstract

The invention discloses a BIM-based green building energy consumption whole-process analysis optimization method and system, and belongs to the field of smart architectures.The method comprises the steps that BIM modeling is conducted on a target building, and a simulation model is output; according to the simulation model, building energy consumption is collected; collecting environmental factors of the target building, and outputting the environmental factors; analyzing the environmental factors to determine a first factor set; performing energy consumption loss identification on the building energy consumption, and outputting N pieces of energy consumption loss data; according to the first factor set, the adjusting parameters of the first factor set and the N pieces of energy consumption loss data, an objective function is built; and according to the objective function, outputting an optimal control parameter set for performing energy consumption adjustment on each process. According to the method and the device, the technical problem of inaccurate energy consumption adjustment caused by incomplete and inaccurate building energy consumption analysis in the prior art is solved, and the technical effect of improving the comprehensiveness and accuracy of building energy consumption analysis so as to accurately adjust the building energy consumption is achieved.

Description

Technical Field

[0001] The present invention relates to the field of smart buildings, and in particular to a BIM-based green building energy consumption full-process analysis and optimization method and system. Background Art

[0002] With the development of the construction industry, the management and regulation of building energy consumption has become one of the important means of building energy conservation. However, in the existing technology, BIM is mainly used in the design and construction stages, and lacks the management of the entire process of the building operation stage. As a result, the energy consumption of the building throughout its life cycle cannot be accurately analyzed and effectively managed. Summary of the invention

[0003] This application provides a BIM-based green building energy consumption full-process analysis and optimization method and system, aiming to solve the technical problem of incomplete and inaccurate building energy consumption analysis in the prior art, resulting in inaccurate energy consumption regulation.

[0004] In view of the above problems, the present application provides a BIM-based green building energy consumption full-process analysis and optimization method and system.

[0005] The first aspect disclosed in the present application provides a BIM-based green building energy consumption full-process analysis and optimization method, the method comprising: performing BIM modeling on the full process of a target building and outputting a full-process simulation model; collecting full-process building energy consumption according to the full-process simulation model, wherein the full-process building energy consumption includes N process nodes, each process node includes a set of energy consumption data, and N is a positive integer greater than or equal to 1; collecting environmental factors of the target building and outputting full-process environmental factors; analyzing the full-process environmental factors according to the full-process building energy consumption and determining a first factor set, wherein the first factor set includes N environmental factors, wherein the N environmental factors are environmental factors obtained by screening corresponding to N process nodes respectively; identifying energy consumption losses of the full-process building energy consumption and outputting N energy consumption loss data; building an objective function using the first factor set as a variable indicator, the adjustment parameters of the first factor set as the target variable, and the N energy consumption loss data as input data; and outputting an optimization control parameter set according to the objective function for adjusting energy consumption of each process.

[0006] Another aspect disclosed in the present application provides a green building energy consumption full-process analysis and optimization system based on BIM, the system comprising: a target building modeling module, used to output a full-process simulation model by performing BIM modeling on the full process of the target building; a building energy consumption collection module, used to collect the full-process building energy consumption according to the full-process simulation model, wherein the full-process building energy consumption includes N process nodes, each process node includes a set of energy consumption data, and N is a positive integer greater than or equal to 1; an environmental factor collection module, used to collect the environmental factors of the target building and output the full-process environmental factors; an environmental factor analysis module, used to collect the full-process building energy consumption according to the full-process simulation model The whole process environmental factors are analyzed to determine the first factor set, wherein the first factor set includes N environmental factors, wherein the N environmental factors are the environmental factors obtained by screening corresponding to the N process nodes respectively; an energy consumption loss identification module is used to identify the energy consumption loss of the whole process building energy consumption and output N energy consumption loss data; an objective function building module is used to build an objective function with the first factor set as the variable indicator, the adjustment parameters of the first factor set as the target variable, and the N energy consumption loss data as the input data; a process energy consumption adjustment module is used to output the optimization control parameter set according to the objective function, which is used to adjust the energy consumption of each process.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: By collecting the full-process data of the target building, detailed modeling of each process node is achieved, and the corresponding relationship between each process node and the environmental factor is determined; on this basis, the energy consumption of the entire building process is analyzed, energy consumption losses are identified, and an objective function between energy consumption losses and environmental factors is established; then, by optimizing the objective function, a set of optimization control parameters are output for accurately adjusting the energy consumption of each process node, thereby realizing a technical solution for green energy management of the entire building process, solving the technical problem of incomplete and inaccurate building energy consumption analysis in the existing technology, which leads to inaccurate energy consumption regulation, and achieving the technical effect of improving the comprehensiveness and accuracy of building energy consumption analysis, thereby accurately adjusting building energy consumption.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A possible flow chart of a BIM-based green building energy consumption full-process analysis and optimization method is provided for the embodiment of the present application; Figure 2A possible flow chart of outputting an optimized control parameter set in a BIM-based green building energy consumption full-process analysis and optimization method is provided for an embodiment of the present application; Figure 3 A possible flow diagram of generating a first factor set in a BIM-based green building energy consumption full-process analysis and optimization method is provided for an embodiment of the present application; Figure 4 A possible structural schematic diagram of a BIM-based green building energy consumption full-process analysis and optimization system is provided for an embodiment of the present application.

