A joint simulation method for studying the impact of urban wind environment on urban building energy consumption

Abstract

The present invention discloses a joint simulation method for studying the impact of urban wind environment on urban building energy consumption. The method comprises the following steps: automatically generating a block model and calculation parameters; simulating urban heat island intensity at a selected block scale and updating temperature and humidity data; simulating wind speed data for eight wind directions in the selected block for 8,760 hours throughout the year; automatically obtaining the average wind speed around each building and generating a new EPW file for each building; simulating and calculating building energy consumption at three time scales in the selected block; using the updated EPW file, simulating and calculating building energy consumption at three time scales in the selected block; quantifying the impact of the wind environment on urban building energy consumption based on the results of the two energy consumption calculations; and deriving the influence patterns of the wind environment on urban building energy consumption in a case city (climate zone) based on the results. This method balances the speed and accuracy of urban wind environment calculations and relatively easily implements quantitative analysis of simulation results.

Description

Technical Field

[0001] The present invention relates to a Grasshopper-based urban planning technology method, and in particular to a joint simulation method for studying the impact of urban wind environment on urban building energy consumption. Background Art

[0002] Cities are among the largest consumers and contributors to carbon emissions. Urban buildings consume 40% of the EU's primary energy and account for approximately 36% of energy-related CO2 emissions. Therefore, urban buildings hold significant potential for energy conservation and greenhouse gas emission reductions. Given that the world's population is projected to grow to approximately 10 billion by 2050, with approximately 68% living in urban areas, climate change in the built environment and cities will have a significant impact on people's physical and mental health and prospects for sustainable development. Given the interplay between the urban environment and climate change, studying the determinants of energy consumption in urban buildings is crucial. Improving energy efficiency through optimized design and urban management is an emerging research priority that can contribute to achieving the Sustainable Development Goals.

[0003] Urban climate affects the human living environment and the energy consumption of urban buildings. Existing studies have quantitatively and qualitatively analyzed the various impacts of urban climate factors, including (1) indoor and outdoor thermal comfort; (2) pollutant diffusion; (3) building cooling and heating loads; and (4) occupant behavior. As one of the main factors of urban climate, the urban wind environment directly affects the energy consumption of urban buildings. Although many environmental performance studies have shown the influence relationship, the relevant laws of how the wind environment in high-density cities affects the energy use of urban buildings have not been fully studied and understood, which is crucial for the design and evaluation of climate-responsive and energy-sustainable cities.

[0004] In the fields of urban planning and building physics, considering the impact of urban climate in urban building energy consumption simulations has become a basic consensus. Urban climate, as a simulation boundary condition, has a significant impact on simulation results. Currently, a common practice is to use typical meteorological year files (TMY), such as the EPW files provided by the EnergyPlus official website, for simulation. However, these meteorological files are often collected by suburban meteorological stations and cannot reflect the actual climate conditions in high-density urban areas, especially the temporal and spatial heterogeneity of the wind environment. Recent research has explored the coupled simulation of urban climate models and urban building energy consumption models, improving the input accuracy of climate boundary conditions and the accuracy of energy consumption simulations.

[0005] However, most methods still primarily focus on microclimate factors such as temperature, failing to consider the dynamic nature of the wind environment and the heterogeneity of urban space. In particular, wind simulation often struggles to balance accuracy and speed. First, many joint simulation methods simulate and update only temperature, ignoring the wind environment as a factor. Second, while some studies or methods do consider the wind environment, the simulation tools used are time-consuming and difficult to apply to large-scale studies or generalize. These methods fail to consider the correlation between wind environment, morphology, and urban building energy consumption, making it difficult to optimize and research this issue. Summary of the Invention

[0006] Purpose of the invention: In order to address the shortcomings of existing methods in studying the impact of wind environment on urban building energy consumption, the present invention proposes a joint simulation method for studying the impact of urban wind environment on urban building energy consumption.

[0007] This method includes two core inventions: first, it proposes a clear and concise process for modeling, interaction and simulation of urban data, comprehensively considering the calculation speed of urban block morphological elements, urban-scale heat island intensity and urban wind environment, as well as flexibility in urban planning applications; second, it proposes a simulation method that takes into account both the speed and accuracy of wind environment calculation, combines the acquisition and input of wind environment data as boundary conditions for energy consumption simulation, and proposes relevant indicators to quantify the impact of wind environment on building energy consumption.

