Multi-objective optimization method for thermal performance of residential building envelope

Abstract

The invention provides a multi-objective optimization method for thermal performance of a residential building envelope structure, and relates to the technical field of building green energy conservation. MATLAB is used for carrying out multivariate nonlinear equation fitting to obtain the relation between the thermal performance parameters of the residential building envelope and the building heating energy consumption; establishing a relation between thermal performance parameters of a residential building envelope structure and full-life-cycle cost and full-life-cycle carbon emission through building heating energy consumption; constructing a thermal performance optimization function of the residential building envelope structure by taking the building full-life-cycle cost and the full-life-cycle carbon emission as the optimization direction; and solving the thermal performance optimization function of the residential building envelope structure by using a multi-objective genetic algorithm NSGA-I I to obtain a Pareto optimal solution set, calculating the close degree of each solution in the Pareto optimal solution set by using an entropy weight-TOPSIS method, and determining the optimal thermal performance parameters of the residential building envelope structure according to the close degree.

Description

Technical Field

[0001] The invention relates to the technical field of green energy-saving buildings, and in particular to a multi-objective optimization method for thermal performance of residential building envelope structures. Background Art

[0002] In the context of sustainable development, the construction industry is facing increasingly severe challenges, especially how to effectively reduce building energy consumption and carbon emissions. With the intensification of global climate change, traditional building design often fails to fully consider the thermal performance of the envelope structure, resulting in excessive energy consumption and increased carbon emissions during the operation of the building. Many existing building design methods mainly focus on the optimization of a single goal, such as reducing initial investment costs or improving energy efficiency, but fail to effectively integrate the relationship between economy, environmental impact and thermal performance. This one-sided design approach not only leads to a waste of resources, but may also increase the long-term economic burden on residents.

[0003] In addition, the importance of life cycle analysis of buildings in the design stage is becoming increasingly prominent. Current design methods often lack systematic life cycle assessment, which makes it difficult to predict and control the energy consumption and related carbon emissions of buildings during use. Therefore, an optimization method that integrates multiple factors is needed to achieve comprehensive optimization of building design, especially to find the best balance between the thermal performance, economy and environmental friendliness of the envelope structure.

[0004] In the prior art, the publication number CN114912788A discloses an evaluation method and system for energy-saving technical solutions for ultra-low energy residential buildings. A benchmark building model of a target project building to be evaluated is established based on the residential building energy-saving design standard, and a benchmark building energy consumption corresponding to the benchmark building model is calculated using building energy consumption simulation software; combining historical building energy-saving data and general ultra-low energy consumption technical factors, a multi-objective optimization method is used to optimize and combine and solve the domain to obtain multiple ultra-low energy consumption technical optimization solutions, and the design building energy consumption of each ultra-low energy consumption technical optimization solution is calculated; based on the benchmark building energy consumption and the design building energy consumption, the comprehensive energy-saving rate corresponding to each ultra-low energy consumption technical optimization solution is obtained, and the ultra-low energy consumption technical optimization solution with the largest comprehensive energy-saving rate is selected as the optimal evaluation technical solution for energy saving of ultra-low energy residential buildings; however, the prior art still has defects. The method mainly focuses on the optimal combination of ultra-low energy consumption technologies. Although it can provide multiple technical solutions, it does not fully consider the comprehensive impact between the thermal performance of the building envelope structure and the full life cycle cost and carbon emissions. This may result in the neglect of the comprehensive economic and environmental benefits of the building's life cycle during the optimization process, making the final selected technical solution not ideal in terms of economic efficiency or environmental sustainability. Existing technologies often rely on historical data and general standards when evaluating ultra-low energy consumption technology optimization solutions, but lack in-depth analysis of the actual conditions of specific projects. This may lead to insufficient applicability and effectiveness of the optimization solution under specific conditions, thereby affecting the actual energy-saving effect of the building. In addition, although the multi-objective optimization method of existing technologies can generate multiple solutions, it lacks clear and scientific decision-making support means in the process of solution selection, which may lead to inaccurate solution selection.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a multi-objective optimization method for the thermal performance of residential building envelope structures to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A multi-objective optimization method for thermal performance of residential building envelope structures, comprising the following specific steps:

[0009] Step 1: Use the uniform design method to design an energy consumption simulation calculation plan, calculate the building heating energy consumption data through the building energy consumption simulation software, and use MATLAB to perform multivariate nonlinear equation fitting to obtain the relationship between the thermal performance parameters of the residential building envelope structure and the building heating energy consumption; the thermal performance parameters of the residential building envelope structure are the thickness of the exterior wall insulation layer, the thickness of the roof insulation layer, and the thickness of the basement top plate insulation layer;

[0010] Step 2: Obtain the relationship between the building heating energy consumption and the annual operation cost of the building heating, as well as the relationship between the initial investment cost of thermal insulation and the thermal performance parameters of the residential building envelope structure, establish a relationship between the thermal performance parameters of the residential building envelope structure and the full life cycle cost, and obtain a first relationship function; the full life cycle cost is calculated by the annual operation cost of the building heating and the initial investment cost of thermal insulation;

[0011] Step 3: Obtain the relationship between the building heating energy consumption and the carbon emissions in the building operation stage through the emission factor, establish a relationship between the thermal performance parameters of the building envelope structure and the carbon emissions in the whole life cycle through the relationship between the building heating energy consumption and the carbon emissions in the building operation stage, and obtain a second relationship function; the whole life cycle carbon emissions include carbon emissions in the building construction stage, carbon emissions in the building demolition stage, and carbon emissions in the building materials production and transportation stage;

[0012] Step 4: Taking the minimum life cycle cost and the minimum life cycle carbon emission of the building as the optimization direction of the first relationship function and the second relationship function, respectively, to construct the thermal performance optimization function of the residential building envelope structure;

[0013] Step 5: Use the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of the residential building envelope structure and obtain the Pareto optimal solution set.

[0014] Step 6: Use the entropy weight-TOPSIS method to calculate the closeness of each solution in the Pareto optimal solution set, and determine the optimal thermal performance parameters of the residential building envelope structure based on the closeness.

