A Multi-Objective Optimization Method for the Renovation of High Side Windows in Industrial Heritage Buildings Based on Lighting and Energy Conservation

Abstract

A multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation is proposed, with the following specific steps: S1, setting optimization objectives and optimization variables, and determining the rationality of optimization variables by establishing a building model and performing calculation and analysis on the optimization objectives to constrain the range of optimization variables; S2, using the Wallacei plugin on the Grasshopper platform to perform multi-objective optimization programming based on the optimization objectives and the range constraints of optimization variables in step S1, obtaining a set of multi-objective optimization solutions for the renovation of high side windows in industrial buildings based on lighting and energy conservation, and obtaining the optimal solution based on the average optimization objective weight; S3, assigning weights to optimization objectives based on the entropy method, and simultaneously assigning priority weights to the solutions in the solution set in step S2 according to each optimization objective based on the analytic hierarchy process; S4, comprehensively calculating the weighted values ​​of the optimization objectives and the priority weighted values ​​of the solutions to obtain the comprehensive priority weighted value of the solutions, and recommending the optimal solution according to the maximum value of the comprehensive priority weighted value.

Description

Technical Field

[0001] This invention belongs to the field of green and low-carbon renovation technology of industrial heritage buildings, and relates to a multi-objective optimization method for the renovation of high side windows of industrial heritage buildings based on lighting and energy saving. Background Technology

[0002] The renovation of high side windows in industrial heritage buildings simultaneously impacts both the indoor lighting environment and energy consumption, and there is a complex coupling relationship between improving the indoor lighting environment and reducing building energy consumption. Simply considering improving the lighting environment or saving energy as a single design objective cannot provide a comprehensive reference for the renovation of high side windows in industrial heritage buildings. Focusing solely on improving the lighting environment may increase building heat gain, affecting the building's thermal environment and thus increasing building energy consumption. Conversely, focusing solely on building energy conservation may reduce the amount of natural light entering the building, hindering the improvement of the indoor lighting environment. The renovation of high side windows in industrial heritage buildings, which significantly affects both lighting and energy consumption, mainly includes window size, visible light transmittance, and external shading.

[0003] While there is considerable research and practice in multi-objective optimization of building physics and energy consumption, multi-objective optimization methods based on building performance have significant shortcomings. First, the importance of optimization objectives varies considerably. Recommending the optimal solution based on the average weight of optimization objectives not only lacks quantified weighting of the objectives but also fails to fully utilize the weighting results in multi-objective optimization decision-making. Second, multi-objective optimization methods based on genetic algorithms recommend solutions containing multiple solutions, but how to select the optimal solution from these solutions remains unknown. Third, existing multi-objective optimization methods do not address the rationality of setting optimization variables and the improvement of multi-objective optimization through quantified weighting of optimization variables. Fourth, there is a lack of a comprehensive multi-objective optimization process framework for industrial heritage buildings with high side windows based on lighting and energy conservation, encompassing optimization objective weighting and utilization, optimization variable weighting and utilization, and solution priority weighting and ranking. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation. This method can assign weights to multiple optimization objectives and then comprehensively rank the renovation schemes to obtain the optimal solution.

[0005] The technical solution adopted in this invention is:

[0006] A multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, the specific steps of which are as follows:

[0007] S1. Set the optimization objective and optimization variables. Calculate and analyze the optimization objective using the established building model to determine the rationality of the optimization variables and constrain their range.

[0008] S2, Based on the optimization objectives and optimization variable range constraints in step S1, multi-objective optimization programming is performed using the Wallacei plugin on the Grasshopper platform to obtain a set of multi-objective optimization solutions for the renovation of high side windows in industrial buildings based on lighting and energy saving. The optimal solution is obtained based on the average optimization objective weight.

[0009] S3, assign weights to the optimization objectives based on the entropy method, and simultaneously assign weights to the solution set in step S2 according to each optimization objective based on the analytic hierarchy process (AHP).

[0010] S4. Normalize the weighted optimization objective and the priority weighting schemes to obtain a comprehensive priority weighting value, and recommend the optimal solution according to the maximum value of the comprehensive priority weighting value.

[0011] Furthermore, the optimization objectives in step S1 include the light environment index O1 and the energy saving index O2, and the optimization variables are C1, C2, ..., C m , where m is the number of optimization variables.

