Simulation method and system based on BIM building energy consumption

Abstract

The invention relates to the technical field of energy consumption simulation, in particular to a simulation method and system based on BIM building energy consumption, and the method comprises the following steps: importing structural data in a BIM building model, recording the actual sizes and material characteristic information of building walls, windows and floors, and generating a building structure parameter table through parameter extraction and data integration. According to the method, the BIM technology and the energy consumption simulation process are utilized, the accuracy of building energy consumption prediction and the practicability of operation are remarkably improved, the building structure parameter table is constructed, the thermal bridge effect is deeply analyzed, so that thermal performance data are more accurate, dynamic adjustment of actual climate change parameters is provided in thermal load simulation, the simulation adaptability is improved, and the simulation efficiency is improved. According to the method, the consistency of an energy consumption prediction result and an actual climate condition is ensured, the simulation precision and practicability are ensured through continuous matching and iterative optimization of actual data and a simulation result, powerful data support is provided for energy efficiency management and optimization, an energy-saving target is helped to be achieved, and the environmental performance of a building is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption simulation, and in particular to a method and system for simulating building energy consumption based on BIM. Background Art

[0002] The field of energy simulation technology mainly involves the use of various calculation methods and models to predict the energy use of buildings in actual operation. This technology can predict and optimize the energy efficiency of buildings during the design stage by simulating the thermal performance and energy consumption characteristics of buildings. Energy simulation tools can take into account a variety of factors, including the building's geographical location, building materials, building structure, HVAC system and other energy-related systems, and conduct a detailed analysis of the building's daily energy consumption, thereby promoting the decision-making process of energy-efficient design and helping to achieve energy conservation and emission reduction goals.

[0003] Among them, the simulation method of building energy consumption aims to evaluate its energy efficiency performance by simulating the energy consumption of buildings, and provide data support for building design and operation. Its main purpose is to help designers and engineers identify energy efficiency potential and energy-saving measures in the early stages of design to optimize the overall energy consumption of buildings. In addition, building energy consumption simulation also supports the formulation of more accurate energy management strategies, thereby improving the sustainability and economic benefits of buildings and reducing environmental impacts.

[0004] Although existing energy consumption simulation technologies provide certain support for building design and operation, they usually fail to fully adapt to changing climate conditions and complex building characteristics, especially in dealing with the dynamic changes of actual material properties and environmental factors. Most of them rely on overly simplified models and assumptions, resulting in inaccurate energy consumption predictions and significant differences from actual energy consumption performance. The lack of effective integration with real-time operational data limits the timely update and optimization of energy consumption management strategies, making it difficult to achieve refined energy efficiency management, resulting in inaccurate assessment of energy efficiency potential, missed opportunities for energy-saving improvements, or unnecessary energy consumption and cost increases in actual operations. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a simulation method and system for building energy consumption based on BIM.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a simulation method of building energy consumption based on BIM, comprising the following steps:

[0007] S1: Import the structural data in the BIM building model, record the actual size and material property information of the building walls, windows and floors, and generate the building structure parameter table through parameter extraction and data integration;

[0008] S2: using the building structure parameter table, performing thermal bridge effect analysis on walls, windows and floors, recording heat flow data, determining the thermal bridge influence of all building components, and obtaining thermal bridge effect analysis results;

[0009] S3: Based on the thermal bridge effect analysis results, heat load simulation is performed, and the energy consumption response under different climate conditions is simulated by adjusting climate change parameters, and the actual climate data is compared with the simulation output information to obtain the climate adjustment energy consumption prediction result;

[0010] S4: Analyze the energy consumption performance of target building components and areas using the climate-adjusted energy consumption prediction results, match the actual energy consumption data, simulate the energy consumption performance, and generate energy consumption simulation results of the target building area through iterative testing;

[0011] S5: Based on the energy consumption simulation results of the target building area, combined with actual operating conditions, including building usage patterns, occupancy rates, indoor and outdoor temperature differences, and usage of air conditioning and lighting systems, a cyclic simulation iteration is performed on the energy consumption behaviors of all building areas to obtain an overall analysis result of the building energy consumption.

[0012] As a further solution of the present invention, the building structure parameter table includes building size details, material properties and structural configuration; the thermal bridge effect analysis results include thermal conductivity, thermal resistance and heat loss records of building components; the climate-adjusted energy consumption prediction results include adjusted energy consumption differences and prediction error range records; the target building area energy consumption simulation results include regional energy consumption distribution results and energy consumption deviation analysis results; the overall building energy consumption analysis results include operating condition adaptability analysis records and total energy consumption prediction records.

[0013] As a further solution of the present invention, the structural data in the BIM building model is imported, the actual size and material property information of the building walls, windows and floors are recorded, and the specific steps of generating the building structure parameter table through parameter extraction and data integration are as follows:

[0014] S101: Based on the BIM building model, extract the geometric data of building walls, windows and floors, record the actual size and material characteristics of each element, perform data cleaning and deduplication, and generate wall size and material information;

[0015] S102: Based on the wall size and material information, classify the attributes of each element, check the accuracy and completeness of each data one by one, and generate a structural data list;

[0016] S103: Based on the structural data list, the data is summarized and sorted, all parameters are arranged, the accuracy of the data is confirmed through manual inspection and proofreading, and a building structure parameter table is generated.