[0010] Explanation of the accompanying drawings: target building modeling module 11, building energy consumption collection module 12, environmental factor collection module 13, environmental factor analysis module 14, energy consumption loss identification module 15, objective function building module 16, process energy consumption adjustment module 17. DETAILED DESCRIPTION

[0011] The overall idea of ​​the technical solution provided by this application is as follows: The embodiment of the present application provides a BIM-based green building energy consumption full-process analysis and optimization method and system, which adopts a BIM-based method to achieve accurate green energy management throughout the entire life cycle of the building through data modeling and analysis of the entire building process.

[0012] First, the full process data of the target building is obtained through BIM modeling, including detailed information of each process node and environmental factors, and the corresponding relationship between each process node and the environmental factors is determined. On this basis, a comprehensive and accurate analysis of the energy consumption of the entire process of the building is carried out to identify the main energy consumption losses in each process node. Afterwards, with environmental factors as variables and energy consumption losses as targets, an objective function between energy consumption losses and environmental factors is established. Through this objective function, the optimization control parameters are accurately output. Finally, the output optimization control parameters are used to accurately adjust the energy consumption of each process node to achieve accurate management and savings of energy throughout the life cycle of the building.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0014] Embodiment 1 like Figure 1 As shown, the embodiment of the present application provides a full-process analysis and optimization method for green building energy consumption based on BIM, the method comprising: Step S100: Outputting a full-process simulation model by performing BIM modeling on the full process of the target building; Specifically, the BIM building information modeling technology is used to model the entire process of the target building and output a full-process simulation model. The full process of the target building refers to the entire life cycle of the building, including planning, design, construction and operation and maintenance stages.

[0015] First, collect the design data of the whole process of the target building, including planning and design data, construction design data and operation and maintenance data; then, based on the collected design data, use BIM software such as Revit to build a three-dimensional model of the target building, including building structure model, MEP system model and equipment management model; then, integrate the building plan, facade design, etc. in the planning and design stage, the structural design, electrical design, HVAC design information in the construction stage, and various monitoring and detection data in the operation and maintenance stage in the three-dimensional model; then, based on the three-dimensional model, use the simulation function of BIM software to establish a simulation model of the whole process of the target building; set the input parameters of each process stage in the simulation model, such as building structure parameters, equipment operation parameters, etc., to realize the simulation model and conduct full-process simulation; among them, the simulation model outputs the full-process building energy consumption, energy consumption details of each process stage, and other related output information, which constitute the full-process simulation model. The full-process simulation model contains the design information, construction information, simulation information and output results of each stage of the target building's life cycle, providing a basic model and data support for subsequent analysis and optimization.

[0016] Step S200: Collecting the full-process building energy consumption according to the full-process simulation model, wherein the full-process building energy consumption includes N process nodes, each process node includes a set of energy consumption data, and N is a positive integer greater than or equal to 1; Specifically, according to the established full-process simulation model, the building energy consumption of the target building in the whole process is collected, where the full-process building energy consumption includes one or more process nodes, and each process node corresponds to a set of energy consumption data. Among them, the process node refers to each stage in the whole life cycle of the building, such as the planning and design stage, the construction stage, and the operation and maintenance stage. Each large stage contains several small stages, and each process node corresponds to a set of energy consumption data, including energy consumption per unit area, energy consumption per unit time, or comprehensive energy consumption index.

[0017] First, confirm the number of process nodes N included in the full-process simulation model and the process stage represented by each node, where N is an integer of 1 or greater; then, set input parameters for each process node, including building structure type, shell material, equipment operation mode, etc. These parameters correspond to different settings such as planning and design stage, construction stage and operation and maintenance stage; then, based on the full-process simulation model and the input parameters of each process node, run the simulation to obtain the output energy consumption of each process node, such as design energy consumption index, construction standard coal and daily energy consumption; among them, the output energy consumption of each process node constitutes a set of energy consumption data, and N sets of energy consumption data constitute the full-process building energy consumption.

[0018] By setting the input parameters of each process node and running the full-process simulation model for simulation, the output energy consumption of each process node is obtained, and these output energy consumptions are combined to obtain the full-process building energy consumption, providing basic data support for achieving green energy management throughout the entire life cycle of the building.

[0019] Step S300: collecting environmental factors of the target building and outputting full-process environmental factors; Specifically, environmental factors refer to environmental parameters that affect building energy consumption, including climate environmental factors and location environmental factors, among which climate environmental factors include temperature, humidity, sunshine, etc., and location environmental factors include enclosure structure type, soil type, etc.

[0020] The full-process environmental factor refers to the parameter set of environmental factors corresponding to each process stage of the target building's entire life cycle. First, according to the climate characteristics and location conditions of the target building's area, select environmental factors that affect the building's energy consumption for collection, such as average temperature, relative humidity, sunshine hours, thermal conductivity of the enclosure structure, etc. Then, according to the process nodes set in the full-process simulation model, the collected environmental factor parameters are respectively mapped to each process node to form the full-process environmental factor. For example, if the process nodes of the full-process simulation model include the planning and design stage, the construction stage, and the operation and maintenance stage. Then first select the environmental factors that affect the building's energy consumption in these three stages, such as the average temperature and sunshine hours of the climate factor corresponding to the planning and design stage, the shell insulation performance of the location factor corresponding to the construction stage, and the daily temperature and humidity of the climate factor corresponding to the operation and maintenance stage. The data of the parameters of these environmental factors constitute the full-process environmental factors.

