A method and system for evaluating energy efficiency of building air conditioning systems based on BIM model

Abstract

The present invention discloses a method and system for evaluating the energy efficiency of building air conditioning systems based on a BIM model, which is applied to the technical field of energy efficiency evaluation of air conditioning. The method comprises obtaining thermal parameters of a target building from its BIM model; screening sample building data from a constructed building database based on the thermal parameters; performing a Pearson correlation analysis on the sample building data to determine an energy consumption behavior correlation coefficient; constructing an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the sample building; inputting the target building's energy consumption peak period and cooling capacity deviation into the air conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value; and performing an energy efficiency evaluation on the target building's air conditioning system based on the temperature adjustment amplitude prediction value. The method provided by the embodiment of the present invention can accurately evaluate the energy efficiency of an air conditioning system, which is conducive to improving the utilization rate of building energy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy efficiency evaluation of air conditioners, and in particular to a method and system for energy efficiency evaluation of building air conditioning systems based on a BIM model. Background Art

[0002] With the rapid development of the economy, the scale of urban buildings continues to expand, and the proportion and absolute value of building energy consumption in the total energy consumption cannot be ignored. Among the building energy consumption, air-conditioning systems account for a large proportion.

[0003] In the existing air-conditioning system energy efficiency evaluation process, the energy efficiency is mostly evaluated based on the energy efficiency level of the air-conditioning itself and the air-conditioning usage time. The energy efficiency of the air-conditioning system cannot be accurately evaluated, which in turn affects the utilization rate of building energy.

[0004] It can be seen that how to accurately evaluate the energy efficiency of air-conditioning systems has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a building air-conditioning system energy efficiency evaluation method and system based on the BIM model to solve the problem that the energy efficiency of the current air-conditioning system cannot be accurately evaluated. The energy efficiency of the air-conditioning system can be accurately evaluated, which is conducive to improving the utilization rate of building energy.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for evaluating the energy efficiency of a building air conditioning system based on a BIM model, the method comprising:

[0007] Obtaining the thermal conductivity coefficient of the exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer of the target building from the BIM model of the target building; and filtering sample building data from the constructed building database based on the thermal conductivity coefficient of the exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer;

[0008] Extracting the temperature adjustment amplitude and mode switching frequency of users in the sample building data, the cooling capacity deviation of the air-conditioning system in the sample building data, and the energy consumption peak period of the sample building data; performing a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine a correlation coefficient of energy consumption behavior;

[0009] Constructing an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data;

[0010] Inputting the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building;

[0011] Based on the predicted value of the temperature adjustment amplitude of the target building, an energy efficiency evaluation is performed on the air-conditioning system of the target building.

[0012] As one preferred solution, the sample building data is obtained by screening from a constructed building database based on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer, including:

[0013] Using the quantile method, range setting processing is performed on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer to obtain the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer with parameter intervals;

[0014] Based on the expert experience method, weights are assigned to the building exterior wall thermal conductivity, window glass transmittance, and roof insulation thickness with parameter intervals to obtain weights of the building exterior wall thermal conductivity, the window glass transmittance, and the roof insulation thickness, respectively;

[0015] The similarity between the target building and other buildings in the constructed building database is quantified using Euclidean distance, and the sample building data is obtained by screening based on the similarity results.

[0016] As one preferred solution, performing a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine the energy consumption behavior correlation coefficient includes:

[0017] Performing matrix processing on the temperature adjustment amplitude to obtain a first matrix;

[0018] performing matrix processing on the mode switching frequency to obtain a second matrix;

[0019] Performing matrix processing on the energy consumption peak period to obtain a third matrix;

[0020] A Pearson correlation analysis is performed on the first matrix, the second matrix, and the third matrix to obtain an energy consumption behavior correlation coefficient.

