A simulation method and system for building energy consumption based on BIM

Abstract

The present invention relates to the field of energy consumption simulation technology, specifically a simulation method and system for building energy consumption based on BIM, comprising the following steps: importing structural data in a BIM building model, recording the actual dimensions and material property information of building walls, windows, and floors, and generating a building structure parameter table through parameter extraction and data integration. In the present invention, BIM technology and energy consumption simulation processes are utilized to significantly improve the accuracy of building energy consumption prediction and the practicality of operation, construct a building structure parameter table and conduct in-depth analysis of thermal bridge effects to make thermal performance data more accurate, and provide dynamic adjustment of actual climate change parameters in thermal 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 simulation accuracy and practicality through continuous matching and iterative optimization of actual data and simulation results, providing strong data support for energy efficiency management and optimization, helping to achieve energy-saving goals and optimize the environmental performance of buildings.

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 primarily involves using various calculation methods and models to predict a building's actual energy use during operation. By simulating a building's thermal performance and energy consumption characteristics, this technology can predict and optimize a building's energy efficiency during the design phase. Energy simulation tools can consider a variety of factors, including a building's location, building materials, structure, HVAC systems, and other energy-related systems, providing a detailed analysis of a building's daily energy consumption. This can facilitate energy-efficiency design decisions and help achieve energy conservation and emission reduction goals.

[0003] Building energy simulation methods aim to evaluate a building's energy efficiency by simulating its energy consumption, providing data support for building design and operation. Their primary purpose is to help designers and engineers identify energy efficiency potential and energy-saving measures early in the design process to optimize the building's overall energy consumption. Furthermore, building energy simulations support the development of more precise energy management strategies, thereby improving a building's sustainability and economic efficiency while reducing its environmental impact.

[0004] While existing energy simulation technologies provide some support for building design and operations, they often fail to fully adapt to changing climate conditions and complex building characteristics. They are particularly limited in addressing the dynamic changes in actual material properties and environmental factors. Most rely on overly simplified models and assumptions, resulting in inaccurate energy consumption forecasts that differ significantly from actual energy consumption performance. The lack of effective integration with real-time operational data limits the timely updating and optimization of energy management strategies, making it difficult to achieve refined energy efficiency management. This leads to inaccurate assessments of energy efficiency potential, missed opportunities for energy-saving improvements, and 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 BIM-based building energy consumption simulation method and system.

[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 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;

[0008] S2: Using the building structure parameter table, perform thermal bridge effect analysis on walls, windows, and floors, record heat flow data, determine the thermal bridge impact of all building components, and obtain thermal bridge effect analysis results;

[0009] S3: Based on the thermal bridge effect analysis results, heat load simulation is performed. By adjusting climate change parameters, energy consumption responses under different climate conditions are simulated. Actual climate data is compared with the simulation output information to obtain climate-adjusted energy consumption prediction results.

[0010] S4: Analyze the energy consumption performance of target building components and areas using the climate-adjusted energy consumption prediction results, match them with actual energy consumption data, perform energy consumption performance simulation, and generate energy consumption simulation results for 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 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 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 value 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 dimensions and material characteristics 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 properties of each element, perform data cleaning and deduplication, and generate wall size and material information;

[0015] S102: 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;

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

[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, and determine the thermal bridge impact of all building components. The specific steps for obtaining the thermal bridge effect analysis results are as follows:

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

[0019] S202: Based on the thermal conductivity evaluation results, comparing and analyzing the temperature differences between different building components, calculating the heat transfer amount at the contact points between the building components, determining the thermal bridge impact value of each component, and generating 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 points between the building components is calculated according to the formula:

[0022]

[0023] 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 building components, d represents the thickness of the material, and T ext Represents the temperature 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, and by adjusting climate change parameters, energy consumption responses under different climate conditions are simulated. The actual climate data is compared with the simulation output information to obtain the climate-adjusted energy consumption prediction results. The specific steps are as follows:

[0025] S301: Based on the thermal bridge effect analysis results, adjust the temperature, humidity, and wind speed parameters to set different climate scenarios. By changing the indoor and outdoor temperature differences, simulate the heat load response of the building, record the building energy consumption under each scenario, and generate heat load data under the 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 to 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, match the actual energy consumption data, perform energy consumption performance simulation, and generate the target building area energy consumption simulation results through iterative testing are as follows:

[0029] S401: Based on the climate-adjusted 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, matching with actual energy consumption data, performing energy consumption deviation analysis, calculating energy consumption difference values ​​of building areas, and generating energy consumption matching difference data;

[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 cyclic 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 i-th area, E actual,i represents the actual energy consumption value of the i-th area, S i represents the building area of ​​the ith area, B i represents the functional category coefficient of the i-th area, and n represents the total number of building areas.