[0012] Furthermore, the optimization objective in step S1 is calculated through orthogonal experiments, and the specific steps are as follows:

[0013] S111, orthogonal experimental design, including setting the number of experimental factors as m, determining the number of levels as n, determining the corresponding orthogonal experimental table based on m and n, and determining the number of experiments p based on the orthogonal experimental table;

[0014] S112, programmatically implements parametric architectural geometry modeling and physical modeling on the Grasshopper platform;

[0015] S113 uses the Ladybug plugin on the Grasshopper platform to obtain local weather parameters;

[0016] S114, using the HB-Daylight plugin on the Grasshopper platform to calculate the light environment index O1;

[0017] S115 uses the HB-Annual load plugin on the Grasshopper platform to calculate the energy-saving index O2.

[0018] Furthermore, in step S1, range analysis is performed on the calculated light environment index O1 and energy-saving index O2, respectively. The specific steps are as follows:

[0019] S121, Calculate K ij ,i=1,2,…,n;j=1,2,…,m,m+1;,K ijK equals the sum of the experimental results of the light environment index O1 or the energy-saving index O2 corresponding to the j-th factor (j=1,2,…,m) taking the i-th level. i,m+1 The sum of the experimental results of the light environment index O1 or the energy saving index O2 corresponding to the blank column of the orthogonal experiment;

[0020] S122, according to calculate Where S is the number of experiments p divided by the number of levels n;

[0021] S123, according to Calculate R j R j R represents the range of the j-th (j=1,2,…,m) factor. m+1 The range of the blank column in the orthogonal experiment;

[0022] S124, based on the range R of the blank column m+1 Is it less than R? j Given j = 1, 2, ..., m, determine the reliability of the orthogonal experimental data.

[0023] Furthermore, in step S1, variance analysis is performed on the calculated light environment index O1 and energy-saving index O2, respectively. The specific steps are as follows:

[0024] S131, calculate the sum of squared deviations Q of the j-th factor according to formula (1). j j = 1, 2, ..., m

[0025]

[0026] In the formula, x k , k = 1, 2, ..., p is the calculation result of the k-th light environment index O1 or the k-th energy saving index O2;

[0027] S132, calculate the mean square G according to formula (2) j j = 1, 2, ..., m

[0028]

[0029] S133, calculate F according to formula (3) j j = 1, 2, ..., m

[0030]

[0031] S134, calculate P according to formula (4) j j = 1, 2, ..., m

[0032] P j=F.DIST.RT(F j (n-1, n-1)(4)

[0033] In the formula, F.DIST.RT is a function of the Microsoft Excel program.

[0034] Furthermore, the rationality of the optimization variables in step S1 is judged as follows: R j The larger the value, the greater the influence of factor j on the optimization objective. If P j A value <0.05 indicates that factor j is significant; the smaller the value, the greater the significance.

[0035] Furthermore, the specific steps of step S2 are as follows:

[0036] S21, set the population size, set the number of iterations, set the crossover rate, and set the mutation rate;

[0037] S22, Run the program to obtain a multi-objective optimization solution set B for the renovation of high side windows in industrial buildings based on lighting and energy saving. z (z = 1, 2, ..., Z), where Z is the number of solutions in the solution set;

[0038] S23, obtain the optimal solution B according to the average optimization objective weight. ao .

[0039] Furthermore, in step S3, the light environment index O1 and the energy-saving index O2 are weighted based on the entropy method, and the specific steps are as follows:

[0040] S311, Use the experimental results of the orthogonal experiment as the sample dataset for the entropy method;

[0041] S312, use Equation (5) to standardize the data of the positive lighting index and Equation (6) to standardize the data of the negative energy saving index, and finally obtain a standardized sample dataset with the differences in the measurement units of each index eliminated.

[0042]

[0043] In the formula, X i ,i=1,2,…,p, refers to the target standard value for daylight optimization of the i-th sample, Y i ,i=1,2,…,p, refers to the standard value of the energy-saving optimization target for the i-th sample;

[0044] S313, In order to avoid some data having low values ​​after standardization, the translation process is performed using equations (7) and (8);

[0045] X Hi =X i +H(7)

[0046] Y Hi =Y i +H(8)

[0047] In the formula, H is the magnitude of the index shift, with a value of 0.01;

[0048] X Hi ,i=1,2,…,p, refers to the target standard value for daylight optimization of the i-th sample after translation processing;

[0049] Y Hi ,i=1,2,…,p, refers to the energy-saving optimization target standard value of the i-th sample after translation processing;

[0050] S314, use equations (9) and (10) to normalize the standardized dataset;

[0051]

[0052] In the formula,

[0053] X Oi ,i=1,2,…,p, refers to the target standard value of daylight optimization for the i-th sample after normalization;

