An ultra-low-energy passive energy-saving external window parameterization design method, electronic equipment and program
By constructing a parametric model of the exterior window on the Grasshopper platform and utilizing orthogonal experiments and range optimization methods, the system analysis problem of multi-parameter coupling in the design of exterior windows for ultra-low energy buildings was solved, achieving rapid and accurate multi-objective optimization and meeting the standards for ultra-low energy buildings.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 中南建筑设计院股份有限公司
- Filing Date
- 2025-12-29
- Publication Date
- 2026-06-26
AI Technical Summary
Existing technologies lack multi-parameter coupling system analysis in the design of exterior windows for ultra-low energy buildings, resulting in long design cycles, low efficiency, and difficulty in meeting multi-objective optimization requirements, often leading to design schemes exceeding budgets.
A parametric simulation technique was used to build an outer window model on the Grasshopper platform. Through orthogonal experiments and range optimization, the combination of multiple parameters of the outer window was optimized. Energy consumption simulation was carried out using Ladybug, Honeybee, Radiance, and EnergyPlus plugins to achieve fast and accurate multi-objective optimization.
It significantly shortens the design cycle, improves design efficiency, reduces R&D costs, achieves precise energy-saving control, is applicable to multiple destination climates and building types, and meets ultra-low energy consumption building standards.
Smart Images
Figure CN122287031A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of green building energy conservation technology, and in particular to a method for designing parameters of ultra-low energy-consuming passive energy-saving windows, a method, electronic equipment and program for multi-objective optimization of passive energy-saving window parameters. Background Technology
[0002] Ultra-low energy buildings and near-zero energy buildings, with their high efficiency and low energy consumption, are gradually becoming important pathways for achieving "dual carbon" goals in the building sector. Meanwhile, near-zero energy buildings and zero-energy buildings, as effective means of reducing energy consumption and carbon emissions, are also gradually gaining widespread attention.
[0003] Different regions experience varying degrees of heat transfer due to differences in external climate conditions. Under heating or cooling conditions, exterior walls account for approximately 30%-45% of the energy consumption of a building's external envelope, while over 50% of heat is transferred through exterior windows. Therefore, improving the thermal insulation performance of exterior windows, effectively controlling air leakage, and enhancing indoor air quality and thermal comfort are of dual significance for improving building interior quality and reducing energy consumption.
[0004] Currently, energy-saving design for ultra-low energy building windows is mainly achieved through optimizing the window-to-wall ratio, selecting high-performance glass, improving window frame profiles, and installing shading components. However, traditional design methods often rely on experience-based judgment or single-factor simulation, lacking systematic analysis of the multi-parameter coupled effects of windows. The design process often requires manual parameter adjustments between different software platforms, which is time-consuming, inefficient, and makes rapid optimization of the solution difficult.
[0005] With the development of building performance simulation technology and parametric modeling tools, parametric design methods for building performance based on platforms such as Rhino-Grasshopper are gradually becoming a new direction for energy-efficient building design. By establishing parametric models, it is possible to quickly adjust and provide performance feedback on the geometric features, material properties, and shading methods of exterior windows in the early stages of design.
[0006] However, existing research focuses on optimizing single performance indicators (such as U-value or solar heat gain coefficient), lacking a design method for multi-parameter integrated coupling of exterior windows under the concept of "passive energy saving". In particular, in meeting the requirements of ultra-low energy consumption building standards (such as near-zero energy buildings and passive buildings), there is still a lack of systematic parameterized optimization paths.
[0007] In the traditional design of ultra-low energy passive energy-saving windows, the baseline model and the optimized energy-saving model with the window are separated into two files. Geometry, mesh, and air conditioning settings are prone to human error. Usually, changes in parameters and climate changes at the destination will cause a "modify once - export once - calculate once" process. The design cycle for just one window can be several weeks, resulting in a long design cycle.
[0008] Furthermore, the design schemes cannot be directly applied to materials and implementation, and the design schemes often exceed the budget. Summary of the Invention
[0009] The technical problem to be solved by the present invention is to provide a parametric design method, electronic device and program for ultra-low energy consumption passive energy-saving windows, which addresses the above-mentioned defects of the prior art. By using parametric simulation technology, the performance of windows under different parameter combinations can be accurately predicted and optimized, which greatly shortens the research and development cycle, reduces the research and development cost, and can be applied to climate switching in multiple destinations, achieving more precise energy-saving control, and making up for the lack of collaborative design of multi-objective passive windows in traditional designs.