[0015] Furthermore, the specific logic for obtaining the first relationship function is as follows: obtaining the relationship between the building heating energy consumption and the annual operation cost of the building heating, the relationship between the initial investment cost of thermal insulation and the thermal performance parameters of the residential building envelope, establishing a relationship between the thermal performance parameters of the residential building envelope and the full life cycle cost, and obtaining the first relationship function; the relationship between the building heating energy consumption and the annual operation cost of the building heating is:

[0016]

[0017] Among them, C H The annual operating cost of heating the building, Q H (x, y, z) is the building heating energy consumption, C fuel is the energy price, qfuel is the low calorific value of the fuel, η 1 is the boiler operating efficiency, η 2 is the outdoor pipe network transmission efficiency, x is the thickness of the exterior wall insulation layer, y is the thickness of the roof insulation layer, and z is the thickness of the basement roof insulation layer;

[0018] The relationship between the initial investment cost of thermal insulation and the thermal performance parameters of residential building envelope structures is:

[0019] C ins (x, y, z) = A 1 (c 1 x+C p1 )+A 2 (C 2 y+C P2 )+A 3 (C 3 z+C P3 )

[0020] Among them, C ins (x, y, z) is the initial investment cost of building insulation, A 1 is the exterior wall area, A 2 is the roof area, A 3 is the basement ceiling area, C 1 is the unit price of exterior wall insulation materials, C 2 is the unit price of roof insulation materials, C 3 C is the unit price of basement roof insulation materials; p1 is the cost of exterior wall construction, C P2 is the cost of roof construction, C P3 Cost of construction of basement roof;

[0021] The first relation function is expressed as:

[0022] LCC(x, y, z) = P 1 ·C H (x,y,z)+P 2 ·C ins (x, y, z)

[0023] Among them, LCC(x, y, z) is the life cycle cost, P 1 is the present value factor of total expenditure within the economic analysis period, P 2 It is the ratio of the total funds spent within the analysis period to the initial investment.

[0024] Furthermore, the specific logic for obtaining the second relationship function is as follows: the relationship between the building heating energy consumption and the carbon emissions in the building operation stage is obtained through the emission factor, and the thermal performance parameters of the building envelope structure are linked to the carbon emissions in the whole life cycle through the relationship between the building heating energy consumption and the carbon emissions in the building operation stage to obtain the second relationship function; the relationship between the building heating energy consumption and the carbon emissions in the building operation stage is:

[0025] C M (x, y, z) = [Q H (x, y, z)*PF-C P ]*N e

[0026] Among them, C M (x, y, z) is the carbon emissions during the building operation phase, Q H (x, y, z) is the building heating energy consumption, PF is the carbon emission factor of heating energy, C P is the annual carbon reduction of the building green space carbon sink system, N e For the analysis period;

[0027] The second relationship function is expressed as:

[0028] C c (x, y, z) = C Jz +C M (x,y,z)+C CC +C JC

[0029] Among them, C C (x, y, z) is the carbon emission of the whole life cycle, C Jz is the carbon emission during the building construction phase, C CC is the carbon emission during the building demolition phase, C JC It is the carbon emissions from the production and transportation of building materials.

[0030] Furthermore, the specific logic for obtaining carbon emissions in the construction stage, the building demolition stage, and the building materials production and transportation stage is as follows: calculate the carbon emissions in the building materials production stage, take 4% of the carbon emissions in the building materials production stage as the carbon emissions in the construction stage, and take 10% of the carbon emissions in the construction stage as the carbon emissions in the building demolition stage. The specific formula for obtaining carbon emissions in the building materials production stage is:

[0031]

[0032] Among them, C SC is the carbon emission in the building materials production stage, B i is the amount of the i-th building material, BPF iis the carbon emission factor of the i-th building material, n is the number of building material types, and i is the building material type index;

[0033] The specific logic for calculating carbon emissions during the production and transportation of building materials is as follows:

[0034]

[0035] Among them, D i is the average transportation distance of the i-th building material, T i is the carbon emission factor of the transportation distance per unit weight of the i-th building material;

[0036] Furthermore, the specific logic for constructing the thermal performance optimization function of residential building envelope structures is: taking the minimum life cycle cost and life cycle carbon emissions of the building as the optimization direction of the first relationship function and the second relationship function, respectively, to construct the thermal performance optimization function of residential building envelope structures. The specific formula of the thermal performance optimization function of residential building envelope structures is:

[0037]

[0038] Among them, F(x, y, z) is the thermal performance optimization function of residential building envelope structure, LCC(x, y, z) is the life cycle cost, C C (x, y, z) is the carbon emission over the entire life cycle, {x ≥ 0, y ≥ 0, z ≥ 0} is the constraint condition of the thermal performance optimization function of the residential building envelope structure, x is the thickness of the exterior wall insulation layer, y is the thickness of the roof insulation layer, and z is the thickness of the basement roof insulation layer.

[0039] Furthermore, the specific logic for solving the thermal performance optimization function of residential building envelope structures using the multi-objective genetic algorithm NSGA-II to obtain the Pareto optimal solution set is as follows: within the constraints, randomly generate N initial thermal performance parameter populations of residential building envelope structures, calculate the optimization function value of each individual in the initial thermal performance parameter population of residential building envelope structures, non-dominated sort the individuals in the population according to the optimization function value, and calculate the crowding degree of each individual, select, crossover and mutate each individual according to the non-dominated sorting and crowding degree, update the population, repeat the selection, crossover, mutation and population update operations until a preset number of iterations is reached, non-dominated sort the initial thermal performance parameter population of residential building envelope structures after the iteration, and use the Pareto frontier obtained by non-dominated sorting of the initial thermal performance parameter population of residential building envelope structures as the Pareto optimal solution set of the thermal performance optimization function of residential building envelope structures {S 1 , S 2 , S 3 , ..., S b};

[0040] The initial population is expressed as:

[0041] P 0 ={(x 1 ,y 1 , z 1 ), (x 2 ,y 2 , z 2 )...(x j ,y j , z j )...(x N ,y N , z N )}

[0042] Among them, P 0 is the initial population of thermal performance parameters of residential building envelope structures, x j is the thickness of the external wall insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelope structures, y j is the thickness of the roof insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelopes, z j is the thickness of the basement roof insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelope structures;

[0043] The non-dominated sorting result can be expressed as:

[0044] F 0 ={F 1 , F 2 ...F L}

[0045] F 0 is the non-dominated sorting result, F 1 is the Pareto front, L is the number of non-dominated sorting layers;

[0046] The specific formula for calculating congestion is:

[0047]

[0048] Among them, M j is the crowding degree of the jth individual, z is the number of optimization targets, and The objective function value of the zth optimized target neighboring individual for the jth individual, where is the maximum value of the zth optimization objective, is the minimum value of the zth optimization objective;

[0049] The specific logic of the selection is: randomly select two individuals from the population, compare the non-dominated sorting levels between the two individuals, and select the individual with a lower non-dominated sorting level. If the two individuals are at the same non-dominated sorting level, compare the crowding of the two individuals and select the individual with a larger crowding level as the parent individual.