[0054] Y Oi ,i=1,2,…,p, refers to the standard value of the energy-saving optimization target of the i-th sample after normalization;

[0055] S315, calculate the information entropy e of the lighting environment index O1 and the energy saving index O2 according to equations (11) and (12) respectively. udi and e energy ;

[0056]

[0057] S316, calculate the difference coefficient g of daylighting environment index O1 and energy saving index O2 according to equations (13) and (14) respectively. udi and g energy ;

[0058] g udi =1-e udi (13)

[0059] g energy =1-e energy (14)

[0060] S317, calculate the normalized weight values ​​V of the daylighting environment index O1 and the energy saving index O2 according to equations (15) and (16) respectively. udi and V energy ;

[0061]

[0062] Furthermore, in step S3, the weights are assigned based on the Analytic Hierarchy Process (AHP) according to the calculation schemes for the light environment index O1 and the energy-saving index O2, respectively. The specific steps are as follows:

[0063] S321, aiming at improving lighting or energy consumption, integrates solutions based on the quality of light environment indicators or energy-saving indicators. z Sort the solutions z = 1, 2, ..., Z, with the optimal solution ranked first, and so on.

[0064] S322, construct judgment matrices A according to light environment index and energy saving index respectively. udi and A energy :

[0065] and

[0066] a ij The value is determined according to the corresponding value selection conditions, a ji =1 / a ij b ij The value of b is determined according to the corresponding value selection conditions. ji =1 / b ij ;

[0067] S323, calculate the judgment matrix A respectively. udi and A energy The eigenvector H corresponding to the largest eigenvalue udi and H energy Its element value is h udi-i (i = 1, 2, ..., z) and h energy-i (i=1,2,…,z), calculate scheme B according to the light environment index according to equation (17). i priority weight h udi-o-i Calculate Scheme B according to the light environment index using formula (18). i priority weight h energy-o-i ;

[0068]

[0069] Furthermore, in step S4, the comprehensive priority weighting value W is... o-i , i = 1, 2, ..., z are calculated according to equation (19);

[0070] W O-i =h energy-o-i ×V energy +h udi-o-i ×V udi (19)

[0071] The optimal solution is recommended based on the maximum value of the comprehensive priority weighting.

[0072] The beneficial effects of this invention are that it can obtain the optimal solution for the renovation of high side windows in industrial heritage buildings by assigning weights to multiple optimization objectives and then comprehensively ranking the renovation schemes. Attached Figure Description

[0073] Figure 1 This is a schematic diagram of the process framework of the present invention.

[0074] Figure 2 This is a schematic diagram of an embodiment of the present invention.

[0075] Figure 3 This is a schematic diagram illustrating part of the programming for calculating the light environment index O1 using the HB-Daylight plugin in this invention.

[0076] Figure 4 This is a schematic diagram illustrating part of the programming for calculating the energy-saving index O2 using the HB-Annual load plugin.

[0077] Figure 5 This is a schematic diagram showing the results of the lighting and energy consumption indicators of a total of 81 orthogonal experiments in this invention.

[0078] Figure 6 This is a schematic diagram illustrating the priority weighting of schemes based on the single objective of daylighting.

[0079] Figure 7 This is a schematic diagram illustrating the priority weighting of schemes based on the single objective of energy saving.

[0080] Figure 8 This is a schematic diagram illustrating the comprehensive priority weighting of the solution in this invention. Detailed Implementation

[0081] 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.

[0082] 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.

[0083] 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.

[0084] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0085] Reference Figure 1 This embodiment provides a multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation. The specific steps are as follows:

[0086] S1. Set optimization objectives and optimization variables. Analyze the established building model to determine the rationality of the optimization variables and constrain their range. The optimization objectives include light environment index O1 and energy saving index O2, with optimization variables C1, C2, ..., C... m .

[0087] The architectural model in this embodiment is as follows: Figure 2 As shown. In this embodiment, the light environment index O1 for optimizing the light environment is set as the effective illuminance (UDI). 150-800 The unit is percentage (%); the energy-saving target O2 is set as the building's annual energy intensity, in kWh / (m³). 2 •a). Set the following 5 optimization variables: C1 window-to-wall ratio, C2 window visible light transmittance, C3 louver width, C4 louver tilt angle, and C5 louver light reflectance; where C4 louver tilt angle refers to the angle between the louver plane and the horizontal plane.