[0017] As a further solution of the present invention, the building structure parameter table is used to perform thermal bridge effect analysis on walls, windows and floors, record heat flow data, determine the thermal bridge influence of all building components, and obtain the thermal bridge effect analysis results in the following specific steps:

[0018] S201: Based on the building structure parameter table, extract the thermal conductivity coefficients of the building walls, windows and floors, combine the contact surfaces of the walls, windows and floors, evaluate the thermal conductivity characteristics of each component, and generate a thermal conductivity evaluation result;

[0019] S202: Based on the thermal conductivity evaluation result, compare and analyze the temperature difference between different building components, calculate the heat transfer amount of the contact points between the building components, determine the thermal bridge impact value of each component, and generate thermal bridge impact data;

[0020] S203: Based on the thermal bridge impact data, analyze the impact of differential thermal bridge areas on the thermal performance of the building, identify key thermal bridge locations, determine the impact on building energy efficiency and temperature distribution, and obtain thermal bridge effect analysis results.

[0021] As a further embodiment of the present invention, the heat transfer at the contact point between the building components is calculated according to the formula:

[0022]

[0023] The calculation is performed, where q represents the amount of heat transferred, K represents the thermal conductivity of the material, A represents the contact area of ​​the material, ΔT represents the temperature difference between the building components, d represents the thickness of the material, and T ext Represents the temperature value outside the building, T int represents the temperature value inside the building, and P represents the perimeter of the building component.

[0024] As a further solution of the present invention, based on the thermal bridge effect analysis results, heat load simulation is performed, by adjusting climate change parameters, simulating energy consumption response under different climate conditions, and comparing actual climate data with simulation output information, the specific steps of obtaining climate adjustment energy consumption prediction results are as follows:

[0025] S301: Based on the thermal bridge effect analysis results, adjust the temperature, humidity and wind speed parameters, set different climate scenarios, simulate the heat load response of the building by changing the indoor and outdoor temperature difference, and record the building energy consumption under each scenario to generate heat load data under climate conditions;

[0026] S302: Based on the heat load data under the climate conditions, the actual climate data is compared with the simulation data, the consistency between the simulation data and the actual data is analyzed, and climate adjustment deviation data is generated;

[0027] S303: Based on the climate adjustment deviation data, combined with actual climate change trends and energy consumption influencing factors, predict the energy consumption changes of the building under different climate conditions, and obtain climate adjustment energy consumption prediction results.

[0028] As a further solution of the present invention, the specific steps of using the climate-adjusted energy consumption prediction results to analyze the energy consumption performance of target building components and areas, matching actual energy consumption data, and simulating energy consumption performance are performed. Through iterative testing, the energy consumption simulation results of the target building area are generated as follows:

[0029] S401: Based on the climate adjustment energy consumption prediction result, extract the heat load data of each building component, analyze the energy consumption performance of the target building components and areas, and generate target building energy consumption analysis data;

[0030] S402: Based on the target building energy consumption analysis data, the target building energy consumption analysis data is matched with the actual energy consumption data, energy consumption deviation analysis is performed, energy consumption difference values ​​of the building areas are calculated, and energy consumption matching difference data is generated;

[0031] S403: Based on the energy consumption matching difference data, compare the gap between the simulation result and the actual data, adjust the parameters and perform iterative testing to generate the energy consumption simulation result of the target building area.

[0032] As a further solution of the present invention, the energy consumption difference value of the building area is calculated according to the formula:

[0033]

[0034] Calculate, where ΔE total Represents the overall energy consumption difference of the building area, E target,i represents the target building energy consumption value of the ith area, E actual,i represents the actual energy consumption value of the ith area, S i represents the building area of ​​the ith area, B i represents the functional category coefficient of the ith area, and n represents the total number of built-up areas.

[0035] As a further solution of the present invention, according to the energy consumption simulation results of the target building area, combined with actual operating conditions, including building usage patterns, occupancy rates, indoor and outdoor temperature differences, and usage of air conditioning and lighting systems, the energy consumption behaviors of all building areas are subjected to cyclic simulation iterations to obtain the overall analysis results of building energy consumption, as follows:

[0036] S501: Based on the energy consumption simulation results of the target building area, combined with the building usage pattern, occupancy rate and actual operating conditions of the indoor and outdoor temperature difference, the on-off cycle of the building air-conditioning system and the on-off duration of the lighting system are extracted, and energy consumption fluctuation data in different time periods are collected to generate building area operating condition data;

[0037] S502: Based on the building area operating condition data, the energy consumption behavior of each building area is simulated, and the effectiveness is verified by setting the upper and lower limits of energy consumption to generate the building area energy consumption performance data;

[0038] S503: Based on the energy consumption performance data of the building area, a cyclic simulation is performed on the energy consumption behavior of all building areas, the overall behavior change trend of the building energy consumption is analyzed, and an overall analysis result of the building energy consumption is generated.