[0021] As one preferred solution, the air conditioning temperature adjustment amplitude prediction model is constructed based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data, including:

[0022] The air conditioning temperature adjustment amplitude prediction model is constructed using a gradient boosting regression tree algorithm with constrained optimization, wherein the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data are used as constraints.

[0023] As one preferred solution, the energy efficiency evaluation of the air conditioning system of the target building based on the predicted value of the temperature adjustment amplitude of the target building includes:

[0024] Performing residual processing on the temperature adjustment amplitude in the sample building data and the predicted value of the temperature adjustment amplitude of the target building to obtain an air conditioning residual parameter;

[0025] An energy efficiency evaluation is performed on the air conditioning system of the target building based on the air conditioning residual parameter.

[0026] As one preferred solution, the obtained energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building are input into the air conditioning temperature adjustment amplitude prediction model to obtain the temperature adjustment amplitude prediction value of the target building, including:

[0027] A feature matrix is ​​constructed using the thermal conductivity of the exterior wall of the target building, the light transmittance of the window glass, the thickness of the roof insulation layer, the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period, wherein the weights of the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period in the feature matrix are adjusted based on the energy consumption behavior correlation coefficient;

[0028] The characteristic matrix and the cooling capacity deviation of the air-conditioning system of the target building are input into the air-conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building.

[0029] As one preferred solution, after obtaining the predicted value of the temperature adjustment amplitude of the target building, the BIM model-based building air-conditioning system energy efficiency evaluation method further includes:

[0030] Bayesian optimization is used to adjust the energy consumption behavior correlation coefficient to optimize the temperature adjustment amplitude prediction value of the target building.

[0031] Another embodiment of the present invention provides a building air conditioning system energy efficiency evaluation system based on a BIM model, comprising:

[0032] an acquisition module, configured to acquire the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer of the target building from the BIM model of the target building; and to filter and obtain sample building data from a constructed building database based on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer;

[0033] an extraction module, configured to extract the temperature adjustment amplitude and mode switching frequency from the sample building data, the cooling capacity deviation of the air-conditioning system from the sample building data, and the energy consumption peak period from the sample building data; perform a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine a correlation coefficient of energy consumption behavior;

[0034] A construction module, configured to construct an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data;

[0035] A prediction module, configured to input the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a predicted value of the temperature adjustment amplitude of the target building;

[0036] An evaluation module is used to evaluate the energy efficiency of the air-conditioning system of the target building based on the predicted value of the temperature adjustment amplitude of the target building.

[0037] Another embodiment of the present invention provides a building air-conditioning system energy efficiency evaluation device based on a BIM model, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the building air-conditioning system energy efficiency evaluation method based on a BIM model as described above.

[0038] Yet another embodiment of the present invention provides a computer-readable storage medium storing a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the building air-conditioning system energy efficiency evaluation method based on the BIM model as described above is implemented.

[0039] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0040] The present invention obtains the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer of the target building from the BIM model of the target building; based on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer, sample building data is screened from a constructed building database; the temperature adjustment amplitude and mode switching frequency of the user in the sample building data, the cooling capacity deviation of the air-conditioning system in the sample building data and the energy consumption peak period of the sample building data are extracted; a Pearson correlation analysis is performed on the temperature adjustment amplitude, the mode switching frequency and the energy consumption peak period to determine the energy consumption behavior correlation coefficient; an air-conditioning temperature adjustment amplitude prediction model is constructed based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air-conditioning system in the sample building data; the obtained energy consumption peak period of the target building and the cooling capacity deviation of the air-conditioning system of the target building are input into the air-conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building; and based on the temperature adjustment amplitude prediction value of the target building, an energy efficiency evaluation is performed on the air-conditioning system of the target building. Compared with the existing technology, this method extracts key thermal performance parameters from the BIM model of the target building, screens sample building data based on the key thermal performance parameters to ensure that the sample data is comparable with the target building, extracts user behavior parameters and performs correlation analysis to generate energy consumption behavior correlation coefficients, constructs an air conditioning temperature adjustment amplitude prediction model, trains and captures the nonlinear relationship between user behavior and equipment performance, inputs the target building parameters to generate temperature adjustment prediction values, dynamically corrects the upper limit of the prediction value according to the real-time cooling capacity deviation of the target building, and performs accurate energy efficiency evaluation based on the prediction value, forming a complete link from prediction to evaluation, providing a quantitative basis for improving building energy efficiency, and is conducive to improving the utilization rate of building energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 1 is a flow chart of a method for evaluating energy efficiency of a building air-conditioning system based on a BIM model in one embodiment of the present invention;