[0035] As a further embodiment of the present invention, based on the energy consumption simulation results of the target building area and 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 is performed on the energy consumption behaviors of all building areas to obtain the overall building energy consumption analysis results. The specific steps are as follows:

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

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

[0038] S503: Based on the energy consumption performance data of the building areas, 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 BIM-based building energy consumption simulation system, 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 building walls, windows, and floors based on the building structure parameter table, evaluates the thermal conductivity characteristics of each component, calculates the amount of heat transfer at contact points between 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 analyzes the consistency between the simulated data and the actual data based on the heat load data under the climate conditions. It also combines the actual climate change trends and energy consumption influencing factors to predict the energy consumption changes of the building under different climate conditions and obtain the climate-adjusted 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 ​​for building areas, adjusts parameters and performs iterative testing to generate energy consumption simulation results for 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. 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, providing strong data support for energy efficiency management and optimization, helping to achieve energy-saving goals and optimize the environmental performance of buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 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 This is a flow chart of the steps of S2 of the present invention;

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

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

[0053] Figure 6 This 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 solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly 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 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;

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

[0060] S3: Based on the results of the thermal bridge effect analysis, heat load simulation is performed. 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-adjusted energy consumption prediction results.

[0061] S4: Analyze the energy consumption performance of target building components and areas using the climate-adjusted energy consumption forecast results, match them with actual energy consumption data, perform energy consumption performance simulations, and generate energy consumption simulation results for the target building area through iterative testing;

[0062] S5: Based on the energy consumption simulation results of the target building area and combined with 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 behavior of all building areas is simulated iteratively to obtain the overall building energy consumption analysis results.

[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 properties of each element, perform data cleaning and deduplication, and generate wall size and material information;

[0066] Based on the BIM building model, the geometric data of the building walls, windows and floors are first extracted from the model. This data includes 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, data cleaning is performed to eliminate duplicate data items, ensure that each wall, window and floor data item is unique, and verify its data integrity 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 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;

[0068] After obtaining the dimensions and material information of the cleaned walls, 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 material 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 each data item one by one to ensure its accuracy and completeness, and finally form a structural data list for subsequent use and analysis.

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

[0070] After generating the structural data list, it is summarized and organized, and all building structural parameters (including the dimensions and materials of walls, windows, and floors) are integrated and arranged according to specifications; each data item is manually reviewed to ensure that each data item is correct, especially the matching of dimensions and materials, to prevent problems in subsequent construction due to data errors; through item-by-item proofreading and inspection of the data, a building structural parameter table is finally formed, and detailed and accurate data support is provided for actual construction to ensure that all parameters meet the requirements of building design and construction.

[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. Combined with the contact surfaces of the walls, windows, and floors, the thermal conductivity characteristics of each component are evaluated 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, based on the geometric shape of the component and the contact surface of each component, the thermal conductivity characteristics are evaluated using the heat conduction formula. 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 calculations. Subsequently, based on the heat conduction formula, the thermal conductivity 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 assessment 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 transferred 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 outside the building, T int represents the temperature value inside the building, P represents the perimeter of the building component, 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 a material's ability to transfer heat. Its units are Watts per meter per Kelvin (W / m·K). It is typically measured experimentally and varies depending on the physical properties of the material. For example, the thermal conductivity of steel is typically 50 W / m·K, while the thermal conductivity of wood is 0.15 W / m·K. Determining thermal conductivity values ​​depends on the properties of specific building materials or standardized testing.

[0079] A (Contact Area): The contact area A represents the actual contact surface area between building components, measured in 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 is measured in meters (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): Perimeter P is the length of the boundary around the contact surface of a building component, measured in meters (m). This is obtained from architectural drawings or measurements. For example, if the contact surface is rectangular, 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 a building component is approximately 6029.39 watts. This value represents the component's contribution to thermal bridging. A higher heat flux indicates a more pronounced thermal bridging effect. This value will be used to further evaluate and optimize the building's thermal performance, particularly to assess whether thermal bridging can be mitigated or enhanced.