[0039] A simulation system for building energy consumption based on BIM, comprising:

[0040] The building data extraction module extracts building geometry data based on the BIM building model, classifies the attributes of each element, arranges all parameters, confirms the accuracy of the data through manual inspection and proofreading, and generates a building structure parameter table;

[0041] The thermal conductivity evaluation module extracts the thermal conductivity of the building walls, windows and floors based on the building structure parameter table, evaluates the thermal conductivity characteristics of each component, calculates the heat transfer at the contact points between the building components, determines the thermal bridge impact value of each component, and generates thermal bridge impact data;

[0042] The thermal bridge location identification module identifies key thermal bridge locations based on the thermal bridge impact data, determines the impact on building energy efficiency and temperature distribution, sets different climate scenarios, simulates the thermal load response of the building, and generates thermal load data under climate conditions;

[0043] The energy consumption forecasting and analysis module is based on the heat load data under the climate conditions, analyzes the consistency between the simulation data and the actual data, combines the actual climate change trend and energy consumption influencing factors, predicts the energy consumption changes of the building under different climate conditions, and obtains the climate adjustment energy consumption forecast results;

[0044] The energy consumption difference analysis module analyzes the energy consumption performance of target building components and areas based on the climate-adjusted energy consumption prediction results, matches them with actual energy consumption data, calculates energy consumption difference values ​​of building areas, adjusts parameters and performs cyclic iterative testing to generate energy consumption simulation results of target building areas;

[0045] The energy consumption behavior analysis module collects energy consumption fluctuation data in different time periods based on the energy consumption simulation results of the target building area and the actual operating conditions, simulates the energy consumption behavior of each building area, analyzes the overall behavior change trend of the building energy consumption, and generates the overall analysis results of the building energy consumption.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, BIM technology and energy consumption simulation process are used to significantly improve the accuracy of building energy consumption prediction and the practicality of operation, a building structure parameter table is constructed and the thermal bridge effect is deeply analyzed to make the thermal performance data more accurate, and dynamic adjustment of actual climate change parameters is provided in the heat load simulation, which not only improves the adaptability of the simulation and ensures the consistency of energy consumption prediction results with actual climate conditions, but also ensures the accuracy and practicality of the simulation through continuous matching and iterative optimization of actual data and simulation results, provides strong data support for energy efficiency management and optimization, helps achieve energy-saving goals and optimizes the environmental performance of buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the steps of the present invention;

[0049] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0050] Figure 3 is a flow chart of the steps of S2 of the present invention;

[0051] Figure 4 is a flow chart of the steps of S3 of the present invention;

[0052] Figure 5 is a flow chart of the steps of S4 of the present invention;

[0053] Figure 6 is a flow chart of the steps of S5 of the present invention;

[0054] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0057] See also Figure 1 , a simulation method of building energy consumption based on BIM, comprising the following steps:

[0058] S1: Import the structural data in the BIM building model, record the actual size and material property information of the building walls, windows and floors, and generate the building structure parameter table through parameter extraction and data integration;

[0059] S2: Use the building structure parameter table to analyze the thermal bridge effect of walls, windows and floors, record the heat flow data, determine the thermal bridge influence of all building components, and obtain the thermal bridge effect analysis results;

[0060] S3: Based on the results of thermal bridge effect analysis, heat load simulation is carried out. By adjusting climate change parameters, the energy consumption response under different climate conditions is simulated. The actual climate data is compared with the simulation output information to obtain the climate adjustment energy consumption prediction results.

[0061] S4: Use the climate-adjusted energy consumption prediction results to analyze the energy consumption performance of target building components and areas, match the actual energy consumption data, simulate the energy consumption performance, and generate the energy consumption simulation results of the target building area through iterative testing;

[0062] S5: Based on the energy consumption simulation results of the target building area, combined with the actual operating conditions, including building usage patterns, occupancy rate, indoor and outdoor temperature difference, and the use of air conditioning and lighting systems, the energy consumption behaviors of all building areas are simulated iteratively to obtain the overall analysis results of the building energy consumption.

[0063] The building structure parameter table includes building size details, material properties and structural configuration; the thermal bridge effect analysis results include thermal conductivity, thermal resistance and heat loss records of building components; the climate-adjusted energy consumption prediction results include adjusted energy consumption differences and prediction error range records; the target building area energy consumption simulation results include regional energy consumption distribution results and energy consumption deviation analysis results; the overall building energy consumption analysis results include operating condition adaptability analysis records and total energy consumption prediction records.

[0064] See also Figure 2, the specific steps of S1 are:

[0065] S101: Based on the BIM building model, extract the geometric data of building walls, windows and floors, record the actual size and material characteristics of each element, perform data cleaning and deduplication, and generate wall size and material information;

[0066] According to the BIM building model, the geometric data of the building walls, windows and floors are first extracted from the model. These data include the length, width and height of each element, as well as the physical properties of the material (such as material, density, strength, etc.). These data are read and stored through the data acquisition interface; then, the data is cleaned to eliminate duplicate data items, ensure that each wall, window and floor data item is unique, and verify the integrity of its data to eliminate format errors or missing information; finally, based on the cleaned data, a detailed list containing wall dimensions, window dimensions and material information is generated to provide reliable data support for subsequent structural analysis.

[0067] S102: Based on the wall size and material information, classify the attributes of each element, check the accuracy and completeness of each data one by one, and generate a structural data list;

[0068] After obtaining the dimensions and material information of the cleaned wall, attribute classification is performed, and each building element is classified according to different material types and sizes to ensure that all attributes are correctly attributed. For example, walls of different materials are classified as concrete walls, brick walls, glass walls, etc., and each type of wall is checked to ensure that the dimension information is consistent with the actual measurement, especially the installation position and size of the windows are repeatedly checked to correct any possible deviations. In this process, the detailed data in the architectural design drawings and BIM models are combined to check and ensure the accuracy and completeness of each data item item by item, and finally a structural data list is formed for subsequent use and analysis.