[0088] The optimization objective is calculated through orthogonal experiments, and the specific steps are as follows:

[0089] S111, orthogonal experimental design, including setting the number of experimental factors as m, determining the number of levels as n, determining the corresponding orthogonal experimental table based on m and n, and determining the number of experiments p based on the orthogonal experimental table; in this embodiment, the number of orthogonal experimental factors is set to 5, the number of levels is determined to be 9, and L81(9) is selected. 6 The orthogonal experimental table has 81 experiments, meaning a total of 81 orthogonal experiments were conducted.

[0090] S112, programmatically implements parametric architectural geometry modeling and physical modeling on the Grasshopper platform;

[0091] S113 uses the Ladybug plugin on the Grasshopper platform to obtain local weather parameters;

[0092] S114, using the HB-Daylight plugin on the Grasshopper platform to calculate the ambient light index O1, the programming details are shown below. Figure 3 As shown;

[0093] S115, using the HB-Annual load plugin on the Grasshopper platform to calculate the energy-saving index O2, the programming part can be found in [link to documentation]. Figure 4 The lighting and energy consumption indicators of a total of 81 orthogonal experiments are as follows: Figure 5 As shown.

[0094] The calculated light environment index O1 and energy-saving index O2 were subjected to range analysis, and the specific steps are as follows:

[0095] S121, Calculate K ij ,i=1,2,…,n;j=1,2,…,m,m+1;K ijK equals the sum of the experimental results of the light environment index O1 or the energy-saving index O2 corresponding to the j-th factor (j=1,2,…,m) taking the i-th level. i,m+1 The sum of the experimental results of the light environment index O1 or the energy saving index O2 corresponding to the blank column of the orthogonal experiment;

[0096] S122, according to calculate Where S is the number of experiments p divided by the number of levels n;

[0097] S123, according to Calculate R j R j R represents the range of the j-th (j=1,2,…,m) factor. m+1 The range of the blank column in the orthogonal experiment;

[0098] S124, based on the range R of the blank column m+1 Is it less than R? j Given j = 1, 2, ..., m, determine the reliability of the orthogonal experimental data.

[0099] The range analysis of the daylighting target in this embodiment is shown in Table 1, and the range analysis of the energy-saving target of the case building is shown in Table 2.

[0100] Table 1. Range Analysis of Daylighting Indicators for Case Study Buildings

[0101]

[0102] Table 2 Range Analysis of Energy Consumption Indicators for Case Study Buildings

[0103]

[0104] In this embodiment, the range R6 of the blank column in the orthogonal experiment is less than R. j Since j = 1, 2, 3, 4, 5, it can be determined that the orthogonal experimental data in this case are reliable.

[0105] The calculated light environment index O1 and energy-saving index O2 were subjected to analysis of variance, and the specific steps are as follows:

[0106] S131, calculate the sum of squared deviations Q of the j-th factor according to formula (1). j j = 1, 2, ..., m

[0107]

[0108] In the formula, x k , k = 1, 2, ..., p is the calculation result of the k-th light environment index O1 or the k-th energy saving index O2;

[0109] S132, calculate the mean square G according to formula (2) j j = 1, 2, ..., m

[0110]

[0111] S133, calculate F according to formula (3) j j = 1, 2, ..., m

[0112]

[0113] S134, calculate P according to formula (4) j j = 1, 2, ..., m

[0114] P j =F.DIST.RT(F j (n-1, n-1)(4)

[0115] In the formula, F.DIST.RT is a function of the Microsoft Excel program.

[0116] The variance analysis of the building lighting target in this embodiment is shown in Table 3, and the variance analysis of the building energy saving target in the case is shown in Table 4.

[0117] Table 3. Analysis of Variance of Daylight Index for Case Study Buildings

[0118] factor Sum of Squares of Deviations Degrees of freedom mean square G F-number p-value Significance <![CDATA[C1]]> 1880.45 8 235.06 3.45 0.05 * <![CDATA[C2]]> 2349.31 8 293.66 4.30 0.03 * <![CDATA[C3]]> 1141.34 8 142.67 2.09 0.16 Not significant <![CDATA[C4]]> 4678.85 8 584.86 8.57 0.00 ** <![CDATA[C5]]> 2584.80 8 323.10 4.74 0.02 *

[0119] Table 4. Analysis of Variance of Energy Consumption Indicators for Case Study Buildings

[0120]

[0121] The rationality of the optimization variables is judged as follows: R j The larger the value, the greater the influence of factor j on the optimization objective. If P j A value <0.05 indicates that factor j is significant; the smaller the value, the greater the significance.