[0021] By collecting environmental factors that affect the building energy consumption of the target building throughout its life cycle and assigning them to each process node, an information set of environmental factors for the entire process is formed, providing data input of environmental factors for subsequent analysis and optimization steps.

[0022] Step S400: Analyze the full-process environmental factors according to the full-process building energy consumption to determine a first factor set, wherein the first factor set includes N environmental factors, wherein the N environmental factors are environmental factors obtained by screening the N process nodes respectively; Specifically, the full-process environmental factors are analyzed according to the full-process building energy consumption to determine the first factor set.

[0023] First, the information of the whole process building energy consumption and the whole process environmental factors needs to be compared and analyzed to evaluate the influence of each environmental factor on the building energy consumption of different process nodes. Specifically, the energy consumption data of each process node in the whole process building energy consumption, such as the design energy consumption index, construction standard coal and daily energy consumption, are obtained through the information of the whole process building energy consumption and the whole process environmental factors, as well as the environmental factor data corresponding to each process node in the whole process environmental factors, such as the average temperature, the thermal performance of the enclosure structure and the number of sunshine hours, etc.; based on these two sets of data, the environmental factor-energy consumption mapping data set of each process node is established, and each data set includes multiple sets of environmental factors and energy consumption data of a certain process node. Subsequently, Pearson correlation analysis is performed on each mapping data set to obtain the correlation coefficient between each environmental factor and the energy consumption data, and measure the linear correlation between them; according to the absolute value of the correlation coefficient, the influence of each environmental factor on the building energy consumption of the corresponding process node is evaluated. The larger the absolute value of the correlation coefficient, the greater the influence. Finally, comparison is made at each process node to determine which environmental factors have the greatest impact on the building energy consumption of the whole process of the building, and these environmental factors constitute the first factor set. For example, if there are three process nodes in the full-process simulation model, namely the design stage, the construction stage, and the operation and maintenance stage, the building energy consumption in the design stage is mainly affected by the average temperature, the building energy consumption in the construction stage is mainly affected by the thermal performance of the enclosure structure, and the building energy consumption in the operation and maintenance stage is mainly affected by the sunshine hours. It can be determined that the first factor set includes three environmental factors, the average temperature corresponds to the design stage, the thermal performance of the enclosure structure corresponds to the construction stage, and the sunshine hours correspond to the operation and maintenance stage. These three environmental factors are used as the first factor set to represent the environmental influencing factors of each process node.

[0024] By analyzing the relationship between the full-process building energy consumption and the full-process environmental factors, the environmental factors that have the greatest impact on the building energy consumption at each process node are screened out to form the first factor set, which provides environmental factor variables for the subsequent establishment of the objective function and output of the optimization control parameters. The composition of the first factor set is not unique, but is determined according to the specific conditions of the target building. Its purpose is to find the main environmental factors that represent the environmental impact of each process node and provide an analytical basis for green energy management throughout the life cycle of the building.

[0025] Step S500: Identify energy loss of the whole process building energy consumption and output N energy loss data; Specifically, energy loss refers to unnecessary or excess energy consumption during building operations, including losses caused by inefficient equipment operation, losses caused by personnel usage habits, and losses caused by environmental factors. By identifying energy loss throughout the entire building process, corresponding measures can be formulated for different types of losses, reducing unnecessary energy waste and improving the green and low-carbon level of building operations.

[0026] First, based on building operation data, expert experience and personal research, we define various types of energy loss that may occur in the entire building process, such as equipment efficiency loss, usage habit loss and environmental impact loss. Among them, green buildings usually have lower inherent energy consumption due to the use of higher-performance building materials and intelligent systems, but they are also more susceptible to changes in the surrounding environment, which increases the cooling or heating load and causes additional energy loss. Then, based on the defined types of energy loss, we analyze the building energy consumption and environmental factors of each process node, and identify the types of energy loss that are prone to or actually occur at different nodes. Subsequently, we quantify the identified types of energy loss, determine the energy consumption percentage or more accurate energy consumption data of different loss types at the corresponding process nodes, and these data constitute the energy loss data of each process node, and obtain N groups of energy loss data. The N groups of energy loss data represent the energy loss of N process nodes, such as the planning and design stage, the construction stage and the operation and maintenance stage, and comprehensively reflect the energy loss of the building throughout its life cycle. Finally, the energy loss types and data of each process node are organized into tables or charts and written into the full-process simulation model for storage, so as to facilitate the formulation of energy-saving measures for different loss types based on the energy loss identification results, and provide decision-making support for the low-carbon management of building operations.

[0027] By identifying the main energy consumption losses at each process node throughout the life cycle of a building, we can obtain data on energy consumption losses, provide a basis for formulating energy consumption loss control strategies and optimizing objective functions, and provide comprehensive data support for low-carbon management of the entire life cycle of buildings.

[0028] Step S600: using the first factor set as a variable indicator, using the adjustment parameter of the first factor set as a target variable, and using the N energy loss data as input data to construct an objective function; Specifically, the objective function is constructed with the first factor set as the variable indicator, the adjustment parameters of the first factor set as the target variable, and N energy loss data as the input data. Among them, the first factor set is a determined set of environmental factors, which contains environmental factors corresponding to each process node, such as average temperature, thermal performance of the enclosure structure, and sunshine hours, etc. The data of these environmental factors are used as variable indicators of the objective function to represent the environmental conditions when the building is in operation; the adjustment parameters of the first factor set refer to various parameters for controlling or adjusting environmental factors, such as the building's lighting and dimming devices, the thickness of the insulation layer of the exterior wall, and the operating parameters of the air-conditioning system, etc. These adjustment parameters can be used as the target variables of the objective function, and the best parameter values ​​are obtained through optimization to adjust the environmental factors to achieve energy saving; the N energy loss data are the energy loss data obtained for each process node, representing the energy waste of different process nodes, as the input data of the objective function.