[0069] S103: Based on the structural data list, the data is summarized and sorted, all parameters are arranged, the accuracy of the data is confirmed through manual inspection and proofreading, and a building structure parameter table is generated;

[0070] After generating the structural data list, summarize and organize it, integrate all building structure parameters (including wall, window, floor size, material and other information), and arrange them according to specifications; manually review each data item to ensure that each data item is correct, especially the matching of size and material, to prevent problems in subsequent construction due to data errors; through item-by-item proofreading and inspection of the data, a building structure parameter table is finally formed, and detailed and accurate data support is provided for actual construction to ensure that all parameters meet the building design and construction requirements.

[0071] See also Figure 3 , the specific steps of S2 are:

[0072] S201: Based on the building structure parameter table, the thermal conductivity coefficients of the building walls, windows and floors are extracted, and the thermal conductivity characteristics of each component are evaluated in combination with the contact surfaces of the walls, windows and floors to generate thermal conductivity evaluation results;

[0073] To extract the thermal conductivity coefficient of building walls, windows and floors, it is first necessary to obtain the material properties of each component from the building structure parameter table, and obtain the thermal conductivity of each component (such as walls, windows, floors) based on the thermal conductivity performance table of building materials or experimental data sets. Then, according to the geometric shape of the component and the contact surface of each component, the heat conduction formula is used to evaluate its thermal conductivity characteristics. The contact surface information of walls, windows and floors needs to be extracted from the model data, and the relevant contact surface parameters such as contact area and contact angle are obtained through geometric calculation. Subsequently, according to the heat conduction formula, the thermal conduction characteristics between different components are calculated to obtain the thermal conductivity characteristics evaluation results of each component. The results of this process show the relevant data of the thermal conductivity coefficients of components such as walls, windows, floors and their influencing factors.

[0074] S202: Based on the thermal conductivity evaluation results, compare and analyze the temperature differences between different building components, calculate the heat transfer at the contact points between the building components, determine the thermal bridge impact value of each component, and generate thermal bridge impact data;

[0075] The amount of heat transfer at the contact points between building components is calculated according to the formula:

[0076]

[0077] The calculation is performed, where q represents the amount of heat transferred, K represents the thermal conductivity of the material, which is a physical quantity that measures the material's ability to conduct heat energy, A represents the contact area of ​​the material, which directly affects the total amount of heat transferred, ΔT represents the temperature difference between building components, which is the driving force in the heat flow calculation, d represents the thickness of the material, which determines the resistance to heat flow, and T ext Represents the temperature value outside the building, T int represents the temperature value inside the building, P represents the circumference of the building components, which is proportional to the probability of thermal bridge formation, affects the size of the heat flow, and ensures the flexibility and accuracy of the heat flow calculation.

[0078] K (material thermal conductivity): Thermal conductivity K is a physical quantity that describes the heat transfer ability of a material. Its unit is Watt per meter per Kelvin (W / m·K), which is usually obtained through experimental measurement and varies according to the physical properties of different materials. For example, the thermal conductivity of steel is usually 50W / m·K, while the thermal conductivity of wood is 0.15W / m·K. The thermal conductivity value needs to be obtained based on the properties of specific building materials or standardized tests.

[0079] A (contact area): The contact area A represents the actual contact surface area between building components, and the unit is square meters (m2). This value is obtained from architectural design drawings or physical measurements. For example, if the contact surface of a building component is a rectangle with a length of 2m and a width of 1m, then the contact area A = 2m × 1m = 2m 2 .

[0080] ΔT (temperature difference): The temperature difference ΔT is the difference between the internal and external surface temperatures of building components, which is obtained through real-time temperature sensor data. For example, the indoor temperature T is obtained through the sensor int =22℃, outdoor temperature T ext =10℃, then the temperature difference ΔT=22-10=12℃.

[0081] d (material thickness): Material thickness d directly affects the heat flow capacity, and the unit is meter (m). This value can be obtained through physical measurement or architectural design drawings, usually measuring the actual material thickness. For example, the material thickness is 0.2m.

[0082] P (perimeter): The perimeter P is the length of the boundary around the contact surface of the building component, in meters (m). It can be obtained from the building design drawings or by measurement. For example, if the shape of the contact surface is a rectangle, the perimeter P = 2 × (2m + 1m) = 6m.

[0083] The first part calculates:

[0084]

[0085] The result represents the material's ability to transfer heat through its thickness.

[0086] The second part calculates:

[0087]

[0088] This result reflects the influence of the relationship between ambient temperature difference and circumference on heat flow.

[0089] Overall calculation results:

[0090] q=6000+29.39≈6029.39W

[0091] The results show that under specific material, temperature difference and perimeter conditions, the heat flux of the building component is about 6029.39 watts. That is, the heat energy transferred through the contact surface of the building component is 6029.39 watts, which represents the contribution of the component to the thermal bridge effect. The greater the heat flux, the more obvious the thermal bridge effect of the component. This value will be used to further evaluate and optimize the thermal performance of the building, especially to evaluate the reduction or enhancement of the thermal bridge effect.

[0092] S203: Based on the thermal bridge impact data, analyze the impact of differential thermal bridge areas on the thermal performance of the building, identify key thermal bridge locations, determine the impact on building energy efficiency and temperature distribution, and obtain thermal bridge effect analysis results;

[0093] When analyzing the thermal bridge impact data, the thermal bridge impact of different areas is first summarized. Based on the temperature difference and heat conduction characteristics of the thermal bridge area, combined with the thermal performance data of the building, the energy efficiency loss of each area is statistically analyzed. Through the thermal bridge effect analysis model, the thermal bridge area is identified and its impact on the temperature distribution of the building is determined, which requires data collection through temperature sensors and heat flow measurement systems. Then, based on the impact analysis of the thermal bridge location, the negative impact of the thermal effect at this location on the overall building energy efficiency is determined, and finally the thermal bridge effect analysis results are obtained. This process shows the degree of influence of different thermal bridge areas on the thermal performance of the building, the contribution of the location of the thermal bridge area to the building energy efficiency, and how it affects the temperature distribution in the building.