[0010] The technical solution adopted in this invention is: A parametric design method for ultra-low energy consumption passive energy-saving windows includes the following steps: a. Obtaining climate data, building model data, and basic window parameters for the target area; b. Constructing a benchmark model in Grasshopper that meets the set design specifications; c. Extracting five window influencing factors: orientation, window-to-wall ratio, glass type, window frame material, and shading method, generating several energy-saving window parameter combinations using orthogonal experiments, and establishing corresponding parametric models; d. Using a building performance plugin to batch simulate the annual energy consumption of the benchmark model and the parametric model; e. Calculating the relative energy saving rate η, retaining the maximum η > 0 and outputting the optimal passive window scheme, and if η ≤ 0, returning to c to adjust the factor level and iterating again until η > 0 is satisfied.
[0011] The above technical solution also includes using the range optimization method to calculate and analyze the optimal design parameters after the energy-saving window based on energy consumption data of different combinations of influencing factors.
[0012] Range analysis can reflect the degree of influence of various factors on building energy consumption. The larger the range value R, the greater the impact of the level change of this factor on the experimental results. The largest change in building energy consumption indicates that the influencing factor is more important to the experimental index. The smaller the range, the smaller the impact on energy consumption. In the above technical solution, the parameterization platform is Rhino-Grasshopper, and it integrates Ladybug, Honeybee, Radiance, and EnergyPlus plugins to complete the simulation.
[0013] In the above technical solution, the wall, roof and openings are automatically generated by inputting the bay opening, depth, floor height and window-to-wall ratio using a slider; Honeybee establishes the materials according to the minimum specifications; the walls, roof, floor and windows are constructed using HB Face and loaded into the local EPW, and the baseline model is obtained after compliance self-check.
[0014] In the above technical solution, the minimum parameters for the materials are as follows: wall K≤0.5, roof K≤0.35, and ordinary hollow window U=2.8.
[0015] In the above technical solution, the climate data includes indoor temperature, outdoor temperature, wind speed, and relative humidity.
[0016] In the above technical solution, the parameters of the building's exterior window model include orientation, window-to-wall ratio, glass type, window frame material, and shading method.
[0017] In the above technical solution, the building model data includes: building orientation, building floor height, building depth, building span, window-to-wall ratio, building envelope structure, personnel parameters, air conditioning and heating, lighting power, equipment power, fresh air volume, and occupancy rate.
[0018] In the above technical solution, the baseline model is a building model that does not include passive energy-saving windows, and the parameter model is a parameterized model of the target building that includes passive energy-saving windows.
[0019] The baseline model serves as a comparison model for the energy-saving effects of passive energy-saving windows.
[0020] In the above technical solutions, the baseline model uses the minimum standard or conventional market windows, employing ordinary double-glazed glass, non-thermal-break aluminum frames, and no shading, serving as the "zero point" for energy consumption comparison. In the parametric model of the above technical solutions, orientation, window-to-wall ratio, glass, window frame, and shading are set as adjustable parameters. Multiple high-performance window combinations are generated through orthogonal arrays to optimize energy-saving solutions.
[0021] In the above technical solution, the orthogonal experiment uses L 16 (4 5 An orthogonal array was used, with 5 influencing factors selected, each factor having 4 levels, resulting in a total of 16 combinations.
[0022] Arrange the factors into the corresponding columns of the orthogonal array, and conduct energy consumption simulations according to the 16 experimental schemes in the table.
[0023] Therefore, by utilizing the properties of orthogonal arrays, representative points can be selected from the full experiment for further testing. This allows for a rapid and economical determination of the impact of each factor on the results with fewer experiments, and the identification of the optimal combination of factors.
[0024] In the above technical solution, the energy consumption simulation outputs the energy consumption of cooling, heating, lighting, and equipment, and calculates the comprehensive energy consumption E=E c +E h +E l +E e； E represents the total energy consumption, E c E represents cooling energy consumption. h E represents heating energy consumption. lE represents lighting energy consumption. e This indicates the energy consumption of the equipment.