[0042] Figure 2 1 is a schematic structural diagram of a building air conditioning system energy efficiency evaluation system based on a BIM model in one embodiment of the present invention;

[0043] Figure 3 It is a structural schematic diagram of a building air-conditioning system energy efficiency evaluation device based on a BIM model in one embodiment of the present invention.

[0044] Reference numerals:

[0045] Among them, 11, acquisition module; 12, extraction module; 13, construction module; 14, prediction module; 15, evaluation module; 21, processor; 22, memory. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0047] In the description of the present invention, the terms "first," "second," "third," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first," "second," "third," etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0048] In the description of the present invention, it should be noted that, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or an indirect connection through an intermediate medium, or it can be a communication between the two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are for illustrative purposes only, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] In describing the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific circumstances.

[0050] An embodiment of the present invention provides a method for evaluating the energy efficiency of a building air conditioning system based on a BIM model. Figure 1 , Figure 1 FIG2 is a flow chart of a method for evaluating energy efficiency of a building air conditioning system based on a BIM model in one embodiment of the present invention, the method comprising:

[0051] S1: Obtain the building exterior wall thermal conductivity, window glass transmittance, and roof insulation thickness of the target building from the BIM model of the target building; and filter sample building data from a constructed building database based on the building exterior wall thermal conductivity, window glass transmittance, and roof insulation thickness.

[0052] S2: extracting the temperature adjustment amplitude and mode switching frequency of users in the sample building data, the cooling capacity deviation of the air-conditioning system in the sample building data, and the energy consumption peak period of the sample building data; performing a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine the energy consumption behavior correlation coefficient;

[0053] S3: Constructing an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data;

[0054] S4: Inputting the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building;

[0055] S5: Based on the predicted value of the temperature adjustment amplitude of the target building, perform energy efficiency evaluation on the air-conditioning system of the target building.

[0056] Specifically, the BIM model file of the target building is obtained, the gbXML file in the model is exported, the exported file is preprocessed, and the building structure and material design parameters and air-conditioning system design parameters in the target building model are read. The parameters include at least the thermal conductivity of the building exterior wall, the light transmittance of the window glass, the thickness of the roof insulation layer, the peak period of energy consumption, and the cooling capacity deviation of the air-conditioning system.

[0057] Based on the thermal conductivity coefficient of the exterior wall of the target building, the light transmittance of the window glass, and the thickness of the roof insulation layer, sample building data is obtained by screening from the constructed building database, including:

[0058] The thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer are range-set by using the quantile method to obtain the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer with parameter intervals; based on the expert experience method, the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer with parameter intervals are weighted to obtain the weights of the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer respectively; the similarity between the target building and other buildings in the constructed building database is quantified by using the Euclidean distance, and the sample building data is obtained by screening based on the similarity results.

[0059] Specifically, the 25% (Q1) and 75% (Q3) quantiles of thermal conductivity, transmittance, and thickness in the database were calculated, and the parameter interval was set to [Q1, Q3] for subsequent normalization or screening.

[0060] The parameter values ​​of the thermal conductivity of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer were normalized and converted into relative values ​​between 0 and 1 to eliminate the dimensional differences.

[0061] The normalized values ​​are weighted using an expert experience method to obtain weights for the thermal conductivity of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer.