[0094] See also Figure 4 , the specific steps of S3 are:

[0095] S301: Based on the results of thermal bridge effect analysis, adjust the temperature, humidity and wind speed parameters, set different climate scenarios, simulate the heat load response of the building by changing the indoor and outdoor temperature difference, and record the building energy consumption under each scenario to generate heat load data under climate conditions;

[0096] Based on the results of thermal bridge effect analysis, the temperature, humidity and wind speed parameters are adjusted, and different climate scenarios are set to simulate the heat load response of the building. First, the indoor and outdoor temperature difference data under different climate conditions need to be collected. The indoor temperature change trend is further calculated based on the outdoor temperature data and the heat exchange conditions of the building's outer surface. Combined with the humidity and wind speed data, the heat conduction equation is used to adjust the indoor and outdoor heat flow exchange. The building energy consumption is evaluated through the heat load response data of different time periods. According to the outdoor temperature and wind speed data, the indoor and outdoor temperature difference is adjusted to the heat load demand of the building through the heat load model, and the corresponding energy consumption data is generated in each climate scenario. Finally, the specific energy consumption values ​​of the building under each different climate scenario are recorded and stored to provide data support for subsequent analysis.

[0097] S302: Based on the heat load data under the climate conditions, the actual climate data is compared with the simulation data, the consistency between the simulation data and the actual data is analyzed, and climate adjustment deviation data is generated;

[0098] According to the heat load data under climatic conditions, the actual climate data is first compared with the simulation data, and the difference analysis method is used to analyze the deviation between the simulation data and the actual data. Through statistical analysis, the error range under different climate scenarios is obtained. On this basis, combined with historical climate data, the climate simulation parameters are adjusted to make the results of the simulation data closer to the actual data, and further accurately predict the energy demand of the building under different climatic conditions. Through this comparison process, the climate adjustment deviation data is obtained to provide data reference for subsequent energy consumption forecasts, ensuring that the forecast results have high reliability and accuracy.

[0099] S303: Based on the climate adjustment deviation data, combined with the actual climate change trend and energy consumption influencing factors, predict the energy consumption change of the building under different climate conditions, and obtain the climate adjustment energy consumption prediction result;

[0100] Based on the climate adjustment deviation data, combined with the actual climate change trends and energy consumption influencing factors, we first analyze the correlation between historical climate data and building energy consumption, select relevant climate factors (such as temperature, humidity, wind speed, etc.) and conduct multivariate regression analysis with the historical building energy consumption data, and further predict the changes in building energy consumption under different climate conditions in the future. According to the climate adjustment deviation data, the impact of climate change on building energy consumption is quantified, and through modeling and prediction of energy consumption change trends, specific climate adjustment energy consumption prediction results are obtained to ensure that the energy consumption prediction results under different climate conditions provide a strong basis for actual decision-making.

[0101] See also Figure 5 , the specific steps of S4 are:

[0102] S401: Based on the climate-adjusted energy consumption prediction results, extract the heat load data of each building component, analyze the energy consumption performance of the target building components and areas, and generate target building energy consumption analysis data;

[0103] According to the energy consumption performance of the target building components and areas, the energy consumption data of each area can be analyzed by extracting the heat load data of each building component. First, it is necessary to conduct a detailed heat load assessment of each component inside the building, and use a heat flow calculation model, such as the heat conduction equation, to simulate the heat flow transfer of each component, so as to obtain the energy consumption data of the building components under different climatic conditions. The climate data is obtained by predicting the heat load of the target building through local climate monitoring stations or historical climate data records. Then, the energy consumption of each area of ​​the building is regionalized and analyzed. By comparing the relationship between the heat load data of different areas and the total energy consumption of the building, the specific energy consumption performance of each component and area is analyzed. Finally, the energy consumption analysis data of the target building is generated to facilitate further energy efficiency optimization.

[0104] S402: Based on the target building energy consumption analysis data, the energy consumption analysis data is matched with the actual energy consumption data, energy consumption deviation analysis is performed, energy consumption difference values ​​of the building areas are calculated, and energy consumption matching difference data is generated;

[0105] The energy consumption difference of the building area is calculated according to the formula:

[0106]

[0107] Calculate, where ΔE total Represents the overall energy consumption difference of the building area, E target,i represents the target building energy consumption value of the ith area, E actual,i represents the actual energy consumption value of the ith area, S i represents the building area of ​​the ith area, B i represents the functional category coefficient of the ith area, and n represents the total number of built-up areas.

[0108] Target building energy consumption value E target,i Acquisition: During the building design phase, architects and energy efficiency engineers use simulation software (such as EnergyPlus, TRNSYS, etc.) to predict the energy consumption of each area based on the building's structure, materials, equipment configuration, etc. The predicted value reflects the energy consumption of the building under standard operating conditions and is calculated based on the functional requirements of the area, equipment operating parameters, etc.

[0109] Actual energy consumption value E actual,i Acquisition: By installing monitoring equipment such as smart meters, temperature and humidity sensors, the actual energy consumption data of each area of ​​the building is collected in real time. The energy consumption of each area is continuously monitored and the actual data is recorded according to a specific period (such as hourly, daily, etc.).