[0008] First, a GIS platform was used to import real-world urban block data, automatically generating models and calculating block morphological parameters. Next, the EPW file was updated twice using two validated numerical simulation tools, obtaining block-scale temperature and humidity data and 8,760 hours of wind speed data at the building level throughout the year. Furthermore, the framework simulated urban building energy consumption data before and after the EPW file update and calculated the difference. The proposed IWE metric was used to measure the impact of the wind environment on urban building energy consumption. Finally, based on the results, the impact of the wind environment on the energy consumption of the case city buildings was analyzed at three time scales, summarizing the patterns of wind environment influence on urban building energy consumption.

[0009] Technical solution: The method for identifying and evaluating urban ventilation corridors in a dynamic environment according to the present invention comprises the following steps:

[0010] S1. Generate a research block model based on urban data and automatically calculate all morphological parameters. The steps include:

[0011] S11, input GIS data to generate a real block model to prepare for subsequent calculations;

[0012] S12. Calculation of urban wind direction and eight block morphological parameters under eight wind directions, including north, northeast, east, southeast, south, southwest, west, and northwest, with an angle of 45° between adjacent wind directions; morphological parameters include frontal area index (FAI), site coverage (SC), average building height (ABH), three-dimensional site coverage (3DSC), facade ratio (FTS), green space ratio (GR), floor area ratio (FAR), and body shape coefficient (BSC);

[0013] S2. Simulate the intensity of the block heat island: Use the urban climate generator to obtain the block-scale EPW file based on the results of S1;

[0014] S3, simulated block-scale annual wind speed: calculate the wind speed of the selected block for 8760 h based on the S1 simulation results;

[0015] S31. Create a computational fluid dynamics grid from the S1 simulation results and set a cylindrical background grid to facilitate the calculation of the results from the eight wind directions;

[0016] S32, calculate and extract the wind speed at a height of 10m around each building in the block, take the average wind speed, and obtain 8760 accurate wind speed data around each building throughout the year;

[0017] S4. Use original EPW for energy consumption simulation: use the same EPW file for all individual buildings in the selected block, without considering the spatiotemporal heterogeneity of the wind environment;

[0018] S5. Use the updated EPW to simulate energy consumption: simulate and calculate the building energy consumption of the selected block at three time scales: full year, month, and extreme climate week;

[0019] S51, simulate the building energy intensity (EUI) of the block under the full-year cycle;

[0020] S52. Simulate the energy consumption intensity (EUI) of the block buildings for each month of the year and derive it in the order of each building;

[0021] S53. Based on relevant standards and meteorological data, select extreme hot weeks and extreme cold weeks to simulate weekly block building energy intensity (EUI);

[0022] S6. Quantify the impact of the wind environment on the energy consumption of urban buildings based on the results of the two energy consumption calculations, mainly the cooling load and heating load; take the difference between the two energy consumption calculations and use the S4 simulation results as a benchmark to derive the degree of impact of the wind environment on the energy consumption of buildings in the block (IWE).

[0023]

[0024] Where i is the building number in the block; n is the total number of buildings in the block; eui2 is the energy intensity simulated by the updated EPW file; eui1 is the energy intensity simulated by the original EPW file. The percentage is used to measure the impact of the wind environment on the energy consumption of urban buildings. The calculation steps of IWE include:

[0025] S61. Taking the difference between the two energy consumption calculation results and taking the first energy consumption calculation result as a benchmark, obtain the value of a single building;

[0026] S62. Calculate the average IWE value of all buildings in the block: accumulate and sum the results of each building in S61 and take the average value to obtain the block IWE value.

[0027] S7. Analyze the influence of the wind environment on the energy consumption of urban buildings in the case city (climate zone) based on the results in S6;

[0028] S71. Analyze the impact patterns of extreme climate on a weekly and annual scale, and provide specific numerical values.

