Method, device and equipment for quantifying carbon emission of building group operation and medium
By dividing urban development areas into grids and setting microclimate data, and simulating thermal interactions between buildings, the problem of inaccurate carbon emission quantification of building groups in existing technologies has been solved, achieving more accurate carbon emission quantification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SICHUAN PROVINCIAL ARCHITECTURAL DESIGN & RES INST
- Filing Date
- 2026-04-24
- Publication Date
- 2026-05-29
AI Technical Summary
Existing technologies quantify building group carbon emissions under standard meteorological conditions, resulting in inaccurate results that fail to reflect actual meteorological changes in the environment.
By dividing the urban development area into grids, setting different microclimate data for each grid, constructing parameterized typical building models, simulating the thermal interaction between buildings, determining energy consumption data, and calculating total carbon emissions based on this.
It accurately captures the microclimate differences between different buildings, improving the accuracy of carbon emission quantification and making the results closer to the actual environment.
Smart Images
Figure CN122114387A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building energy consumption analysis technology, specifically to a method, apparatus, equipment, and medium for quantifying carbon emissions from building complex operations. Background Technology
[0002] The carbon emissions from the operation of a building complex refer to the total amount of carbon dioxide emissions indirectly or directly generated by the building complex during its daily use phase due to the consumption of various energy sources (electricity, gas, diesel, etc.) to meet energy needs such as heating, cooling, lighting, equipment operation, and domestic hot water. A building complex refers to a group of multiple buildings within an urban development area (such as the design scope of TOD (Transit-Oriented Development)).
[0003] Currently, methods for quantifying carbon emissions from building complexes typically include: establishing independent and detailed energy consumption models for each building within the complex; conducting long-term dynamic simulations of each building under standard meteorological conditions using a building energy consumption simulation engine to determine the energy consumption of each building; and determining the total carbon emissions of the building complex based on the energy consumption of each building.
[0004] The aforementioned method for quantifying carbon emissions from building complexes is based on dynamic simulations of energy consumption under standard meteorological conditions. However, in reality, meteorological conditions vary considerably over long periods, making the total carbon emissions of building complexes determined by existing methods inaccurate. Summary of the Invention
[0005] The purpose of this invention is to provide a method, apparatus, equipment, and medium for quantifying the carbon emissions of a building complex. By dividing the urban development area where the building complex is located into grids and setting different microclimate data for each grid, the microclimate environment of different buildings is refined, and the determined carbon emissions are closer to the actual environment, thus solving the problem of inaccurate total carbon emissions of building complexes determined by existing methods.
[0006] This invention is achieved through the following technical solution:
[0007] The first aspect of this application provides a method for quantifying carbon emissions from the operation of a building complex, including:
[0008] For building clusters within urban development areas, obtain multi-dimensional characteristics that can affect the energy consumption of building clusters;
[0009] Based on the multidimensional features, a group of buildings containing N buildings is clustered into K building clusters; and for each building cluster, a parameterized typical building model corresponding to the statistical values of the key features of the buildings within the cluster is constructed; where N and K are both integers, and N is greater than K.
[0010] Based on the spatial location of the typical building models, each typical building model is placed in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models and determine the energy consumption data of each typical building model; the urban microclimate model includes: the urban development area is divided into grids, and different microclimate data are set for each grid.
[0011] Based on the energy consumption data of each typical building model and the total building area of the building cluster to which the typical building model belongs, the total operating carbon emissions of the building group are determined.
[0012] In one feasible implementation, the method further includes:
[0013] Based on the energy consumption data of a typical building model and the total building area of the building cluster to which the typical building model belongs, the cluster-level operational carbon emissions of the building cluster are determined.
[0014] The cluster-level operational carbon emissions are mapped to a geographic information layer, and the carbon emission intensity corresponding to the cluster-level operational carbon emissions is displayed differently on the geographic information layer.
[0015] In one feasible implementation, the multidimensional feature data includes at least the geometric features, physical features, and functional features of the building complex: the geometric features include at least parameters characterizing the building volume, parameters characterizing the building form, and parameters characterizing the building spatial layout; the physical features include at least parameters characterizing the thermal performance of the building envelope and parameters characterizing the building's shading characteristics; and the functional features include at least parameters characterizing the building's functional type and parameters characterizing the configuration of equipment within the building.
[0016] In one feasible implementation, the key feature is at least one of the multidimensional features, and the statistical value is the average of the statistical values corresponding to each key feature.