[0110] Building area i Acquisition: The building area of ​​each area can be obtained through architectural design drawings or actual on-site measurement data, in square meters. Larger areas may involve more equipment and personnel, and the energy consumption difference may be more significant, so the quantification accuracy of the area directly affects the accuracy of the calculation.

[0111] Functional category coefficient B i Acquisition: Functional category coefficient B i It is set according to the functional complexity of the area. For example, the energy consumption coefficient of the office area is set to 1.2, the residential area is 1.0, and the commercial area is 1.5. This coefficient reflects the impact of different areas on energy consumption, based on a comprehensive assessment of the actual use of the building, equipment density, etc.

[0112] Obtaining the total number of areas n: n is the total number of building areas, usually defined by architects during the building design phase. Each area represents an independent energy management unit within the building. The total number of areas is the basis for various monitoring devices and energy consumption data collection.

[0113] Take a building as an example, consider four areas n = 4, namely office area, restaurant, dormitory area and store area. The target building energy consumption E of each area is target,i and actual energy consumption E actual,i , and the corresponding building area A i and functional category coefficient B i as follows:

[0114]

[0115] Calculate the energy consumption difference part: For each area, calculate the difference between the target energy consumption and the actual energy consumption and multiply it by the square root of the building area of ​​the area. Sum the difference results for all areas:

[0116] Office Area:

[0117]

[0118] Dining room:

[0119]

[0120] Dormitory area:

[0121]

[0122] Shop Area:

[0123]

[0124] but:

[0125]

[0126] Calculate the sum of the functional category coefficients:

[0127]

[0128] Calculate the overall energy consumption difference:

[0129]

[0130] Calculation result ΔE total=730.8 represents the energy consumption difference value of the entire building area. This value reflects the overall deviation between the target energy consumption and the actual energy consumption, taking into account the influence of the building area and functional complexity. The size of this value indicates the optimization space of building energy consumption. When the value is high, it means that there are large differences in energy efficiency in certain areas, and further optimization of building design or operation management is needed.

[0131] S403: Based on the energy consumption matching difference data, compare the gap between the simulation result and the actual data, adjust the parameters and perform iterative testing to generate the energy consumption simulation result of the target building area;

[0132] Compare the difference between the simulation results and the actual energy consumption data, adjust the parameters and perform iterative testing. In order to optimize the energy consumption prediction model, first analyze and determine the direction of model parameter adjustment based on the energy consumption matching difference data. Through the error feedback mechanism, gradually adjust the parameters such as temperature conduction and air flow in the simulation calculation, and re-simulate the energy consumption. After each parameter adjustment, recalculate the difference between the simulation results and the actual energy consumption data until a smaller error range is reached. Through multiple iterative calculations, the accurate simulation results of the energy consumption of the target building area are finally generated.

[0133] See also Figure 6 , the specific steps of S5 are:

[0134] S501: Based on the energy consumption simulation results of the target building area, combined with the building usage pattern, occupancy rate and actual operating conditions of the indoor and outdoor temperature difference, the on-off cycle of the building air-conditioning system and the on-off duration of the lighting system are extracted, and the energy consumption fluctuation data in different time periods are collected to generate the building area operating condition data;

[0135] According to the energy consumption simulation results of the target building area, the energy consumption fluctuation data of the building can be collected by extracting the switching cycle of the building air conditioning system and the on-off duration of the lighting system, combined with the actual operating conditions such as the building usage pattern, occupancy rate and indoor and outdoor temperature difference. In the specific operation, first extract the operation log of the air conditioning and lighting system from the building control system, record the switching cycle and on-off duration of the system, and then further analyze these operation cycles in combination with the building usage pattern and occupancy rate. Use temperature sensors and meteorological station data to measure the impact of indoor and outdoor temperature differences on the energy consumption of the building area. Through data collection and analysis, the energy consumption fluctuation data of the building area under different conditions can be obtained, which helps to understand the energy consumption dynamics of the building, and finally generate operating condition data for each area, providing a basis for subsequent energy efficiency optimization.

[0136] S502: Based on the building area operating condition data, the energy consumption behavior of each building area is simulated, and the effectiveness is verified by setting the upper and lower limits of energy consumption to generate the building area energy consumption performance data;

[0137] According to the building area operating condition data, by simulating the energy consumption behavior of each building area and setting the upper and lower limits of energy consumption, the rationality of the energy consumption simulation can be effectively verified. First, for the operating condition data of each area, an energy consumption behavior model is established, and various factors such as the air conditioning system and lighting system are included in the model calculation. The upper and lower limits of energy consumption are set as verification standards to ensure that the energy consumption behavior is within a reasonable range. During the simulation process, the results will be compared according to the set upper and lower limits to determine whether there is an energy consumption fluctuation that exceeds the reasonable range. By analyzing the simulation results of each area, the energy consumption performance data of the building area is generated. The data can not only reflect the actual energy consumption of the building, but also provide data support for subsequent energy efficiency management.

