Industrial heritage building high side window reconstruction multi-objective optimization method based on lighting and energy saving

Abstract

An industrial heritage building high side window reconstruction multi-objective optimization method based on lighting and energy saving comprises the following specific steps: S1, setting an optimization objective and an optimization variable, and calculating and analyzing the optimization objective by establishing a building model to judge the rationality of the optimization variable so as to perform optimization variable range constraint; s2, performing multi-objective optimization programming on a Grasshopper platform by adopting a Wallacei plug-in according to the optimization objective and optimization variable range constraint in the step S1, obtaining a lighting and energy-saving-based industrial building high side window transformation multi-objective optimization solution set, and obtaining an optimal solution according to an average optimization objective weight; s3, weighting optimization targets on the basis of an entropy method, and performing priority weighting on the schemes in the scheme set in the step S2 according to the optimization targets on the basis of an analytic hierarchy process; and S4, performing comprehensive calculation on the weighting value of the optimization target and the priority weighting value of the scheme to obtain a comprehensive priority weighting value of the scheme, and recommending an optimal solution according to the maximum value of the comprehensive priority weighting value.

Description

Technical Field

[0001] The present invention belongs to the technical field of green and low-carbon renovation 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 daylighting and energy conservation. Background Technique

[0002] The renovation of high-side windows of industrial heritage buildings has an impact on both the indoor light environment and energy consumption, and there is a complex coupling relationship between improving the indoor light environment of buildings and reducing building energy consumption. Taking only the improvement of the light environment or energy conservation as a single design goal cannot provide a comprehensive reference for the renovation of high-side windows of industrial heritage buildings. Only considering the improvement of the light environment may increase the heat gain of the building, affect the thermal environment of the building, and thus increase building energy consumption. Only considering building energy conservation may damage the indoor daylighting quantity of the building and is not conducive to the improvement of the indoor light environment. The renovation of high-side windows of industrial heritage buildings, which has an important impact on both daylighting and energy consumption, mainly includes the size of the window area, the visible light transmittance of the window, and external shading, etc.

[0003] Although there are many current studies and practices on multi-objective optimization of building physical environment and energy consumption, the multi-objective optimization method based on building performance has obvious deficiencies. First, there are obvious differences in the importance of optimization objectives. Recommending the optimal solution based on the average weight of optimization objectives not only lacks the quantitative weighting of optimization objectives, but also fails to fully utilize the weighting results of optimization objectives in multi-objective optimization decision-making. Second, the solution set recommended by the multi-objective optimization method based on the genetic algorithm contains multiple solutions, but it is unknown how to select the optimal solution from the multiple solutions in the solution set. Third, the existing multi-objective optimization methods do not involve the improvement of the rationality and multi-objective optimization of the setting of optimization variables by the quantitative weight of optimization variables. Fourth, there is currently a lack of a comprehensive multi-objective optimization process framework for high-side windows of industrial heritage buildings based on daylighting and energy conservation that includes the weighting and utilization of optimization objectives, the weighting and utilization of optimization variables, and the comprehensive ranking of solution priorities by weighting. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a multi-objective optimization method for the renovation of high-side windows of industrial heritage buildings based on daylighting and energy conservation, which can perform weighting on multiple optimization objectives and comprehensively rank the renovation plans to obtain the optimal solution.

[0005] The technical solution adopted by the present invention is as follows:

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

[0007] S1, set optimization objectives and optimization variables, and judge the rationality of the optimization variables through the established building model to calculate and analyze the optimization objectives, so as to perform the range constraint of the optimization variables;

[0008] S2. Based on the optimization objectives and the constraints of the optimization variable range in step S1, use the Wallacei plug-in on the Grasshopper platform for multi-objective optimization programming to obtain a multi-objective optimization solution set for the transformation of the high-side windows of industrial buildings based on daylighting and energy conservation, and obtain the optimal solution according to the average optimization objective weight;

[0009] S3. Assign weights to the optimization objectives respectively based on the entropy method, and at the same time, based on the analytic hierarchy process (AHP), prioritize and rank the solution set in step S2 according to each optimization objective;

[0010] S4. Normalize the weighted optimization objectives and the solutions with prioritized weights to obtain the comprehensive prioritized weight value, and recommend the optimal solution according to the maximum value of the comprehensive prioritized weight value.