[0017] In one feasible implementation, based on the spatial location corresponding to each typical building model, each typical building model is placed in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models, thereby determining the energy consumption data of each typical building model, including:
[0018] Based on the building density and terrain complexity within the urban development area, the urban development area is divided into grids of different scales; each grid is used as a meteorological node, and the meteorological data corresponding to each grid is determined.
[0019] Based on the spatial location corresponding to the typical building model, each typical building model is placed in a grid;
[0020] Based on the meteorological data of the grid where each typical building model is located and the spatial location of the typical building model, the thermal interaction data of each typical building model is determined.
[0021] The thermal interaction data, the parameter data corresponding to each typical building model, and the meteorological data of each grid are input into the building energy consumption simulation engine to obtain the energy consumption data of each typical building model output by the building energy consumption simulation engine.
[0022] In one feasible implementation, the thermal interaction data includes solar shading data and long-wave radiation heat transfer data.
[0023] In one feasible implementation, determining the thermal interaction data of each typical building model based on the meteorological data of the grid where each typical building model is located and the spatial location of the typical building model includes:
[0024] Based on the geometric features of a typical building model, the solar shading data of the outer surface of the typical building model is determined.
[0025] The long-wave radiation heat transfer data between the surface of a typical building model and other surfaces are determined by the viewing angle coefficient; the other surfaces include at least the surfaces of other typical building models, the ground, and the sky.
[0026] The second aspect of this application provides a device for quantifying carbon emissions from the operation of a building complex, comprising:
[0027] Multidimensional feature extraction unit; for building clusters within urban development areas, acquire multidimensional features that can affect the energy consumption of the building clusters;
[0028] A typical building model construction unit, based on the multidimensional features, clusters a group of buildings containing N buildings into K building clusters; and for each building cluster, based on the statistical values of the key features of the buildings within the cluster, constructs a parameterized typical building model corresponding to the statistical values; where N and K are both integers, and N is greater than K;
[0029] The energy consumption simulation unit, based on the spatial location corresponding to the typical building model, places each typical building model in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models in order to determine the energy consumption data of each typical building model; the urban microclimate model includes: the urban development area is divided into grids, and different microclimate data are set for each grid.
[0030] The carbon emission determination unit determines the total operating carbon emissions of the building group based on the energy consumption data of each typical building model and the total building area of the building cluster to which the typical building model belongs.
[0031] A third aspect of this application provides an electronic device, including: a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the above-described method.
[0032] A fourth aspect of this application provides a storage medium, comprising: storing a program or instructions on the storage medium, wherein the program or instructions, when executed by a processor, implement the steps of the above-described method.
[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:
[0034] This application embodiment places each typical building model in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models and determine the energy consumption data of each typical building model. Since the urban microclimate model divides the urban development area where the building group is located into grids and sets different microclimate data for each grid, it accurately captures the microclimate differences between different typical building models. Furthermore, it simulates the thermal interaction data of each typical building model and uses the thermal interaction data for energy consumption assessment, making the final determined operating carbon emissions closer to the actual environment and solving the problem of inaccurate total carbon emissions results of building groups determined by existing methods. Attached Figure Description
[0035] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:
[0036] Figure 1 A flowchart illustrating a method for quantifying carbon emissions from building complex operation, provided as an embodiment of this application;
[0037] Figure 2 A schematic diagram illustrating the formation process of a typical building model in a method for quantifying carbon emissions from building complex operation provided in this application embodiment;
[0038] Figure 3 A schematic diagram illustrating the coupling of a simulated environment to determine thermal interaction data in a method for quantifying carbon emissions from building complex operation provided in an embodiment of this application;
[0039] Figure 4 A schematic diagram of a device for quantifying carbon emissions from building complex operation, provided as an embodiment of this application;
[0040] Figure 5This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation
[0041] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explanation only and are not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this application.
[0042] As will be known to those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.
[0043] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, product, or apparatus.
[0044] Example 1:
[0045] Embodiment 1 of this application provides a method for quantifying the carbon emissions of a building complex, in order to solve the problem that the total carbon emissions of a building complex determined by existing methods are inaccurate.
[0046] The subject executing this method can be any computing device capable of implementing the method, such as a server, mobile phone, personal computer, smart wearable device, smart robot, etc.
[0047] Furthermore, the embodiments of this application do not limit the execution order of different steps. When using the method provided in the embodiments of this application, the execution order of different steps can be adjusted according to actual needs.
[0048] For ease of description, the following uses a device for quantifying carbon emissions from a building complex as an example to illustrate the method provided in this application.