[0025] In the above technical solution, the relative energy saving rate E0 represents the overall energy consumption of the baseline model; E1 represents the overall energy consumption of the parametric model.
[0026] In the above technical solution, the building geometry and window openings are created in Grasshopper at once; the "window-frame-shading" parameters are divided into two branches using Boolean switches: one branch calls ordinary glass / aluminum frame to generate a baseline model, and the other branch calls energy-saving glass, low U-frame and shading components to generate an energy-saving parameterized model; the two models share climate, material and energy consumption simulation components.
[0027] This shared model allows for one-click output of load and calculation of relative energy saving rate, enabling rapid comparison of "one model for two uses".
[0028] In the above technical solution, in Grasshopper, all five external window influence factors are made into sliders and numbered; such as A1-A4, B1-B4, ..., E1-E4; Read in the L16 orthogonal array, and automatically adjust the slider 16 times. Each modification immediately triggers the Honeybee-Energy calculator to calculate the total annual energy consumption; The results panel synchronously writes the "Comprehensive Energy Consumption" and slider value into a CSV file. After running 16 sets, a batch energy consumption list is obtained, and no manual parameter changes are required throughout the process.
[0029] Based on the above method, the present invention also provides: An electronic device includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, performs the steps of any of the methods described above.
[0030] A computer-readable storage medium storing a program that, when executed by a processor, implements the steps of any of the methods described above.
[0031] Compared with the prior art, the present invention has the following beneficial effects: This invention provides an efficient, accurate, and economical method for optimizing passive energy-saving windows. Compared with traditional methods, this invention has higher accuracy, stronger adaptability, and wider applicability, providing an effective way for the development of ultra-low energy consumption.
[0032] It can simultaneously take into account ventilation, lighting, energy saving, and indoor thermal comfort, achieving multiple optimizations in building energy consumption and comfort.
[0033] One-click parametric batch modeling allows for the slider-based processing of five factors: orientation, window-to-wall ratio, glass, window frame, and shading. The entire orthogonal array of these five factors at four levels is then imported at once. Sixteen models run for an average of 4 minutes per run (on a standard laptop, using EnergyPlus-ideal loads), requiring no manual intervention. Compared to the traditional "modify once, export once, calculate once" process, the design cycle is reduced from weeks to hours, improving efficiency by more than 10 times.
[0034] This invention uses a baseline model and an energy-saving model built from the same source, ensuring complete consistency in geometry, climate, and energy systems, with an energy-saving rate calculation error of <0.3%. Traditional methods often separate the baseline and optimized schemes into two separate files, making it easy for human error to occur in geometry, mesh, and air conditioning settings. This invention uses a "same Grasshopper script + Boolean switch" approach, replacing only window construction and shading parameters, while completely locking the rest of the geometry, materials, and energy systems. Actual test comparisons show that for the same office building, calculations using "dual files" and "same source switch" show an annual difference of up to 3.8% in heating and cooling loads, while the difference using the same source method is <0.3%, meeting the ±5% accuracy requirement of civil building energy consumption standards.
[0035] The entire process is based on Ladybug Tools + EnergyPlus, directly outputting hourly load data for the entire year, avoiding data loss caused by cross-software data transfer.
[0036] The output is the procurable construction method (number of glass layers, frame material, shading dimensions), which does not require secondary detailing, and incremental costs can be accurately calculated in advance.
[0037] The script has a built-in energy-saving rate judge: if all 16 energy-saving rates are ≤0, it automatically increases the number of glass layers or reduces the window-to-wall ratio, regenerates the orthogonal array, and runs it a second time until an energy-saving rate >0 appears. The rollback process does not require manual boundary reset, avoiding omissions and budget overruns caused by "trial and error based on experience".
[0038] The method is applicable to any climate zone and building type. The same script can be reused by changing the EPW (Energy Processing Unit), supporting the rapid implementation of near-zero energy, passive house, and dual-carbon demonstration projects. The same script has been validated in Beijing (cold A), Guangzhou (hot summer and warm winter), and Xining (severe cold), with energy savings ranging from 2.1% to 4.3%, proving that it is not dependent on specific climates. In terms of building types, templates for four types—office, school, residence, and hospital—have been completed, with built-in sliders for floor height, depth, and window-to-wall ratio, achieving "out-of-the-box" usability. Attached Figure Description
[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a schematic diagram of the parametric design method for ultra-low energy consumption passive energy-saving windows according to Embodiment 1 of the present invention.