[0029] Specifically, a multivariable nonlinear function is constructed as the objective function. In the function, environmental factor data is used as a variable, adjustment parameters are used as the target variable, and process node energy consumption loss data is used as input to establish a mapping relationship between environmental factors, adjustment parameters and building energy consumption loss. Then, the objective function is solved by genetic algorithms, etc., so as to obtain a set of ideal adjustment parameters to achieve energy consumption regulation and control of the entire process of the building.

[0030] By constructing an objective function that describes the multivariable composite relationship between environmental factors, adjustment measures and energy consumption losses throughout the entire life cycle of a building, the ideal adjustment parameters can be output by optimizing the objective function, and the environmental factors and energy systems of the entire building process can be controlled to achieve the purpose of reducing the building's energy consumption losses.

[0031] Step S700: According to the objective function, output an optimized control parameter set for adjusting energy consumption of each process.

[0032] Specifically, the set of optimized control parameters is a set of parameters obtained by solving the objective function, which can control and adjust the environmental factors or energy consumption systems of each process node in the entire life cycle of the building to achieve the purpose of reducing energy consumption loss and improving energy efficiency. In the process of solving the objective function, when it is detected that the building is at a certain process node, such as the planning and design stage, the real-time data of the environmental factors corresponding to the node, such as the average temperature, will be collected and brought into the objective function. At the same time, the energy consumption loss data of the node, such as the energy consumption index in the design stage, is collected and put into the objective function. Subsequently, the optimization algorithm solves the objective function for multiple iterations, and finally outputs a set of environmental factors and adjustment parameters, such as the optimal operating parameters of the building lighting system. This set of parameters constitutes the optimized control parameters of the current process node. According to this set of parameters, the environmental factors and energy systems of the process node can be adjusted to achieve the purpose of reducing energy consumption loss and saving energy.

[0033] When the building process advances to the next node, the data of the node will be resampled and input into the objective function for optimization and solution to obtain the optimized control parameters of the next node. In this way, each process node in the entire life cycle of the building will update and solve a set of optimal control parameters based on the node data. This set of parameters constitutes the set of optimized control parameters for the entire process of the building. Among them, by optimizing and solving the objective function according to the latest data update at each process node, it can be ensured that the optimal control parameters corresponding to the node are obtained to maximize the minimum energy consumption of each process node. In the entire building process, the main environmental factors and energy consumption types of each process node are different, and the optimal control parameters must be different.

[0034] By optimizing the objective function, the optimal control parameters for each stage of the building's life cycle are obtained, and these parameters are dynamically adjusted according to the real-time status of the building's operation to achieve the lowest carbon emissions and maximize energy-saving potential throughout the entire building process, thereby achieving optimal energy management and utilization at each stage of the building's life cycle.

[0035] Further, such as Figure 2 As shown, the embodiment of the present application also includes: Step S710: when it is detected that the target building is in a first process, based on the first factor set, a first environmental factor corresponding to the first process is determined, and first energy consumption loss data corresponding to the first process is acquired; Step S720: collecting real-time environmental parameters according to the first environmental factors; Step S730: Input the real-time environmental parameters and the first energy loss data into the objective function, output the first optimization control parameter, and so on. When it is detected that the target building is in the Nth process, output the optimization control parameter set, wherein the optimization control parameter set includes N optimization control parameters.

[0036] Specifically, when it is detected that the target building enters a certain process node, such as the first process (planning and design stage), the environmental factors corresponding to the first process, such as the average temperature, are determined according to the corresponding relationship between the environmental factors in the first factor set and the first process; at the same time, the energy consumption loss data of the first process, such as the energy consumption index in the design stage, is obtained. Among them, the first factor set is a determined environmental factor set, in which the first environmental factor is associated with the first process node to represent the environmental status of the node; the first energy consumption loss data is the energy consumption loss situation of the first node.

[0037] Then, according to the data type of the first environmental factor, the corresponding environmental parameters are collected at the beginning of the first process, such as collecting historical outdoor average temperature data. Subsequently, the collected real-time environmental parameters and the obtained first energy loss data are input into the objective function, the objective function is solved, and the first optimization control parameters are output, such as the optimal operation mode of the building lighting system.

[0038] If it is detected that the target building has entered the second process (such as the construction phase), steps S710 and S720 will be re-executed to collect environmental factor data and energy loss data related to the second process. After inputting the objective function, the second optimization control parameter will be output, such as the optimal thickness of the exterior wall insulation layer. The final optimization control parameter set will include the first optimization control parameter and the second optimization control parameter. Similarly, when it is detected that the target building has entered the Nth process node, the environmental factor and energy loss data corresponding to different nodes will be repeatedly obtained, the objective function will be optimized and solved, and the corresponding optimization control parameters will be output. These N groups of optimization control parameters ultimately constitute the optimization control parameter set, which corresponds to the optimal control schemes for N process nodes in the entire life cycle of the building. According to the real-time stage of the building operation, the corresponding parameter set is selected to activate, and the building system and equipment are precisely controlled to achieve the lowest carbon emissions and maximum energy saving of the process nodes.