[0049] like Figure 1 The diagram shown is a flowchart illustrating the specific implementation of a method for quantifying carbon emissions from the operation of a building complex, as provided in this application embodiment. The method includes the following steps 11-14:
[0050] Step 11: For building clusters within an urban development area, obtain multidimensional characteristics that can affect the energy consumption of the building clusters.
[0051] The urban development area in this embodiment can be an area to be developed, an area under development, or an area that has been developed. For areas to be developed and areas under development, the method of steps 11-14 of this embodiment can be used to predict the carbon emissions of the building group operation in order to plan building groups that meet carbon emission requirements. For areas that have been developed, the method of this embodiment can be used to assess the carbon emissions of the building group operation in order to optimize the surrounding environment of the building group so that carbon emissions meet the requirements.
[0052] The multidimensional characteristics that affect the energy consumption of a building complex include at least its geometric, physical, and functional characteristics.
[0053] Geometric features include at least parameters that characterize the building's volume, its form, and its spatial layout.
[0054] Among these, parameters characterizing building volume, such as building height, building area, and number of floors, can influence the energy consumption of a building complex as follows: building height affects wind speed and the angle at which solar radiation is received (high-rise buildings experience higher wind speeds and faster heat dissipation; lower-rise buildings are more easily shaded, potentially leading to higher heating energy consumption); building area determines the base amount of total energy consumption and also affects internal heat dissipation (large-area buildings have more internal heat sources, reducing the heating load in winter); the number of floors is positively correlated with building height and also affects the proportion of the building envelope (multi-story buildings have a higher proportion of exterior walls, resulting in greater heat loss than high-rise buildings).
[0055] Parameters characterizing building form, such as shape coefficient and window-to-wall ratio, can influence the energy consumption of a building complex. Shape coefficient represents the ratio of a building's external surface area to its volume; a higher shape coefficient indicates greater heat loss (e.g., towers with a smaller shape coefficient consume less energy than slab buildings of the same area). Window-to-wall ratio is also important; since windows have a higher heat transfer coefficient than walls, a higher window-to-wall ratio results in higher cooling / heating loads (a higher window-to-wall ratio on south-facing buildings allows for better solar heat gain, while a higher ratio on north-facing buildings increases heat loss).
[0056] Parameters characterizing the spatial layout of buildings, such as building orientation, building spacing, and building plan shape, can influence the energy consumption of a building complex as follows: orientation determines the duration of direct solar radiation (south-facing buildings receive more heat in winter, resulting in lower heating energy consumption; west-facing buildings experience strong sunlight in summer, leading to higher cooling energy consumption); spacing affects sunlight shading (too small a spacing results in lower-rise buildings being shaded, increasing heating energy consumption in winter); circular buildings have the smallest shape coefficient and the best energy consumption; irregular shapes (such as L-shapes) are prone to creating local shadows, increasing differences in energy consumption.
[0057] Physical characteristics include at least parameters characterizing the thermal performance of the building envelope and parameters characterizing the building's shading characteristics.
[0058] Among these, parameters characterizing the thermal performance of the building envelope include the heat transfer coefficient of the exterior walls, the heat transfer coefficient of the roof, and the type of window glass. Their impact on the energy consumption of the building complex can be summarized as follows: a lower average heat transfer coefficient of the exterior walls indicates better insulation (e.g., the average heat transfer system of new insulated exterior walls). Compared to old exterior walls Heating energy consumption is reduced by more than 30%; roofs directly receive solar radiation, and roofs with low heat transfer coefficients and poor insulation absorb more heat in summer, increasing cooling energy consumption; window glass type affects the shading coefficient, such as a low shading coefficient, which can reduce solar heat gain and lower cooling energy consumption in summer.
[0059] Parameters characterizing building shading features include whether external shading is installed and the type of shading (e.g., movable shading, fixed shading). The impact on building group energy consumption can be: external shading can block direct radiation (e.g., installing a south-facing awning reduces summer cooling energy consumption by 10-15%); movable shading allows for flexible adjustment (it can be retracted in winter without affecting heat gain, and deployed in summer, making it more energy-efficient than fixed shading).
[0060] The functional characteristics include at least parameters characterizing the functional type of the building and parameters characterizing the equipment configuration within the building.
[0061] Among them, parameters that characterize the land use type of a building, such as residential, commercial, public service facilities, and transportation hubs, can have an impact on the energy consumption of a building group: different land use types have large differences in energy consumption intensity (e.g., commercial buildings have high energy consumption for lighting / equipment, while residential buildings have a high proportion of energy consumption for heating / cooling).