[0122] According to the range analysis in Table 1, the degree of influence of each factor on the lighting target of the case building from largest to smallest is as follows: C4 louver tilt angle > C5 louver light reflectivity > C2 window visible light transmittance > C1 window-to-wall ratio > C3 louver width.

[0123] According to the range analysis in Table 2, the degree of influence of each factor on the energy-saving target of the case building from largest to smallest is: C1 window-to-wall ratio > C4 louver tilt angle > C3 louver width > C2 window visible light transmittance > C5 louver reflectance.

[0124] According to the analysis of variance in Table 3, the factors of the case building have the following impacts on the building's daylighting target: C4 louver tilt angle has the greatest significant impact, C1 window-to-wall ratio, C2 window visible light transmittance, and C5 louver reflectance have moderately significant impacts, and C3 louver width has no significant impact.

[0125] According to the variance analysis in Table 4, the impact of each factor on the energy-saving target of the case building is as follows: C1 window-to-wall ratio and C4 louver tilt angle have significant effects, while other factors have no significant effects.

[0126] Based on the above analysis, it can be seen that no significant unreasonableness was found in the influencing factors of the case building, and it is preliminarily determined that the optimization variable settings in this case are basically reasonable.

[0127] Extreme values ​​were removed to improve the range of optimization variables, and the range constraints for the multi-objective optimization variables of high side windows for daylighting and energy saving in the case building based on Wallacei were set as shown in Table 5:

[0128] Table 5. Range of optimization variables for case study buildings based on Wallacei

[0129]

[0130] S2, Based on the optimization objectives and optimization variable range constraints in step S1, multi-objective optimization programming is performed using the Wallacei plugin on the Grasshopper platform to obtain a set of multi-objective optimization solutions for the renovation of high side windows in industrial buildings based on lighting and energy saving. The optimal solution is obtained based on the average optimization objective weight.

[0131] The specific steps are as follows:

[0132] S21, set the population size, set the number of iterations, set the crossover rate, and set the mutation rate;

[0133] S22, Run the program to obtain a multi-objective optimization solution set B for the renovation of high side windows in industrial buildings based on lighting and energy saving. z (z = 1, 2, ..., Z);

[0134] S23, obtain the optimal solution B according to the average optimization objective weight. ao .

[0135] In this embodiment, the population size is set to 10 and the number of iterations to 20, while other parameters remain at their default values. Based on the program's results, a multi-objective solution set for the renovation of high side windows in the case building based on lighting and energy conservation is obtained. The solution set contains 16 solutions (B1, B2, ..., B...). 16 As shown in Table 6.

[0136] Table 6: Multi-objective solutions for the renovation of high side windows in case studies of buildings based on lighting and energy consumption.

[0137]

[0138]

[0139] The optimal solution recommended by Wallacei based on the average optimization objective weight is B3.

[0140] S3, weight the optimization objectives based on the entropy method, and at the same time, prioritize the solutions in the solution set in step S2 according to each optimization objective based on the analytic hierarchy process (AHP).

[0141] The light environment index O1 and energy-saving index O2 are weighted based on the entropy method. The specific steps are as follows:

[0142] S311, Use the experimental results of the orthogonal experiment as the sample dataset for the entropy method;

[0143] S312, use Equation (5) to standardize the data of the positive lighting index and Equation (6) to standardize the data of the negative energy saving index, and finally obtain a standardized sample dataset with the differences in the measurement units of each index eliminated.

[0144]

[0145] In the formula, X i ,i=1,2,…,p, refers to the target standard value for daylight optimization of the i-th sample, Y i ,i=1,2,…,p, refers to the standard value of the energy-saving optimization target for the i-th sample;

[0146] S313, In order to avoid some data having low values ​​after standardization, the translation process is performed using equations (7) and (8);

[0147] X Hi =X i +H(7)

[0148] Y Hi =Y i +H(8)

[0149] In the formula,

[0150] H represents the magnitude of the index shift, with a value of 0.01.