[0039] Furthermore, the embodiment of the present application also includes: Step S510: establishing an invalid energy consumption label, identifying invalid energy consumption of the entire process building energy consumption according to the invalid energy consumption label, and determining invalid energy consumption; Step S520: outputting the ineffective energy consumption as energy consumption loss data to obtain N energy consumption loss data corresponding to the N process nodes.

[0040] Specifically, first, based on the output results of the building full-process simulation model and on-site detection data, various types of energy consumption losses that may occur during the operation of the building are analyzed, such as equipment efficiency loss, operating parameter errors, and environmental impact losses. Then, each type of energy consumption loss is defined as an invalid energy consumption label, representing the energy waste that is easy to occur in the operation of the building system. Among them, the invalid energy consumption label is represented by coding, such as EL for equipment loss, OP for operating parameter errors, and EI for environmental impact losses. Subsequently, the energy consumption data of each process node in the full-process building energy consumption is obtained, such as the energy consumption index in the design stage and the material consumption in the construction stage. Then, based on the obtained invalid energy consumption labels, the full-process building energy consumption is scanned and matched to find the energy consumption data corresponding to the invalid energy consumption labels. This part of the energy consumption is the invalid energy consumption. For example, if frequent refrigeration starts and stops during the building operation and maintenance process, it can be identified as invalid energy consumption caused by environmental impact losses; if the actual operating efficiency of a certain equipment is far lower than the theoretical index, it can be identified as equipment efficiency loss.

[0041] Then, according to the various types of invalid energy consumption identified by the invalid energy consumption labels, the corresponding process nodes of the entire building process are determined to obtain the invalid energy consumption types and data of each node; for example, equipment efficiency loss may correspond to the operation and maintenance stage, operating parameter errors may correspond to the design and construction stages, and environmental impact losses may correspond to the operation and maintenance stage. Then, the invalid energy consumption data of each process node are counted and calculated, and converted into standard energy consumption units. These invalid energy consumption data after calculation and conversion constitute the energy consumption loss data of the node. For example, in the construction stage, the excessive energy consumption caused by the inefficiency of mechanical equipment has an energy loss data of fuel consumption exceeding the standard; in the operation and maintenance stage, the air conditioner is repeatedly started due to frequent changes in ambient temperature, and its energy loss data is the kilowatt-hours consumed by the startup loss.

[0042] By identifying the types and data of ineffective energy consumption throughout the entire building process, discovering common energy waste phenomena during building operation, and strengthening the monitoring and control of key links and equipment, it is helpful to formulate targeted energy-saving measures and minimize the building's carbon emissions and energy consumption.

[0043] Further, such as Figure 3 As shown, the embodiment of the present application also includes: Step S410: establishing N sets of environment-energy consumption mapping data sets according to the full-process building energy consumption and the full-process environmental factors; Step S420: performing data correlation analysis based on the N sets of environment-energy consumption mapping data sets, outputting N correlation coefficient sets, and screening out a corresponding first correlation coefficient from each correlation coefficient set to obtain N first correlation coefficients; Step S430: Generate the first factor set using the N first correlation coefficients.

[0044] Specifically, first, select environmental factors and energy consumption data within a certain time range, such as quarterly data or monthly data for the most recent year; based on each set of selected data, establish an environmental factor-energy consumption data set for each process node, and each data set includes multiple sets of environmental factor data and corresponding energy consumption data.

[0045] Then, the environment-energy consumption mapping data set is imported, and the environmental factor data is selected as the independent variable X, and the energy consumption data is selected as the dependent variable Y; the Pearson correlation analysis function of the SPSS statistical software is used to perform a correlation test on the X and Y variables in the data set, and the correlation coefficient r of X and Y is output; the absolute value of the correlation coefficient reflects the strength of the linear correlation between X and Y, greater than 0.8 indicates a strong correlation, 0.5-0.8 indicates a moderate correlation, and less than 0.5 indicates a low or no correlation. Correlation analysis is performed on the mapping data set of each process node to obtain a set of N correlation coefficients. In each set of correlation coefficients, the correlation coefficient r corresponding to the environmental factor X with the strongest correlation with the energy consumption data Y is selected as the first correlation coefficient. These N first correlation coefficients correspond to the key environmental impact factors of the N process nodes and represent the dominant environmental factors of each node. For example, in the correlation coefficient set of the planning and design stage, if the correlation coefficient between the average temperature and the design energy consumption index is the highest, reaching 0.9, then the correlation coefficient of the average temperature of 0.9 is the first correlation coefficient of the node. Finally, N first correlation coefficients are obtained, which correspond to the dominant environmental impact factors of the N process nodes in the entire life cycle of the building.

[0046] By establishing an environment-energy consumption mapping model and correlation analysis, the impact of various environmental factors on the energy consumption of different nodes in the entire building process is evaluated, and the most critical environmental influencing factors are determined based on this, forming a first factor set, accurately and objectively screening out the dominant environmental influencing factors of building energy consumption, providing a basis for constructing accurate objective functions and output control parameters.

[0047] Furthermore, the embodiment of the present application also includes: Step S441: Obtaining the environmental demand characteristics of the target building; Step S442: generating a first constraint condition according to the environmental requirement characteristics, wherein the first constraint condition is used to constrain the first factor set output by screening, and output an optimized first factor set.