[0062] The weighted Euclidean distance formula is used to calculate the similarity between the target and the database buildings, and the buildings are sorted from small to large in distance. The sample with the smallest distance is selected as the sample building.

[0063] In this step, quantiles ensure that the parameter range is reasonable and reduce the impact of noise; weight distribution reflects the importance of parameters and enhances the targetedness of the model; weighted Euclidean distance comprehensively considers normalization parameters and weights to accurately quantify similarity.

[0064] Extract the temperature adjustment amplitude and mode switching frequency of users in the sample building data, the cooling capacity deviation of the air-conditioning system in the sample building data, and the energy consumption peak period of the sample building data; perform Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine the energy consumption behavior correlation coefficient.

[0065] In step S2, the analysis process includes matrixing the temperature adjustment amplitude to obtain a first matrix; matrixing the mode switching frequency to obtain a second matrix; matrixing the energy consumption peak period to obtain a third matrix; and performing Pearson correlation analysis on the first matrix, the second matrix, and the third matrix to obtain an energy consumption behavior correlation coefficient.

[0066] From the screened sample building data, key parameters are extracted, such as temperature adjustment amplitude, mode switching frequency, cooling capacity deviation, and energy consumption peak period, focusing on the correlation analysis between user behavior and energy consumption.

[0067] The temperature adjustment amplitude reflects the user's active adjustment intensity of the indoor temperature (such as the set temperature change range), which directly affects the air-conditioning load; the mode switching frequency is used to quantify the frequency of users operating the air-conditioning mode (such as switching between cooling / heating / air supply modes), reflecting the dynamic nature of user usage habits; the cooling capacity deviation is the difference between the actual cooling capacity of the air-conditioning and the theoretical demand, which is used to measure the degree of match between the system operation efficiency and user needs; the energy consumption peak period is the time period with the highest daily energy consumption, revealing the temporal distribution characteristics of building energy consumption.

[0068] Data preprocessing is performed on the temperature adjustment amplitude, mode switching frequency, cooling capacity deviation, and energy consumption peak period to ensure that the parameter data format is unified before analysis.

[0069] Convert unstructured parameter data into matrix form to facilitate pattern mining.

[0070] Specifically, the temperature adjustment amplitude matrix (first matrix): each row represents a sample building, and each column represents the temperature setting change value in different time periods (such as day / week / month).

[0071] Mode switching frequency matrix (second matrix): each row corresponds to a building, and each column is the count or frequency statistics of different mode switching events.

[0072] Energy consumption peak period matrix (third matrix): Each row records the peak period distribution of a building (such as the energy consumption intensity matrix in hours).

[0073] The relationship between the three variables was evaluated by calculating the Pearson Correlation Coefficient:

[0074] Temperature adjustment range vs. peak energy consumption period: Whether the user's active temperature adjustment behavior leads to a surge in energy consumption during a specific period.

[0075] Mode switching frequency vs. energy consumption peak period: Whether frequent switching of air conditioning modes is related to the distribution of energy consumption peak periods.

[0076] Temperature adjustment range vs. mode switching frequency: The linkage of user operations (for example, whether large temperature adjustment ranges are accompanied by high-frequency mode switching).

[0077] Based on the above Pearson analysis results, the energy consumption behavior correlation coefficient is formed.

[0078] In this process, user behavior (temperature adjustment, mode switching) is linked to energy consumption characteristics (peak period, cooling capacity deviation), breaking through the traditional energy management logic that only relies on equipment parameters. Moreover, through matrix and correlation analysis, the temporal and spatial variation patterns of user behavior are captured to support personalized energy consumption prediction.

[0079] In step S3, based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air-conditioning system in the sample building data, an air-conditioning temperature adjustment amplitude prediction model is constructed, including: using a gradient boosting regression tree algorithm with constrained optimization to construct the air-conditioning temperature adjustment amplitude prediction model, wherein the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air-conditioning system in the sample building data are used as constraints.