[0011] Furthermore, the optimization objectives in step S1 include the lighting environment index O 1 and the energy conservation index O 2 , and the optimization variables are C 1 , C 2 , …, C m , where m is the number of optimization variables.

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

[0013] S111. Orthogonal experiment design, including setting the number of experimental factors as m, determining the number of levels as n, determining the corresponding orthogonal experiment table according to m and n, and determining the number of experiments p according to the orthogonal experiment table;

[0014] S112. Program on the Grasshopper platform to realize parametric building geometry modeling and physical modeling;

[0015] S113. Use the Ladybug plug-in on the Grasshopper platform to obtain local meteorological parameters;

[0016] S114. Use the HB-Daylight plug-in on the Grasshopper platform to calculate the lighting environment index O 1 ;

[0017] S115. Use the HB-Annual load plug-in on the Grasshopper platform to calculate the energy conservation index O 2 .

[0018] Furthermore, in step S1, perform range analysis on the calculated lighting environment index O 1 and the energy conservation index O 2 respectively, and the specific steps are as follows:

[0019] S121, Calculate K ij , where \(i = 1, 2, \cdots, n\); \(j = 1, 2, \cdots, m, m + 1\), and K ij equals the sum of the experimental results corresponding to the \(i\)-th level of the \(j\)-th (\(j = 1, 2, \cdots, m\)) factor for the light environment index O 1 or the corresponding energy-saving index O 2 ; K i,m+1 refers to the sum of the experimental results corresponding to the light environment index O 1 or the corresponding energy-saving index O 2 for the blank column of the orthogonal experiment;

[0020] S122, Calculate according to where S is the number of experiments p divided by the number of levels n; S123, Calculate R according to

[0021] S123, Calculate R according to where R j , R j represents the range of the \(j\)-th (\(j = 1, 2, \cdots, m\)) factor, and R m+1 represents the range of the blank column of the orthogonal experiment;

[0022] S124, Judge the reliability of the orthogonal experiment data based on whether the range value R m+1 of the blank column is less than R j , where \(j = 1, 2, \cdots, m\).

[0023] Furthermore, in step S1, variance analysis is performed on the calculated light environment index O 1 and energy-saving index O 2 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 , where \(j = 1, 2, \cdots, m\),

[0025]

[0026] In the formula, \(x\ k , where \(k = 1, 2, \cdots, p\) is the calculation result of the \(k\)-th light environment index O 1 or the \(k\)-th energy-saving index O 2 ;

[0027] S132, Calculate the mean square G according to formula (2) j , where \(j = 1, 2, \cdots, m\),

[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 judgment of the rationality of the optimization variables in step S1 is as follows: R j The larger the value, the greater the influence of factor j on the optimization goal. If P j < 0.05 indicates that factor j is significant, and the smaller the value, the greater the significance.

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

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

[0037] S22, run the program to obtain the multi-objective optimization solution set B for the retrofit of high-side windows in industrial buildings based on daylighting and energy conservation 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 goal weight ao .

[0039] Furthermore, in step S3, the entropy value method is used to assign weights to the light environment index O 1 and the energy conservation index O 2 respectively. The specific steps are as follows:

[0040] S311, use the experimental results of the orthogonal experiment as the sample data set for the entropy value method;

[0041] S312, use formula (5) to perform data standardization processing on the positive daylighting index, and use formula (6) to perform data standardization processing on the negative energy conservation index, and finally obtain the standardized sample data set 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 standard value of the daylighting optimization goal of the i-th sample, Yi , where \(i = 1, 2, \ldots, p\), represents the standard value of the energy-saving optimization objective for the \(i\)-th sample;

[0044] S313. To avoid the situation where some data after standardization have relatively low values, equations (7) and (8) are used for translation processing;

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

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

[0047] In the formula, \(H\) is the amplitude of index translation, with a value of 0.01;

[0048] X Hi , where \(i = 1, 2, \ldots, p\), represents the standard value of the daylighting optimization objective for the \(i\)-th sample after translation processing;

