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

Abstract

The present invention provides a multi-objective optimization method for the thermal performance of a residential building envelope structure, which relates to the technical field of green energy-saving buildings. The present invention comprises the following steps: calculating the building heating energy consumption by using building energy consumption simulation software, and fitting a multivariate nonlinear equation using MATLAB to obtain a relationship between the thermal performance parameters of the residential building envelope structure and the building heating energy consumption; establishing a relationship between the thermal performance parameters of the residential building envelope structure and the life cycle cost and the life cycle carbon emission through the building heating energy consumption; constructing a thermal performance optimization function for the residential building envelope structure with minimizing the building life cycle cost and the life cycle carbon emission as the optimization direction; solving the thermal performance optimization function for the residential building envelope structure by using a multi-objective genetic algorithm (NSGA-II) to obtain a Pareto optimal solution set, calculating the closeness 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 closeness.

Description

Technical Field

[0001] The present 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 faces increasingly severe challenges, particularly in effectively reducing building energy consumption and carbon emissions. As global climate change intensifies, traditional building designs often fail to fully consider the thermal performance of building envelopes, resulting in excessive energy consumption and increased carbon emissions during building operation. Many existing building design methods focus primarily on optimizing a single objective, such as reducing initial investment costs or improving energy efficiency, but fail to effectively integrate the relationship between economic efficiency, environmental impact, and thermal performance. This one-sided design approach not only leads to a waste of resources but also may increase the long-term financial burden on residents.

[0003] Furthermore, the importance of lifecycle analysis of buildings is growing during the design phase. Current design methods often lack systematic lifecycle assessments, making it difficult to predict and control a building's energy consumption and associated carbon emissions during its use. Therefore, an optimization approach that integrates multiple factors is needed to achieve comprehensive optimization of building design, specifically finding the optimal balance between the thermal performance, economic efficiency, and environmental friendliness of the building envelope.

[0004] In the prior art, publication number CN114912788A discloses an evaluation method and system for energy-saving technology solutions for ultra-low energy residential buildings. The method comprises the following steps: establishing a benchmark building model of a target project building to be evaluated based on energy-saving design standards for residential buildings, and using building energy consumption simulation software to calculate the benchmark building energy consumption corresponding to the benchmark building model; combining historical building energy-saving data and general ultra-low energy consumption technology factors, optimizing and combining multiple ultra-low energy consumption technology optimization solutions through a multi-objective optimization method, and solving the domain to obtain multiple ultra-low energy consumption technology optimization solutions, and calculating the design building energy consumption of each ultra-low energy consumption technology optimization solution; obtaining a comprehensive energy-saving rate corresponding to each ultra-low energy consumption technology optimization solution based on the benchmark building energy consumption and the design building energy consumption, and selecting the ultra-low energy consumption technology optimization solution with the largest comprehensive energy-saving rate as the optimal evaluation technology 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 comprehensive economic and environmental benefits of the building's life cycle being overlooked during the optimization process, resulting in the final selected technical solution being suboptimal 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, lacking in-depth analysis of the actual conditions of specific projects. This can lead to insufficient applicability and effectiveness of the optimization solution under specific conditions, thereby affecting the actual energy-saving effects of the building. In addition, while existing multi-objective optimization methods can generate multiple solutions, the lack of clear and scientific decision-making support during the solution selection process can result in 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 form the prior art that is already known to a person 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 steps:

[0009] Step 1: Design an energy consumption simulation calculation plan using the uniform design method, calculate building heating energy consumption data using 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 and the building heating energy consumption; the thermal performance parameters of the residential building envelope 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;

[0010] Step 2: Obtain the relationship between building heating energy consumption and annual building heating operating costs, as well as the relationship between initial insulation investment costs and thermal performance parameters of residential building envelopes, and establish a relationship between the thermal performance parameters of residential building envelopes and the full life cycle costs to obtain a first relationship function; the full life cycle costs are calculated from the annual building heating operating costs and the initial insulation investment costs;

[0011] Step 3: Obtain the relationship between building heating energy consumption and carbon emissions during the building operation phase through emission factors. Establish a relationship between the thermal performance parameters of the building envelope and the carbon emissions during the entire life cycle through this relationship to obtain a second relationship function. The carbon emissions during the entire life cycle include carbon emissions during the building construction phase, carbon emissions during the building demolition phase, and carbon emissions during the building material production and transportation phase.