[0080] Among them, the energy consumption behavior correlation coefficient serves as prior knowledge to guide the model to focus on highly correlated variables, while the cooling capacity deviation reflects the actual operating status of the air-conditioning system. It serves as a constraint condition to ensure that the prediction results meet the physical performance limitations of the equipment.

[0081] Specifically, the gradient boosting regression tree algorithm is suitable for complex dynamic modeling of building energy consumption because it is good at handling nonlinear relationships and feature interactions.

[0082] When the gradient boosting regression tree algorithm generates each decision tree, it ensures that the splitting rule meets the constraints through Lagrange multiplier method or projected gradient descent.

[0083] During this process, the model can automatically adjust the prediction logic as building usage patterns change (such as seasons and occupancy rates), supporting long-term energy efficiency optimization.

[0084] The obtained energy consumption peak period of the target building and the cooling capacity deviation of the air-conditioning system of the target building are input into the air-conditioning temperature adjustment amplitude prediction model to obtain the temperature adjustment amplitude prediction value of the target building, including: using the thermal conductivity coefficient of the building exterior wall of the target building, the window glass transmittance, the thickness of the roof insulation layer, the temperature adjustment amplitude, the mode switching frequency and the energy consumption peak period of the target building to construct a feature matrix, wherein the weights of the temperature adjustment amplitude, the mode switching frequency and the energy consumption peak period in the feature matrix are adjusted respectively based on the energy consumption behavior correlation coefficient; the feature matrix and the cooling capacity deviation of the air-conditioning system of the target building are input into the air-conditioning temperature adjustment amplitude prediction model to obtain the temperature adjustment amplitude prediction value of the target building.

[0085] Specifically, the building's physical properties (exterior wall thermal conductivity, window transmittance, roof insulation thickness) and user behavior data (temperature adjustment amplitude, mode switching frequency, energy consumption peak period) are integrated into a unified feature matrix to form a structured input that comprehensively describes the building's characteristics.

[0086] Among them, physical properties determine the thermal performance of the building itself (such as insulation capacity and natural lighting effects), which directly affect the basic value of the air-conditioning load; user behavior reflects dynamic usage habits (such as frequent temperature adjustments and concentrated energy consumption during peak hours), which determines the load fluctuation characteristics.

[0087] Using the energy consumption behavior correlation coefficient obtained above, differentiated weights are assigned to user behavior characteristics to enhance the model's sensitivity to key influencing factors.

[0088] The target building's cooling capacity deviation (the difference between actual cooling capacity and theoretical cooling capacity) is used as an independent input parameter and fed into the model along with the feature matrix. This deviation reflects the real-time performance degradation of the air conditioning system (e.g., equipment aging, filter clogs), and the model adjusts the predicted upper temperature adjustment range accordingly to avoid recommending a setpoint that exceeds the equipment's capabilities. If the deviation remains consistently negative (actual cooling capacity is insufficient), the model automatically reduces the predicted temperature adjustment range to prevent the user from setting an excessively low temperature and overloading the compressor.

[0089] Specifically, sliding window statistics are performed on the cooling capacity deviation and converted into a time series vector consistent with the time dimension of the feature matrix.

[0090] Through the interaction of splicing or attention mechanism with the feature matrix, the weighted feature matrix and the cooling capacity deviation are input into the gradient boosted regression tree (GBRT) model. The tree structure of GBRT is used to capture the complex interaction between features to obtain the predicted value of the temperature adjustment amplitude of the target building.

[0091] After obtaining the predicted value of the temperature adjustment amplitude of the target building, the energy consumption behavior correlation coefficient is adjusted using Bayesian optimization to optimize the predicted value of the temperature adjustment amplitude of the target building.