[0029] S72. Use machine learning methods to study the model prediction characteristics of different months under seasonal factors, explain the influence characteristics of different months behind the annual influence patterns, rank the importance of features, and provide the block morphological characteristics that should be prioritized under different scales and optimization objectives.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. This invention quantifies the impact of wind environment on urban building energy consumption. It proposes the Impact of Wind Environment on Urban Energy (IWE) metric for the first time and proposes a comprehensive simulation framework for data acquisition, calculation, and quantification. This allows for the evaluation of how wind environment affects urban building energy consumption in a case study city (climate zone). Existing methods for evaluating the impact of microclimate on urban energy consumption fail to reflect the role of wind environment factors in affecting urban building energy consumption.

[0032] 2. The conclusions drawn by the present invention are more accurate and reliable. The present invention comprehensively proposes a process and results analysis combining four aspects: urban block morphology indicators, urban heat island intensity, year-round wind environment simulation, and urban building energy consumption simulation. This can be used to evaluate the impact of the wind environment, a highly variable indicator, on urban building energy consumption through data quantification. By setting eight incoming wind directions during the simulation, the accuracy of the wind environment boundary conditions used to calculate energy consumption can be guaranteed. Therefore, compared with existing methods that ignore or incompletely consider the impact of the wind environment, the present invention has higher accuracy and reliability.

[0033] 3. The present invention has strong flexibility in the application of urban planning. The present invention conducts research by selecting target research blocks in case cities, without expanding to the entire urban area or the entire city scale to increase the amount of calculation. At the same time, the present invention is very flexible in data selection and input, taking into account the differences in background meteorological conditions between different regions, and different original EPW files can be input to quickly switch research cities. Compared with the more cumbersome coupling process of existing methods, the present invention is highly scalable, and the original code (battery pack) is simple and easy to understand, providing a basic method for subsequent research on more cities and climate zones.

[0034] 4. Compared with traditional methods, the method disclosed in the present invention effectively improves the convenience and accuracy of calculating the wind environment in high-density cities, and can quantify the impact of the wind environment on urban energy consumption through relatively simple steps, and has greater accuracy and applicability in urban planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is the overall flow chart of the present invention;

[0036] Figure 2 An analysis diagram of the implementation case area of ​​the present invention;

[0037] Figure 3 A schematic diagram of all batteries in Grasshopper of the present invention;

[0038] Figure 4 It is a flowchart of the simulation stage;

[0039] Figure 5 is the IWE value of each block at the extreme cold and hot week scale in the implementation case of the present invention; the horizontal axis represents the case block code, HFAI-UO represents an office block with a U-shaped plane type with a high FAI value, HFAI-CO represents an office block with a courtyard plane type with a high FAI value, HFAI-LO represents an office block with an L-shaped plane type with a high FAI value, HFAI-TO represents an office block with a tower point type distribution with a high FAI value, HFAI-SNS-O represents an office block with a slab-type north-south distribution with a high FAI value, HFAI-SEW-O represents an office block with a slab-type east-west distribution with a high FAI value, MFAI-O and LFAI-O and so on represent office block types with medium and low FAI values, and HFI-XXX-R, MFAI-XXX-R, LFAI-XXX-R and so on represent the types of corresponding residential blocks;

[0040] Figure 6is the IWE value of each block at the annual scale in the implementation case of the present invention; the horizontal axis represents the case block code, HFAI-UO represents an office block with a U-shaped plane type with a high FAI value, HFAI-CO represents an office block with a courtyard plane type with a high FAI value, HFAI-LO represents an office block with an L-shaped plane type with a high FAI value, HFAI-TO represents an office block with a tower point type distribution with a high FAI value, HFAI-SNS-O represents an office block with a slab-type north-south distribution with a high FAI value, HFAI-SEW-O represents an office block with a slab-type east-west distribution with a high FAI value, MFAI-O and LFAI-O and so on represent office block types with medium and low FAI values, and HFI-XXX-R, MFAI-XXX-R, LFAI-XXX-R and so on represent the types of corresponding residential blocks;

[0041] Figure 7 This is the ranking of the importance of block morphology under six types of optimization objectives at three time scales in the implementation case of the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] In order to demonstrate the implementation mode of the present invention more clearly and easily, this embodiment selects a case to describe the embodiment.

