A design method for the courtyard space morphology of traditional courtyard houses based on multi-objective optimization

Abstract

A design method for the courtyard space form of traditional Chinese dwellings based on multi-objective optimization includes the following steps: S1, setting optimization objectives, optimization variables, and constraints on the range of optimization variables; S2, calculating the optimization objective index values ​​using programming on the Grasshopper platform based on the optimization objectives, optimization variables, and constraints on the range of optimization variables in step S1, and obtaining a set of design solutions for the courtyard space form of traditional Chinese dwellings based on multi-objective optimization through multi-objective optimization programming; S3, assigning weights to the optimization objectives based on the CRITIC index weighting method based on index correlation; S4, ranking the solutions based on CRITIC+TOPSIS, and recommending the optimal solution according to the maximum value of the comprehensive score index. This invention provides a scientific and reasonable method for the green and low-carbon design and renovation of courtyard spaces in traditional Chinese dwellings. The comprehensive scheme ranking provides a reference for the selection of comprehensive schemes, thereby obtaining the optimal design scheme.

Description

Technical Field

[0001] This invention belongs to the field of green and low-carbon design and renovation technology of courtyard-style dwellings, and relates to a design method for the courtyard space morphology of courtyard-style dwellings based on multi-objective optimization. Background Technology

[0002] Traditional dwellings contain rich regional culture and passive energy-saving technologies. Among them, the traditional courtyard dwellings in the north and south have strong regional characteristics. The traditional courtyard dwellings in the north are mainly courtyards, while the courtyard dwellings in the south are widely distributed. Related studies indicate that the courtyard not only plays a role in cultural inheritance, but also plays an important role in regulating the physical environment of traditional dwellings.

[0003] Courtyard spaces are the primary source of natural light in traditional courtyard-style dwellings. Increasing the amount of natural light can meet indoor lighting needs, but it can also easily lead to excessive areas of extreme glare. Simultaneously, the heat gained from natural light can cause fluctuations in the indoor thermal environment, thus impacting building energy consumption. Therefore, there is a complex coupling relationship between improving the lighting environment, avoiding glare, and reducing building energy consumption. Optimizing only one objective can easily result in poorer outcomes for other design goals in residential architecture, failing to achieve a comprehensive improvement in the physical environment. The morphological elements of traditional courtyard spaces include the length, width, height, shading, window size, and visible light transmittance.

[0004] With the development of computer simulation technology, research and practice on multi-objective optimization and transformation of building form using parametric platforms and artificial intelligence algorithms to improve indoor lighting environment and reduce building energy consumption have made some progress. Although there are many studies and practices that use building form as an optimization parameter for multi-objective optimization of building physical environment and building energy consumption, research on multi-objective optimization based on lighting environment and energy conservation still has significant shortcomings. First, the importance of optimization objectives varies significantly in multi-objective optimization calculations, but current research recommends the optimal solution based on the average weight of optimization objectives, lacking in-depth research on the importance of different optimization objectives and failing to fully utilize the results of optimization objective weighting in multi-objective optimization decision-making. Second, the Pareto front solution based on multi-objective genetic algorithms is usually a non-dominated solution set containing multiple solutions, requiring further decision-making. Current research lacks a comprehensive and scientific method for ranking multiple solutions within the solution set, failing to provide decision-makers with a comprehensive reference for selecting the optimal solution. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a design method for the courtyard space morphology of traditional courtyard houses based on multi-objective optimization. This method offers a scientific and reasonable approach for the green and low-carbon design and renovation of courtyard spaces in traditional courtyard houses, thereby obtaining the optimal design scheme.

[0006] The technical solution adopted in this invention is:

[0007] A design method for the courtyard space morphology of traditional courtyard houses based on multi-objective optimization, the specific steps of which are as follows:

[0008] S1, set the optimization objective, optimization variables, and constraints on the range of optimization variables;

[0009] S2, Based on the optimization objectives, optimization variable settings and optimization variable range constraints in step S1, the optimization objective index values ​​are calculated in the Grasshopper platform through programming. Through multi-objective optimization programming, a set of courtyard-style residential courtyard space form design solutions based on multi-objective optimization is obtained.