[0049] Y Hi , where \(i = 1, 2, \ldots, p\), represents the standard value of the energy-saving optimization objective for the \(i\)-th sample after translation processing;

[0050] S314. Equations (9) and (10) are used to normalize the standardized data set;

[0051]

[0052] In the formula,

[0053] X Oi , where \(i = 1, 2, \ldots, p\), represents the standard value of the daylighting optimization objective for the \(i\)-th sample after normalization processing;

[0054] Y Oi , where \(i = 1, 2, \ldots, p\), represents the standard value of the energy-saving optimization objective for the \(i\)-th sample after normalization processing;

[0055] S315. Calculate the information entropy \(e\) 1 of the daylighting environment index \(O\) 2 and the information entropy \(e\) udi of the energy-saving index \(O\) energy respectively according to equations (11) and (12);

[0056]

[0057] S316. Calculate the coefficient of variation \(g\) 1 of the daylighting environment index \(O\) 2 and the coefficient of variation \(g\) udi of the energy-saving index \(O\) energy respectively according to equations (13) and (14);

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

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

[0060] S317, calculate the daylighting environment index O 1 and the energy-saving index O 2 of the normalized weight values V udi and V energy ;

[0061]

[0062] Furthermore, in step S3, based on the analytic hierarchy process AHP, calculate the priority weights of the solutions according to the daylighting environment index O 1 and the energy-saving index O 2 specifically as follows:

[0063] S321, with the goal of improving daylighting or energy consumption, sort the solution set B z , z = 1, 2,..., Z according to the pros and cons of the daylighting environment index or the energy-saving index. The optimal solution ranks first, and so on;

[0064] S322, construct the judgment matrices A udi and A energy respectively according to the daylighting environment index and the energy-saving index:

[0065] and

[0066] a ij Take values according to the corresponding value-taking conditions, a ji = 1 / a ij ; b ij Take values according to the corresponding value-taking conditions, b ji = 1 / b ij ;

[0067] S323, calculate the eigenvectors H udi and H energy corresponding to the maximum eigenvalues of the judgment matrices A udi and H energy , whose element values are h udi-i (i = 1, 2,..., z) and h energy-i (i = 1, 2,..., z), and calculate the priority weight h i of the solution B udi-o-i, calculate the solution B according to the light environment index according to formula (18). i The priority weight h energy-o-i ;

[0068]

[0069] Furthermore, the comprehensive priority weighting value W o-i , i = 1, 2, …, z is calculated according to formula (19);

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

[0071] Recommend the optimal solution according to the maximum value of the comprehensive priority weighting value.

[0072] Advantages of the present invention: After weighting multiple optimization objectives, the comprehensive ranking of the renovation solutions can be carried out to obtain the optimal solution for the high side window renovation of industrial heritage buildings. Brief Description of the Drawings

[0073] Figure 1 It is a schematic flow framework diagram of the present invention.

[0074] Figure 2 It is a schematic diagram of the building in the embodiment of the present invention.

[0075] Figure 3 It is a schematic diagram of part of the content programmed for calculating the light environment index O by using the HB-Daylight plug-in in the present invention. 1 of the present invention.

[0076] Figure 4 It is a schematic diagram of part of the content programmed for calculating the energy saving index O by using the HB-Annual load plug-in in the present invention. 2 of the present invention.

[0077] Figure 5 It is a schematic diagram of the results of the daylighting and energy consumption indexes of the total 81 orthogonal experiments of the present invention.

[0078] Figure 6 It is a schematic diagram of expressing the priority weighting value of the solution based on the single daylighting objective.

[0079] Figure 7 It is a schematic diagram of expressing the priority weighting value of the solution based on the single energy saving objective.

[0080] Figure 8 It is a schematic diagram of expressing the comprehensive priority weighting value of the solution of the present invention. Detailed Embodiments

[0081] The present invention will be further described below in conjunction with specific embodiments, but the present invention is not limited to these specific embodiments. Those skilled in the art should recognize that the present invention covers all alternative solutions, improvement solutions, and equivalent solutions that may be included within the scope of the claims.