[0092] S203: Based on the thermal bridge impact data, analyze the impact of differential thermal bridge areas on the building's thermal performance, 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 thermal bridge impact data, the impact of thermal bridges in different areas is first summarized. Based on the temperature differences and heat conduction characteristics of the thermal bridge areas, combined with the building's thermal performance data, the energy efficiency losses in each area are calculated. Using a thermal bridge effect analysis model, thermal bridge areas are identified and their impact on the building's temperature distribution is determined. This requires collecting data using 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 that location on the overall building energy efficiency is determined, ultimately resulting in the thermal bridge effect analysis results. This process demonstrates the extent to which different thermal bridge areas affect the building's thermal performance, the contribution of the thermal bridge area's location to the building's energy efficiency, and how it affects the temperature distribution within the building.

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

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

[0096] Based on the results of the thermal bridge effect analysis, temperature, humidity, and wind speed parameters are adjusted to set different climate scenarios to simulate the building's heat load response. First, indoor and outdoor temperature difference data under different climate conditions is collected. The indoor temperature trend is then calculated based on the outdoor temperature data and the heat exchange conditions on the building's exterior surface. Combined with humidity and wind speed data, the heat conduction equation is used to adjust the indoor and outdoor heat flow exchange. The building's energy consumption is then evaluated using the heat load response data from different time periods. Based on the outdoor temperature and wind speed data, the indoor and outdoor temperature difference is adjusted to the building's heat load demand through a heat load model. Corresponding energy consumption data is generated for each climate scenario. Finally, the specific energy consumption values ​​for 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] Based on the heat load data under different climate conditions, we first compare the actual climate data with the simulated data. Using difference analysis, we analyze the deviations between the simulated and actual data. Through statistical analysis, we determine the error ranges for each different climate scenario. Based on this, we combine historical climate data to adjust the climate simulation parameters, bringing the simulated data closer to the actual data and further accurately predicting the building's energy demand under different climate conditions. This comparison process generates climate-adjusted deviation data, which provides a reference for subsequent energy consumption forecasts, ensuring the reliability and accuracy of the forecast results.

[0099] 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 the climate adjustment energy consumption prediction results;

[0100] Based on climate-adjusted deviation data, combined with actual climate change trends and factors influencing energy consumption, we first analyzed the correlation between historical climate data and building energy consumption. We then selected relevant climate factors (such as temperature, humidity, and wind speed) and combined them with historical building energy consumption data for a multivariate regression analysis to further predict changes in building energy consumption under different future climate conditions. Using climate-adjusted deviation data, we quantified the impact of climate change on building energy consumption. By modeling and predicting energy consumption trends, we derived specific climate-adjusted energy consumption forecasts, ensuring that energy consumption forecasts under different climate conditions provide a strong basis for practical 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] Based on 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, a detailed heat load assessment of each component inside the building is required, and a heat flow calculation model, such as the heat conduction equation, is used to simulate the heat flow transfer of each component 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 actual energy consumption data is matched, 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 value of the building area is based on 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 i-th area, E actual,i represents the actual energy consumption value of the i-th area, S i represents the building area of ​​the ith area, B i represents the functional category coefficient of the i-th area, and n represents the total number of building 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 and TRNSYS) to predict the energy consumption of each area based on the building's structure, materials, and equipment configuration. The predicted value reflects the building's energy consumption under standard operating conditions and is calculated based on the area's functional requirements and equipment operating parameters.

[0109] Actual energy consumption value E actual,i Acquisition: By installing monitoring equipment such as smart meters and temperature and humidity sensors, actual energy consumption data for 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 or daily).

[0110] Building area S i Obtaining the building area of ​​each area: The building area of ​​each area can be obtained from architectural design drawings or actual on-site measurement data, in square meters. Larger areas may involve more equipment and personnel, and energy consumption differences may be more significant. Therefore, 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 The energy consumption coefficient is set based on the functional complexity of the area. For example, the energy consumption coefficient for office areas is set at 1.2, for residential areas at 1.0, and for commercial areas at 1.5. This coefficient reflects the impact of different areas on energy consumption and is based on a comprehensive assessment of the building's actual use, equipment density, and other factors.

