Joint simulation method for researching influence of urban wind environment on urban building energy consumption

Abstract

The invention discloses a joint simulation method for researching the influence of an urban wind environment on urban building energy consumption. The method comprises the following steps: automatically generating a block model and calculating parameters; simulating selected block scale urban heat island intensity, and updating temperature and humidity data; simulating wind speed data of eight wind directions of the selected block for 8760h in the whole year; the average wind speed around each building is automatically obtained, and a new EPW file is generated for each building; building energy consumption of the selected block under three time scales is simulated and calculated; using the updated EPW file to simulate and calculate the building energy consumption of the selected block under three time scales; the influence of the wind environment on the urban building energy consumption is quantified according to the two energy consumption calculation results; and obtaining an influence rule of the stroke environment of the case city (climate zone) on the urban building energy consumption according to a result. According to the method, the speed and accuracy of urban wind environment calculation are both considered, and quantitative analysis of simulation results is simply achieved.

Description

Technical Field

[0001] The present invention relates to a city planning technical method based on a Grasshopper platform, and in particular to a joint simulation method for studying the influence of urban wind environment on energy consumption of urban buildings. Background Art

[0002] Cities are one of the largest consumers and contributors to carbon emissions, with urban buildings consuming 40% of the EU's primary energy and accounting for about 36% of energy-related CO2 emissions. Therefore, urban buildings have great potential for energy conservation and reduction of greenhouse gas emissions. Given that the world's population is expected to increase to about 10 billion by 2050, and about 68% of the population lives in urban areas, climate change in the built environment and cities will have a significant impact on people's physical and mental health and the prospects for sustainable development. Considering the interaction between the urban environment and climate change, it is crucial to study the determinants of energy consumption in urban buildings. Improving energy efficiency through optimized design and urban management is an emerging research focus that will help achieve the Sustainable Development Goals.

[0003] Urban climate affects the living environment and 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, 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 climate-responsive and energy-sustainable urban design and evaluation.

[0004] In the field of urban planning and building physics, it has become a basic consensus to consider the impact of urban climate in the simulation of urban building energy consumption. Urban climate as a boundary condition of simulation has a great influence on the simulation results. At present, the common practice is to use typical meteorological year files (TMY) such as the EPW files provided by the EnergyPlus official website for simulation, but this kind of meteorological file is often collected by suburban meteorological stations and cannot reflect the real climate conditions in high-density urban areas, especially the temporal and spatial heterogeneity of the wind environment. In recent years, many studies have explored the coupled simulation of urban climate models and urban building energy consumption models, improved the input accuracy of climate boundary conditions, and improved the accuracy of energy consumption simulation.

[0005] However, most methods still focus on microclimate factors such as temperature, without considering the dynamics of the wind environment and the heterogeneity of urban space. In particular, the simulation of the wind environment often finds it difficult to balance accuracy and speed. First, many joint simulation methods only simulate and update temperature, ignoring the wind environment. Second, although some studies or methods have taken the wind environment into consideration, the simulation tools used are time-consuming and difficult to study on a large scale or to promote. None of these methods can consider the correlation between wind environment, morphology, and urban building energy consumption, and cannot optimize and study this issue. Summary of the invention

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

[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, the GIS platform is used to import real urban block data, automatically generate models and calculate block morphological parameters. Then, the EPW file is updated twice in combination with two verified numerical simulation tools to obtain block-scale temperature and humidity data and 8760h wind speed data for the whole year at the building level. At the same time, the urban building energy consumption data before and after the EPW file is updated are simulated in the framework and the difference is calculated. The proposed IWE index is used to measure the impact of wind environment on urban building energy consumption. Finally, based on the results, the characteristics of the impact of wind environment on the energy consumption of case city buildings are analyzed at three time scales, and the law of the impact of wind environment on urban building energy consumption is summarized.

[0009] Technical solution: The method for identifying and evaluating urban ventilation corridors under dynamic environment described in 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 8 block morphological parameters under 8 wind directions, including north, northeast, east, southeast, south, southwest, west and northwest, with the angle between adjacent wind directions being 45°; 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: according to the results of S1, use the urban climate generator to obtain the block-scale EPW file;

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

[0015] S31, establish a computational fluid dynamics grid from the simulation results of S1, and set a cylindrical background grid to facilitate the calculation of the results of 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: the same EPW file is used 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: annual, monthly, and extreme climate weeks;

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

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

[0021] S53. According to 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 wind environment on urban building energy consumption 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 wind environment on building energy consumption in the block (IWE).

[0023]

[0024] In the formula, 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 represents the energy intensity simulated by the original EPW, and the percentage is finally used to measure the impact of wind environment on urban building energy consumption. 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, obtaining a single building value;

[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 IWE value of the block.

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

[0028] S71. Analyze the influence of extreme climate on a weekly and annual scale and give specific values;

[0029] S72. Use machine learning methods to study the model prediction characteristics of different months under seasonal factors, explain the impact characteristics of different months behind the annual impact rules, rank the importance of features, and give 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. The present invention realizes the quantification of the impact of wind environment on urban building energy consumption. The present invention first proposes the impact of wind environment on urban energy consumption (IWE) index, and proposes a complete simulation framework for data acquisition, data calculation, and data quantification, so as to evaluate how the wind environment in the case city (climate zone) affects the energy consumption of urban buildings. However, the existing methods cannot reflect the performance of wind environment factors in affecting the energy consumption of urban buildings when evaluating the impact of microclimate on urban energy consumption.