[0082] In the description of the present 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", "counterclockwise", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more, unless otherwise clearly defined.

[0083] In the present invention, unless otherwise clearly specified and defined, the terms "mounted", "connected", "coupled", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0084] In the present invention, unless otherwise clearly specified and defined, the first feature being "above" or "below" the second feature may include the direct contact between the first and second features, or may include the situation where the first and second features are not in direct contact but in contact through other features between them. Moreover, the first feature being "above", "over", and "on top of" the second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the horizontal height of the first feature is higher than that of the second feature. The first feature being "below", "beneath", and "underneath" the second feature includes the first feature being directly below and obliquely below the second feature, or simply indicating that the horizontal height of the first feature is lower than that of the second feature.

[0085] Refer to Figure 1 , this embodiment provides a multi-objective optimization method for the transformation of high-side windows in industrial heritage buildings based on daylighting and energy conservation. The specific steps are as follows:

[0086] S1. Set the optimization objectives and variables, and determine the rationality of the optimization variables by calculating and analyzing the optimization objectives through the established building model, so as to constrain the range of the optimization variables. The optimization objectives include the lighting environment index O 1 and the energy-saving index O 2 . The optimization variables are C 1 , C 2 , …, C m .

[0087] The building model in this embodiment is as shown in Figure 2 . In this embodiment, the lighting environment index O 1 of the lighting environment optimization objective is set as the useful daylight illuminance UDI 150-800 , with the unit of percentage (%). The energy-saving objective O 2 is set as the annual energy consumption intensity of the building, with the unit of (kwh / (m 2 ·a)). The following 5 optimization variables are set: C 1 window-wall ratio, C 2 visible light transmittance of the window, C 3 louver width, C 4 louver tilt angle, C 5 louver light reflectance. Among them, the louver tilt angle C 4 refers to the angle between the louver plane and the horizontal plane.

[0088] The optimization objectives are calculated through orthogonal experiments. The specific steps are as follows:

[0089] S111. Orthogonal experiment design, including setting the number of experimental factors as m, determining the number of levels as n, determining the corresponding orthogonal experiment table according to m and n, and determining the number of experiments p according to the orthogonal experiment table. In this embodiment, according to the number of factors, the number of orthogonal experiment factors is set as 5, the number of levels is determined as 9, and the L81(9 6 ) orthogonal experiment table is selected, and the number of experiments is 81, that is, a total of 81 orthogonal experiments are completed.

[0090] S112. Program on the Grasshopper platform to realize parametric building geometry modeling and physical modeling;

[0091] S113. Use the Ladybug plug-in on the Grasshopper platform to obtain local meteorological parameters;

[0092] S114. Use the HB-Daylight plug-in on the Grasshopper platform to calculate the lighting environment index O 1 . For part of the programming content, see Figure 3 shown;

[0093] S115. Calculate the energy-saving index O using the HB-Annual load plug-in on the Grasshopper platform 2 . For the programming part, see Figure 4 . The daylighting and energy consumption indexes of a total of 81 orthogonal experiments are as shown in Figure 5 .

[0094] Among them, for the calculated daylighting environment indexes O 1 and energy-saving indexes O 2 , perform range analysis respectively. The specific steps are as follows:

[0095] S121. Calculate K ij , where i = 1, 2,..., n; j = 1, 2,..., m, m + 1; K ij is equal to the sum of the experimental results of the corresponding daylighting environment index O 1 or the corresponding energy-saving index O 2 when the jth (j = 1, 2,..., m) factor takes the ith level, and K i,m+1 refers to the sum of the experimental results of the daylighting environment index O 1 or the corresponding energy-saving index O 2 corresponding to the blank column of the orthogonal experiment;

[0096] S122. Calculate where S is the number of experiments p divided by the number of levels n;

[0097] S123. Calculate Raccording to j , where R j represents the range of the jth (j = 1, 2,..., m) factor, and R m+1 represents the range of the blank column of the orthogonal experiment;

[0098] S124. According to whether the range value R m+1 of the blank column is less than R j , j = 1, 2,..., m, judge the reliability of the orthogonal experiment 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 table of daylighting indexes of the case building

[0101]

[0102] Table 2 Range analysis table of energy consumption indexes of the case building

[0103]

[0104] ​The range R of the blank column in the orthogonal experiment of this embodiment 6 is less than R j , j = 1, 2, 3, 4, 5. It can be judged that the orthogonal experiment data of this case is reliable.