[0050] The specific logic of the crossover operation is: randomly select two parent individuals (x C ,y C , z C ) and (x V ,y V , z V )

[0051] x xnew =αx C +(1-α)x V

[0052] y ynew =αy C +(1-α)y V

[0053] z znew =αz C +(1-α)z V

[0054] Among them, x xnew is the thickness of the external wall insulation layer after crossing, y ynew is the thickness of the roof insulation layer after crossing, z znew is the thickness of the basement roof insulation layer after crossing, and α is the crossing ratio randomly generated in the range of [0, 1];

[0055] The specific formula on which the mutation operation is based is:

[0056] x new =x xnew +σ x

[0057] y new =y ynew +σ y

[0058] Z new =z znew +σ z

[0059] Among them, x new is the thickness of the external wall insulation layer after variation, y new is the thickness of the roof insulation layer after variation, z new is the thickness of the basement roof insulation layer after the intersection, σ x , σ y and σz is a random disturbance;

[0060] The specific logic for updating the population is as follows: merge the mutated parent populations into a joint population, select the first N individuals to form the next generation population, and the selection principle is as follows: start selecting from the first non-dominated sorting layer, select the next non-dominated sorting layer after the non-dominated sorting layers with lower numbers are selected, and give priority to individuals with high crowding in the same non-dominated sorting layer.

[0061] Furthermore, the specific logic for determining the optimal thermal performance parameters of residential building envelope structures is as follows: the entropy weight-TOPSIS method is used to calculate the closeness of each solution in the Pareto optimal solution set, and the optimal thermal performance parameters of the residential building envelope structure are determined according to the closeness; the specific logic for calculating the closeness is as follows: the Pareto optimal solution set and the life cycle cost and carbon emissions constitute a joint matrix, each element in the joint matrix is ​​standardized, the indicator feature proportion of the joint matrix is ​​calculated, the indicator entropy value of the joint matrix is ​​calculated by the indicator feature proportion, and then the indicator weight is calculated by the indicator entropy value of the joint matrix, the joint matrix is ​​weighted by the indicator weight, the positive ideal solution distance and the negative ideal solution distance corresponding to each scheme in the weighted joint matrix are calculated, the closeness of each scheme is calculated by the positive ideal solution distance and the negative ideal solution distance, and the thermal performance parameters of the residential building envelope structure with the closeness closest to 1 are defined as the optimal thermal performance parameters of the residential building envelope structure.

[0062] The joint matrix is ​​expressed as:

[0063]

[0064] Among them, R is the joint matrix, LCC b1 is the life cycle cost corresponding to the bth solution in the Pareto optimal solution set, C Cb2 is the full life cycle carbon emission corresponding to the bth solution in the Pareto optimal solution set, and b is the number of optimal solutions in the Pareto optimal solution set;

[0065] The specific logic for calculating the characteristic weight of indicators is as follows:

[0066]

[0067] Among them, S uh is the characteristic weight of the indicator, a uh is the matrix element at the uth row and hth column after the joint matrix elements are normalized;

[0068] The specific formula for calculating the index entropy value is:

[0069]

[0070] Among them, E his the index entropy value;

[0071] The specific formula for calculating the indicator weights is:

[0072]

[0073] Among them, w h is the indicator weight;

[0074] The specific formula for weighting the joint matrix by indicator weights is:

[0075] Z uh =w h a uh

[0076] Among them, Z uh is the weighted matrix element in the uth row and hth column;

[0077] The specific formula for calculating the positive ideal solution distance and the negative ideal solution distance is:

[0078]

[0079] in, is the distance between u solutions and their ideal solutions, is the distance between u solutions and the ideal solution, is the maximum positive ideal solution, is the minimum negative ideal solution;

[0080] The specific formula for calculating closeness is:

[0081]

[0082] Among them, V u is the closeness of the u-th solution. Compared with the prior art, the beneficial effects of the present invention are:

[0083] The present invention not only fully considers the relationship between the thermal performance, life cycle cost and carbon emissions of the building envelope during the building design stage, but also comprehensively evaluates the impact of different thermal performance parameters on building energy efficiency and environmental protection by establishing a scientific optimization model. The multi-objective genetic algorithm NSGA-II is used for optimization, which can effectively generate a series of Pareto optimal solutions, allowing designers to make scientific trade-offs between different goals and ensure that the final selected solution achieves the best balance in energy saving, economic and environmental benefits.

[0084] The present invention also introduces the entropy weight-TOPSIS method to evaluate the Pareto optimal solution. This solution can also provide designers with a more accurate selection basis among multiple solutions to ensure that the selected best solution has the best comprehensive performance. The implementation of this method can effectively improve the overall energy efficiency of buildings, reduce carbon emissions, promote economic sustainable development, and provide strong support for the green transformation of the construction industry. In addition, comprehensive consideration of the analysis of the entire life cycle allows architectural design to not only focus on initial investment, but also from a long-term perspective, improve resource utilization efficiency and environmental protection awareness, and contribute to the realization of sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0086] Figure 2 Schematic diagram of the Pareto optimal solution set when the heating heat source type is a coal boiler.

[0087] Figure 3 Schematic diagram of the Pareto optimal solution set when the heating heat source type is a gas boiler.

[0088] Figure 4 Schematic diagram of the Pareto optimal solution set when the heating heat source type is an air source heat pump.

[0089] Figure 5 Schematic diagram of the impact of the life cycle cost weight on the life cycle cost of the optimal solution.