[0040] Figure 2 This is a flowchart of the parametric design method for ultra-low energy consumption passive energy-saving windows in Wuhan area according to Embodiment 2 of the present invention.
[0041] Figure 3 This is the interface for downloading meteorological data files for the Wuhan area in Embodiment 2 of the present invention.
[0042] Figure 4 This is a flowchart illustrating the parameterization setting procedure for the benchmark building envelope in Embodiment 2 of the present invention.
[0043] Figure 5 This is a baseline model diagram based on Grasshopper, as shown in Embodiment 2 of the present invention.
[0044] Figure 6 This is a flowchart illustrating the parameter setting procedure for the outer window model built using Grasshopper in Embodiment 2 of the present invention.
[0045] Figure 7 This is a parametric model diagram based on Grasshopper, as shown in Embodiment 2 of the present invention.
[0046] Figure 8-11 This is a logical block diagram of the architectural model running program based on Grasshopper in Embodiment 2 of the present invention (due to the large size of the overall diagram, the logic of each module is shown separately).
[0047] Figure 12 This is a flowchart of the energy consumption analysis system built based on Grasshopper in Embodiment 2 of the present invention. Detailed Implementation
[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0049] Example 1 like Figure 1 As shown, the ultra-low energy consumption passive energy-saving window parameter design method according to the present invention includes the following steps: Step 1: Obtain data for the target building area, including climate data for the target area, window model parameters for the target building, and model data for the target building. Step 2: Based on the steps described in Step 1, construct a benchmark model of the target building that meets the "Code for Thermal Design of Civil Buildings" using the data of the target building area in the parametric software Grasshopper.
[0050] Step 3: Extract five different energy-saving window influencing factors, apply orthogonal experiments to combine different influencing factors, and establish corresponding combination parameterized models including energy-saving windows; Step 4: Use the building performance plugin in Grasshopper to build and run the program for the baseline model and parametric model, perform building energy consumption calculations, and output the calculation results; Step 5: Based on the above parameters, calculate the relative energy saving rate of the target building model. When the relative energy saving rate is greater than 0, select the parameter combination with the largest relative energy saving rate to output the solution result; when the relative energy saving rate is less than 0, reset the values of the influencing factors for analysis and calculation until the result is met.
[0051] Step 6: Based on the above parameters, provide design options for ultra-low energy passive windows.
[0052] In the above technical solution, the climate data in step 1 includes indoor temperature, outdoor temperature, wind speed, and relative humidity.
[0053] In the above technical solution, the parameters of the target building's exterior window model in step 1 include orientation, window-to-wall ratio, glass type, window frame material, and shading method.
[0054] In the above technical solution, the target building model data in step 1 includes building orientation, building height, building depth, building span, window-to-wall ratio, building envelope structure, personnel parameters, air conditioning and heating, lighting power, equipment power, fresh air volume, and occupancy rate.
[0055] In the above technical solution, the benchmark model in step 2 is a building model that does not include passive energy-saving windows. The benchmark model serves as a comparison model of the energy-saving effect of passive energy-saving windows.
[0056] In the above technical solution, the five different energy-saving window influencing factors in step 4 include orientation, window-to-wall ratio, glass type, window frame material, and shading method.
[0057] In the above technical solution, the parameter model in step 3 is a parameterized model of the target building that includes passive energy-saving windows.
[0058] In the above technical solution, the formula for the orthogonal experimental design matrix in step 3 is: L n (m¹ × m² × ... × m K L represents Latin Square or Orthogonal Array, n represents the total number of experiments to be conducted, m is the number of levels for each factor, and K represents the number of factors.
[0059] In the above technical solution, step 3 uses an orthogonal experiment to select 5 influencing factors, with 4 levels for each factor, and selects L... 16 (4^5) An orthogonal array is used as the experimental design matrix, and the five factors are assigned to the five independent columns of the orthogonal array respectively. Based on the orthogonal array, 16 experimental schemes are formed, and each experimental scheme corresponds to a set of external window parameter combinations.
[0060] In the above technical solution, the orthogonal experiment in step 3 is an efficient experimental design method based on mathematical principles, used to study different combinations of schemes under multi-factor and multi-level conditions. Its core idea is to use the properties of orthogonal arrays to select some representative points from the comprehensive experiment for experimentation, so as to quickly and economically determine the influence of each factor on the results with fewer experiments.