[0048] Specifically, the design data of the building is consulted to understand its functional positioning, energy-saving rating of use type and functional positioning; for example, the building is positioned as a high-star hotel, the use type is residential and commercial, and it meets the 3-star green building standard. Then, according to the environmental demand characteristics of the building, determine which environmental factors it relies on the most or is most sensitive to. For example, high-star hotels are most sensitive to the indoor temperature and humidity environmental quality, which determines that it relies the most on environmental factors related to temperature and humidity. Subsequently, the focus and dominant environmental factors of building environmental control are determined as the basis of the first constraint condition; for example, the indoor thermal environment quality control of high-star hotels is the focus of environmental control, and temperature and humidity are the dominant environmental factors. Next, a constraint criterion for the focus of building environmental control is established, stipulating the environmental impact types that must be included in the first factor set, such as the first constraint condition requiring that the first factor set must include environmental factors related to indoor temperature and humidity control. Finally, the environmental factors in the first factor set are screened according to the criterion, and if a certain environmental factor does not meet the constraint criterion, it is deleted from the set. For example, in a high-star hotel building, if the first factor set contains environmental factors such as light hours that are not related to the indoor thermal environment, it is deleted from the set according to the constraint criterion. After screening and optimization, the environmental factors recorded in the first factor set are the key influencing factors of building environment control, and the optimized first factor set is output.

[0049] By screening and optimizing the first factor set according to the environmental demand characteristics of the building, the key environmental influencing factors in the building operation process are obtained, providing key references for subsequent objective function construction and control optimization.

[0050] Furthermore, the embodiment of the present application also includes: Step S610: The expression of the objective function is as follows: ; in, is the objective function, and energy consumption is adjusted by minimizing the objective function. is the total number of process nodes; , is the first factor set, The real-time location of the target building is Environmental factors corresponding to each process node; is the real-time location of the target building Based on environmental factors Energy loss data; is the real-time location of the target building Based on environmental factors Preset energy loss data; It is the fusion loss data of the previous process node and the next process node.

[0051] Specifically, the expression of the objective function preferably constructed is as follows: ; in, As the objective function, the energy consumption regulation of the whole life cycle of the building is achieved by minimizing the objective function; The total number of process nodes in the entire life cycle of the building, such as the planning and design stage, the construction stage, and the operation and maintenance stage; For the The first factor set corresponding to the process nodes, is the environmental factor of the node, such as temperature or humidity; For the Based on environmental factors Calculated energy loss data; For the Environmental factors at each process node Preset energy loss data; It is the sum of the energy loss data of the previous node and the current node, representing the energy loss relationship between the two nodes. The objective function expression is expressed by the environmental factor data With preset data The difference between the upper and lower nodes and the energy loss relationship between the upper and lower nodes are used to establish the coupling correspondence between the nodes to achieve energy consumption regulation of the entire process of the building. By optimizing and solving this expression, a set of environmental factors and adjustment parameters can be output to achieve the lowest energy consumption of each node and the entire process.

[0052] Furthermore, the embodiment of the present application also includes: Step S621: Obtain historical environmental change samples of the area where the target building is located; Step S622: simulating and predicting the full-process simulation model according to the historical environmental change samples, and outputting full-process simulated energy consumption data, wherein the full-process simulated energy consumption data includes N simulated energy consumption data corresponding to the N process nodes; Step S623: Generate feedback adjustment parameters according to the full-process simulation energy consumption data and the full-process building energy consumption, and optimize the objective function with the feedback adjustment parameters.

[0053] Specifically, first, determine the time range for data collection, such as environmental change data in the last 10 years, to reflect the long-term dynamic change characteristics of the regional environment. Then, consult the historical meteorological data released by the regional meteorological station or environmental protection department to obtain data such as temperature, humidity, and sunshine hours within the target time range; at the same time, collect regional land use change data, vegetation cover change data, etc. Next, organize, analyze and process the collected environmental data to generate intuitive images and charts to intuitively reflect the dynamic change process of the regional environment. According to the dynamic change characteristics of environmental data, extract environmental elements of different time periods, such as temperature, humidity and sunshine hours in the last 10 years.

[0054] Then, according to the environmental factor data selected from the historical environmental change samples, the input parameters of the full-process simulation model are set, such as selecting the temperature and humidity data of the past 10 years as the input climate conditions of the model. After that, multiple sets of environmental inputs are input to the full-process simulation model. According to different environmental inputs, the simulation is run to obtain the simulated energy consumption output of each process node, such as the simulated energy consumption index in the design stage, the material loss in the construction stage, etc. The simulated energy consumption data under different environmental inputs of each process node can constitute the simulated energy consumption sample of the node, and the simulated energy consumption samples of N nodes constitute the simulated energy consumption data of the full process.

[0055] Next, the simulated energy consumption data of each process node is compared with the actual energy consumption data to analyze the deviation between the two; according to the actual situation of the deviation, a set of feedback adjustment parameters is generated to correct the environmental factor data or adjustment parameters in the objective function; if the simulated design energy consumption index is too high, the influence of the environmental factor in the objective function on the design energy consumption calculation can be reduced, or the temperature setting value in the design stage of the objective function can be reduced. Then, the feedback adjustment parameters are input into the objective function and optimized again until the simulated energy consumption data of each process node is close to the actual energy consumption data; the optimized objective function can more accurately simulate the actual energy consumption level of the entire process of the building, providing an important basis for outputting the optimal control parameters.