[0105] Among them, for the calculated lighting environment index O 1 and the energy-saving index O 2 perform variance analysis respectively, and the specific steps are as follows:

[0106] S131. Calculate the sum of squares of deviations Q of the jth 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 kth lighting environment index O 1 or the kth energy-saving index O 2 ;

[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 Variance analysis table of the lighting index of the case building

[0118] Factor Sum of squared deviations Degree of freedom Mean square G F value P value Significance <![CDATA[C 1 > 1880.45 8 235.06 3.45 0.05 * <![CDATA[C 2 > 2349.31 8 293.66 4.30 0.03 * <![CDATA[C 3 > 1141.34 8 142.67 2.09 0.16 Not significant <![CDATA[C 4 > 4678.85 8 584.86 8.57 0.00 ** <![CDATA[C 5 > 2584.80 8 323.10 4.74 0.02 *

[0119] Table 4 Variance analysis table of the energy consumption index of the case building

[0120]

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

[0122] According to the range analysis in Table 1, the influence degrees of various factors of the case building on the daylighting goal from large to small are: C 4 Louver tilt angle > C 5 Louver light reflectance > C 2 Visible light transmittance of the window > C 1 Window-wall ratio > C 3 Louver width.

[0123] According to the range analysis in Table 2, the influence degrees of various factors of the case building on the energy-saving goal from large to small are: C 1 Window-wall ratio > C 4 Louver tilt angle > C 3 Louver width > C 2 Visible light transmittance of the window > C 5 Louver reflectance.

[0124] According to the variance analysis in Table 3, the influence of various factors of the case building on the building daylighting goal is as follows, C 4 The louver tilt angle has the greatest significant influence, C 1 The window-wall ratio, C 2 The visible light transmittance of the window and C 5 The louver reflectance has a general significant influence, C 3 The louver width has no significant influence.

[0125] According to the variance analysis in Table 4, the influence of various factors of the case building on the energy-saving goal is as follows, C 1 The window-wall ratio and C 4 The louver tilt angle both have significant influences, and other factors have no significant influences.

[0126] From the above analysis, it can be seen that no obvious irrationality exists in the influencing factors of the set case building, and it is initially determined that the setting of the optimization variables in this case is basically reasonable.

[0127] Extreme values are screened out to improve the range of optimization variables, and the range constraints of the multi-objective optimization variables for daylighting and energy saving of the high-side windows of the case building based on wallacei in the next step are set as shown in Table 5:

[0128] Table 5 Range of optimization variables of the case building based on wallacei

[0129]

[0130] S2. Based on the optimization objectives and the constraints of the optimization variable range in step S1, multi-objective optimization programming is carried out on the Grasshopper platform using the Wallacei plug-in to obtain a multi-objective optimization solution set for the retrofit of high-side windows in industrial buildings based on daylighting and energy conservation. The optimal solution is obtained according to 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 retrofit of high-side windows in industrial buildings based on daylighting and energy conservation 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 is set to 20, and other parameters remain the original default values. According to the results of running the program, a multi-objective solution set for the retrofit of high-side windows in the case building based on daylighting and energy conservation is obtained. The solution set contains 16 solutions (B 1 , B 2 , …, B 16 ) as shown in Table 6.

[0136] Table 6 Multi-objective solution set for the retrofit of high-side windows in the case building based on daylighting and energy consumption

[0137]

[0138]

[0139] The optimal solution recommended by Wallacei according to the average optimization objective weight is B 3 .