[0061] In the above technical solution, step 4 involves establishing building climate data based on Ladybug, establishing building operation programs based on Radiance, establishing building envelope parameter information based on Honeybee-Energy, and establishing a building energy consumption simulation system based on Honeybee AnnualLoads.
[0062] In the above technical solution, the comprehensive energy consumption calculation formula in step 5 is: E=E c +E h +E l +E e (E represents total energy consumption, E) c E represents cooling energy consumption. h E represents heating energy consumption. l E represents lighting energy consumption. e (This indicates the equipment's energy consumption).
[0063] In the above technical solution, the specific formula for calculating the relative energy saving rate in step 5 is as follows: (E0 represents the total energy consumption of the baseline model; E1 represents the total energy consumption of the parametric model).
[0064] In the above technical solution, in step 5, the energy consumption of the target building model is calculated based on the above parameters. When the relative energy saving rate is greater than 0, the parameter combination with the largest relative energy saving rate is selected to output the solution result; when the relative energy saving rate is less than 0, the values of the influencing factors are reset for analysis and calculation until the result is met.
[0065] Example 2 Based on Example 1, such as Figure 2-12 As shown, this embodiment selects a specific region for parametric design of ultra-low energy consumption passive energy-saving windows, including the following steps: Step 1: Taking office buildings in Wuhan as an example, obtain climate data for Wuhan, building window model parameters, and target building data, such as... Figure 2 As shown; Specifically, sub-step 1 includes: see Figure 3 Run the LB EPW map program in the LBT component to download meteorological data files for the Wuhan area.
[0066] The LBT component is the Ladybug Tools software package, which is open source and available for free download. It includes the LB EPW Map program, providing one-click access to the EnergyPlus official weather database. Website: energyplus.net / weather. The specific weather data file is "CHN_Wuhan.574940_CSWD.epw", provided by China Standard Meteorological Data (CSWD).
[0067] Specifically, sub-step 2 includes: obtaining the target building as a room with dimensions of 3.0×6.0×3.0m (width×depth×height).
[0068] Specifically, sub-step 3 includes: See Table 1, obtaining the parameter settings for the building envelope of the target building; Table 1 shows the structural form and thermal performance parameter settings for the building envelope.
[0069] Table 1
[0070] Step 2, see Figure 4 , Figure 5 In the parametric software Grasshopper, the reference standard that meets the requirements of the "Code for Thermal Design of Civil Buildings" (GB50176-2016) is constructed according to the parameters in Table 1.
[0071] Step 3: Identify the five influencing factors of energy-saving windows and use orthogonal experiments to combine different influencing factors.
[0072] Specifically, sub-step 1 includes: as shown in Table 2, the five building influencing factors extracted are orientation, window-to-wall ratio, shading coefficient, window frame material, and glass material. An orthogonal experiment is conducted on these five influencing factors, with four levels set for each factor, and L... 16 (4) 5 The experimental design using orthogonal arrays is shown in Table 2. Table 2 is a table of orthogonal experimental design factors and levels.
[0073] Table 2
[0074] Specifically, sub-step 2 includes: (see Table 3) arranging the level values of the five influencing factors into the corresponding columns of the orthogonal array, establishing a parameterized model according to the 16 experimental schemes in the table, and conducting energy consumption simulation analysis. Table 3 is a table of orthogonal experimental combinations for the five influencing factors.
[0075] Table 3
[0076] Specifically, sub-step 3 includes: see Figure 6 , Figure 7 By setting parameters such as window-to-wall ratio, windowsill height, window height, window orientation, and external structure in Grasshopper, an energy-saving window model was established, and 16 sets of parametric models were established according to the experimental scheme in Table 3.
[0077] Step 4: Use the building performance plugin in Grasshopper to build and run the program for the baseline model and parametric model, perform building energy consumption calculations, and output the calculation results.
[0078] Specifically, sub-step 1 includes: see Figure 8-11 By using Ladybug, Honeybee, and Radiance building performance plugins, and by setting meteorological files, air conditioning operating time, air conditioning operating set temperature, personnel usage time, lighting equipment time, ventilation system time, indoor lighting, and other procedures, a building operation program can be established.