[0062] Parameters characterizing the configuration of equipment within a building, such as the type of air conditioning system (central air conditioning, split air conditioning, ground source heat pump) and the type of lighting system (LED (Light-Emitting Diode) lamps, fluorescent lamps, incandescent lamps), etc. The impact on the building's overall energy consumption can be: heat pump systems have a high energy efficiency ratio and consume less energy than traditional air conditioning for cooling / heating; LED lamps consume only 1 / 10 the energy of incandescent lamps, reducing the proportion of lighting energy consumption from 15% to 3%.
[0063] Multidimensional features can be obtained from the basic database of urban design information models, geographic information systems (GIS), and / or existing building ledgers.
[0064] Step 12: Based on the multidimensional features, cluster the building group containing N buildings into K building clusters; and for each building cluster, construct a parameterized typical building model corresponding to the statistical values of the key features of the buildings within the cluster. Here, N and K are both integers, N is greater than K, and N and K differ in order of magnitude; for example, K is usually a single-digit or tens digit, while N is a hundreds digit or higher.
[0065] like Figure 2 As shown, for the multidimensional features extracted from the multi-source building database in step 11, a feature vector is formed for each building in the building group; through clustering algorithms, such as K-means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise), in the high-dimensional feature space, based on the multidimensional features, the building group containing N buildings is clustered into K building clusters, decomposing the group problem into multiple subgroup problems; the feature center of each building cluster is calculated, and the feature center contains the mean of each feature in the multidimensional features, such as the mean of the shape system number, the mean of the building height, etc.; the mean of each feature in the multidimensional features contained in the feature center is input into the parameterized model generator to generate each parameterized typical building model.
[0066] The feature center, also known as the statistical average of key features of buildings within a building cluster, can be at least one of the multidimensional features obtained in step 11. The parameter values corresponding to parameterization are the statistical values (means) of each key feature. In other words, the generated typical building model is determined based on the parameter values corresponding to the feature center of the building cluster, rather than from real building models selected from the building cluster. The geometric and physical properties of the generated parameterized typical building model are determined by the feature center values of the building cluster, ensuring that its energy consumption characteristics represent the statistical average level of the building cluster and can more purely and unbiasedly represent the commonalities of buildings within the building cluster.
[0067] The parametric model generator is a data-to-model conversion engine that uses preset parameter mapping rules and modeling logic to construct typical architectural models.
[0068] Step 13: Based on the spatial location of each typical building model, place each typical building model within a pre-constructed urban microclimate model to simulate the thermal interaction between them, thereby determining the energy consumption data for each typical building model. The urban microclimate model includes: a grid-based division of the urban development area, with different microclimate data set for each grid.
[0069] This step includes an urban microclimate model and a parallel building energy consumption simulation engine. All typical building models are spatially positioned within the environment created by the urban microclimate model. Based on the principles of urban microclimate and building thermophysics, the energy consumption of a building complex depends not only on its own properties but also on the microclimate formed by its surrounding environment (local variations in temperature, wind speed, and solar radiation) and the radiation and shading interactions with other buildings.
[0070] The specific implementation process of step 13 includes the following steps 1301-1304:
[0071] Step 1301: Based on the building density and terrain complexity within the urban development area, divide the urban development area into grids of different scales; use each grid as a meteorological node and determine the meteorological data corresponding to each grid.
[0072] To accurately capture the microclimate differences in urban space, the entire urban development area is divided into several regular or irregular computational grids, such as... Figure 3 Grids 1 through N in the model.
[0073] The grid division is based on several criteria, including: spatial resolution, where the grid size is determined according to building density, terrain complexity, and simulation accuracy requirements within the urban development area, typically ranging from 10m×10m to 50m×50m; each grid is treated as an independent meteorological node, receiving local meteorological data interpolated from weather stations or mesoscale meteorological models; for example, grid 1 corresponds to a temperature of 26.5℃ and a wind speed of 1.2m / s, while grid 2 corresponds to a temperature of 27.1℃ and a wind speed of 0.8m / s. Regarding building layout, grid boundaries are usually aligned with spatial elements such as building outlines, street orientation, and green belts to more accurately depict shading and radiation exchange.
[0074] Through the above-mentioned gridded microclimate environment, each grid is represented as an "environmental unit" with relatively homogeneous microclimate characteristics. The grid is assigned independent meteorological data such as temperature, humidity, wind speed, and solar radiation. The urban development area where the entire building complex is located forms a "non-uniform meteorological field", which reflects the microclimate differentiation within the urban development area caused by building layout, underlying surface properties, etc.