[0092] In step S5, based on the predicted value of the temperature adjustment amplitude of the target building, the energy efficiency of the air conditioning system of the target building is evaluated, including:

[0093] Residual processing is performed on the temperature adjustment amplitude in the sample building data and the predicted value of the temperature adjustment amplitude of the target building to obtain air conditioning residual parameters; and energy efficiency evaluation is performed on the air conditioning system of the target building based on the air conditioning residual parameters.

[0094] By calculating the temperature adjustment amplitude residual, the gap between the operating status of the air-conditioning system and the ideal model is quantified.

[0095] Based on the evaluation results, suggestions for equipment maintenance, control strategy adjustment or user behavior intervention are put forward to form a "prediction-evaluation-optimization" closed loop.

[0096] An embodiment of the present invention provides a building air conditioning system energy efficiency evaluation system based on the BIM model. For details, see Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a BIM-based building air conditioning system energy efficiency evaluation system according to one embodiment of the present invention. The system includes:

[0097] An acquisition module 11 is configured to obtain the thermal conductivity coefficient of the exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer of the target building from the BIM model of the target building; and based on the thermal conductivity coefficient of the exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer, filter and obtain sample building data from a constructed building database;

[0098] An extraction module 12 is configured to extract the temperature adjustment amplitude and mode switching frequency from the sample building data, the cooling capacity deviation of the air conditioning system from the sample building data, and the energy consumption peak period from the sample building data; perform a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine a correlation coefficient of energy consumption behavior;

[0099] A construction module 13 is configured to construct an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data;

[0100] The prediction module 14 is configured to input the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a predicted value of the temperature adjustment amplitude of the target building;

[0101] The evaluation module 15 is configured to perform energy efficiency evaluation on the air-conditioning system of the target building based on the predicted value of the temperature adjustment amplitude of the target building.

[0102] See also Figure 3 , which is a structural block diagram of a building air-conditioning system energy efficiency evaluation device based on a BIM model provided by an embodiment of the present invention. The building air-conditioning system energy efficiency evaluation device 20 based on a BIM model provided by an embodiment of the present invention includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, the steps in the embodiment of the building air-conditioning system energy efficiency evaluation method based on a BIM model are implemented, for example Figure 1 or, when the processor 21 executes the computer program, the functions of the modules in the above-mentioned device embodiments are realized, such as the acquisition module 11.

[0103] Exemplarily, the computer program can be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the BIM model-based building air conditioning system energy efficiency assessment device 20. For example, the computer program can be divided into an acquisition module 11, an extraction module 12, a construction module 13, etc. The specific functions of each module are as follows:

[0104] An acquisition module 11 is configured to obtain the thermal conductivity coefficient of the exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer of the target building from the BIM model of the target building; and based on the thermal conductivity coefficient of the exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer, filter and obtain sample building data from a constructed building database;

[0105] An extraction module 12 is configured to extract the temperature adjustment amplitude and mode switching frequency from the sample building data, the cooling capacity deviation of the air conditioning system from the sample building data, and the energy consumption peak period from the sample building data; perform a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine a correlation coefficient of energy consumption behavior;

[0106] A construction module 13 is configured to construct an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data;

[0107] The prediction module 14 is configured to input the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a predicted value of the temperature adjustment amplitude of the target building;

[0108] The evaluation module 15 is configured to perform energy efficiency evaluation on the air-conditioning system of the target building based on the predicted value of the temperature adjustment amplitude of the target building.

[0109] The BIM model-based building air conditioning system energy efficiency assessment device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will appreciate that the schematic diagram is merely an example of a BIM model-based building air conditioning system energy efficiency assessment device and does not limit the BIM model-based building air conditioning system energy efficiency assessment device 20. The BIM model-based building air conditioning system energy efficiency assessment device 20 may include more or fewer components than shown, or may combine certain components, or may include different components. For example, the BIM model-based building air conditioning system energy efficiency assessment device 20 may also include input and output devices, network access devices, buses, and the like.