[0090] Figure 6 Schematic diagram of the impact of life cycle cost weights on the life cycle carbon emissions of the optimal solution. DETAILED DESCRIPTION

[0091] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0092] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0093] Example:

[0094] See also Figure 1-6 , the present invention provides a technical solution:

[0095] A multi-objective optimization method for thermal performance of residential building envelope structures, comprising the following specific steps:

[0096] Step 1: Use the uniform design method to design an energy consumption simulation calculation plan, calculate the building heating energy consumption data through the building energy consumption simulation software, and use MATLAB to perform multivariate nonlinear equation fitting to obtain the relationship between the thermal performance parameters of the residential building envelope structure and the building heating energy consumption; the thermal performance parameters of the residential building envelope structure are the thickness of the exterior wall insulation layer, the thickness of the roof insulation layer, and the thickness of the basement top plate insulation layer;

[0097] Life cycle costs and life cycle carbon emissions are important data for evaluating the thermal performance of buildings. Building heating energy consumption is related to both life cycle costs and life cycle carbon emissions. This embodiment fits the multivariate linear relationship between the thermal performance parameters of residential building envelope structures and building heating energy consumption, which can provide an important basis for subsequently establishing a connection between the thermal performance parameters of residential building envelope structures and life cycle costs and life cycle carbon emissions.

[0098] The uniform design method was used to design the energy consumption simulation calculation scheme, and the DeST software was used to calculate the building heating energy consumption. The building area, building height, wall-to-window ratio, average winter temperature, average winter humidity, building thermal performance parameters, and thickness of the exterior wall insulation layer, roof insulation layer thickness, and basement ceiling insulation layer thickness were input into the DeST software to obtain the building heating energy consumption. The thermal performance parameters of the residential building envelope structure and the calculated building heating energy consumption were sorted out to obtain Table 1. The thickness of the building's exterior wall insulation layer, roof insulation layer thickness, basement ceiling insulation layer thickness, and building heating energy consumption were input into the MATLAB software, and the multivariate nonlinear fitting tool in the MATLAB software was used to fit the complex relationship between these parameters and energy consumption. Through fitting, a multivariate nonlinear equation can be obtained;

[0099]

[0100] Table 1 Energy consumption simulation calculation scheme and heating energy consumption

[0101] Step 2: Obtain the relationship between the building heating energy consumption and the annual operation cost of the building heating, as well as the relationship between the initial investment cost of thermal insulation and the thermal performance parameters of the residential building envelope structure, establish a relationship between the thermal performance parameters of the residential building envelope structure and the full life cycle cost, and obtain a first relationship function; the full life cycle cost is calculated by the annual operation cost of the building heating and the initial investment cost of thermal insulation;

[0102] The specific logic for obtaining the first relationship function is: obtain the relationship between building heating energy consumption and annual building heating operating costs, and the relationship between the initial investment cost of insulation and the thermal performance parameters of residential building envelopes. Establish a relationship between the thermal performance parameters of residential building envelopes and the full life cycle costs to obtain the first relationship function; the relationship between building heating energy consumption and annual building heating operating costs is:

[0103]

[0104] Among them, C H The annual operating cost of heating the building, Q H (x, y, z) is the building heating energy consumption, C fuel is the energy price, q fuel is the low calorific value of the fuel, η 1 is the boiler operating efficiency, η 2 is the outdoor pipe network transmission efficiency, x is the thickness of the exterior wall insulation layer, y is the thickness of the roof insulation layer, and z is the thickness of the basement roof insulation layer; the annual operating cost of building heating C H It reflects the annual heating cost of a building and provides an important basis for calculating the full life cycle cost.

[0105] The relationship between the initial investment cost of thermal insulation and the thermal performance parameters of residential building envelope structures is:

[0106] C ins (x, y, z) = A 1 (C 1 x+C p1 )+A 2 (C 2 y+C P2 )+A 3 (C 3 z+C P3 )

[0107] Among them, C ins (x, y, z) is the initial investment cost of building insulation, A 1 is the exterior wall area, A 2 is the roof area, A 3 is the basement ceiling area, C 1 is the unit price of exterior wall insulation materials, C 2 is the unit price of roof insulation materials, C 3 C is the unit price of basement roof insulation materials; p1 is the cost of exterior wall construction, C P2 is the cost of roof construction, C P3 C is the construction cost of the basement roof; ins (x, y, z) The initial investment cost of building insulation reflects the initial investment cost under certain thermal performance parameters of the residential building envelope structure. The larger the value, the higher the initial investment cost and the higher the full life cycle cost.

[0108] The first relation function is expressed as:

[0109] LCC(x, y, z) = P 1 ·C H (x,y,z)+P 2 ·C ins (x, y, z)

[0110] Among them, LCC(x, y, z) is the life cycle cost, P 1 is the present value factor of total expenditure within the economic analysis period, P 2 To analyze the ratio of the total amount of funds spent within the years to the initial investment. The life cycle cost LCC (x, y, z) reflects the life cycle cost under certain thermal performance parameters of residential building envelopes. The life cycle cost is an important indicator for optimizing the thermal performance parameters of residential building envelopes. The size of this indicator can directly reflect the thermal performance of residential building envelopes.

[0111] Step 3: Obtain the relationship between the building heating energy consumption and the carbon emissions in the building operation stage through the emission factor, establish a relationship between the thermal performance parameters of the building envelope structure and the carbon emissions in the whole life cycle through the relationship between the building heating energy consumption and the carbon emissions in the building operation stage, and obtain a second relationship function; the whole life cycle carbon emissions include carbon emissions in the building construction stage, carbon emissions in the building demolition stage, and carbon emissions in the building materials production and transportation stage;

[0112] The specific logic for obtaining the second relationship function is as follows: the relationship between the building heating energy consumption and the carbon emissions in the building operation stage is obtained through the emission factor, and the thermal performance parameters of the building envelope structure are linked to the carbon emissions in the whole life cycle through the relationship between the building heating energy consumption and the carbon emissions in the building operation stage to obtain the second relationship function; the relationship between the building heating energy consumption and the carbon emissions in the building operation stage is:

[0113] C M (x, y, z) = [Q H (x, y, z)*PF-C P ]*Ne

[0114] Among them, C M (x, y, z) is the carbon emission during the building operation phase, Q H (x, y, z) is the building heating energy consumption, PF is the carbon emission factor of heating energy, C P is the annual carbon reduction of the building green space carbon sink system, and Ne is the analysis period;

[0115] The second relationship function is expressed as:

[0116] C C (x, y, z) = C Jz +C M (x,y,z)+C CC +C JC

[0117] Among them, C C (x, y, z) is the carbon emission of the whole life cycle, C JZ is the carbon emission during the building construction phase, C CC is the carbon emission during the building demolition phase, C JC It is the carbon emissions from the production and transportation of building materials.