[0079] Specifically, sub-step 2 includes: see Figure 12 The EnergyPlus building performance plugin can be used to create an energy consumption calculation program and output visualized calculation results for cooling energy consumption, heating energy consumption, lighting energy consumption, and equipment energy consumption.
[0080] Step 5: Compare and analyze the energy consumption data of the baseline model and each combined parameterized model, and calculate the relative energy saving rate.
[0081] Specifically, sub-step 1 includes: See Table 4, performing energy consumption simulation calculations on the benchmark building model. The energy consumption simulation data is shown in Table 4, which is a parameterized energy consumption simulation data table for the benchmark energy consumption.
[0082] Table 4
[0083] Specifically, sub-step 2 includes: as shown in Table 5, performing energy consumption simulation calculations on 16 sets of building parametric models. The energy consumption simulation data is shown in Table 5. Table 5 is a table of energy consumption simulation data for 16 sets of parametric models.
[0084] Table 5
[0085] Calculate the energy consumption of 16 sets of parametric models of the target building, select the parameter combination with the highest relative energy saving rate, and determine whether it meets the requirements for ultra-low energy consumption windows. If it does not meet the requirements, reset the parameters and recalculate.
[0086] Specifically, sub-step 1 includes: (See Table 5) Selecting a parameterized model to compare and analyze energy consumption data with that of the benchmark model. The parameter selection combination that meets the ultra-low energy consumption design requirements for the westward direction is Experiment No. 13, with a cooling energy consumption of 25.58 kWh / m². 2 •a, Heating energy consumption: 23.90 kWh / m³ 2 •a, Lighting energy consumption is 14.61 kWh / m² 2 • a, The equipment energy consumption is 36.89 kWh / m³ 2 •a, the comprehensive energy consumption is 100.98 kWh / m³ 2 •a, The relative energy saving rate is 2.77%, which meets the requirement that the relative energy saving rate is greater than 0, and thus satisfies the requirements.
[0087] Specifically, sub-step 2 includes: (See Table 5) Selecting a parameterized model to compare and analyze energy consumption data with that of the benchmark model. The parameter combination that meets the ultra-low energy consumption design requirements for the northbound direction is Experiment No. 2, with a cooling energy consumption of 25.01 kWh / m³. 2 •a, Heating energy consumption: 25.06 kWh / m³ 2 •a, Lighting energy consumption is 13.91 kWh / m² 2 • a, The equipment energy consumption is 36.89 kWh / m³ 2 •a, the comprehensive energy consumption is 100.87 kWh / m³ 2 •a, The relative energy saving rate is 0.85%, which meets the requirement that the relative energy saving rate is greater than 0, and thus satisfies the requirements.
[0088] Specifically, sub-step 3 includes: (See Table 5) Selecting a parameterized model to compare and analyze energy consumption data with that of the benchmark model. The parameter selection combination that meets the ultra-low energy consumption design requirements for the eastward direction is Experiment No. 5, with a cooling energy consumption of 24.66 kWh / m³. 2 •a, Heating energy consumption: 24.27 kWh / m³ 2 •a, Lighting energy consumption is 14.74 kWh / m² 2 • a, The equipment energy consumption is 36.89 kWh / m³ 2 •a, the comprehensive energy consumption is 100.56 kWh / m³ 2 •a, The relative energy saving rate is 2.01%, which meets the requirement that the relative energy saving rate is greater than 0, and thus satisfies the requirements.
[0089] Specifically, sub-step 4 includes: (See Table 5) Selecting a parameterized model to compare and analyze energy consumption data with that of the benchmark model. The parameter selection combination that meets the ultra-low energy consumption design requirements for the south-facing side is Experiment No. 11, with a cooling energy consumption of 26.32 kWh / m². 2 •a, Heating energy consumption: 22.91 kWh / m³ 2 •a, Lighting energy consumption is 12.98 kWh / m² 2 • a, The equipment energy consumption is 36.89 kWh / m³ 2 •a, the overall energy consumption is 99.1 kWh / m³ 2 •a, The relative energy saving rate is 0.49%, which meets the requirement that the relative energy saving rate is greater than 0, and thus satisfies the requirements.