[0012] Step 4: Taking the minimization of the building's life cycle cost and life cycle carbon emissions 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 based on the closeness.

[0015] Furthermore, the specific logic for obtaining the first relationship function is as follows: obtaining 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 the residential building envelope. The thermal performance parameters of the residential building envelope are linked to 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:

[0016]

[0017] in, The annual operating cost of heating the building, Energy consumption for heating buildings, is the energy price, The fuel has low calorific value. is the boiler operating efficiency, For outdoor pipe network transmission efficiency, is the thickness of the exterior wall insulation layer, is the thickness of the roof insulation layer, 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]

[0020] in, The initial investment cost for building insulation, is the exterior wall area, is the roof area, is the basement ceiling area, The unit price of exterior wall insulation materials is The unit price of roof insulation materials is The unit price of basement roof insulation material; For the exterior wall construction costs, For roof construction costs, Cost of basement roof construction;

[0021] The first relation function is expressed as:

[0022]

[0023] in, is the total life cycle cost, is the present value factor of total expenditure within the economic analysis period, It is the ratio of the total amount of funds spent during the analysis period to the initial investment.

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

[0025]

[0026] in, is the carbon emissions during the building operation phase, Energy consumption for heating buildings, is the carbon emission factor of heating energy, The annual carbon reduction of the building green space carbon sink system, For the analysis period;

[0027] The second relationship function is expressed as:

[0028]

[0029] in, For the whole life cycle carbon emissions, Carbon emissions during the building construction phase, Carbon emissions from the building demolition phase, It refers to the carbon emissions during the production and transportation of building materials.

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

[0031]

[0032] in, is the carbon emissions during the building materials production stage, For the The amount of building materials used, For the Carbon emission factors of various building materials, is the number of building material types, Index of building material types;

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

[0034]

[0035] in, For the Average transportation distance of various building materials, For the Carbon emission factors per unit weight of the transport distance of the building materials;

[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, 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:

[0037]

[0038] in, is the thermal performance optimization function of residential building envelope structure, is the total life cycle cost, For the whole life cycle carbon emissions, is the constraint condition of the thermal performance optimization function of residential building envelope structure, is the thickness of the exterior wall insulation layer, is the thickness of the roof insulation layer, The thickness of the basement roof insulation layer.

[0039] Furthermore, the specific logic underlying the use of the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of residential building envelope structures and obtain the Pareto optimal solution set is as follows: within the constraints, randomly generate N initial residential building envelope structure thermal performance parameter populations, calculate the optimization function value of each individual in the initial residential building envelope structure thermal performance parameter population, perform non-dominated sorting of the population individuals according to the optimization function value, and calculate the crowding degree of each individual, perform selection, crossover and mutation operations on each individual according to 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, perform non-dominated sorting of the initial residential building envelope structure thermal performance parameter population after the iteration, and use the Pareto frontier obtained by non-dominated sorting of the initial residential building envelope structure thermal performance parameter population after the iteration as the Pareto optimal solution set of the residential building envelope structure thermal performance optimization function. ;

[0040] The initial population is expressed as:

[0041]

[0042] in, is the initial population of thermal performance parameters of residential building envelope structures, is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the exterior wall insulation layer of each individual is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the roof insulation layer of each individual is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the basement roof insulation layer for each individual;

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

[0044]

[0045] is the non-dominated sorting result, is the Pareto frontier, is the number of non-dominated sorting levels;

[0046] The specific formula for calculating congestion is:

[0047]

[0048] in, For the The crowding of individuals, To optimize the target number, and for The individual Optimize the objective function value of the target adjacent individuals, where , For the The maximum value of the optimization objectives, For the The minimum value of the optimization objectives;

[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 the 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 the larger crowding level as the parent individual.