[0044] As attached Figure 2 As shown, the implementation case is 36 blocks in the Central District of Shanghai, including 18 residential blocks and 18 office blocks, and all blocks are coded and classified.

[0045] like Figure 1 As shown, the joint simulation method for studying the impact of urban wind environment on urban building energy consumption in this embodiment includes the following steps:

[0046] S1. Generate a research block model based on Shanghai city data and automatically calculate all morphological parameters. The schematic diagram of all battery packs in Grasshopper is as follows: Figure 3 As shown, the steps include:

[0047] S11. Input GIS data of 36 real blocks within Shanghai Middle Ring Road to generate real block models to prepare for subsequent calculations. The scope and spatial analysis of the study area are as follows: Figure 2 ;

[0048] S12. Calculate urban wind direction and eight street block morphological parameters under eight wind directions, including north, northeast, east, southeast, south, southwest, west, and northwest, with an angle of 45° between adjacent wind directions. Morphological parameters include frontal area index (FAI), site coverage (SC), average building height (ABH), three-dimensional site coverage (3DSC), facade ratio (FTS), green space ratio (GR), floor area ratio (FAR), and body shape coefficient (BSC).

[0049] S2. Simulate the intensity of the block heat island: Use the urban climate generator to obtain the block-scale EPW file based on the results of S1;

[0050] S3, simulated block-scale annual wind speed: calculate the wind speed of the selected block for 8760 h based on the S1 simulation results;

[0051] S31. Create a computational fluid dynamics grid from the S1 simulation results and set a cylindrical background grid to facilitate the calculation of the results from the eight wind directions;

[0052] S32, calculate and extract the wind speed at a height of 10m around each building in the block, take the average wind speed, and obtain 8760 accurate wind speed data around each building throughout the year. All processes of S2 and S3 are as follows Figure 4 As shown;

[0053] S4. Use original EPW for energy consumption simulation: use the same EPW file for all individual buildings in the selected block, without considering the spatiotemporal heterogeneity of the wind environment;

[0054] S5. Use the updated EPW to simulate energy consumption: simulate and calculate the building energy consumption of the selected block at three time scales: annual, monthly, and extreme climate weeks. All processes in S4 and S5 are as follows Figure 5 As shown;

[0055] S51. Simulate the building energy intensity (EUI) of the block under the full-year cycle to obtain the annual IWE index of each block;

[0056] S52. Simulate the energy consumption intensity (EUI) of the block buildings for each month of the year and derive it in the order of each building;

[0057] S53. Based on relevant standards and meteorological data, the extremely hot week (July 23-29) and the extremely cold week (January 20-26) in Shanghai were selected to simulate weekly block building energy consumption intensity (EUI);

[0058] S6. Quantify the impact of the wind environment on the energy consumption of urban buildings based on the results of the two energy consumption calculations, mainly the cooling load and heating load; take the difference between the two energy consumption calculations and use the S4 simulation results as a benchmark to derive the degree of impact of the wind environment on the energy consumption of buildings in the block (IWE).

[0059]

[0060] Where i is the building number in the block; n is the total number of buildings in the block; eui2 is the energy intensity simulated by the updated EPW file; eui1 is the energy intensity simulated by the original EPW file. The percentage is used to measure the impact of the wind environment on the energy consumption of urban buildings. The calculation steps of IWE include:

[0061] S61. Taking the difference between the two energy consumption calculation results and taking the first energy consumption calculation result as a benchmark, obtain the value of a single building;

[0062] S62. Calculate the average IWE value of all buildings in the block: accumulate and average the results of each building in S61 to obtain the block IWE value. The block IWE value in the case of extreme cold and hot weeks is as follows: Figure 5 As shown, the block IWE values ​​in the annual case are as follows Figure 6 shown.

[0063] S7. Analyze the influence of the wind environment on the energy consumption of urban buildings in the case city (climate zone) based on the results in S6;

[0064] S71. Analyze the impact of extreme climate on a weekly and annual scale, and give specific numerical values. The results are as follows: Figure 5 and Figure 6 As shown,

[0065] S72. Use machine learning methods to study the model prediction characteristics of different months under seasonal factors, explain the influence characteristics of different months behind the annual influence pattern, and rank the importance of features, and give the block morphological characteristics that should be prioritized under different scales and optimization goals, such as Figure 7 shown.