[0151] X Hi ,i=1,2,…,p, refers to the target standard value for daylight optimization of the i-th sample after translation processing;

[0152] Y Hi,i=1,2,…,p, refers to the energy-saving optimization target standard value of the i-th sample after translation processing;

[0153] S314, use equations (9) and (10) to normalize the standardized dataset;

[0154]

[0155] X Oi ,i=1,2,…,p, refers to the target standard value of daylight optimization for the i-th sample after normalization;

[0156] Y Oi ,i=1,2,…,p, refers to the standard value of the energy-saving optimization target for the i-th sample after normalization;

[0157] S315, calculate the information entropy e of the lighting environment index O1 and the energy saving index O2 according to equations (11) and (12) respectively. udi and e energy ;

[0158]

[0159] S316, calculate the difference coefficient g of daylighting environment index O1 and energy saving index O2 according to equations (13) and (14) respectively. udi and g energy ;

[0160] g udi =1-e udi (13)

[0161] g energy =1-e energy (14)

[0162] S317, calculate the normalized weight values ​​V of the daylighting environment index O1 and the energy saving index O2 according to equations (15) and (16) respectively. udi and V energy ;

[0163]

[0164] In this embodiment, the normalized weight V of the optimization objective is calculated according to steps S311 to S317. udi and V energy V udi =0.97, V energy =0.03.

[0165] The weighting of light environment index O1 and energy saving index O2 based on the analytic hierarchy process (AHP) is prioritized according to the calculation schemes. The specific steps are as follows:

[0166] S321, aiming to improve lighting or reduce energy consumption, integrates solutions based on the quality of light environment indicators or energy-saving indicators. z Sort the solutions z = 1, 2, ..., Z, with the optimal solution ranked first, and so on.

[0167] This embodiment categorizes solutions B according to the quality of the lighting environment. z Sort the solutions (z = 1, 2, ..., Z) to obtain the priority ranking of the high side window renovation schemes of the case building. The optimal scheme B6 ranks first, scheme B3 ranks 16th, and the ranking of the other schemes follows the same pattern.

[0168] Based on the merits of the energy-saving targets, the solutions are grouped into B. z Sort the solutions (z = 1, 2, ..., Z) to obtain the priority ranking of the high side window renovation schemes of the case building. The optimal scheme B3 ranks first, scheme B6 ranks 16th, and the ranking of the other schemes follows the same pattern.

[0169] S322, construct judgment matrices A according to light environment index and energy saving index respectively. udi and A energy :

[0170] and

[0171] a ij and b ij The method for determining the value of a is shown in Table 7. ji =1 / a ij b ji =1 / b ij d is the value obtained by rounding z / 4.

[0172] Table 7: Determining matrix element a ij Method of obtaining values

[0173] <![CDATA[a ij and b ij Values]]> Value conditions 1 Scheme i and Scheme j have the same ranking. 3 Option i ranks higher than option j, and the difference in rank is less than d. 5 Option i ranks higher than option j, and the difference in rank is greater than or equal to d and less than 2d. 7 Solution i ranks higher than solution j, and the difference in rank is greater than or equal to 2d and less than 3d. 9 Option i ranks higher than option j, and the difference in rank is greater than or equal to 3d.

[0174] The judgment matrix A for the lighting target in this implementation case. udi :

[0175]

[0176] The judgment matrix A for the energy-saving target in this implementation case energy :

[0177]

[0178] S323, calculate the judgment matrix A respectively. udi and A energy The eigenvector H corresponding to the largest eigenvalue udi and H energyIts element value is h udi-i (i = 1, 2, ..., z) and h energy-i (i=1,2,…,z), calculate scheme B according to the light environment index according to equation (17). i priority weight h udi-o-i Calculate Scheme B according to the light environment index using formula (18). i priority weight h energy-o-i ;

[0179]

[0180] In this implementation example, A is calculated using [VD] = eig(A) in Matlab. udi and A energy The corresponding maximum eigenvalue and its eigenvector are used to obtain the normalized priority weights of the high side window solution for the building in this embodiment, as shown in Table 8.

[0181] Table 8 Priority Weighting Values ​​for AHP-based Solutions

[0182]

[0183] S4, the weighting of the optimization objective and the priority weighting of the scheme are calculated together, and the comprehensive priority weighting W of the scheme is calculated according to formula (19). o-i For i = 1, 2, ..., z, the optimal solution is recommended based on the maximum value of the comprehensive priority weight.

[0184] W O-i =h energy-o-i ×V energy +h udi-o-i ×V udi (19)

[0185] The comprehensive priority weighting of the schemes for the multi-objective optimization method of high side window renovation of industrial heritage buildings based on lighting and energy saving in this embodiment is shown in Table 9.