[0050] The specific logic of the crossover operation is: randomly select two parent individuals and

[0051]

[0052]

[0053]

[0054] in, is the thickness of the exterior wall insulation layer after crossing, is the thickness of the roof insulation layer after crossing, is the thickness of the basement roof insulation layer after crossing, The crossover ratio is randomly generated in the range [0,1];

[0055] The specific formula for the mutation operation is:

[0056]

[0057]

[0058]

[0059] in, is the thickness of the exterior wall insulation layer after variation, is the thickness of the roof insulation layer after variation, is the thickness of the basement roof insulation layer after crossing, , and is a random disturbance;

[0060] The specific logic of updating the population is: the mutated parent Merge into a joint population, select the first N individuals to form the next generation population, and the selection principle is The selection starts from the first non-dominated sorting layer. After the non-dominated sorting layers with lower numbers are selected, the next non-dominated sorting layer is selected. Individuals with higher congestion in the same non-dominated sorting layer are given priority.

[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 residential building envelope structures are determined according to the closeness. The specific logic for calculating the closeness is as follows: The Pareto optimal solution set, life cycle costs and carbon emissions are combined into a joint matrix, each element in the joint matrix is ​​standardized, and the characteristic weight of the joint matrix is ​​calculated. The index entropy value of the joint matrix is ​​calculated by the index feature proportion, and then the index weight is calculated by the index entropy value of the joint matrix. The joint matrix is ​​weighted by the index weight, and the positive ideal solution distance and 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. The thermal performance parameter of the residential building envelope structure with the closest closeness to 1 is defined as the optimal thermal performance parameter of the residential building envelope structure

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

[0063] in, is the joint matrix, is the Pareto optimal solution set The life cycle cost corresponding to each solution is is the Pareto optimal solution set The full life cycle carbon emissions corresponding to each solution are: is the number of optimal solutions in the Pareto optimal solution set;

[0064] The specific logic for calculating the characteristic weight of indicators is as follows:

[0065]

[0066] in, is the characteristic weight of the indicator, The first element after the joint matrix is ​​normalized Rank Matrix elements of columns;

[0067] The specific formula for calculating the index entropy value is:

[0068]

[0069] in, is the index entropy value;

[0070] The specific formula for calculating indicator weights is:

[0071]

[0072] in, is the indicator weight;

[0073] The specific formula for weighting the joint matrix by indicator weight is:

[0074]

[0075] in, After weighting Rank Matrix elements of columns;

[0076] The specific formula for calculating the positive ideal solution distance and the negative ideal solution distance is:

[0077]

[0078]

[0079]

[0080]

[0081] in, for The ideal solution distance is for The distance between the solution and the ideal solution is is the maximum positive ideal solution, is the minimum negative ideal solution;

[0082] The specific formula for calculating closeness is:

[0083]

[0084] in, For the Compared with the prior art, the present invention has the following advantages:

[0085] This invention not only fully considers the relationship between the thermal performance, lifecycle costs, and carbon emissions of the building envelope during the building design phase, but also establishes a scientific optimization model to comprehensively evaluate the impact of different thermal performance parameters on the building's energy efficiency and environmental impact. Using the multi-objective genetic algorithm NSGA-II for optimization, it effectively generates a series of Pareto optimal solutions, enabling designers to scientifically balance different objectives and ensure that the final selected solution achieves the optimal balance between energy conservation, economic, and environmental benefits.

[0086] 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, ensuring that the best solution selected 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, the 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, thereby improving resource utilization efficiency and environmental protection awareness, and achieving sustainable development goals. . BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 Schematic diagram of the overall method of the present invention.

[0088] Figure 2 Schematic diagram of the Pareto optimal solution set when the heating heat source type is a coal boiler.

[0089] Figure 3 Schematic diagram of the Pareto optimal solution set when the heating heat source type is a gas boiler.

[0090] Figure 4 Schematic diagram of the Pareto optimal solution set when the heating heat source type is an air source heat pump.

[0091] Figure 5 Schematic diagram of the impact of life cycle cost weight on the life cycle cost of the optimal solution.