[0066] Throughout this specification, references to terms such as "embodiment," "case," or "for example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0067] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A joint simulation method for studying the impact of urban wind environment on urban building energy consumption, characterized by: The following steps are involved: S1. Generate a research block model based on urban data and automatically calculate all morphological parameters; S2. Simulate the intensity of the block heat island: Use the urban climate generator to obtain the block-scale EPW file based on the results of S1; S3, simulated block-scale annual wind speed: calculate the wind speed of the selected block for 8760 h based on the S1 simulation results; S4. Use original EPW for energy consumption simulation: use the same EPW file for all individual buildings in the selected block, without considering the spatiotemporal heterogeneity of the wind environment; S5. Use the updated EPW to simulate energy consumption: simulate and calculate the building energy consumption of the selected block at three time scales: full year, month, and extreme climate week; S6. quantifying the impact of the wind environment on urban building energy consumption based on the two energy consumption calculation results, where the urban building energy consumption is cooling load and heating load; S7. Based on the results of S6, analyze the influence of the wind environment on the energy consumption of urban buildings in the case city climate zone. Step S6 includes: taking the difference between the two energy consumption calculations and using the simulation results of S4 as a benchmark, the degree of influence of the wind environment on the energy consumption of the buildings in the block, IWE, can be obtained. IWE= ； Where, i Number the buildings in the block; n is the total number of buildings in the block; EUI 2 The energy consumption intensity simulated by the updated EPW file; EUI 1 It represents the energy consumption intensity obtained from the original EPW simulation, and is ultimately used as a percentage to measure the impact of the wind environment on the energy consumption of urban buildings.

2. The combined simulation method for studying the impact of urban wind environment on urban building energy consumption according to claim 1 is characterized by: Step S1 includes: S11, input GIS data to generate a real block model to prepare for subsequent calculations; S12. Select the city wind direction from eight wind directions: north, northeast, east, southeast, south, southwest, west, and northwest; and automatically calculate eight morphological parameters: windward area index, site coverage, average building height, three-dimensional site coverage, facade ratio, green area ratio, volume ratio, and shape coefficient.

3. The combined simulation method for studying the impact of urban wind environment on urban building energy consumption according to claim 2 is characterized by: Step S3 includes: S31. Create a computational fluid dynamics grid from the S1 simulation results and set a cylindrical background grid to facilitate the calculation of the results from the eight wind directions; S32. Calculate and extract the wind speed at a height of 10 m around each building in the block, take the average wind speed, and obtain 8760 accurate wind speed data around each building throughout the year.

4. The combined simulation method for studying the impact of urban wind environment on urban building energy consumption according to claim 3 is characterized by: Step S5 includes: S51, simulated building energy intensity EUI of the block under the full-year cycle; S52. Simulate the EUI of the building energy intensity of the block for each month of the year and derive it in the order of each building; S53. Based on relevant standards and meteorological data, the extremely hot week of July 23-29 and the extremely cold week of January 20-26 in Shanghai were selected to simulate the weekly block building energy intensity EUI.

5. The combined simulation method for studying the impact of urban wind environment on urban building energy consumption according to claim 4 is characterized by: The calculation steps of IWE include: S61. Taking the difference between the two energy consumption calculation results and taking the first energy consumption calculation result as a benchmark, obtain the value of a single building; S62. Calculate the average IWE value of all buildings in the block: accumulate and sum the results of each building in S61 and take the average value to obtain the block IWE value.

6. The combined simulation method for studying the impact of urban wind environment on urban building energy consumption according to claim 5 is characterized by: Step S7 includes: S71. Analyze the impact patterns of extreme climate on a weekly and annual scale, and provide specific numerical values. S72. Use machine learning methods to study the model prediction characteristics of different months under seasonal factors, explain the influence characteristics of different months behind the annual influence patterns, rank the importance of features, and provide the block morphological characteristics that should be prioritized under different scales and optimization objectives.

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