[0075] Step 1302: Place each typical building model in the grid according to its corresponding spatial location.
[0076] Each typical building model is placed in one or more grids based on the spatial distribution of the building cluster it represents and the spatial location of each grid, such as... Figure 3 In the diagram, typical building model T1 is in grid 1, typical building model T2 is in grid 2, typical building model T3 is in grid 3, etc., and Tk represents the k-th typical building model. Each typical building model receives meteorological data from the corresponding grid.
[0077] The model's position in the grid determines its microclimate environment (such as received solar radiation, surrounding wind speed, etc., which are affected by the grid's meteorological data). Multiple typical building models may be located in the same grid, in which case they share the microclimate environment of that grid, but still independently calculate the radiation and heat interaction received by the surfaces of these multiple typical building models.
[0078] Step 1303: Based on the meteorological data of the grid where each typical building model is located and the spatial location of the typical building model, determine the thermal interaction data of each typical building model.
[0079] Thermal interaction data includes solar shading data and long-wave radiation heat transfer data.
[0080] Each grid receives hourly meteorological data (temperature, relative humidity, wind speed and direction, total solar radiation, etc.) from urban weather stations, reanalysis data, or CFD (Computational Fluid Dynamics) simulation outputs. Differentiated meteorological boundary conditions are assigned to each grid using spatial interpolation methods (such as inverse distance weighting and Kriging interpolation).
[0081] For the calculation of solar shading data: Based on the geometric features of a typical building model, such as its three-dimensional geometric location, the solar shading data of the outer surface of the typical building model is determined, such as calculating the solar shading of the outer surface (especially windows and exterior walls). Ray projection or projection methods are used to determine whether direct solar radiation is blocked by other surrounding models at each moment. The shading results directly affect the effective solar heat gain of the building surface, and thus affect the cooling and heating loads.
[0082] The determined solar shading data can be in the form of the shading ratio of a typical building model surface that includes both time and spatial dimensions. For example, for a typical building model T1 with coordinate position X, the shading ratio of its southeast-facing surface at time t is 0.45.
[0083] For the calculation of long-wave radiation heat transfer data: the long-wave radiation heat transfer data between the surface of a typical building model and other surfaces is determined by the viewing angle coefficient; the other surfaces include at least the surfaces of other typical building models, the ground and the sky.
[0084] Considering the long-wave radiation exchange between the outer surfaces of typical building models and between the surface of the typical building model and the ground and sky, the long-wave radiation heat exchange between the model surface and other surfaces is calculated through the viewing angle coefficient. This process significantly affects the temperature of the building's outer surface under nighttime or cloudy conditions, especially in high-density urban areas.
[0085] The viewing angle coefficient is a core geometric parameter for quantifying long-wave radiation heat transfer between building surfaces. It represents the proportion of total radiation energy emitted by surface i that directly reaches surface j, and its value ranges from 0 to 1 (unitless). It is related to the shape, size, and relative position of the two surfaces (i and j), but is independent of physical properties such as material and temperature.
[0086] Long-wave radiation heat transfer between surface i and surface j It can be represented as: = ;in, This represents the viewing angle coefficients of the two building surfaces (i and j). This represents the Stefan-Boltzmann constant. The temperatures of surface i and surface j are respectively. Let i be the area of surface i.
[0087] For the long-wave radiation exchange between a typical building model and the ground, when calculating the corresponding long-wave radiation heat transfer, the ground of the grid where the typical model is located and the surrounding associated grids are divided into ground surface elements that match the outer surface of the building; and the outer surface (exterior wall, roof) of the typical model is divided into building surface elements; the ray transmission method is used to determine the shading of the building surface elements on the ground surface elements, thereby determining the effective radiation path; the viewing angle coefficient between the building surface and the ground is determined based on the effective radiation path, and the long-wave radiation heat transfer between the surface of the typical building model and the ground is further determined through the expression of the long-wave radiation heat transfer.
[0088] When calculating the corresponding long-wave radiation heat exchange between a typical building model and the sky, the sky can be regarded as a hemispherical dome with an infinite radius. Then all the unobstructed outer surfaces of the building can exchange radiation with the sky, thereby determining the viewing angle coefficient of the building surface to the sky. Furthermore, the long-wave radiation heat exchange between the surface of the typical building model and the sky can be determined through the expression of the long-wave radiation heat exchange.