[0186] Table 9: Overall Priority Weighting Values ​​for Case Study Buildings

[0187] Solution ID <![CDATA[B1]]> <![CDATA[B2]]> <![CDATA[B3]]> <![CDATA[B4]]> <![CDATA[B5]]> <![CDATA[B6]]> <![CDATA[B7]]> <![CDATA[B8]]> <![CDATA[B9]]> <![CDATA[B 10 ]]> <![CDATA[B 11 ]]> <![CDATA[B 12 ]]> <![CDATA[B 13 ]]> <![CDATA[B 14 ]]> <![CDATA[B 15 ]]> <![CDATA[B 16 ]]> <![CDATA[W o-i ]]> 0.1480 0.1242 0.0162 0.0178 0.0512 0.2131 0.0233 0.0511 0.0886 0.0206 0.0177 0.0486 0.0191 0.0776 0.0531 0.0297

[0188] The priority weighting values ​​for schemes based on the single objective of lighting, the priority weighting values ​​for schemes based on the single objective of energy saving, and the priority weighting values ​​for schemes based on the method of this invention are respectively described in... Figure 6 , Figure 7 and Figure 8According to the priority weighting value based on the single objective of light collection, Scheme 6 is the optimal scheme; according to the priority weighting value based on the single objective of energy saving, Scheme 3 is the best scheme; running Wallacei yields Scheme 3 as the optimal solution; and according to the priority weighting value based on the method of this invention, Scheme 6 is the optimal scheme.

[0189] This invention significantly improves the scientific rigor, comprehensiveness, and flexibility of prioritizing solutions, fully considering the optimization objective weights and the solutions derived by Wallacei, providing an important reference for multi-objective optimization of high-side-window renovation of industrial heritage buildings based on light environment improvement and energy conservation. This invention can assign weights to multiple optimization objectives and then comprehensively prioritize renovation schemes to obtain the optimal solution for high-side-window renovation of industrial heritage buildings.

Claims

1. A multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, the specific steps of which are as follows: S1. Set optimization objectives and variables. Analyze the established building model to determine the rationality of the optimization variables and impose range constraints on them. The optimization objectives include light environment index O1 and energy saving index O2, with optimization variables C1, C2, ..., C... m m is the number of optimization variables; The optimization objective is calculated through orthogonal experiments, and the specific steps are as follows: S111, orthogonal experimental design, including setting the number of experimental factors as m, determining the number of levels as n, determining the corresponding orthogonal experimental table based on m and n, and determining the number of experiments p based on the orthogonal experimental table; S112, programmatically implements parametric architectural geometry modeling and physical modeling on the Grasshopper platform; S113 uses the Ladybug plugin on the Grasshopper platform to obtain local weather parameters; S114, using the HB-Daylight plugin on the Grasshopper platform to calculate the light environment index O1; S115 uses the HB-Annual load plugin on the Grasshopper platform to calculate the energy-saving index O2; In step S1, range analysis is performed on the calculated light environment index O1 and energy-saving index O2, respectively. The specific steps are as follows: S121, Calculate K ij ,i=1,2,…,n;j=1,2,…,m,m+1;K ij K is equal to the sum of the experimental results of the light environment index O1 or the corresponding energy saving index O2 when the j-th factor (j = 1, 2, ..., m) takes the i-th level. i,m+1 The sum of the experimental results of the light environment index O1 or the energy saving index O2 corresponding to the blank column of the orthogonal experiment; S122, according to calculate Where S is the number of experiments p divided by the number of levels n; S123, according to Calculate R j R j R represents the range of the j-th factor, j = 1, 2, ..., m. m+1 The range of the blank column in the orthogonal experiment; S124, based on the range R of the blank column m+1 Is it less than R? j Given j = 1, 2, ..., m, determine the reliability of the orthogonal experimental data; S2, Based on the optimization objectives and optimization variable range constraints in step S1, multi-objective optimization programming is performed using the Wallacei plugin on the Grasshopper platform to obtain a set of multi-objective optimization solutions for the renovation of high side windows in industrial buildings based on lighting and energy saving. The optimal solution is obtained based on the average optimization objective weight. S3, weight the optimization objectives based on the entropy method, and at the same time, prioritize the solutions in the solution set in step S2 according to each optimization objective based on the analytic hierarchy process (AHP). S4 calculates the combined weight of the optimization objective and the priority weight of the solution to obtain the combined priority weight of the solution, and recommends the optimal solution according to the maximum value of the combined priority weight.

2. The multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, as described in claim 1, is characterized in that: In step S1, variance analysis is performed on the calculated light environment index O1 and energy saving index O2, respectively. The specific steps are as follows: S131, calculate the sum of squared deviations Q of the j-th factor according to formula (1). j j = 1, 2, ..., m In the formula, x k , k = 1, 2, ..., p is the calculation result of the k-th light environment index O1 or the k-th energy saving index O2; S132, calculate the mean square G according to formula (2) j j = 1, 2, ..., m S133, calculate F according to formula (3) j j = 1, 2, ..., m S134, calculate P according to formula (4) j j = 1, 2, ..., m P j =F.DIST.RT(F j ,n-1,n-1) (4) In the formula, F.DIST.RT is a function of the Microsoft Excel program.