[0010] S3, the CRITIC (Criteria Importance Through Intercriteria Correlation) method, which assigns weights to the optimization objective based on the correlation of indicators;

[0011] S4. Based on CRITIC+TOPSIS, the solutions are comprehensively weighted and ranked. The optimal solution is recommended according to the maximum value of the comprehensive score index. TOPSIS stands for Technique for Order Preference by Similarity to Ideal Solution.

[0012] Furthermore, the optimization objectives in step S1 include the daylight index O1 and glare index O2 of the light environment optimization objectives, and the energy-saving index O3 of the energy-saving optimization objectives, with optimization variables C1, C2, ..., C q q represents the number of optimization variables.

[0013] Furthermore, the optimization objective in step S1 is calculated through parametric programming, and the specific steps are as follows:

[0014] S21, implements parametric architectural geometry modeling and physical modeling through programming on the Grasshopper platform;

[0015] S22, using the Ladybug plugin on the Grasshopper platform to obtain local weather parameters;

[0016] S23, use the HB-Daylight plugin on the Grasshopper platform to calculate the daylight index O1;

[0017] S24, Calculate the glare index O2 using the HB-Annual Glare plugin on the Grasshopper platform;

[0018] S25 uses the HB-ModelToOSM plugin on the Grasshopper platform to calculate the energy-saving index O3;

[0019] S26 uses the Wallacei plugin on the Grasshopper platform for multi-objective optimization programming to obtain a set of courtyard-style residential space form design solutions based on multi-objective optimization.

[0020] Furthermore, in step S3, the optimization objective is weighted based on the CRITIC weighting method. The specific steps are as follows:

[0021] S31, Construct a standardized decision matrix A ij As shown in equation (1),

[0022]

[0023] In formula (1): a ij This refers to the value of the j-th optimization objective index for the i-th scheme; i = 1, 2, ..., m; j = 1, 2, ..., n; m refers to the total number of schemes; n refers to the number of optimization objectives, which is equal to 3 in this method;

[0024] S32, use equation (2) to perform min-max data standardization on the positive optimization target index in equation (1);

[0025]

[0026] In equation (2): x ij The standardized value of the j-th optimization objective index for the i-th scheme;

[0027] S33, use equation (3) to perform min-max data standardization on the negative index in equation (1);

[0028]

[0029] S34, use equation (4) to calculate the standardized arithmetic mean of the optimization target index. The variability S of the optimization target index is calculated using equation (5). j ;

[0030]

[0031] S35, use equation (6) to construct the correlation coefficient matrix R of the optimized target index. jj ;

[0032]

[0033] In the formula r 11 =r 22 =r 33 =1, r 12 =r 21 , r 13 =r 31 , r 23 =r 32 ;

[0034] S36, r is calculated using equations (7), (8), and (9) respectively. 12 、r 13 、r 23 And use equations (10), (11), and (12) to calculate the conflict indices B1, B2, and B3 of the optimization targets, respectively.

[0035]

[0036]

[0037] S37, use equation (13) to calculate the information carrying capacity C of the optimized target index. j ;

[0038] C j =S j ×B j (13)

[0039] S38: Calculate the weight W of the optimization objective using equation (14). j ;

[0040]

[0041] Furthermore, in step S4, the schemes are comprehensively weighted and ranked based on CRITIC+TOPSIS. The specific steps are as follows:

[0042] S41, use equation (15) to calculate the standardized decision matrix Z with target weights. ij :

[0043]

[0044] S42, the ideal solution Z is defined using equation (16). + ;

[0045]

[0046] S43, using equation (17) to define the negative ideal solution Z - ;

[0047]

[0048] S44, use equation (18) to calculate the distance between scheme i and the ideal solution.

[0049]

[0050] S45, use equation (19) to calculate the distance between scheme i and the negative ideal solution.