[0118] Due to the various operations of various facilities in the building demolition design during the construction phase, it is difficult to directly calculate the carbon emissions of the corresponding phases through formulaic calculations. Therefore, this embodiment obtains the carbon emissions of the construction phase and the building demolition phase through estimation methods; the specific logic for obtaining the carbon emissions of the construction phase, the building demolition phase, and the carbon emissions of the building materials production and transportation phase is as follows: calculate the carbon emissions of the building materials production phase, and use 4% of the carbon emissions of the building materials production phase as the carbon emissions of the construction phase, and use 10% of the carbon emissions of the construction phase as the carbon emissions of the building demolition phase.

[0119] The specific formula for obtaining carbon emissions during the building materials production phase is:

[0120]

[0121] Among them, C SC is the carbon emission of building materials in the production stage, B i is the amount of the i-th building material, BPF i is the carbon emission factor of the i-th building material, n is the number of building material types, and i is the building material type index; the carbon emission in the building material production stage C SC Reflects the carbon emissions released during the production of building materials. Since the construction process of building envelope structures is similar, the carbon emissions during the production of building materials are C SC The generation of can provide an important basis for estimating carbon emissions in the construction phase and carbon emissions in the building demolition phase.

[0122] The specific logic for calculating carbon emissions during the production and transportation of building materials is as follows:

[0123]

[0124] Among them, D i is the average transportation distance of the i-th building material, T i is the carbon emission factor of the transportation distance per unit weight of the i-th building material;

[0125] Step 4: Taking the minimum life cycle cost and the minimum life cycle carbon emission of the building as the optimization direction of the first relationship function and the second relationship function, respectively, to construct the thermal performance optimization function of the residential building envelope structure;

[0126] According to the thermal design code for civil buildings, the thermal performance of building envelope structures in extremely cold and cold areas is mainly to meet the needs of winter insulation. The thickness of the insulation layer can be determined according to the winter heating conditions. With the increase of the thickness of the insulation layer, the annual heating operating costs and carbon emissions of the building during the operation stage are reduced. When the cost and carbon emissions of the building throughout its life cycle are minimized, the thickness of the insulation layer of the building envelope structure is the optimal insulation layer thickness, which has the best thermal performance parameters of the envelope structure. In this way, the optimization direction of the thermal performance of the envelope structure of residential buildings is determined, and the thermal performance optimization function of the envelope structure of residential buildings is constructed.

[0127] The specific logic for constructing the thermal performance optimization function of residential building envelope structures is as follows: taking the minimum life cycle cost and life cycle carbon emissions of the building as the optimization direction of the first relationship function and the second relationship function respectively, and constructing the thermal performance optimization function of residential building envelope structures; the specific formula of the thermal performance optimization function of residential building envelope structures is:

[0128]

[0129] Among them, F(x, y, z) is the thermal performance optimization function of residential building envelope structure, LCC(x, y, z) is the life cycle cost, C C (x, y, z) is the carbon emission over the entire life cycle, {x ≥ 0, y ≥ 0, z ≥ 0} is the constraint condition of the thermal performance optimization function of the residential building envelope structure, x is the thickness of the exterior wall insulation layer, y is the thickness of the roof insulation layer, and z is the thickness of the basement roof insulation layer.

[0130] Step 5: Use the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of the residential building envelope structure and obtain the Pareto optimal solution set;

[0131] The specific logic of using the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of the residential building envelope structure to obtain the Pareto optimal solution set is as follows: within the constraint range, randomly generate N initial thermal performance parameter populations of the residential building envelope structure, calculate the optimization function value of each individual in the initial thermal performance parameter population of the residential building envelope structure, non-dominated sort the individuals in the population according to the optimization function value, and calculate the crowding degree of each individual, select, crossover and mutate each individual through the non-dominated sorting and crowding degree, update the population, repeat the selection, crossover, mutation and population update operations until the preset number of iterations is reached, non-dominated sort the initial thermal performance parameter population of the residential building envelope structure after the iteration, and the Pareto frontier obtained by non-dominated sorting of the initial thermal performance parameter population of the residential building envelope structure after the iteration is used as the Pareto optimal solution set of the thermal performance optimization function of the residential building envelope structure {S 1 , S 2 , S 3 , ..., S b}; Algorithm related calculation parameter settings are shown in Table 2

[0132]

[0133] Table 2 NSGA-II algorithm related calculation parameters

[0134] The initial population is expressed as:

[0135] P 0={(x 1 ,y 1 , z 1 ), (x 2 ,y 2 , z 2 )...(x j ,y j , z j )...(x N ,y N , z N )}

[0136] Among them, P 0 is the initial population of thermal performance parameters of residential building envelope structures, x j is the thickness of the external wall insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelope structures, y j is the thickness of the roof insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelopes, z j is the thickness of the basement roof insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelope structures;

[0137] The non-dominated sorting result can be expressed as:

[0138] F 0 ={F 1 , F 2 ...F L}

[0139] F 0 is the non-dominated sorting result, F 1 is the Pareto front, L is the number of non-dominated sorting layers;

[0140] For this scenario, if the life cycle cost and life cycle carbon emissions of an individual are both smaller than those of another individual, then this individual is deemed to be dominated by another individual. We arrange all non-dominated individuals as the first non-dominated sorting layer, and repeat the above operation for the remaining individuals until all individuals are sorted, and we can get the non-dominated sorting result.

[0141] The specific formula for calculating congestion is:

[0142]

[0143] Among them, M j is the crowding degree of the jth individual, z is the number of optimization targets, and The objective function value of the zth optimized target neighboring individual for the jth individual, where is the maximum value of the zth optimization objective, is the minimum value of the zth optimization objective; the crowding degree M of the jth individual j Reflecting the sparseness of the jth individual in the target space relative to its neighbors can maintain the diversity of the population and avoid the concentration of solutions in certain specific areas. By giving priority to solutions with large crowding distance, it can ensure that the algorithm can explore the entire solution space instead of converging to a local area too early.

[0144] The specific logic of the selection is: randomly select two individuals from the population, compare the non-dominated sorting levels between the two individuals, and select the individual with a lower non-dominated sorting level. If the two individuals are at the same non-dominated sorting level, compare the crowding of the two individuals and select the individual with a larger crowding level as the parent individual.