[0138] S503: Based on the energy consumption performance data of the building area, the energy consumption behavior of all building areas is cyclically simulated, the overall behavior change trend of the building energy consumption is analyzed, and the overall analysis result of the building energy consumption is generated;

[0139] Based on the energy consumption performance data of the building area, the energy consumption behavior of all areas of the building is simulated in a cycle to analyze the changing trend of the overall energy consumption behavior of the building. During the simulation process, the energy consumption data of each area will be integrated first, and the fluctuation of the overall energy consumption of the building will be tracked through multiple cycles of simulation. Through trend analysis of the simulation results, the changes in building energy consumption in different time periods can be obtained, and potential problems in building energy efficiency optimization can be further identified. The analysis results can help identify the peak and valley values ​​of energy consumption in each area, as well as the impact of various energy consumption factors on the overall building energy consumption, generate overall analysis results of building energy consumption, and provide data support for the building's energy efficiency evaluation and optimization plan.

[0140] See also Figure 7 , a BIM-based building energy consumption simulation system, including:

[0141] The building data extraction module extracts building geometry data based on the BIM building model, classifies the attributes of each element, arranges all parameters, confirms the accuracy of the data through manual inspection and proofreading, and generates a building structure parameter table;

[0142] The thermal conductivity evaluation module extracts the thermal conductivity of building walls, windows and floors based on the building structure parameter table, evaluates the thermal conductivity characteristics of each component, calculates the heat transfer at the contact points between building components, determines the thermal bridge impact value of each component, and generates thermal bridge impact data;

[0143] The thermal bridge location identification module identifies key thermal bridge locations based on thermal bridge impact data, determines the impact on building energy efficiency and temperature distribution, sets different climate scenarios, simulates the thermal load response of the building, and generates thermal load data under climate conditions;

[0144] The energy consumption forecasting and analysis module is based on the heat load data under climate conditions, analyzes the consistency between the simulation data and the actual data, and combines the actual climate change trend and energy consumption influencing factors to predict the energy consumption changes of buildings under different climate conditions and obtain climate-adjusted energy consumption forecast results;

[0145] The energy consumption difference analysis module adjusts the energy consumption prediction results based on the climate, analyzes the energy consumption performance of the target building components and areas, matches them with the actual energy consumption data, calculates the energy consumption difference values ​​of the building areas, adjusts the parameters and performs iterative tests to generate energy consumption simulation results for the target building areas;

[0146] The energy consumption behavior analysis module is based on the energy consumption simulation results of the target building area and combines the actual operating conditions to collect energy consumption fluctuation data in different time periods, simulates the energy consumption behavior of each building area, analyzes the overall behavior change trend of the building energy consumption, and generates the overall analysis results of the building energy consumption.

[0147] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A simulation method for building energy consumption based on BIM, characterized in that: The following steps are involved: Import structural data from the BIM building model, record the actual dimensions and material properties of building walls, windows and floors, and generate a building structure parameter table through parameter extraction and data integration; Using the building structure parameter table, thermal bridge effect analysis is performed on walls, windows and floors, heat flow data is recorded, thermal bridge influence of all building components is determined, and thermal bridge effect analysis results are obtained; Based on the thermal bridge effect analysis results, heat load simulation is performed, and the energy consumption response under different climate conditions is simulated by adjusting climate change parameters, and the actual climate data is compared with the simulation output information to obtain the climate adjustment energy consumption prediction results; Utilizing the climate-adjusted energy consumption prediction results, analyzing the energy consumption performance of target building components and areas, matching actual energy consumption data, performing energy consumption performance simulation, and generating target building area energy consumption simulation results through iterative testing; Based on the energy consumption simulation results of the target building area, combined with actual operating conditions, including building usage patterns, occupancy rate, indoor and outdoor temperature difference, and usage of air conditioning and lighting systems, a cyclic simulation iteration is performed on the energy consumption behavior of all building areas to obtain the overall analysis results of the building energy consumption.

2. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: The building structure parameter table includes building size details, material properties and structural configuration; the thermal bridge effect analysis results include thermal conductivity, thermal resistance and heat loss records of building components; the climate adjustment energy consumption prediction results include adjusted energy consumption differences and prediction error range records; the target building area energy consumption simulation results include regional energy consumption distribution results and energy consumption deviation analysis results; The overall analysis results of building energy consumption include operating condition adaptability analysis records and total energy consumption forecast records.

3. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: Import the structural data in the BIM building model, record the actual size and material property information of the building walls, windows and floors, and generate the building structure parameter table through parameter extraction and data integration. The specific steps are as follows: Based on the BIM building model, the geometric data of building walls, windows and floors are extracted, the actual size and material characteristics of each element are recorded, data cleaning and deduplication are performed, and wall size and material information are generated; Based on the wall dimensions and material information, the attributes of each element are classified, the accuracy and completeness of each data item are checked one by one, and a structural data list is generated; Based on the structural data list, the data is summarized and sorted, all parameters are arranged, the accuracy of the data is confirmed through manual inspection and proofreading, and a building structure parameter table is generated.

4. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: The building structure parameter table is used to analyze the thermal bridge effect of walls, windows and floors, record heat flow data, determine the thermal bridge influence of all building components, and obtain the specific steps of the thermal bridge effect analysis results as follows: Based on the building structure parameter table, the thermal conductivity coefficients of the building walls, windows and floors are extracted, and the thermal conductivity characteristics of each component are evaluated in combination with the contact surfaces of the walls, windows and floors to generate a thermal conductivity evaluation result; Based on the thermal conductivity evaluation results, the temperature differences between different building components are compared and analyzed, the heat transfer at the contact points between the building components is calculated, the thermal bridge impact value of each component is determined, and thermal bridge impact data is generated; Based on the thermal bridge impact data, the impact of differential thermal bridge areas on the thermal performance of the building is analyzed, the key thermal bridge locations are identified, the impact on the building energy efficiency and temperature distribution is determined, and the thermal bridge effect analysis results are obtained.