[0051]

[0052] S46, use formula (20) to calculate the comprehensive score index Y of scheme i. i The higher the overall score index value, the better the solution and the higher its ranking.

[0053]

[0054] The beneficial effects of this invention are: it provides a scientific and reasonable method for the green and low-carbon transformation of courtyard-style residential courtyard spaces, and the comprehensive scheme ranking provides a reference for the selection of comprehensive schemes, thereby obtaining the optimal design scheme. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the process framework of the present invention.

[0056] Figure 2 This is a schematic diagram of an embodiment of the present invention.

[0057] Figure 3 This is a partial schematic diagram of the program for the present invention, which utilizes the effective illuminance index of the HB-Daylight plug-in.

[0058] Figure 4 This is a partial schematic diagram of the program used in this paper to calculate glare indexes using the HB-Annual Glare plugin.

[0059] Figure 5 This is a partial schematic diagram of the program used by the HB-ModelToOSM plugin to calculate energy consumption indicators according to the present invention. Detailed Implementation

[0060] The present invention will be further described below with reference to specific embodiments, but the invention is not limited to these specific embodiments. Those skilled in the art should recognize that the present invention covers all alternatives, improvements, and equivalents that may be included within the scope of the claims.

[0061] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more, unless otherwise expressly defined.

[0062] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0063] In the present invention, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Furthermore, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly below or obliquely below the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0064] Reference Figure 1 This embodiment provides a design method for the courtyard space morphology of traditional courtyard houses based on multi-objective optimization. The specific steps are as follows:

[0065] S1, set the optimization objective, optimization variables, and constraints on the range of optimization variables;

[0066] This embodiment takes a residential building plot in a certain community in the urban-rural integration area of a certain city in a certain province as a case demonstration. The case building is designed with the most basic "mouth" shape in the patio-style courtyard residence. The building faces southeast from the northwest. The building shape is a regular square with a length of 12 meters and a width of 10.5 meters, and the total designed building land area is 173 square meters. The patio space is located in the center of the case building, and there are windows on all four sides around the patio. The method of the present invention is used to conduct multi-objective optimization design of the patio space form of the case building based on lighting, glare and energy conservation. The case building is shown as Figure 2 shown below.

[0067] Referring to the residential building design standard of this province in 2021 edition, the heat transfer coefficient of the building wall is set to 0.9 W / (m 2 ·°C), the heat transfer coefficient of the roof is set to 0.25 W / (m 2 ·°C), the heat transfer coefficient of the glass is set to 1.8 W / (m 2 ·°C), and the solar heat gain coefficient is set to 0.375.

[0068] Set the lighting index O udi of the light environment optimization target as the useful daylight illuminance UDI 300-2000 , with the unit of percentage (%). Set the glare index O dgp of the light environment optimization target as the extreme daylight glare probability DGP, with the unit of percentage (%). Set the energy conservation optimization target O energy as the annual energy consumption intensity of the building, with the unit of (kwh / (m 2 ·a)).

[0069] Set the following 7 optimization variables: C1 patio length, C2 patio width, C3 patio height, C4 internal overhanging ratio of the patio, C5 visible light transmittance of the window, C6 east-west window-wall ratio inside the patio, C7 north-south window-wall ratio inside the patio; among them, the internal overhanging ratio C4 of the patio refers to the ratio of the overhanging length of the overhanging eaves in the patio space to the total length of the patio in the same direction.