[0092] Figure 6 Schematic diagram of the impact of life cycle cost weights on the life cycle carbon emissions of the optimal solution. DETAILED DESCRIPTION

[0093] 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 with reference to specific embodiments.

[0094] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "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 position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0095] Example:

[0096] See also Figure 1-6 , the present invention provides a technical solution:

[0097] A multi-objective optimization method for thermal performance of residential building envelope structures, comprising the following steps:

[0098] Step 1: Design an energy consumption simulation calculation plan using the uniform design method, calculate building heating energy consumption data using 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 and the building heating energy consumption; the thermal performance parameters of the residential building envelope 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;

[0099] 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 relationship between the thermal performance parameters of residential building envelope structures and life cycle costs and life cycle carbon emissions.

[0100] The uniform design method was used to design an energy consumption simulation calculation plan. The building heating energy consumption was calculated using the DeST software. The building area, building height, wall-to-window ratio, average winter temperature, average winter humidity, building thermal performance parameters, and the thickness of the exterior wall insulation layer, roof insulation layer thickness, and basement ceiling insulation layer were input into the DeST software to obtain the building heating energy consumption. The thermal performance parameters of the residential building envelope and the calculated building heating energy consumption were compiled to obtain Table 1. The building exterior wall insulation layer thickness, roof insulation layer thickness, basement ceiling insulation layer thickness, and building heating energy consumption were input into the MATLAB software. The complex relationship between these parameters and energy consumption was fitted using the multivariate nonlinear fitting tool in the MATLAB software. Through fitting, a multivariate nonlinear equation was obtained;

[0101]

[0102] Table 1 Energy consumption simulation calculation scheme and heating energy consumption

[0103] Step 2: Obtain the relationship between building heating energy consumption and annual building heating operating costs, as well as the relationship between initial insulation investment costs and thermal performance parameters of residential building envelopes, and establish a relationship between the thermal performance parameters of residential building envelopes and the full life cycle costs to obtain a first relationship function; the full life cycle costs are calculated from the annual building heating operating costs and the initial insulation investment costs;

[0104] The specific logic for obtaining the first relationship function is as follows: obtaining 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. The thermal performance parameters of residential building envelopes are linked to 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:

[0105]

[0106] in, The annual operating cost of heating the building, Energy consumption for heating buildings, is the energy price, The fuel has low calorific value. is the boiler operating efficiency, For outdoor pipe network transmission efficiency, is the thickness of the exterior wall insulation layer, is the thickness of the roof insulation layer, is the thickness of the basement roof insulation layer; the annual operating cost of building heating It reflects the annual heating cost of a building and provides an important basis for calculating the full life cycle cost.

[0107] The relationship between the initial investment cost of thermal insulation and the thermal performance parameters of residential building envelope structures is:

[0108]

[0109] in, The initial investment cost for building insulation, is the exterior wall area, is the roof area, is the basement ceiling area, The unit price of exterior wall insulation materials is The unit price of roof insulation materials is The unit price of basement roof insulation material; For the exterior wall construction costs, For roof construction costs, Cost of basement roof construction; 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.

[0110] The first relation function is expressed as:

[0111]

[0112] in, is the total life cycle cost, is the present value factor of total expenditure within the economic analysis period, The ratio of total expenditure to initial investment within the analysis period. Reflects the full life cycle of certain residential building envelope thermal performance parameters , the life cycle cost is optimized It is an important indicator of the thermal performance parameters of building envelope structures. The size of this indicator can directly reflect the quality of the thermal performance of residential building envelope structures.

[0113] Step 3: Obtain the relationship between building heating energy consumption and carbon emissions during the building operation phase through emission factors. Establish a relationship between the thermal performance parameters of the building envelope and the carbon emissions during the entire life cycle through this relationship to obtain a second relationship function. The carbon emissions during the entire life cycle include carbon emissions during the building construction phase, carbon emissions during the building demolition phase, and carbon emissions during the building material production and transportation phase.