5. The method for simulating building energy consumption based on BIM according to claim 4 is characterized in that: The heat transfer at the contact points between the building components is calculated according to the formula: The calculation is performed, where q represents the amount of heat transferred, K represents the thermal conductivity of the material, A represents the contact area of ​​the material, ΔT represents the temperature difference between the building components, d represents the thickness of the material, which determines the resistance to heat flow, and T ext Represents the temperature value outside the building, T int represents the temperature value inside the building, and P represents the perimeter of the building component.

6. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: Based on the thermal bridge effect analysis results, heat load simulation is performed, and the energy consumption response under different climate conditions is simulated by adjusting the climate change parameters. The actual climate data is compared with the simulation output information to obtain the climate adjustment energy consumption prediction results. The specific steps are as follows: Based on the thermal bridge effect analysis results, adjust the temperature, humidity and wind speed parameters, set different climate scenarios, simulate the heat load response of the building by changing the indoor and outdoor temperature difference, and record the building energy consumption under each scenario to generate heat load data under climate conditions; Based on the heat load data under the climate conditions, the actual climate data is compared with the simulation data, the consistency between the simulation data and the actual data is analyzed, and climate adjustment deviation data is generated; Based on the climate adjustment deviation data, combined with actual climate change trends and energy consumption influencing factors, the energy consumption changes of buildings under different climate conditions are predicted to obtain climate adjustment energy consumption prediction results.

7. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: The specific steps of using the climate-adjusted energy consumption prediction results to analyze the energy consumption performance of target building components and areas, matching actual energy consumption data, and simulating energy consumption performance are as follows: Based on the climate-adjusted energy consumption prediction results, extract the heat load data of each building component, analyze the energy consumption performance of the target building components and areas, and generate target building energy consumption analysis data; Based on the target building energy consumption analysis data, the energy consumption difference value of the building area is calculated by matching the target building energy consumption analysis data with the actual energy consumption data to generate energy consumption matching difference data; Based on the energy consumption matching difference data, the gap between the simulation results and the actual data is compared, the parameters are adjusted and a cyclic iterative test is performed to generate the energy consumption simulation results of the target building area.

8. The method for simulating building energy consumption based on BIM according to claim 7, characterized in that: The energy consumption difference value of the building area is according to the formula: Calculate, where ΔE total Represents the overall energy consumption difference of the building area, E target,i represents the target building energy consumption value of the ith area, E actual,i represents the actual energy consumption value of the ith area, S i represents the building area of ​​the ith area, B i represents the functional category coefficient of the ith area, and n represents the total number of built-up areas.

9. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: According to the energy consumption simulation results of the target building area, combined with the actual operating conditions, including building usage patterns, occupancy rate, indoor and outdoor temperature difference, and the use of air conditioning and lighting systems, the energy consumption behaviors of all building areas are simulated and iterated cyclically to obtain the overall analysis results of building energy consumption. The specific steps are as follows: Based on the energy consumption simulation results of the target building area, combined with the building usage pattern, occupancy rate and actual operating conditions of the indoor and outdoor temperature difference, the on-off cycle of the building air-conditioning system and the on-off duration of the lighting system are extracted, and the energy consumption fluctuation data in different time periods are collected to generate the building area operating condition data; Based on the building area operating condition data, the energy consumption behavior of each building area is simulated, and the effectiveness is verified by setting upper and lower limits of energy consumption to generate building area energy consumption performance data; Based on the energy consumption performance data of the building area, the energy consumption behavior of all building areas is simulated cyclically, the changing trend of the overall building energy consumption behavior is analyzed, and the overall analysis result of the building energy consumption is generated.

10. A BIM-based building energy consumption simulation system, characterized in that: According to a method for simulating building energy consumption based on BIM according to any one of claims 1 to 9, the system comprises: The building data extraction module extracts building geometry data based on the BIM building model, classifies the attributes of each element, arranges all parameters, confirms the accuracy of the data through manual inspection and proofreading, and generates a building structure parameter table; The thermal conductivity evaluation module extracts the thermal conductivity of the building walls, windows and floors based on the building structure parameter table, evaluates the thermal conductivity characteristics of each component, calculates the heat transfer at the contact points between the building components, determines the thermal bridge impact value of each component, and generates thermal bridge impact data; The thermal bridge location identification module identifies key thermal bridge locations based on the thermal bridge impact data, determines the impact on building energy efficiency and temperature distribution, sets different climate scenarios, simulates the thermal load response of the building, and generates thermal load data under climate conditions; The energy consumption forecasting and analysis module is based on the heat load data under the climate conditions, analyzes the consistency between the simulation data and the actual data, combines the actual climate change trend and energy consumption influencing factors, predicts the energy consumption changes of the building under different climate conditions, and obtains the climate adjustment energy consumption forecast results; The energy consumption difference analysis module analyzes the energy consumption performance of target building components and areas based on the climate-adjusted energy consumption prediction results, matches them with actual energy consumption data, calculates energy consumption difference values ​​of building areas, adjusts parameters and performs cyclic iterative testing to generate energy consumption simulation results of target building areas; The energy consumption behavior analysis module collects energy consumption fluctuation data in different time periods based on the energy consumption simulation results of the target building area and the actual operating conditions, simulates the energy consumption behavior of each building area, analyzes the overall behavior change trend of the building energy consumption, and generates the overall analysis results of the building energy consumption.

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