[0089] Figure 3 The diagram illustrates coupling simulation examples between typical building models that may exhibit shading and longwave radiation exchange. For instance, shading and longwave radiation exchange may exist between typical building models T1 and T2, between typical building models T2 and T3, and between typical building models Tk and T3, etc.
[0090] Step 1304: Input the thermal interaction data, the parameter data corresponding to each typical building model, and the meteorological data of each grid into the building energy consumption simulation engine to obtain the energy consumption data of each typical building model output by the building energy consumption simulation engine.
[0091] For each typical building model, the aforementioned non-uniform meteorological environment and thermal interaction effects are integrated, and a building energy consumption simulation engine (such as the Energy Plus kernel) is invoked to perform dynamic hourly simulations. During the simulation, the building model not only responds to its own properties and internal loads, but also responds in real time to microclimate parameters from the grid and the shading and radiation effects from surrounding models. Finally, the hourly energy consumption results of each typical building model (including sub-items such as heating, cooling, lighting, and equipment energy consumption) are output, and these are summarized into the total annual energy consumption.
[0092] The internal calculation logic of the building energy consumption simulation engine can be as follows: Based on the parameter data of the input typical building model (the feature data corresponding to the feature center of the building cluster), a basic physical model is constructed; the basic physical model is associated with micro-meteorological data, and non-uniform meteorological data is bound to the spatial location of the model; based on thermal interaction data, the heat gain from solar shading and the heat transfer from long-wave radiation are input into the model as additional heat flux; the heat budget of the typical building model is calculated, such as real-time calculation of heat loss / gain of the building envelope, solar heat gain (after correction for shading), heat transfer from long-wave radiation, and heat loss / gain of fresh air; based on the heat budget results, the hourly heating / cooling load (the energy required to maintain the indoor set temperature) is calculated; combined with the equipment energy efficiency ratio (such as the air conditioning energy efficiency ratio of 3.5), the load is converted into actual energy consumption (energy consumption = load / energy efficiency ratio); the hourly energy consumption values of each item are stored to form an annual energy consumption time series of 8760 hours; and the total annual energy consumption is obtained by summing them up.
[0093] Step 14: Based on the energy consumption data of each typical building model and the total building area of the building cluster to which the typical building model belongs, determine the total operating carbon emissions of the building group.
[0094] Based on the energy consumption data of each typical building model and the total building area of the building cluster to which the typical building model belongs, the total energy consumption of the building cluster to which the typical building model belongs is determined.
[0095] Based on pre-defined carbon emission accounting rules, such as the international standard ISO 14064, the total energy consumption results are converted into the total operating carbon emissions of the building complex.
[0096] In one feasible implementation, this embodiment further includes: determining the cluster-level operational carbon emissions of a building cluster based on energy consumption data of a typical building model and the total building area of the building cluster to which the typical building model belongs; mapping the cluster-level operational carbon emissions to a geographic information layer, and displaying them differently on the geographic information layer based on the carbon emission intensity corresponding to the cluster-level operational carbon emissions. The differentiating display can be based on carbon emission intensity, using different colors to distinguish each building cluster.
[0097] Among them, the geographic information layer is a digital carrier that carries the spatial geographic data of urban development areas. Specifically, it is an electronic map layer that organizes the geographical location and attribute information of entities such as terrain, buildings, roads, and blocks in a layered manner.
[0098] This application embodiment places each typical building model in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models and determine the energy consumption data of each typical building model. Since the urban microclimate model divides the urban development area where the building group is located into grids and sets different microclimate data for each grid, it accurately captures the microclimate differences between different typical building models. Furthermore, it simulates the thermal interaction data of each typical building model and uses the thermal interaction data for energy consumption assessment, making the final determined operating carbon emissions closer to the actual environment and solving the problem of inaccurate total carbon emissions results of building groups determined by existing methods.
[0099] Example 2:
[0100] To address the problem of inaccurate total carbon emissions results for building groups determined by existing methods, and based on the same inventive concept as Embodiment 1, this application also provides a device for quantifying carbon emissions from the operation of building groups.
[0101] A schematic diagram of the specific structure of the device is shown below. Figure 4 As shown, it includes the following functional units 41-44:
[0102] Multidimensional feature extraction unit 41: For building groups within urban development areas, acquire multidimensional features that can affect the energy consumption of the building groups.
[0103] The multidimensional feature data includes at least the geometric, physical, and functional features of the building complex: the geometric features include at least parameters characterizing the building volume, the building form, and the building spatial layout; the physical features include at least parameters characterizing the thermal performance of the building envelope and the building shading characteristics; and the functional features include at least parameters characterizing the functional type of the building and the configuration of equipment within the building.