[0110] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor 21 is the control center of the BIM-based building air conditioning system energy efficiency assessment device 20, and utilizes various interfaces and lines to connect various parts of the BIM-based building air conditioning system energy efficiency assessment device 20.

[0111] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements the various functions of the BIM model-based building air conditioning system energy efficiency assessment device 20 by running or executing the computer programs and / or modules stored in the memory 22 and accessing the data stored in the memory 22. The memory 22 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the mobile phone (such as audio data and a phone book). Furthermore, the memory 22 may include high-speed random access memory (RAM) and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0112] If the modules integrated into the BIM-based building air conditioning system energy efficiency assessment device 20 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0113] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0114] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the steps of the energy efficiency evaluation method of a building air-conditioning system based on a BIM model as described in the above embodiment, for example Figure 1 Steps S1 to S5 described in .

[0115] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0116] The present invention obtains the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer of the target building from the BIM model of the target building; based on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer, sample building data is screened from a constructed building database; the temperature adjustment amplitude and mode switching frequency of the user in the sample building data, the cooling capacity deviation of the air-conditioning system in the sample building data and the energy consumption peak period of the sample building data are extracted; a Pearson correlation analysis is performed on the temperature adjustment amplitude, the mode switching frequency and the energy consumption peak period to determine the energy consumption behavior correlation coefficient; an air-conditioning temperature adjustment amplitude prediction model is constructed based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air-conditioning system in the sample building data; the obtained energy consumption peak period of the target building and the cooling capacity deviation of the air-conditioning system of the target building are input into the air-conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building; and based on the temperature adjustment amplitude prediction value of the target building, an energy efficiency evaluation is performed on the air-conditioning system of the target building. Compared with the existing technology, this method extracts key thermal performance parameters from the BIM model of the target building, screens sample building data based on the key thermal performance parameters to ensure that the sample data is comparable with the target building, extracts user behavior parameters and performs correlation analysis to generate energy consumption behavior correlation coefficients, constructs an air conditioning temperature adjustment amplitude prediction model, trains and captures the nonlinear relationship between user behavior and equipment performance, inputs the target building parameters to generate temperature adjustment prediction values, dynamically corrects the upper limit of the prediction value according to the real-time cooling capacity deviation of the target building, and performs accurate energy efficiency evaluation based on the prediction value, forming a complete link from prediction to evaluation, providing a quantitative basis for improving building energy efficiency, and is conducive to improving the utilization rate of building energy.

[0117] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A building air conditioning system energy efficiency evaluation method based on BIM model, characterized in that: include: Obtaining the thermal conductivity of the exterior walls, the light transmittance of the window glass, and the thickness of the roof insulation layer of the target building from the BIM model of the target building; Based on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer, sample building data is obtained from the constructed building database; Extracting the temperature adjustment amplitude and mode switching frequency of users in the sample building data, the cooling capacity deviation of the air-conditioning system in the sample building data, and the energy consumption peak period of the sample building data; Performing a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine a correlation coefficient of energy consumption behavior; Constructing an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data; Inputting the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building; Based on the predicted value of the temperature adjustment amplitude of the target building, an energy efficiency evaluation is performed on the air-conditioning system of the target building.

2. The energy efficiency evaluation method for building air conditioning systems based on a BIM model according to claim 1, characterized in that: The method of obtaining sample building data by screening from a constructed building database based on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer includes: Using the quantile method, range setting processing is performed on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer to obtain the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer with parameter intervals; Based on the expert experience method, weights are assigned to the building exterior wall thermal conductivity, window glass transmittance, and roof insulation thickness with parameter intervals to obtain weights of the building exterior wall thermal conductivity, the window glass transmittance, and the roof insulation thickness, respectively; The similarity between the target building and other buildings in the constructed building database is quantified using Euclidean distance, and the sample building data is obtained by screening based on the similarity results.