[0114] The specific logic for obtaining the second relationship function is as follows: the relationship between building heating energy consumption and carbon emissions during the building operation phase is obtained through the emission factor, and the thermal performance parameters of the building envelope structure are linked to the carbon emissions during the entire life cycle through the relationship between building heating energy consumption and carbon emissions during the building operation phase to obtain the second relationship function; the relationship between building heating energy consumption and carbon emissions during the building operation phase is:

[0115]

[0116] in, is the carbon emissions during the building operation phase, Energy consumption for heating buildings, is the carbon emission factor of heating energy, The annual carbon reduction of the building green space carbon sink system, For the analysis period;

[0117] The second relationship function is expressed as:

[0118]

[0119] in, For the whole life cycle carbon emissions, Carbon emissions during the building construction phase, Carbon emissions from the building demolition phase, It refers to the carbon emissions during the production and transportation of building materials.

[0120] Since the building demolition design during the construction phase involves multiple operations of various facilities, it is difficult to directly calculate the carbon emissions of the corresponding phases through formulaic calculations. Therefore, this embodiment uses an estimation method to obtain the carbon emissions of the construction phase and the building demolition phase. The specific logic for obtaining the carbon emissions of the construction phase, the building demolition phase, and the building materials production and transportation phase is as follows: calculate the carbon emissions of the building materials production phase, 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.

[0121] The specific formula for obtaining carbon emissions during the building materials production stage is:

[0122]

[0123] in, is the carbon emissions of building materials during the production phase, For the The amount of building materials used, For the Carbon emission factors of various building materials, is the number of building material types, Index of building materials types; carbon emissions during the building materials production stage 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 The generation of can provide an important basis for estimating carbon emissions during the construction phase and carbon emissions during the building demolition phase.

[0124] The specific logic for calculating carbon emissions from the production and transportation of building materials is as follows:

[0125]

[0126] in, For the Average transportation distance of various building materials, For the Carbon emission factors per unit weight of the transport distance of the building materials;

[0127] Step 4: Taking the minimization of the building's life cycle cost and life cycle carbon emissions 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;

[0128] According to the thermal design specifications for civil buildings, the thermal performance of building envelope structures in extremely cold and cold areas is mainly to meet winter insulation needs. The thickness of the insulation layer can be determined according to the winter heating conditions. As the thickness of the insulation layer increases, the annual operating costs of building heating and carbon emissions during the operation phase decrease. When the building's full life cycle costs and carbon emissions are minimized, the thickness of the insulation layer of the building envelope structure is the optimal insulation layer thickness, and it has the best thermal performance parameters of the envelope structure. This is used to determine the optimization direction of the thermal performance of residential building envelope structures and construct a thermal performance optimization function for residential building envelope structures.

[0129] 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 and second relationship functions, 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:

[0130]

[0131] in, is the thermal performance optimization function of residential building envelope structure, is the total life cycle cost, For the whole life cycle carbon emissions, is the constraint condition of the thermal performance optimization function of residential building envelope structure, is the thickness of the exterior wall insulation layer, is the thickness of the roof insulation layer, The thickness of the basement roof insulation layer.

[0132] 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;

[0133] The specific logic for using the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of residential building envelope structures and obtain the Pareto optimal solution set is as follows: within the constraint range, randomly generate N initial residential building envelope structure thermal performance parameter populations, calculate the optimization function value of each individual in the initial residential building envelope structure thermal performance parameter population, non-dominated sort the population individuals according to the optimization function value, and calculate the crowding degree of each individual, select each individual according to the non-dominated sorting and crowding degree, perform crossover and mutation operations, 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 residential building envelope structure thermal performance parameter population after the iteration, and use the Pareto frontier obtained by non-dominated sorting of the initial residential building envelope structure thermal performance parameter population after the iteration as the Pareto optimal solution set of the residential building envelope structure thermal performance optimization function. ; Algorithm related calculation parameter settings are shown in Table 2

[0134]

[0135] Table 2 NSGA-II algorithm related calculation parameters

[0136] The initial population is expressed as:

[0137]

[0138] in, is the initial population of thermal performance parameters of residential building envelope structures, is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the exterior wall insulation layer of each individual is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the roof insulation layer of each individual is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the basement roof insulation layer for each individual;

[0139] The non-dominated sorting result can be expressed as:

[0140]

[0141] is the non-dominated sorting result, is the Pareto frontier, is the number of non-dominated sorting levels;

[0142] 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 the other 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, thus obtaining the non-dominated sorting result.