[0104] Typical building model construction unit 42, based on the multidimensional features, clusters a group of buildings containing N buildings into K building clusters; and for each building cluster, constructs a parameterized typical building model corresponding to the statistical values of the key features of the buildings in the building cluster; where N and K are both integers, and N is greater than K.
[0105] The key feature is at least one of the multidimensional features, and the statistical value is the average of the statistical values corresponding to each key feature.
[0106] Energy consumption simulation unit 43, based on the spatial location corresponding to the typical building model, places each typical building model in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models in order to determine the energy consumption data of each typical building model; the urban microclimate model includes: the urban development area is divided into grids, and different microclimate data are set for each grid.
[0107] Energy consumption simulation unit 43 is specifically used for:
[0108] Based on the building density and terrain complexity within the urban development area, the urban development area is divided into grids of different scales. Each grid is treated as a meteorological node, and meteorological data corresponding to each grid are determined. According to the spatial location of each typical building model, each typical building model is placed in a grid. Based on the meteorological data of the grid in which each typical building model is located and the spatial location of the typical building model, the thermal interaction data of each typical building model is determined. The thermal interaction data, the parameter data corresponding to each typical building model, and the meteorological data of each grid are input into the building energy consumption simulation engine to obtain the energy consumption data of each typical building model output by the building energy consumption simulation engine.
[0109] The thermal interaction data includes solar shading data and long-wave radiation heat transfer data.
[0110] The determination of thermal interaction data specifically includes: determining the solar shading data of the outer surface of the typical building model based on the geometric features of the typical building model; determining the long-wave radiation heat transfer data between the surface of the typical building model and other surfaces through the viewing angle coefficient; the other surfaces include at least the surfaces of other typical building models, the ground and the sky.
[0111] Carbon emission determination unit 44 determines the total operating carbon emissions of the building group based on the energy consumption data of each typical building model and the total building area of the building cluster to which the typical building model belongs.
[0112] In one feasible implementation, the quantification device of this embodiment further includes a display unit, specifically used to determine the cluster-level operational carbon emissions of a building cluster based on the energy consumption data of a typical building model and the total building area of the building cluster to which the typical building model belongs; map the cluster-level operational carbon emissions to a geographic information layer, and display them differently on the geographic information layer based on the carbon emission intensity corresponding to the cluster-level operational carbon emissions.
[0113] This application embodiment places each typical building model in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models and determine the energy consumption data of each typical building model. Since the urban microclimate model divides the urban development area where the building group is located into grids and sets different microclimate data for each grid, it accurately captures the microclimate differences between different typical building models. Furthermore, it simulates the thermal interaction data of each typical building model and uses the thermal interaction data for energy consumption assessment, making the final determined operating carbon emissions closer to the actual environment and solving the problem of inaccurate total carbon emissions results of building groups determined by existing methods.
[0114] Based on the same inventive concept as the foregoing embodiments of this application, this application also provides a computing device.
[0115] like Figure 5 As shown, the computing device includes a memory 51 and a processor 52. The memory 51 can be configured to store various other data to support operation on the electronic device. Examples of such data include instructions for any application or method used to operate on the electronic device. The memory 51 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0116] The processor 52, coupled to the memory 51, is used to execute the computer program stored in the memory 51 to perform a method for quantifying carbon emissions from building complex operations as described in the foregoing embodiments.
[0117] When the processor 52 executes the computer program to perform a method for quantifying the carbon emissions of a building complex, it simulates the thermal interaction between each typical building model by placing each typical building model in a pre-built urban microclimate model to determine the energy consumption data of each typical building model. Since the urban microclimate model divides the urban development area where the building complex is located into grids and sets different microclimate data for each grid, it accurately captures the microclimate differences between different typical building models. Furthermore, it simulates the thermal interaction data of each typical building model and uses the thermal interaction data for energy consumption assessment, making the final determined operating carbon emissions closer to the actual environment and solving the problem of inaccurate total carbon emissions results of building complexes determined by existing methods.
[0118] When the processor 52 executes the computer program in the memory 51, in addition to the functions described above, it can also perform other functions, as detailed in the descriptions of the preceding embodiments.
[0119] Furthermore, such as Figure 5 As shown, the computing device also includes other components such as a display 54, a communication component 53, a power supply component 55, and an audio component 56. Figure 5 The diagram only shows some components and does not mean that the computing device includes only these components. Figure 5 The components shown.