[0090] Step 6: After performing energy consumption analysis on the parametric model, the parameters selected for the west-facing window to meet the ultra-low energy consumption design requirements are: a window-to-wall ratio of 0.2, a shading coefficient of 0.6, a wood-aluminum composite window frame, and double-glazed windows with solar coating for Low-E glass. The parameters selected for the north-facing window to meet the ultra-low energy consumption design requirements are: a window-to-wall ratio of 0.3, a shading coefficient of 0.35, a wood-aluminum composite window frame, and triple-glazed Low-E glass. The parameters selected for the east-facing window to meet the ultra-low energy consumption design requirements are: a window-to-wall ratio of 0.2, a shading coefficient of 0.35, a uPVC window frame, and double-glazed Low-E glass with warm edge spacing. The parameters selected for the south-facing window to meet the ultra-low energy consumption design requirements are: a window-to-wall ratio of 0.35, a shading coefficient of 0.25, a wood-aluminum composite window frame, and double-glazed Low-E glass with warm edge spacing.
[0091] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.
Claims
1. A parametric design method for ultra-low energy consumption passive energy-saving windows, characterized in that, The process includes the following steps: a) Obtain climate data, building model data, and basic parameters of exterior windows for the target area; b) Construct a benchmark model in Grasshopper that meets the set design specifications; c) Extract five exterior window influencing factors: orientation, window-to-wall ratio, glass type, window frame material, and shading method; generate several energy-saving exterior window parameter combinations using orthogonal experiments and establish corresponding parametric models; d) Call the building performance plugin to batch simulate the annual energy consumption of the benchmark model and the parametric model; e) Calculate the relative energy saving rate η, retain the one with η > 0 and the largest as the optimal passive exterior window scheme, and if η ≤ 0, return to step c to adjust the factor level and iterate again until η > 0 is satisfied.
2. The parametric design method for ultra-low energy consumption passive energy-saving windows according to claim 1, characterized in that, The parameterization platform is Rhino-Grasshopper, and it integrates Ladybug, Honeybee, Radiance, and EnergyPlus plugins to complete the simulation.
3. The parametric design method for ultra-low energy consumption passive energy-saving windows according to claim 1, characterized in that, The baseline model is a building model that does not include passive energy-saving windows, while the parametric model is a parametric model of the target building that includes passive energy-saving windows.
4. The parametric design method for ultra-low energy consumption passive energy-saving windows according to claim 1, characterized in that, In Grasshopper, the building geometry and window openings are created all at once; Boolean switches are used to divide the "window-frame-shading" parameters into two branches: one branch calls ordinary glass / aluminum frame to generate a baseline model, and the other branch calls energy-saving glass, low U-frame and shading components to generate an energy-saving parametric model; the two models share climate, material and energy consumption simulation components.
5. The parametric design method for ultra-low energy consumption passive energy-saving windows according to claim 1, characterized in that, The orthogonal experiment uses L 16 (4 5 An orthogonal array was used, with 5 influencing factors selected, each factor having 4 levels, resulting in a total of 16 combinations.
6. The parametric design method for ultra-low energy consumption passive energy-saving windows according to claim 1, characterized in that, The energy consumption simulation outputs the energy consumption of cooling, heating, lighting, and equipment, and calculates the comprehensive energy consumption E=E c +E h +E l +E e E represents the total energy consumption. c E represents cooling energy consumption. h E represents heating energy consumption. l E represents lighting energy consumption. e This indicates the energy consumption of the equipment.
7. The parametric design method for ultra-low energy consumption passive energy-saving windows according to claim 1, characterized in that, The relative energy saving rate E0 represents the overall energy consumption of the baseline model; E1 represents the overall energy consumption of the parametric model.
8. The parametric design method for ultra-low energy consumption passive energy-saving windows according to claim 1, characterized in that, In Grasshopper, make all five outer window influence factors into sliders and number them; read in the L16 orthogonal array and loop 16 times to automatically change the sliders; Each modification immediately triggers the Honeybee-Energy calculator to calculate the total annual energy consumption.
9. An electronic device comprising a processor, a memory, and a program stored in the memory and executable on the processor, characterized in that: When the program is executed by the processor, it implements the steps of the method according to any one of claims 1-8.
10. A computer-readable storage medium storing a program thereon, characterized in that: When the program is executed by a processor, it implements the steps of the method described in any one of claims 1-8.