[0070] According to the case building site conditions and general qualitative knowledge, the range constraints of the set optimization variables are shown in Table 1:

[0071] Table 1 Range of optimization variables for the case building

[0072]

[0073] S2. According to the optimization objectives, optimization variable settings and optimization variable range constraints in step S1, program and calculate the optimization target index values on the Grasshopper platform, and obtain a set of design solutions for the patio space form of the courtyard-style residence based on multi-objective optimization through multi-objective optimization programming;

[0074] The specific steps are as follows:

[0075] S21, implements parametric architectural geometry modeling and physical modeling through programming on the Grasshopper platform;

[0076] S22, using the Ladybug plugin on the Grasshopper platform to obtain local weather parameters;

[0077] S23, calculate the daylight index O1 using the HB-Daylight plugin on the Grasshopper platform; in this embodiment, the effective illuminance UDI is calculated using HB-Daylight on the Grasshopper platform. 300-2000 The simulation was performed, and the metric was constrained to be within the effective illuminance range for at least 30% of the time and at least 60% of the space. The programming portion can be found in [link to programming documentation]. Figure 3 .

[0078] S24, calculate the glare index O2 using the HB-Annual Glare plugin on the Grasshopper platform; this embodiment uses HB-Annual Glare on the Grasshopper platform to simulate the probability of sunlight glare, and the programming part is described in [link to documentation]. Figure 4 .

[0079] S25, calculate the energy-saving index O3 using the HB-ModelToOSM plugin on the Grasshopper platform; perform energy consumption simulation using HB-ModelToOSM on the Grasshopper platform. See [link to programming details]. Figure 5 .

[0080] S26. Using the Wallacei plugin on the Grasshopper platform, multi-objective optimization programming was performed to obtain a set of design solutions for the courtyard-style residential courtyard space form based on multi-objective optimization. In this embodiment, the population size was set to 10, the iteration count to 20, and other parameters were kept at their default values. Running the program yielded a set of design solutions for the courtyard-style residential courtyard space form based on multi-objective optimization of lighting, glare, and energy conservation. This embodiment's solution set contains 50 solutions, as shown in Table 2. The solution ID is Gen.A|Ind.B, where A refers to the (A+1)th iteration and B refers to the (B+1)th population size.

[0081] Table 2. Design Solutions for Courtyard-Style Residential Courtyard Space Form Based on Multi-Objective Optimization of Lighting, Glare, and Energy Saving

[0082]

[0083]

[0084] S3, the CRITIC (Criteria Importance Through Intercriteria Correlation) method, which assigns weights to the optimization objective based on the correlation of indicators;

[0085] The specific steps are as follows:

[0086] S31, Construct a standardized decision matrix A ij As shown in equation (1),

[0087]

[0088] In formula (1): a ij This refers to the value of the j-th optimization objective index for the i-th scheme; i = 1, 2, ..., m; j = 1, 2, ..., n; m refers to the total number of schemes, which equals 50 in this case; n refers to the number of optimization objectives, which equals 3 in this method.

[0089] S32, use equation (2) to perform min-max data standardization on the positive optimization target index (the larger the index value, the better) in equation (1);

[0090]

[0091] In equation (2): x ij The standardized value of the j-th optimization objective index for the i-th scheme;

[0092] S33, use equation (3) to perform min-max data standardization on the negative index (the smaller the index value, the better) in equation (1);

[0093]

[0094] S34, use equation (4) to calculate the standardized arithmetic mean of the optimization target index. The variability S of the optimization target index is calculated using equation (5). j ;

[0095]

[0096] S35, construct the correlation coefficient matrix R of the optimization index using equation (6). jj ;

[0097]

[0098] In the formula r 11 =r 22 =r 33 =1, r 12 =r 21 , r 13=r 31 , r 23 =r 32 ;

[0099] S36, r is calculated using equations (7), (8), and (9) respectively. 12 、r 13 、r 23 And use equations (10), (11), and (12) to calculate the conflict indices B1, B2, and B3 respectively;

[0100]

[0101] S37, use formula (13) to calculate the information carrying capacity C of the evaluation index. j ;

[0102] C j =S j ×B j (13)

[0103] S38: Calculate the objective weight W of the target using equation (14). j ;

[0104]

[0105] In this embodiment, steps S31 to S33 are performed to standardize the target value.

[0106] Execute step S34 to calculate the variability S of the optimization objective. udi S dgp and S energy S udi =0.293, S dgp =0.302, S energy =0.304.