[0145] The specific logic of the crossover operation is: randomly select two parent individuals (x C ,y C , z C ) and (x V ,y V , z V )

[0146] x xnew =αx C +(1-α)x V

[0147] y ynew =αy C +(1-α)y V

[0148] z znew =αz C +(1-α)z V

[0149] Among them, x xnew is the thickness of the external wall insulation layer after crossing, y ynew is the thickness of the roof insulation layer after crossing, z znew is the thickness of the basement roof insulation layer after crossing, and α is the crossing ratio randomly generated in the range of [0, 1];

[0150] The specific formula on which the mutation operation is based is:

[0151] x new =x xnew +σ x

[0152] y new =y ynew +σ y

[0153] z new =z znew +σz

[0154] Among them, x new is the thickness of the external wall insulation layer after variation, y new is the thickness of the roof insulation layer after variation, z new is the thickness of the basement roof insulation layer after the intersection, σ x , σ y and σ z is a random disturbance;

[0155] The specific logic for updating the population is as follows: merge the mutated parent populations into a joint population, select the first N individuals to form the next generation population, and the selection principle is as follows: start selecting from the first non-dominated sorting layer, select the next non-dominated sorting layer after the non-dominated sorting layers with lower numbers are selected, and give priority to individuals with high crowding in the same non-dominated sorting layer.

[0156] This embodiment mainly studies three heating heat sources, namely coal boiler, gas boiler and air source heat pump; the Pareto optimal solution set of the three heating heat sources is as follows: Figure 2 , Figure 3 and Figure 4 As shown;

[0157] Step 6: Use the entropy weight-TOPSIS method to calculate the closeness of each solution in the Pareto optimal solution set, and determine the optimal thermal performance parameters of the residential building envelope structure based on the closeness.

[0158] The specific logic for determining the optimal thermal performance parameters of residential building envelope structures is as follows: the entropy weight-TOPSIS method is used to calculate the closeness of each solution in the Pareto optimal solution set, and the optimal thermal performance parameters of the residential building envelope structure are determined according to the closeness; the specific logic for calculating the closeness is as follows: the Pareto optimal solution set, the life cycle cost and the carbon emission constitute a joint matrix, each element in the joint matrix is ​​standardized, the indicator feature proportion of the joint matrix is ​​calculated, the indicator entropy value of the joint matrix is ​​calculated by the indicator feature proportion, and then the indicator weight is calculated by the indicator entropy value of the joint matrix, the joint matrix is ​​weighted by the indicator weight, and the positive ideal solution distance and the negative ideal solution distance corresponding to each scheme of the weighted joint matrix are calculated, and the closeness of each scheme is calculated by the positive ideal solution distance and the negative ideal solution distance, and the thermal performance parameters of the residential building envelope structure with the closest closeness to 1 are defined as the optimal thermal performance parameters of the residential building envelope structure.

[0159] The joint matrix is ​​expressed as:

[0160]

[0161] Among them, R is the joint matrix, LCC b1is the life cycle cost corresponding to the bth solution in the Pareto optimal solution set, C Cb2 is the life cycle carbon emissions corresponding to the bth solution in the Pareto optimal solution set, and b is the number of optimal solutions in the Pareto optimal solution set; the joint matrix R reflects the performance of each plan in terms of life cycle cost indicators and life cycle carbon emissions indicators; it provides an important basis for the subsequent selection of the best plan.

[0162] The specific logic for calculating the characteristic weight of indicators is as follows:

[0163]

[0164] Among them, S uh is the characteristic weight of the indicator, a uh is the matrix element at the uth row and hth column after the joint matrix elements are normalized;

[0165] Index characteristic weight S uh Reflecting the relative performance of each plan under the full life cycle cost index and the full life cycle carbon emission index can eliminate the dimensional differences of the data.

[0166]

[0167] Among them, E h is the index entropy value;

[0168] Index entropy value E h It reflects the differentiation between the life cycle cost index and the life cycle carbon emission index among the options. The lower the entropy value, the greater the importance of the index.

[0169] The specific formula for calculating the indicator weights is:

[0170]

[0171] Among them, w h is the indicator weight;

[0172] Index weight w h The relative importance of the life cycle cost indicator and the life cycle carbon emission indicator in the decision-making process. The greater the weight, the greater the role of the indicator in the decision-making.

[0173] The specific formula for weighting the joint matrix by indicator weights is:

[0174] Z uh =w h a uh

[0175] Among them, Z uh is the weighted matrix element in the uth row and hth column;

[0176] After weighting, the values ​​in the joint matrix not only represent the performance of each plan in terms of the life cycle cost indicator and the life cycle carbon emission indicator, but also reflect the importance of different indicators.

[0177] The specific formula for calculating the positive ideal solution distance and the negative ideal solution distance is:

[0178]

[0179] in, is the distance between u solutions and their ideal solutions, is the distance between u solutions and the ideal solution, is the maximum positive ideal solution, is the minimum negative ideal solution; the maximum positive ideal solution Represents the theoretical optimal effect, the minimum negative ideal The solution represents the theoretical worst result. u solutions are far from the ideal solution Reflects the Euclidean distance between u solutions and the maximum positive ideal solution; the distance between u solutions and the negative ideal solution Reflects the Euclidean distance between u solutions and the minimum positive ideal solution; u solutions positive ideal solution distance The distance between u solutions and the negative ideal solution The pros and cons of each solution are measured; the closer the solution is to the positive ideal solution, the better it is; the farther it is from the negative ideal solution, the better its performance is.

[0180] The specific formula for calculating closeness is:

[0181]

[0182] Among them, V u is the closeness of the u-th solution. u Reflects the "closeness" of the u-th solution to the maximum positive ideal solution, and the value is between 0 and 1. The closer the value is to 1, the closer the solution is to the ideal state; the closer the value is to 0, the closer the solution is to the minimum negative ideal solution.

[0183] During optimization, the life cycle cost and carbon emission are two optimization targets, and the sum of their weights is equal to 1. The weights of the life cycle cost target are set to 0, 0.2, 0.4, 0.6, 0.8 and 1 respectively, and the weights of the life cycle carbon emission target change accordingly according to the additive relationship, and different optimal solutions can be obtained respectively. The impact of different weights of the life cycle cost target on the life cycle cost and carbon emission is shown in the figure. Figure 5 and Figure 6 .