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

[0113] Take a building as an example, and consider four areas n = 4, namely office area, restaurant area, 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: 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] Compute 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 variance for the entire building area. This value reflects the overall deviation between target and actual energy consumption, taking into account the impact of building area and functional complexity. This value indicates potential for energy efficiency optimization. A high value indicates significant energy efficiency discrepancies in certain areas, requiring further optimization of building design or operational management.

[0131] S403: Based on the energy consumption matching difference data, the gap between the simulation results and the actual data is compared, parameters are adjusted, and iterative testing is performed to generate energy consumption simulation results for the target building area;

[0132] Comparing the difference between the simulation results and actual energy consumption data, parameter adjustments are made and iterative testing is performed. To optimize the energy consumption prediction model, the energy consumption matching discrepancy data is first analyzed and the direction of model parameter adjustment is determined. Through an error feedback mechanism, simulation parameters such as temperature conduction and air flow are gradually adjusted, and the energy consumption simulation is re-run. After each parameter adjustment, the difference between the simulation results and actual energy consumption data is recalculated until a narrow error range is achieved. Through multiple iterative calculations, an accurate simulation result for the target building area's energy consumption is ultimately 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's usage pattern, occupancy rate, and actual operating conditions of the indoor and outdoor temperature difference, the on / off cycle of the building's air conditioning system and the on / off duration of the lighting system are extracted, and energy consumption fluctuation data within different time periods is collected to generate building area operating condition data;

[0135] Based on the energy consumption simulation results for the target building area, data on the building's energy consumption fluctuations can be collected by extracting the on / off cycles of the building's air conditioning system and the on / off duration of the lighting system, combined with actual operating conditions such as the building's usage patterns, occupancy rate, and indoor / outdoor temperature differences. Specifically, the operation logs of the air conditioning and lighting systems are first extracted from the building control system, recording the system's on / off cycles and on / off durations. These operation cycles are then further analyzed in combination with the building's usage patterns and occupancy rate. Temperature sensors and weather station data are used to measure the impact of indoor / outdoor temperature differences on building area energy consumption. Through data collection and analysis, energy consumption fluctuation data for building areas under different conditions can be obtained, which helps understand the building's energy consumption dynamics. Ultimately, operating condition data for each area is generated, 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 upper and lower limits of energy consumption to generate building area energy consumption performance data;

[0137] By simulating the energy consumption behavior of each building area based on the building area's operating condition data and setting upper and lower limits for energy consumption, the rationality of the energy consumption simulation can be effectively verified. First, an energy consumption behavior model is established based on the operating condition data of each area, incorporating various factors such as the air conditioning system and lighting system into the model calculation. Upper and lower limits for energy consumption are set as verification standards to ensure that energy consumption behavior is within a reasonable range. During the simulation process, the results are compared based on the set upper and lower limits to determine whether there are any energy consumption fluctuations that exceed the reasonable range. By analyzing the simulation results for each area, energy consumption performance data for the building area is generated. This data not only reflects the actual energy consumption of the building but also provides data support for subsequent energy efficiency management.

[0138] S503: Based on the energy consumption performance data of the building areas, a cyclic simulation is performed on the energy consumption behavior of all building areas, the overall trend of building energy consumption behavior is analyzed, and an overall analysis result of building energy consumption is generated;

[0139] Based on the building's regional energy consumption performance data, a cyclical simulation of energy consumption behavior across all building areas is conducted to analyze the changing trends in the building's overall energy consumption behavior. During the simulation process, energy consumption data for each area is first integrated. Through multiple cyclical simulations, fluctuations in the building's overall energy consumption are tracked. Trend analysis of the simulation results reveals how building energy consumption varies over time, further identifying potential issues for optimizing building energy efficiency. The results can help identify peaks and valleys in energy consumption for each area, as well as the impact of various energy consumption factors on overall building energy consumption. This generates a comprehensive analysis of building energy consumption, providing data support for building energy efficiency assessment and optimization plans.

[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 their impact on building energy efficiency and temperature distribution, sets different climate scenarios, simulates the building's thermal load response, and generates thermal load data under these climate conditions.