[0107] Execution steps S35-S36: Calculate the conflict B of the optimization objectives based on the correlation coefficients of the optimization indicators. udi B dgp and B energy B udi =2.945, B dgp =3.024, B energy =2.643.

[0108] Execution step S37: Calculate the information carrying capacity C of the optimization target respectively. udi C dgp and C energy C udi =0.862, C dgp =0.913, C energy =0.803.

[0109] Execute step S38: Calculate the target weight W of the optimization target based on the information carrying capacity. udi W dgp and W energy , W udi =33.446%, W dgp =35.422%, W energy =31.133%.

[0110] S4 uses CRITIC+TOPSIS to perform comprehensive weighting and ranking of solutions, and recommends the optimal solution based on the maximum comprehensive score index.

[0111] The specific steps are as follows:

[0112] S41, use equation (15) to calculate the standardized decision matrix Z with target weights. ij :

[0113]

[0114] S42, the ideal solution Z is defined using equation (16). + ;

[0115]

[0116] S43, using equation (17) to define the negative ideal solution Z - ;

[0117]

[0118] S44, use equation (18) to calculate the distance between the scheme and the ideal solution.

[0119]

[0120] S45, use equation (19) to calculate the distance between the scheme and the negative ideal solution.

[0121]

[0122] S46, use formula (20) to calculate the comprehensive score index Y i The higher the overall score index value, the better the solution and the higher its ranking.

[0123]

[0124] According to Y i The order of preference for sorting the values ​​from largest to smallest, Y iThe solution with the largest value is the optimal solution, and the ranking of comprehensive solutions provides a reference for the selection of all solutions.

[0125] In this embodiment, following steps S41-46, the data is standardized using a weighted normalization matrix, and the distance between each scheme and the ideal solution is evaluated. Distance of negative ideal solution The comprehensive score index Y of each solution is calculated by assessing its relative closeness to the ideal solution. i The higher the comprehensive score index value, the better the solution and the higher its ranking. The ranking of the solutions is shown in Table 3.

[0126] Table 3. Design Case Studies of Courtyard Space Forms in Traditional Courtyard Dwellings Based on Lighting, Glare Reduction, and Energy Saving (Ranked by CRITIC+TOPSIS)

[0127]

[0128]

[0129] According to Table 3, the comprehensive score index of scheme Gen.0|Ind.3 is the highest, ranking first, and is the optimal scheme. The comprehensive score index and ranking of the other schemes provide a reference for the design of this building scheme.

[0130] This embodiment presents the following comparative analysis of experimental results. Using the TOPSIS method, the results are compared based on a single light-gathering target O. udi (Ultimate Illuminance UDI) 300-2000 The scheme prioritizes weighting values ​​and is based on a single target O for glare. dgp (Probability of extreme solar glare) scheme priority weighting value, based on the single goal of energy saving O energy The priority weighting values ​​for (annual building energy consumption intensity) are presented in Tables 4, 5, and 6. According to Table 4, scheme Gen.19|Ind.1 is the optimal scheme. According to Table 5, scheme Gen.9|Ind.2 is the optimal scheme. According to Table 6, scheme Gen.19|Ind.0 is the best scheme. Running Wallacei yields the optimal solution as Gen.12|Ind.5. Based on the priority weighting values ​​of this invention, Gen.0|Ind.3 is the optimal scheme. It is evident that the method of this invention significantly improves the scientific rigor, comprehensiveness, and flexibility of scheme priority ranking, fully considering the optimization objective weights and the solution obtained from Wallacei, providing a more comprehensive reference for the design of courtyard-style residential space morphology based on multi-objective optimization of lighting, glare, and energy conservation.