[0184] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0185] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0186] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A multi-objective optimization method for thermal performance of residential building envelope structure, characterized in that: The specific steps include: Step 1: Use the uniform design method to design an energy consumption simulation calculation plan, calculate the building heating energy consumption data through the building energy consumption simulation software, and use MATLAB to perform multivariate nonlinear equation fitting to obtain the relationship between the thermal performance parameters of the residential building envelope structure and the building heating energy consumption. The thermal performance parameters of the residential building envelope structure are the thickness of the exterior wall insulation layer, the thickness of the roof insulation layer, and the thickness of the basement roof insulation layer; Step 2: Obtain the relationship between the building heating energy consumption and the annual operation cost of the building heating, as well as the relationship between the initial investment cost of thermal insulation and the thermal performance parameters of the residential building envelope structure, establish a relationship between the thermal performance parameters of the residential building envelope structure and the full life cycle cost, and obtain a first relationship function; the full life cycle cost is calculated by the annual operation cost of the building heating and the initial investment cost of thermal insulation; Step 3: Obtain the relationship between the building heating energy consumption and the carbon emissions in the building operation stage through the emission factor, establish a relationship between the thermal performance parameters of the building envelope structure and the carbon emissions in the whole life cycle through the relationship between the building heating energy consumption and the carbon emissions in the building operation stage, and obtain a second relationship function; the whole life cycle carbon emissions include carbon emissions in the building construction stage, carbon emissions in the building demolition stage, and carbon emissions in the building materials production and transportation stage; Step 4: Taking the minimum life cycle cost and the minimum life cycle carbon emission of the building as the optimization direction of the first relationship function and the second relationship function, respectively, to construct the thermal performance optimization function of the residential building envelope structure; Step 5: Use the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of the residential building envelope structure and obtain the Pareto optimal solution set; Step 6: Use the entropy weight-TOPSIS method to calculate the closeness of each solution in the Pareto optimal solution set, and determine the optimal thermal performance parameters of the residential building envelope structure based on the closeness.

2. A multi-objective optimization method for thermal performance of residential building envelope structure according to claim 1, characterized in that: The specific logic for obtaining the first relationship function is: The relationship between building heating energy consumption and annual building heating operation cost, the relationship between initial investment cost of thermal insulation and thermal performance parameters of residential building envelope structure are obtained, and the thermal performance parameters of residential building envelope structure are linked with the full life cycle cost to obtain the first relationship function; the relationship between building heating energy consumption and annual building heating operation cost is: Among them, C H The annual operating cost of heating the building, Q H (x, y, z) is the building heating energy consumption, C fuel is the energy price, q fuel is the low calorific value of the fuel, η1 is the boiler operation efficiency, η2 is the outdoor pipe network transmission efficiency, x is the thickness of the exterior wall insulation layer, y is the thickness of the roof insulation layer, and z is the thickness of the basement roof insulation layer; The relationship between the initial investment cost of thermal insulation and the thermal performance parameters of residential building envelope structures is: <h2 style=";text-align:left;direction:ltr">C<h2 style=";text-align:left;direction:ltr"> ins <h2 style=";text-align:left;direction:ltr"> (x, y, z) = A1(C1x+C<h2 style=";text-align:left;direction:ltr"> p1 <h2 style=";text-align:left;direction:ltr"> )+A2(C2y+C<h2 style=";text-align:left;direction:ltr"> P2 <h2 style=";text-align:left;direction:ltr"> )+A3(C3z+C<h2 style=";text-align:left;direction:ltr"> P3 <h2 style=";text-align:left;direction:ltr"> ) Among them, C ins (x, y, z) is the initial investment cost of building insulation, A1 is the exterior wall area, A2 is the roof area, A3 is the basement ceiling area, C1 is the unit price of exterior wall insulation materials, C2 is the unit price of roof insulation materials, and C3 is the unit price of basement ceiling insulation materials; C p1 is the cost of exterior wall construction, C P2 is the cost of roof construction, C P3 Cost of construction of basement roof; The first relation function is expressed as: LCC(x,y,z)=P1·C H (x,y,z)+P2·C ins (x,y,z) Among them, LCC (x, y, z) is the life cycle cost, P1 is the present value factor of the total expenditure within the economic analysis period, and P2 is the ratio of the total expenditure within the analysis period to the initial investment.

3. The multi-objective optimization method for thermal performance of residential building envelope structure according to claim 2 is characterized by: The specific logic for obtaining the second relationship function is: The relationship between the building heating energy consumption and the carbon emissions in the building operation stage is obtained through the emission factor. The thermal performance parameters of the building envelope structure are linked to the carbon emissions in the whole life cycle through the relationship between the building heating energy consumption and the building operation stage, and the second relationship function is obtained. The relationship between the building heating energy consumption and the carbon emissions in the building operation stage is: C M (x, y, z)=[Q H (x, y, z)*PF-C P ]*N e Among them, C M (x, y, z) is the carbon emissions during the building operation phase, Q H (x, y, z) is the building heating energy consumption, PF is the carbon emission factor of heating energy, C P is the annual carbon reduction of the building green space carbon sink system, N e For the analysis period; The second relationship function is expressed as: C C (x,y,z)=CJ Z +C M (x,y,z)+C CC +C JC Among them, C C (x, y, z) is the carbon emission of the whole life cycle, C JZ is the carbon emission during the construction phase of the building, C CC is the carbon emission during the building demolition phase, C JC It is the carbon emissions from the production and transportation of building materials.

4. A multi-objective optimization method for thermal performance of residential building envelope structure according to claim 3, characterized in that: The specific logic for obtaining carbon emissions in the construction stage, building demolition stage, and building materials production and transportation stage is as follows: Calculate the carbon emissions in the building materials production stage, use 4% of the carbon emissions in the building materials production stage as the carbon emissions in the construction stage, and use 10% of the carbon emissions in the construction stage as the carbon emissions in the building demolition stage. The specific formula for obtaining carbon emissions in the building materials production stage is as follows: Among them, C SC is the carbon emission in the building materials production stage, B i is the amount of the i-th building material, BPF i is the carbon emission factor of the i-th building material, n is the number of building material types, and i is the building material type index; The specific logic for calculating carbon emissions during the production and transportation of building materials is as follows: Among them, D i is the average transportation distance of the i-th building material, T i is the carbon emission factor per unit weight of the transportation distance of the i-th type of building material.