[0032] 2. The conclusions drawn by the present invention have higher accuracy and reliability. The present invention comprehensively proposes a process and result analysis combining four aspects: urban block morphology index, urban heat island intensity, year-round wind environment simulation, and urban building energy consumption simulation. The wind environment, a highly variable index, can be used to evaluate its impact on urban building energy consumption through data quantification. Eight wind directions are set during the simulation to ensure the accuracy of the wind environment boundary conditions used to calculate energy consumption. 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 has strong scalability, 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 by 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 stronger 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 It is 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 phase;

[0039] Figure 5 It is the IWE value of each block under the extreme cold and hot cycle scale in the implementation case of the present invention; the horizontal axis represents the case block code, HFAI-UO represents an office block of a U-shaped plane type with a high FAI value, HFAI-CO represents an office block of a courtyard plane type with a high FAI value, HFAI-LO represents an office block of an L-shaped plane 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 plate-type north-south distribution with a high FAI value, HFAI-SEW-O represents an office block with a plate-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 6It is 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 of a U-shaped plane type with a high FAI value, HFAI-CO represents an office block of a courtyard plane type with a high FAI value, HFAI-LO represents an office block of an L-shaped plane 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 plate-type north-south distribution with a high FAI value, HFAI-SEW-O represents an office block with a plate-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 cases of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work 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 understandably, 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 ring area 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. All battery packs are shown in Grasshopper 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 a real block model to prepare for subsequent calculations. The scope and spatial analysis of the study area are as follows: Figure 2 ;

[0048] S12. Calculation of urban wind direction under 8 wind directions and 8 block morphological parameters, 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 area ratio (GR), floor area ratio (FAR), and body shape coefficient (BSC). ;

[0049] S2, simulate the intensity of the block heat island: according to the results of S1, use the urban climate generator to obtain the block-scale EPW file;

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

[0051] S31, establish a computational fluid dynamics grid from the simulation results of S1, and set a cylindrical background grid to facilitate the calculation of the results of 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 for 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: the same EPW file is used 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 block building energy consumption intensity (EUI) for each month of the year and derive it in the order of each building;

[0057] S53. According to 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 the weekly block building energy intensity (EUI);

[0058] S6. Quantify the impact of wind environment on urban building energy consumption 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 wind environment on building energy consumption in the block (IWE).

[0059]

[0060] In the formula, 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 represents the energy intensity simulated by the original EPW, and the percentage is finally used to measure the impact of wind environment on urban building energy consumption. 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, obtaining a single building value;

[0062] 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. The block IWE value in the case of extreme cold and hot weeks is as follows: Figure 5 As shown in Figure 2, the block IWE values ​​in the annual case are as follows: Figure 6 shown.

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

[0064] S71. Analyze the influence of extreme climate on a weekly and annual scale, and give specific 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 rules, and rank the importance of features, and give the block morphological characteristics that should be given priority under different scales and optimization goals, such as Figure 7 shown.

[0066] In the description of this specification, the description with reference to the terms "embodiment", "case", "take ... as an example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0067] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

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: according to the results of S1, use the urban climate generator to obtain the block-scale EPW file; S3, simulated block-scale wind speed throughout the year: calculate the wind speed of the selected block for 8760 h based on the simulation results of S1; S4. Use original EPW for energy consumption simulation: the same EPW file is used 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: annual, monthly, and extreme climate weeks; S6. Quantify the impact of wind environment on urban building energy consumption based on the results of two energy consumption calculations, mainly cooling load and heating load; S7. Based on the results in S6, analyze the influence of the stroke environment on the energy consumption of urban buildings in the case city climate zone.

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 under 8 wind directions: north, northeast, east, southeast, south, southwest, west and northwest; automatically calculate 8 morphological parameters including windward area index, site coverage rate, average building height, three-dimensional site coverage rate, 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, establish a computational fluid dynamics grid from the simulation results of S1, and set a cylindrical background grid to facilitate the calculation of the results of eight wind directions; S32. Calculate and extract the wind speed at a height of 10m in the grid 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, simulate the building energy intensity EUI of the block under the full-year cycle to obtain the annual IWE index of each block; 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. According to relevant standards and meteorological data, the extremely hot week 7.23-29 and the extremely cold week 1.20-26 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 in that: Step S6 includes: taking the difference between the two energy consumption calculations and taking the simulation result of S4 as a benchmark, the degree of influence of the wind environment on the energy consumption of the building in the block IWE can be obtained. In the formula, i is the building number in the block; n is the total number of buildings in the block; eui2 is the energy consumption intensity simulated by the updated EPW file; eui1 represents the energy consumption intensity simulated by the original EPW, and the percentage is finally taken to measure the impact of wind environment on urban building energy consumption.

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: 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, obtaining a single building value; 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 IWE value of the block.

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

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