[0131] Table 4 Based on the single objective of light collection O udi (Ultimate Illuminance UDI) 300-2000 TOPSIS sorting of the solution set

[0132]

[0133]

[0134]

[0135] Table 5 Based on glare from a single target O dgp TOPSIS ranking of schemes for (extreme solar glare probability)

[0136]

[0137]

[0138] Table 6 Based on the single objective of energy saving energy TOPSIS ranking of design schemes based on (annual building energy intensity)

[0139]

[0140]

Claims

1. A design method for the courtyard space morphology of traditional courtyard houses based on multi-objective optimization, the specific steps of which are as follows: S1 sets the optimization objectives, optimization variables, and constraints on the range of optimization variables; the optimization objectives include the daylight index O1 and glare index O2 for the light environment optimization objective, and the energy saving index O3 for the energy saving optimization objective; the optimization variables are C1, C2, ..., C q q represents the number of optimization variables; S2, Based on the optimization objectives, optimization variable settings and optimization variable range constraints in step S1, the optimization objective index values ​​are calculated in the Grasshopper platform through programming. Through multi-objective optimization programming, a set of courtyard-style residential courtyard space form design solutions based on multi-objective optimization is obtained. S3, the CRITIC method, which assigns weights to the optimization objective based on the correlation of indicators; The optimization objective is weighted using the CRITIC weighting method, and the specific steps are as follows: S31, Construct a standardized decision matrix A ij As shown in equation (1), In formula (1): a ij This refers to the value of the j-th optimization objective index for the i-th scheme; i = 1, 2, ..., m; j = 1, 2, ..., n; m refers to the total number of schemes; n refers to the number of optimization objectives, and n equals 3; S32, use equation (2) to perform min-max data standardization on the positive optimization target index in equation (1); In equation (2): x ij The standardized value of the j-th optimization objective index for the i-th scheme; S33, use equation (3) to perform min-max data standardization on the negative index in equation (1); S34, use equation (4) to calculate the standardized arithmetic mean of the optimization target index. The variability S of the optimization target index is calculated using equation (5). j ; S35, use equation (6) to construct the correlation coefficient matrix R of the optimized target index. jj ; In the formula r 11 =r 22 =r 33 =1, r 12 =r 21 r 13 =r 31 r 23 =r 32 ; S36, r is calculated using equations (7), (8), and (9) respectively. 12 、r 13 、r 23 And use equations (10), (11), and (12) to calculate the conflict indices B1, B2, and B3 of the optimization targets, respectively. S37, use equation (13) to calculate the information carrying capacity C of the optimized target index. j ; C j =S j ×B j (13) S38: Calculate the weight W of the optimization objective using equation (14). j ; S4. Based on CRITIC+TOPSIS, the solutions are comprehensively weighted and ranked, and the optimal solution is recommended according to the maximum value of the comprehensive score index. The specific steps for comprehensive weighting and ranking of solutions based on CRITIC+TOPSIS are as follows: S41, use equation (15) to calculate the standardized decision matrix Z with target weights. ij : S42, using equation (16) to define the ideal solution Z + ; S43, using equation (17) to define the negative ideal solution Z - ; S44, use equation (18) to calculate the distance between scheme i and the ideal solution. S45, use equation (19) to calculate the distance between scheme i and the negative ideal solution. S46, use formula (20) to calculate the comprehensive score index Y of scheme i. i The higher the overall score index value, the better the solution and the higher its ranking.

2. The design method for the courtyard space morphology of a courtyard-style residential building based on multi-objective optimization according to claim 1, characterized in that: The optimization objective in step S1 is calculated through parametric programming, and the specific steps are as follows: S21, implements parametric architectural geometry modeling and physical modeling through programming on the Grasshopper platform; S22, using the Ladybug plugin on the Grasshopper platform to obtain local weather parameters; S23, use the HB-Daylight plugin on the Grasshopper platform to calculate the daylight index O1; S24, Calculate the glare index O2 using the HB-Annual Glare plugin on the Grasshopper platform; S25 uses the HB-ModelToOSM plugin on the Grasshopper platform to calculate the energy-saving index O3; S26 uses the Wallacei plugin on the Grasshopper platform for multi-objective optimization programming to obtain a set of courtyard-style residential space form design solutions based on multi-objective optimization.

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