[0140] S3. Based on the entropy method, weights are assigned to the optimization objectives. At the same time, based on the analytic hierarchy process (AHP), weights are assigned to the solutions in the solution set in step S2 according to each optimization objective;

[0141] Based on the entropy method, weights are assigned to the light environment index O 1 and the energy conservation index O 2 respectively. The specific steps are as follows:

[0142] S311. Take the experimental results of the orthogonal experiment as the sample data set for the entropy method;

[0143] In S312, the data of the positive daylighting index is standardized by Equation (5), and the data of the negative energy-saving index is standardized by Equation (6), and finally a standardized sample data set with the differences in the measurement units of each index eliminated is obtained;

[0144]

[0145] where X i , i = 1, 2, …, p, refers to the standard value of the daylighting optimization target of the i-th sample, and Y i , i = 1, 2, …, p, refers to the standard value of the energy-saving optimization target of the i-th sample;

[0146] In S313, to avoid the situation where some data after the standardization process have relatively low values, translation processing is performed using Equation (7) and Equation (8);

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

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

[0149] where

[0150] H is the amplitude of the index translation, with a value of 0.01;

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

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

[0153] In S314, the standardized data set is normalized using Equations (9) and (10);

[0154]

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

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

[0157] In S315, the information entropy e 1 of the daylighting environment index O 2 and the information entropy e udi of the energy-saving index O energy;

[0158]

[0159] S316, calculate the lighting environment index O according to formula (13) and formula (14) respectively 1 and energy saving index O 2 The coefficient of variation g udi and g energy ;

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

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

[0162] S317, calculate the lighting environment index O according to formula (15) and formula (16) respectively 1 and energy saving index O 2 The normalized weight value V udi and V energy ;

[0163]

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

[0165] Based on the analytic hierarchy process (AHP), the light environment index O 1 and energy saving index O 2 The calculation scheme is given priority. The specific steps are as follows:

[0166] S321, with the goal of improving daylighting or reducing energy consumption, according to the quality of light environment indicators or energy saving indicators, solution set B z , z=1,2,…,Z for sorting, the best solution is ranked first, and so on;

[0167] In this embodiment, solution set B is divided into z (z=1,2,…,Z) are sorted, and the priority ranking of the high side window renovation schemes of the case building can be obtained. The optimal scheme B 6 Ranked 1st, Option B 3 Ranked 16th, and the rest of the options are ranked similarly.

[0168] According to the advantages and disadvantages of energy saving goals, solutions are grouped into B z(z = 1, 2, …, Z) are sorted to obtain the priority ranking of the renovation plans for the high-side windows of the case building, and the optimal plan is Plan B 3 Ranks 1st, Plan B 6 Ranks 16th, and the ranking of the remaining plans follows accordingly.

[0169] S322, construct the judgment matrices A udi and A energy :

[0170] and

[0171] a ij and b ij are obtained as shown in Table 7. a ji = 1 / a ij and b ji = 1 / b ij , where d is the value obtained by rounding z / 4.

[0172] Table 7: Method for obtaining the elements a ij of the judgment matrix

[0173] <![CDATA[a ij and b ij value]]> Value-taking condition 1 The rankings of Plan i and Plan j are the same 3 The ranking of Plan i is higher than that of Plan j, and the ranking gap is less than d 5 The ranking of Plan i is higher than that of Plan j, and the ranking gap is greater than or equal to d and less than 2d 7 The ranking of Plan i is higher than that of Plan j, and the ranking gap is greater than or equal to 2d and less than 3d 9 The ranking of Plan i is higher than that of Plan j, and the ranking gap is greater than or equal to 3d

[0174] The judgment matrix A udi for the daylighting objective of this implementation case:

[0175]

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

[0177]

[0178] S323, calculate the eigenvectors H udi corresponding to the maximum eigenvalues of the judgment matrices A energy and A udi and H energy , whose element values are h udi-i (i = 1, 2, …, z) and h energy-i (i = 1, 2, …, z). Calculate the priority weight h i of Plan B according to the daylighting index using Equation (17), and calculate the priority weight h udi-o-i of Plan B according to the daylighting index using Equation (18); i energy-o-i ;

[0179]

[0180] ​In this embodiment, [V.D]=eig(A) is used in Matlab to calculate the maximum eigenvalue and its eigenvector corresponding to A udi and A energy respectively, and the normalized priority weights of the high-side window solution of the building in this embodiment are shown in Table 8