[0120] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a computer, can implement the methods provided in the above embodiments.
[0121] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.
[0123] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for quantifying carbon emissions from the operation of a building complex, characterized in that, include: For building clusters within urban development areas, obtain multi-dimensional characteristics that can affect the energy consumption of building clusters; Based on the multidimensional features, a group of buildings containing N buildings is clustered into K building clusters; and for each building cluster, a parameterized typical building model corresponding to the statistical values of the key features of the buildings within the cluster is constructed; where N and K are both integers, and N is greater than K. Based on the spatial location of the typical building models, each typical building model is placed in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models and determine the energy consumption data of each typical building model; the urban microclimate model includes: the urban development area is divided into grids, and different microclimate data are set for each grid. Based on the energy consumption data of each typical building model and the total building area of the building cluster to which the typical building model belongs, the total operating carbon emissions of the building group are determined.
2. The method for quantifying carbon emissions from building complex operation according to claim 1, characterized in that, The method further includes: Based on the energy consumption data of a typical building model and the total building area of the building cluster to which the typical building model belongs, the cluster-level operational carbon emissions of the building cluster are determined. The cluster-level operational carbon emissions are mapped to a geographic information layer, and the carbon emission intensity corresponding to the cluster-level operational carbon emissions is displayed differently on the geographic information layer.
3. The method for quantifying carbon emissions from building complex operation according to claim 1, characterized in that, The multidimensional feature data includes at least the geometric, physical, and functional features of the building complex: the geometric features include at least parameters characterizing the building volume, the building form, and the building spatial layout; the physical features include at least parameters characterizing the thermal performance of the building envelope and the building shading characteristics; and the functional features include at least parameters characterizing the functional type of the building and the configuration of equipment within the building.
4. The method for quantifying carbon emissions from building complex operation according to claim 1, characterized in that, The key feature is at least one of the multidimensional features, and the statistical value is the average of the statistical values corresponding to each key feature.
5. The method for quantifying carbon emissions from building complex operation according to claim 1, characterized in that, Based on the spatial location corresponding to the typical building models, each typical building model is placed in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models, thereby determining the energy consumption data of each typical building model, including: Based on the building density and terrain complexity within the urban development area, the urban development area is divided into grids of different scales; each grid is used as a meteorological node, and the meteorological data corresponding to each grid is determined. Based on the spatial location corresponding to the typical building model, each typical building model is placed in a grid; Based on the meteorological data of the grid where each typical building model is located and the spatial location of the typical building model, the thermal interaction data of each typical building model is determined. The thermal interaction data, the parameter data corresponding to each typical building model, and the meteorological data of each grid are input into the building energy consumption simulation engine to obtain the energy consumption data of each typical building model output by the building energy consumption simulation engine.
6. The method for quantifying carbon emissions from building complex operation according to claim 5, characterized in that, The thermal interaction data includes solar shading data and long-wave radiation heat transfer data.
7. The method for quantifying carbon emissions from building complex operation according to claim 6, characterized in that, The determination of thermal interaction data for each typical building model, based on meteorological data of the grid in which each typical building model is located and the spatial location of the typical building model, includes: Based on the geometric features of a typical building model, the solar shading data of the outer surface of the typical building model is determined. The long-wave radiation heat transfer data between the surface of a typical building model and other surfaces are determined by the viewing angle coefficient; the other surfaces include at least the surfaces of other typical building models, the ground, and the sky.
8. A device for quantifying carbon emissions from the operation of a building complex, characterized in that, include: Multidimensional feature extraction unit; For building clusters within urban development areas, obtain multi-dimensional characteristics that can affect the energy consumption of building clusters; A typical building model construction unit, based on the multidimensional features, clusters a group of buildings containing N buildings into K building clusters; and for each building cluster, based on the statistical values of the key features of the buildings within the cluster, constructs a parameterized typical building model corresponding to the statistical values; where N and K are both integers, and N is greater than K; The energy consumption simulation unit, based on the spatial location corresponding to the typical building model, places each typical building model in a pre-constructed urban microclimate model to simulate the thermal interaction between the typical building models in order to determine the energy consumption data of each typical building model; the urban microclimate model includes: the urban development area is divided into grids, and different microclimate data are set for each grid. The carbon emission determination unit determines the total operating carbon emissions of the building group based on the energy consumption data of each typical building model and the total building area of the building cluster to which the typical building model belongs.
9. An electronic device, characterized in that, include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method as claimed in any one of claims 1-7.
10. A storage medium, characterized in that, include: The storage medium stores a program or instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-7.