5. The multi-objective optimization method for thermal performance of residential building envelope structure according to claim 1, characterized in that: The specific logic for constructing the thermal performance optimization function of residential building envelope structures is: taking the minimum life cycle cost and the minimum life cycle carbon emissions of the building as the optimization direction of the first relationship function and the second relationship function, respectively, to construct the thermal performance optimization function of residential building envelope structures; the specific formula of the thermal performance optimization function of residential building envelope structures is: Among them, F(x, y, z) is the thermal performance optimization function of residential building envelope structure, LCC(x, y, z) is the life cycle cost, C C (x, y, z) is the carbon emission over the entire life cycle, {x ≥ 0, y ≥ 0, z ≥ 0} is the constraint condition of the thermal performance optimization function of the residential building envelope structure, x is the thickness of the exterior wall insulation layer, y is the thickness of the roof insulation layer, and z is the thickness of the basement roof insulation layer.

6. The multi-objective optimization method for thermal performance of residential building envelope structure according to claim 1, characterized in that: The specific logic of using the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of residential building envelope structures to obtain the Pareto optimal solution set is: Within the constraints, N initial populations of thermal performance parameters of residential building envelope structures are randomly generated, the optimization function value of each individual in the initial population of thermal performance parameters of residential building envelope structures is calculated, the individuals of the population are non-dominated sorted by the optimization function value, and the crowding degree of each individual is calculated, and each individual is selected, crossed and mutated by non-dominated sorting and crowding degree, the population is updated, and the selection, crossover, mutation and population update operations are repeated until a preset number of iterations is reached, and the initial population of thermal performance parameters of residential building envelope structures that have been iterated are non-dominated sorted, and the Pareto frontier obtained by non-dominated sorting of the initial population of thermal performance parameters of residential building envelope structures that have been iterated is used as the Pareto optimal solution set {S1, S2, S3, ..., S b }; The initial population is expressed as: P0={(x1,y1,z1),(x2,y2,z2)...(x j y j ,z j )…(x N y N ,z N )} Among them, P0 is the initial population of thermal performance parameters of residential building envelope structure, x j is the thickness of the external wall insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelope structures, y j is the thickness of the roof insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelopes, z j is the thickness of the basement roof insulation layer of the jth individual in the initial population of thermal performance parameters of residential building envelope structures; The non-dominated sorting result can be expressed as: <h2 style=";text-align:left;direction:ltr">F0={F1,F2...F<h2 style=";text-align:left;direction:ltr"> L <h2 style=";text-align:left;direction:ltr">} F0 is the non-dominated sorting result, F1 is the Pareto frontier, and L is the number of non-dominated sorting layers; The specific formula for calculating congestion is: Among them, M j is the crowding degree of the jth individual, z is the number of optimization targets, and The objective function value of the zth optimized target neighboring individual for the jth individual, where is the maximum value of the zth optimization objective, is the minimum value of the zth optimization objective; The specific logic of the selection is: randomly select two individuals from the population, compare the non-dominated sorting levels between the two individuals, and select the individual with a lower non-dominated sorting level. If the two individuals are at the same non-dominated sorting level, compare the crowding of the two individuals and select the individual with a larger crowding level as the parent individual. The specific logic of the crossover operation is: randomly select two parent individuals (x C ,y C , z C ) and (x V ,y V , z V ) x xnew =αx C +(1-a)x V y ynew ay C +(1-α)y V With znew =αz C +(1-α)z V Among them, x xnew is the thickness of the external wall insulation layer after crossing, y ynew is the thickness of the roof insulation layer after crossing, z znew is the thickness of the basement roof insulation layer after crossing, and α is the crossing ratio randomly generated in the range of [0, 1]; The specific formula on which the mutation operation is based is: x new =x xnew +s x and new =and ynew +σ y With new =from znew +σ z Among them, x new is the thickness of the external wall insulation layer after variation, y new is the thickness of the roof insulation layer after variation, z new is the thickness of the basement roof insulation layer after the intersection, σ x , σ y and σ z is a random disturbance; The specific logic for updating the population is as follows: merge the mutated parent populations into a joint population, select the first N individuals to form the next generation population, and the selection principle is as follows: start selecting from the first non-dominated sorting layer, select the next non-dominated sorting layer after the non-dominated sorting layers with lower numbers are selected, and give priority to individuals with high crowding in the same non-dominated sorting layer.

7. The multi-objective optimization method for thermal performance of residential building envelope structure according to claim 1, characterized in that: The specific logic for determining the optimal thermal performance parameters of residential building envelopes is: The entropy weight-TOPSIS method is used to calculate the closeness of each solution in the Pareto optimal solution set, and the optimal thermal performance parameters of the envelope structure of residential buildings are determined according to the closeness. The specific logic for calculating the closeness is as follows: the Pareto optimal solution set, the full life cycle cost and the carbon emission constitute a joint matrix, each element in the joint matrix is ​​standardized, the indicator feature proportion of the joint matrix is ​​calculated, the indicator entropy value of the joint matrix is ​​calculated by the indicator feature proportion, and then the indicator weight is calculated by the indicator entropy value of the joint matrix. The joint matrix is ​​weighted by the indicator weight, and the positive ideal solution distance and the negative ideal solution distance corresponding to each scheme of the weighted joint matrix are calculated. The closeness of each scheme is calculated by the positive ideal solution distance and the negative ideal solution distance, and the thermal performance parameters of the envelope structure of residential buildings with the closest closeness to 1 are defined as the thermal performance parameters of the envelope structure of the optimal residential building. The joint matrix is ​​expressed as: Among them, R is the joint matrix, LCC b1 is the life cycle cost corresponding to the bth solution in the Pareto optimal solution set, C Cb2 is the full life cycle carbon emission corresponding to the bth solution in the Pareto optimal solution set, and b is the number of optimal solutions in the Pareto optimal solution set; The specific logic for calculating the characteristic weight of indicators is as follows: Among them, S uh is the characteristic weight of the indicator, a uh is the matrix element at the uth row and hth column after the joint matrix elements are normalized; The specific formula for calculating the index entropy value is: Among them, E h is the index entropy value; The specific formula for calculating the indicator weights is: Among them, w h is the indicator weight; The specific formula for weighting the joint matrix by indicator weights is: WITH uh =in h and uh Among them, Z uh is the weighted matrix element in the uth row and hth column; The specific formula for calculating the positive ideal solution distance and the negative ideal solution distance is: in, is the distance between u solutions and their ideal solutions, is the distance between u solutions and the ideal solution, is the maximum positive ideal solution, is the minimum negative ideal solution; The specific formula for calculating closeness is: Among them, V u is the closeness of the u-th solution.

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