[0181] Table 8 Priority weighting values of solutions based on AHP

[0182]

[0183] S4. The weighted values of the optimization objectives and the priority weighted values of the solutions are comprehensively calculated, and the comprehensive priority weight W of the solution is calculated according to Equation (19) o-i , i = 1, 2, …, z, and the optimal solution is recommended according to the maximum value of the comprehensive priority weight value

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

[0185] The comprehensive priority weights of the solutions of the multi-objective optimization method for the high-side window renovation of industrial heritage buildings based on daylighting and energy conservation in this embodiment of the building are shown in Table 9

[0186] Table 9 Comprehensive priority weighting values of the solutions of the case building

[0187] Plan ID <![CDATA[B 1 > <![CDATA[B 2 > <![CDATA[B 3 > <![CDATA[B 4 > <![CDATA[B 5 > <![CDATA[B 6 > <![CDATA[B 7 > <![CDATA[B 8 > <![CDATA[B 9 > <![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 of the solutions based on the single daylighting objective, the priority weighting values of the solutions based on the single energy conservation objective, and the priority weighting values of the solutions based on the method of the present invention are respectively expressed in Figure 6 , Figure 7 and Figure 8 . According to the priority weighting value of the solution based on the single daylighting objective, Solution 6 is the optimal solution. According to the priority weighting value of the solution based on the single energy conservation objective, Solution 3 is the best solution. Running wallacei gives the optimal solution as Solution 3. According to the priority weighting value of the solution based on the method of the present invention, Solution 6 is the optimal solution

[0189] The present invention significantly improves the scientificity, comprehensiveness, and flexibility of the solution priority ranking, fully considers the optimization objective weights and the solutions obtained by wallacei, and provides an important reference for the multi-objective optimization of the high-side window renovation of industrial building heritage based on light environment improvement and energy conservation. The present invention can assign weights to multiple optimization objectives and then comprehensively rank the renovation solutions to obtain the optimal solution for the high-side window renovation of industrial heritage buildings

Claims

1. A multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving, the specific steps are as follows: S1, set the optimization target and optimization variables, calculate and analyze the optimization target through the established building model to judge the rationality of the optimization variables, and constrain the range of the optimization variables; S2, according to the optimization objectives and optimization variable range constraints in step S1, multi-objective optimization programming is performed on the Grasshopper platform using the Wallacei plug-in to obtain a multi-objective optimization solution set for the transformation of high side windows of industrial buildings based on daylighting and energy saving, and the optimal solution is obtained according to the average optimization objective weight; S3, weighting the optimization objectives based on the entropy method, and at the same time, prioritizing the weights of the solutions in the solution set in step S2 according to each optimization objective based on the analytic hierarchy process AHP; S4, comprehensively calculate the weight of the optimization target and the priority weight value of the solution to obtain the comprehensive priority weight value of the solution, and recommend the optimal solution according to the maximum value of the comprehensive priority weight value.

2. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 1 is characterized by: 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 , m is the number of optimization variables.

3. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 2 is characterized by: The optimization target in step S1 is calculated by orthogonal experiment, and the specific steps are as follows: S111, orthogonal experimental design, including setting the number of experimental factors to m, determining the number of levels to n, determining the corresponding orthogonal experimental table according to m and n, and determining the number of experiments p according to the orthogonal experimental table; S112, parametric architectural geometry modeling and physical modeling are implemented by programming on the Grasshopper platform; S113, using Ladybug plug-in on Grasshopper platform to obtain local meteorological parameters; S114, calculate the light environment index O1 using the HB-Daylight plug-in on the Grasshopper platform; S115, calculate the energy saving index O2 using the HB-Annual load plug-in on the Grasshopper platform.

4. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 3 is characterized by: In step S1, the calculated light environment index O1 and energy saving index O2 are respectively subjected to range analysis, and the specific steps are as follows: S121, calculate K ij ,i=1,2,…,n;j=1,2,…,m,m+1;,K ij It is equal to the sum of the experimental results of the corresponding light environment index O1 or the corresponding energy saving index O2 when the jth factor (j = 1, 2, ..., m) takes the i-th level, K i,m+1 Refers to the sum of the experimental results of the light environment index O1 or the corresponding 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 represents the range of the jth (j=1,2,…,m) factor, R m+1 Represents the range of the blank column of the orthogonal experiment; S124, according to the range value R of the blank column m+1 Is it less than R j , j = 1, 2,…, m, to judge the reliability of orthogonal experimental data.

5. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 4 is characterized by: 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 squares of deviations Q of the jth 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) Where F.DIST.RT is a function of the Microsoft Excel program.

6. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 1 is characterized by: The rationality of the optimized variables in step S1 is judged as follows: j The larger the value, the greater the influence of factor j on the optimization target. j <0.05 indicates that factor j is significant, and the smaller the value, the greater the significance.

7. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 1 is characterized by: 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 the multi-objective optimization solution set B for the transformation of high side windows of industrial buildings based on lighting and energy saving z (z=1,2,…,Z), Z is the number of solutions in the solution set; S23, obtain the optimal solution B according to the average optimization target weight ao .

8. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 1 is characterized by: 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, using the experimental results of the orthogonal experiment as a sample data set for the entropy method; S312, using formula (5) to perform data standardization processing on the positive index of daylighting, and using formula (6) to perform data standardization processing on the negative index of energy saving, and finally obtaining a standardized sample data set that has eliminated the difference in measurement units of each index; Where, X i ,i=1,2,…,p, refers to the lighting optimization target standard value of the i-th sample, Y i ,i=1,2,…,p, refers to the energy-saving optimization target standard value of the i-th sample; S313, in order to avoid the situation where some data after standardization have low values, formula (7) and formula (8) are used for translation processing; X Hi =X i +H(7) Y Hi =Y i +H(8) In the formula, H is the amplitude of the index translation, which is 0.01; X Hi ,i=1,2,…,p, refers to the lighting optimization target standard value 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, normalize the standardized data set using formula (9) (10); In the formula, X Oi ,i=1,2,…,p, refers to the lighting optimization target standard value of the i-th sample after normalization; Y Oi ,i=1,2,…,p, refers to the energy-saving optimization target standard value of the i-th sample after normalization; S315, according to formula (11) and formula (12), the information entropy e of the lighting environment index O1 and the energy saving index O2 are calculated respectively. udi and e energy ; S316, calculate the difference coefficient g of the lighting environment index O1 and the energy saving index O2 according to formula (13) and formula (14) respectively udi and g energy ; g udi =1-e udi (13) g energy =1-e energy (14) S317, according to formula (15) and formula (16), the normalized weight value V of the lighting environment index O1 and the energy saving index O2 are calculated respectively. udi and V energy ; 9. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 8 is characterized by: In step S3, the calculation schemes are weighted according to the light environment index O1 and the energy saving index O2 based on the analytic hierarchy process AHP. The specific steps are as follows: S321, with the goals of improving daylighting and reducing energy consumption, respectively, according to the merits of the indicators, solution set B z , z=1,2,…,Z, sorting, the best solution ranks first, and so on; S322: construct a judgment matrix A according to the light environment index and the energy saving index respectively. udi and A energy : a ij According to the corresponding value conditions, a ji =1 / a ij ; b ij According to the corresponding value conditions, b ji =1 / b ij ; S323, respectively calculate the judgment matrix A udi and A energy The eigenvector H corresponding to the maximum eigenvalue udi and H energy , whose element value is h udi-i (i=1,2,…,z) and h energy-i (i=1,2,…,z), calculate the solution B according to the light environment index according to formula (17) i The priority weight h udi-o-i , according to formula (18), calculate the scheme B according to the light environment index i The priority weight h energy-o-i ; 10. The multi-objective optimization method for the transformation of high side windows of industrial heritage buildings based on lighting and energy saving according to claim 8 is characterized by: Scheme B in step S4 i Comprehensive priority weight value W o-i , i = 1, 2, ..., Z, calculated according to formula (19); W O-i =h energy-o-i ×V energy +h udi-o-i ×V udi (19) Recommend the optimal solution according to the maximum value of the comprehensive priority weight.

Images