3. The multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, as described in claim 1, is characterized in that: The rationality of the optimization variables in step S1 is judged as follows: R j The larger the value, the greater the influence of factor j on the optimization objective. If P j A value <0.05 indicates that factor j is significant; the smaller the value, the greater the significance.

4. The multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, as described in claim 1, is characterized in that: The specific steps of step S2 are as follows: S21, set the population size, set the number of iterations, set the crossover rate, and set the mutation rate; S22, Run the program to obtain a multi-objective optimization solution set B for the renovation of high side windows in industrial buildings based on lighting and energy saving. z z = 1, 2, ..., Z, where Z is the number of schemes in the scheme set; S23, obtain the optimal solution B according to the average optimization objective weight. ao .

5. The multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, as described in claim 1, is characterized in that: In step S3, the light environment index O1 and the energy-saving index O2 are weighted based on the entropy method. The specific steps are as follows: S311, Use the experimental results of the orthogonal experiment as the sample dataset for the entropy method; S312, use Equation (5) to standardize the data of the positive lighting index and Equation (6) to standardize the data of the negative energy saving index, and finally obtain a standardized sample dataset with the differences in the measurement units of each index eliminated. In the formula, X i ,i=1,2,…,p, refers to the target standard value for daylight optimization of the i-th sample, Y i ,i=1,2,…,p, refers to the standard value of the energy-saving optimization target for the i-th sample; S313, In order to avoid some data having low values ​​after standardization, the translation process is performed using equations (7) and (8); X Hi =X i +H (7) Y Hi =Y i +H (8) In the formula, H is the magnitude of the index shift, with a value of 0.01; X Hi ,i=1,2,…,p, refers to the target standard value for daylight optimization of the i-th sample after translation processing; Y Hi ,i=1,2,…,p, refers to the energy-saving optimization target standard value of the i-th sample after translation processing; S314, use equations (9) and (10) to normalize the standardized dataset; In the formula, X Oi ,i=1,2,…,p, refers to the target standard value of daylight optimization for the i-th sample after normalization; Y Oi ,i=1,2,…,p, refers to the standard value of the energy-saving optimization target for the i-th sample after normalization; S315, calculate the information entropy e of the lighting environment index O1 and the energy saving index O2 according to equations (11) and (12) respectively. udi and e energy ; S316, calculate the difference coefficient g of daylighting environment index O1 and energy saving index O2 according to equations (13) and (14) respectively. udi and g energy ; g udi =1-e udi (13) g energy =1-e energy (14) S317, calculate the normalized weight values ​​V of the daylighting environment index O1 and the energy saving index O2 according to equations (15) and (16) respectively. udi and V energy ; 6. The multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, as described in claim 5, is characterized in that: In step S3, the weights are assigned based on the Analytic Hierarchy Process (AHP) according to the calculation schemes for the light environment index O1 and the energy saving index O2, respectively. The specific steps are as follows: S321, with the goals of improving daylighting and reducing energy consumption respectively, categorizes solutions into groups B based on the performance of these indicators. z Let z = 1, 2, ..., Z. Sort the solutions and rank the optimal solution first, and so on. S322, construct judgment matrices A according to light environment index and energy saving index respectively. udi and A energy : and a ij The value is determined according to the corresponding value selection conditions, a ji =1 / a ij b ij The value of b is determined according to the corresponding value selection conditions. ji =1 / b ij ; S323, calculate the judgment matrix A respectively. udi and A energy The eigenvector H corresponding to the largest eigenvalue udi and H energy Its element value is h udi-i i = 1, 2, ..., z and h energy-i Let i = 1, 2, ..., z. Calculate Scheme B according to the light environment index using Equation (17). i priority weight h udi-o-i Calculate Scheme B according to the light environment index using formula (18). i priority weight h energy-o-i ; 7. The multi-objective optimization method for the renovation of high side windows in industrial heritage buildings based on lighting and energy conservation, as described in claim 6, is characterized in that: In step S4, scheme B i Comprehensive priority weighting value W o-i , i = 1, 2, ..., Z, are calculated according to formula (19); W O-i =h energy-o-i ×V energy +h udi-o-i ×V udi (19) The optimal solution is recommended based on the maximum value of the comprehensive priority weighting.

Images