[0056] By predictively simulating the full-process simulation model based on the historical environmental changes in the area where the building is located, the simulated energy consumption data under the corresponding environmental conditions of each process node in the entire life cycle of the building are obtained. Then the simulation results are compared and analyzed with the measured full-process building energy consumption data, feedback adjustment parameters are generated, and input into the objective function for correction and optimization, which can improve the accuracy of the objective function in reflecting the actual operating characteristics of the building and provide an important reference for outputting the optimal control parameters.

[0057] In summary, the green building energy consumption full-process analysis and optimization method based on BIM provided in the embodiment of the present application has the following technical effects: Through BIM modeling of the entire process of the target building, a full-process simulation model is output to provide a basis for subsequent analysis and management; based on the full-process simulation model, the full-process building energy consumption is collected, where the full-process building energy consumption includes N process nodes, each process node includes a set of energy consumption data, N is a positive integer greater than or equal to 1, and provides data support for establishing the objective function; the environmental factors of the target building are collected, and the full-process environmental factors are output to provide variables for establishing the objective function; the full-process environmental factors are analyzed according to the full-process building energy consumption to determine the first factor set, where the first factor set includes N environmental factors, where N The environmental factors are respectively corresponding to the screened environmental factors for N process nodes, providing variables for establishing the objective function; energy consumption loss is identified for the whole process of building energy consumption, and N energy consumption loss data are output to provide a target for establishing the objective function; the first factor set is used as the variable indicator, the adjustment parameters of the first factor set are used as the target variable, and the N energy consumption loss data are used as the input data to build the objective function, providing a basis for subsequent optimization of the output control parameters; according to the objective function, the optimized control parameter set is output for energy consumption adjustment of each process, thereby improving the comprehensiveness and accuracy of the building energy consumption analysis and achieving the technical effect of accurately adjusting the building energy consumption.

[0058] Embodiment 2 Based on the same inventive concept as the BIM-based green building energy consumption full-process analysis and optimization method in the aforementioned embodiment, Figure 4 As shown, the embodiment of the present application provides a green building energy consumption full-process analysis and optimization system based on BIM, the system comprising: The target building modeling module 11 is used to output a full-process simulation model by performing BIM modeling on the full process of the target building; The building energy consumption collection module 12 is used to collect the full-process building energy consumption according to the full-process simulation model, wherein the full-process building energy consumption includes N process nodes, each process node includes a set of energy consumption data, and N is a positive integer greater than or equal to 1; The environmental factor collection module 13 is used to collect the environmental factors of the target building and output the full-process environmental factors; The environmental factor analysis module 14 is used to analyze the full-process environmental factors according to the full-process building energy consumption to determine a first factor set, wherein the first factor set includes N environmental factors, wherein the N environmental factors are environmental factors obtained by screening the N process nodes respectively; The energy consumption loss identification module 15 is used to identify the energy consumption loss of the whole process building energy consumption and output N energy consumption loss data; An objective function building module 16 is used to build an objective function using the first factor set as a variable indicator, the adjustment parameter of the first factor set as a target variable, and the N energy loss data as input data; The process energy consumption adjustment module 17 is used to output an optimized control parameter set according to the objective function, so as to adjust the energy consumption of each process.

[0059] Furthermore, the process energy consumption adjustment module 17 includes the following execution steps: When it is detected that the target building is in a first process, based on the first factor set, a first environmental factor corresponding to the first process is determined, and first energy consumption loss data corresponding to the first process is acquired; According to the first environmental factor, collecting real-time environmental parameters; The real-time environmental parameters and the first energy loss data are input into the objective function, and the first optimization control parameter is output. Similarly, when it is detected that the target building is in the Nth process, the optimization control parameter set is output, wherein the optimization control parameter set includes N optimization control parameters.

[0060] Furthermore, the energy loss identification module 15 includes the following execution steps: Establishing an invalid energy consumption label, identifying the invalid energy consumption of the entire process building energy consumption according to the invalid energy consumption label, and determining the invalid energy consumption; The ineffective energy consumption is used as energy consumption loss data output to obtain N energy consumption loss data corresponding to the N process nodes.

[0061] Furthermore, the environmental factor analysis module 14 includes the following execution steps: According to the full-process building energy consumption and the full-process environmental factors, N sets of environment-energy consumption mapping data sets are established; Performing data correlation analysis based on the N sets of environment-energy consumption mapping data sets, outputting N correlation coefficient sets, and screening out a corresponding first correlation coefficient from each correlation coefficient set to obtain N first correlation coefficients; The first factor set is generated using the N first correlation coefficients.

[0062] Furthermore, the environmental factor analysis module 14 also includes the following execution steps: Obtaining environmental demand characteristics of the target building; A first constraint condition is generated according to the environmental requirement characteristics, wherein the first constraint condition is used to constrain the first factor set output by screening, and output an optimized first factor set.

[0063] Furthermore, the objective function building module 16 includes the following contents: The expression of the objective function is as follows: ; in, is the objective function, and energy consumption is adjusted by minimizing the objective function. is the total number of process nodes; , is the first factor set, The real-time location of the target building is Environmental factors corresponding to each process node; is the real-time location of the target building Based on environmental factors Energy loss data; is the real-time location of the target building Based on environmental factors Preset energy loss data; It is the fusion loss data of the previous process node and the next process node.