3. The energy efficiency evaluation method for building air conditioning systems based on a BIM model according to claim 1, characterized in that: The performing of a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine the energy consumption behavior correlation coefficient includes: Performing matrix processing on the temperature adjustment amplitude to obtain a first matrix; performing matrix processing on the mode switching frequency to obtain a second matrix; Performing matrix processing on the energy consumption peak period to obtain a third matrix; A Pearson correlation analysis is performed on the first matrix, the second matrix, and the third matrix to obtain an energy consumption behavior correlation coefficient.

4. The energy efficiency evaluation method for building air conditioning systems based on a BIM model according to claim 1, wherein: The step of constructing an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data includes: The air conditioning temperature adjustment amplitude prediction model is constructed using a gradient boosting regression tree algorithm with constrained optimization, wherein the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data are used as constraints.

5. The energy efficiency evaluation method for building air conditioning systems based on a BIM model according to claim 1, wherein: The energy efficiency evaluation of the air conditioning system of the target building based on the predicted value of the temperature adjustment amplitude of the target building includes: Performing residual processing on the temperature adjustment amplitude in the sample building data and the predicted value of the temperature adjustment amplitude of the target building to obtain an air conditioning residual parameter; An energy efficiency evaluation is performed on the air conditioning system of the target building based on the air conditioning residual parameter.

6. The energy efficiency evaluation method for building air conditioning systems based on a BIM model according to claim 1, wherein: The step of inputting the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building includes: A feature matrix is ​​constructed using the thermal conductivity of the exterior wall of the target building, the light transmittance of the window glass, the thickness of the roof insulation layer, the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period, wherein the weights of the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period in the feature matrix are adjusted based on the energy consumption behavior correlation coefficient; The characteristic matrix and the cooling capacity deviation of the air-conditioning system of the target building are input into the air-conditioning temperature adjustment amplitude prediction model to obtain a temperature adjustment amplitude prediction value of the target building.

7. The energy efficiency evaluation method for building air conditioning systems based on a BIM model according to claim 1, wherein: After obtaining the predicted value of the temperature adjustment amplitude of the target building, the BIM model-based building air-conditioning system energy efficiency evaluation method further includes: Bayesian optimization is used to adjust the energy consumption behavior correlation coefficient to optimize the temperature adjustment amplitude prediction value of the target building.

8. A building air conditioning system energy efficiency evaluation system based on BIM model, characterized by: include: An acquisition module is used to obtain the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass, and the thickness of the roof insulation layer of the target building from the BIM model of the target building; Based on the thermal conductivity coefficient of the building exterior wall, the light transmittance of the window glass and the thickness of the roof insulation layer, sample building data is obtained from the constructed building database; an extraction module, configured to extract the temperature adjustment amplitude and mode switching frequency in the sample building data, the cooling capacity deviation of the air-conditioning system in the sample building data, and the energy consumption peak period of the sample building data; Performing a Pearson correlation analysis on the temperature adjustment amplitude, the mode switching frequency, and the energy consumption peak period to determine a correlation coefficient of energy consumption behavior; A construction module, configured to construct an air conditioning temperature adjustment amplitude prediction model based on the energy consumption behavior correlation coefficient and the cooling capacity deviation of the air conditioning system in the sample building data; A prediction module, configured to input the acquired energy consumption peak period of the target building and the cooling capacity deviation of the air conditioning system of the target building into the air conditioning temperature adjustment amplitude prediction model to obtain a predicted value of the temperature adjustment amplitude of the target building; An evaluation module is used to evaluate the energy efficiency of the air-conditioning system of the target building based on the predicted value of the temperature adjustment amplitude of the target building.

9. A building air conditioning system energy efficiency evaluation device based on a BIM model, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for energy efficiency evaluation of a building air-conditioning system based on a BIM model as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the building air-conditioning system energy efficiency evaluation method based on the BIM model as described in any one of claims 1 to 7 is implemented.

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