[0144] The energy consumption forecasting and analysis module analyzes the consistency between simulated data and actual data based on heat load data under climatic conditions. It also combines actual climate change trends and energy consumption influencing factors to predict energy consumption changes of buildings under different climatic conditions and obtain climate-adjusted energy consumption forecast results.

[0145] The energy consumption difference analysis module analyzes the energy consumption performance of target building components and areas based on climate-adjusted energy consumption forecast results, matches them with actual energy consumption data, calculates energy consumption differences across building areas, adjusts parameters, and performs iterative testing to generate energy consumption simulation results for the target building area.

[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. It simulates the energy consumption behavior of each building area, analyzes the overall behavior change trend of building energy consumption, and generates the overall analysis results of building energy consumption.

[0147] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for simulating 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, perform thermal bridge effect analysis on walls, windows, and floors, record heat flow data, determine the thermal bridge impact of all building components, and obtain thermal bridge effect analysis results; Based on the thermal bridge effect analysis results, heat load simulation is performed. By adjusting climate change parameters, energy consumption responses under different climate conditions are simulated. The actual climate data is compared with the simulation output information to obtain climate-adjusted 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 simulations, 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 rates, indoor and outdoor temperature differences, and the use of air conditioning and lighting systems, the energy consumption behavior of all building areas is simulated iteratively to obtain the overall building energy consumption analysis results; Based on the thermal bridge effect analysis results, heat load simulation is performed. 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-adjusted 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 to set different climate scenarios. By changing the indoor and outdoor temperature differences, simulate the building's heat load response, record the building energy consumption under each scenario, and 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, predict the energy consumption changes of the building under different climate conditions to obtain climate adjustment energy consumption prediction results; The specific steps of using 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, perform energy consumption performance simulation, and generate the target building area energy consumption simulation results through iterative testing 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 deviation analysis is performed by matching the target building energy consumption analysis data with the actual energy consumption data, calculating the energy consumption difference value of the building area, and generating 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.

2. A 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-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 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 from the BIM building model, record the actual dimensions and material properties 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, and data is cleaned and deduplicated to generate wall size and material information; 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, and the accuracy of the data is confirmed through manual inspection and proofreading to generate a building structure parameter table.

4. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: Using the building structure parameter table, perform thermal bridge effect analysis on walls, windows, and floors, record heat flow data, and determine the thermal bridge impact of all building components. The specific steps for obtaining the thermal bridge effect analysis results are as follows: Based on the building structure parameter table, extract the thermal conductivity coefficients of the building walls, windows, and floors, and evaluate the thermal conductivity characteristics of each component 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, key thermal bridge locations are identified, the impact on the building's 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, characterized in that: The amount of heat transfer at the contact points between the building components is calculated according to the formula: 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 building components, d represents the thickness of the material, which determines the resistance to heat flow, and T ext Represents the temperature 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: The energy consumption difference value of the building area is based on 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 i-th area, E actual，i represents the actual energy consumption value of the i-th area, S i represents the building area of ​​the ith area, B i represents the functional category coefficient of the i-th area, and n represents the total number of building areas.

7. The method for simulating building energy consumption based on BIM according to claim 1, characterized in that: 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 the use of air conditioning and lighting systems, the energy consumption behavior of all building areas is simulated iteratively to obtain the overall building energy consumption analysis results. The specific steps are as follows: Based on the energy consumption simulation results of the target building area, combined with the building's usage pattern, occupancy rate, and actual operating conditions of the indoor and outdoor temperature difference, the on-off cycle of the building's air-conditioning system and the on-off duration of the lighting system are extracted, and energy consumption fluctuation data within different time periods are collected to generate 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 areas, 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 the overall analysis results of the building energy consumption are generated.

8. 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 7, 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 building walls, windows, and floors based on the building structure parameter table, evaluates the thermal conductivity characteristics of each component, calculates the amount of heat transfer at contact points between 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 analyzes the consistency between the simulated data and the actual data based on the heat load data under the climate conditions. It also combines the actual climate change trends and energy consumption influencing factors to predict the energy consumption changes of the building under different climate conditions and obtain the climate-adjusted 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 ​​for building areas, adjusts parameters and performs iterative testing to generate energy consumption simulation results for 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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