[0064] Furthermore, the objective function building module 16 further includes the following execution steps: Obtaining a sample of historical environmental changes in the area where the target building is located; Performing simulation prediction on the full-process simulation model according to the historical environmental change samples, and outputting full-process simulation energy consumption data, wherein the full-process simulation energy consumption data includes N simulation energy consumption data corresponding to the N process nodes; Feedback adjustment parameters are generated based on the full-process simulation energy consumption data and the full-process building energy consumption, and the objective function is optimized with the feedback adjustment parameters.

[0065] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0066] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A full-process analysis and optimization method for green building energy consumption based on BIM, characterized in that: The method comprises: Through BIM modeling of the entire process of the target building, a full-process simulation model is output; According to the full-process simulation model, the full-process building energy consumption is collected, wherein the full-process building energy consumption includes N process nodes, each process node includes a set of energy consumption data, and N is a positive integer greater than or equal to 1; Collect environmental factors of the target building and output full-process environmental factors; Analyze the full-process environmental factors according to the full-process building energy consumption to determine a first factor set, wherein the first factor set includes N environmental factors, wherein the N environmental factors are environmental factors obtained by screening the N process nodes respectively; Identify energy consumption losses for the entire building energy consumption process and output N energy consumption loss data; Taking the first factor set as a variable indicator, taking the adjustment parameter of the first factor set as a target variable, and taking the N energy loss data as input data, constructing an objective function; According to the objective function, an optimized control parameter set is output for adjusting the energy consumption of each process.

2. The method according to claim 1, characterized in that According to the objective function, an optimized control parameter set is outputted, and the method further comprises: When it is detected that the target building is in a first process, based on the first factor set, a first environmental factor corresponding to the first process is determined, and first energy consumption loss data corresponding to the first process is acquired; According to the first environmental factor, collecting real-time environmental parameters; The real-time environmental parameters and the first energy loss data are input into the objective function, and the first optimization control parameter is output. Similarly, when it is detected that the target building is in the Nth process, the optimization control parameter set is output, wherein the optimization control parameter set includes N optimization control parameters.

3. The method according to claim 2, characterized in that Energy loss identification is performed on the whole process building energy consumption, and N energy loss data are outputted. The method further includes: Establishing an invalid energy consumption label, identifying the invalid energy consumption of the entire process building energy consumption according to the invalid energy consumption label, and determining the invalid energy consumption; The ineffective energy consumption is used as energy consumption loss data output to obtain N energy consumption loss data corresponding to the N process nodes.

4. The method according to claim 1, characterized in that The whole process environmental factors are analyzed according to the whole process building energy consumption, and the method includes: Establishing N sets of environment-energy consumption mapping data sets according to the full-process building energy consumption and the full-process environmental factors; Performing data correlation analysis based on the N sets of environment-energy consumption mapping data sets, outputting N correlation coefficient sets, and screening out a corresponding first correlation coefficient from each correlation coefficient set to obtain N first correlation coefficients; The first factor set is generated using the N first correlation coefficients.

5. The method according to claim 4, characterized in that The method further comprises: Obtaining environmental demand characteristics of the target building; A first constraint condition is generated according to the environmental requirement characteristics, wherein the first constraint condition is used to constrain the first factor set output by screening, and output an optimized first factor set.

6. The method according to claim 1, characterized in that The expression of the objective function is as follows: ； in, is the objective function, and energy consumption is adjusted by minimizing the objective function. is the total number of process nodes; , is the first factor set, Based on the real-time location of the target building Environmental factors corresponding to each process node; is the real-time location of the target building Based on environmental factors Energy loss data; is the real-time location of the target building Based on environmental factors Preset energy loss data; It is the fusion loss data of the previous process node and the next process node.

7. The method according to claim 6, characterized in that The method further comprises: Obtaining a sample of historical environmental changes in the area where the target building is located; Performing simulation prediction on the full-process simulation model according to the historical environmental change samples, and outputting full-process simulation energy consumption data, wherein the full-process simulation energy consumption data includes N simulation energy consumption data corresponding to the N process nodes; Feedback adjustment parameters are generated based on the full-process simulation energy consumption data and the full-process building energy consumption, and the objective function is optimized with the feedback adjustment parameters.

8. A BIM-based green building energy consumption full-process analysis and optimization system, characterized in that: A method for analyzing and optimizing the energy consumption of green buildings based on BIM in the whole process according to any one of claims 1 to 7, the system comprising: A target building modeling module, wherein the target building modeling module is used to output a full-process simulation model by performing BIM modeling on the full process of the target building; A building energy consumption collection module, wherein the building energy consumption collection module is used to collect the full-process building energy consumption according to the full-process simulation model, wherein the full-process building energy consumption includes N process nodes, each process node includes a set of energy consumption data, and N is a positive integer greater than or equal to 1; An environmental factor collection module, which is used to collect environmental factors of the target building and output full-process environmental factors; An environmental factor analysis module, the environmental factor analysis module is used to analyze the full-process environmental factors according to the full-process building energy consumption, and determine a first factor set, wherein the first factor set includes N environmental factors, wherein the N environmental factors are environmental factors obtained by screening corresponding to the N process nodes respectively; An energy consumption loss identification module, the energy consumption loss identification module is used to identify energy consumption losses of the whole process building energy consumption and output N energy consumption loss data; An objective function building module, the objective function building module is used to build an objective function using the first factor set as a variable indicator, the adjustment parameter of the first factor set as a target variable, and the N energy consumption loss data as input data; A process energy consumption adjustment module is used to output an optimized control parameter set according to the objective function, so as to adjust the energy consumption of each process.

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