[0143] The specific formula for calculating congestion is:

[0144]

[0145] in, For the The crowding of individuals, To optimize the target number, and for The individual Optimize the objective function value of the target adjacent individuals, where , For the The maximum value of the optimization objectives, For the The minimum value of the optimization objective; The crowding degree of each individual Reflect the The sparseness of each individual relative to its neighbors in the target space can maintain the diversity of the population and prevent the concentration of solutions in certain specific areas. By prioritizing solutions with large crowding distances, the algorithm can be ensured to explore the entire solution space instead of converging prematurely on a local area.

[0146] 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 the 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 the larger crowding level as the parent individual.

[0147] The specific logic of the crossover operation is: randomly select two parent individuals and

[0148]

[0149]

[0150]

[0151] in, is the thickness of the exterior wall insulation layer after crossing, is the thickness of the roof insulation layer after crossing, is the thickness of the basement roof insulation layer after crossing, The crossover ratio is randomly generated in the range [0,1];

[0152] The specific formula on which the mutation operation is based is:

[0153]

[0154]

[0155]

[0156] in, is the thickness of the exterior wall insulation layer after variation, is the thickness of the roof insulation layer after variation, is the thickness of the basement roof insulation layer after crossing, , and is a random disturbance;

[0157] The specific logic for updating the population is: 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: start 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 higher congestion in the same non-dominated sorting layer.

[0158] 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;

[0159] 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 based on the closeness.

[0160] 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 full 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 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 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 parameter of the residential building envelope structure with the closeness closest to 1 is defined as the optimal thermal performance parameter of the residential building envelope structure.

[0161] The joint matrix is ​​expressed as:

[0162]

[0163] in, is the joint matrix, is the Pareto optimal solution set The life cycle cost corresponding to each solution is is the Pareto optimal solution set The full life cycle carbon emissions corresponding to each solution are: is the number of optimal solutions in the Pareto optimal solution set; joint matrix Reflect the performance of each plan in terms of life cycle cost indicators and life cycle carbon emission indicators; provide important basis for the subsequent selection of the best plan.

[0164] The specific logic for calculating the characteristic weight of indicators is as follows:

[0165]

[0166] in, is the characteristic weight of the indicator, The first element after the joint matrix is ​​normalized Rank Matrix elements of columns;

[0167] Indicator feature proportion 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.

[0168]

[0169] in, is the index entropy value;

[0170] Indicator entropy 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.

[0171] The specific formula for calculating indicator weights is:

[0172]

[0173] in, is the indicator weight;

[0174] Indicator weight 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 decision-making.

[0175] The specific formula for weighting the joint matrix by indicator weight is:

[0176]

[0177] in, After weighting Rank Matrix elements of columns;

[0178] After weighting, the values ​​in the joint matrix not only represent the performance of each plan in terms of the life cycle cost index and the life cycle carbon emission index, but also reflect the importance of different indicators.

[0179] The specific formula for calculating the positive ideal solution distance and the negative ideal solution distance is:

[0180]

[0181]

[0182]

[0183]

[0184] in, for The ideal solution distance is for The distance between the solution and the ideal solution is 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-case scenario. The ideal solution distance reflect The Euclidean distance between each solution and the maximum positive ideal solution; Negative ideal solution distance reflect The Euclidean distance between each solution and the minimum positive ideal solution; The ideal solution distance and Negative ideal solution distance 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.

[0185] The specific formula for calculating closeness is:

[0186]

[0187] in, For the The closeness of the scheme. The closeness of the scheme Reflect the The degree of closeness of each solution to the maximum positive ideal solution 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.

[0188] During the optimization, the life cycle cost and carbon emissions 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. The weights of the life cycle carbon emissions target are changed 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 emissions is shown in the figure below. Figure 5 and Figure 6 .

[0189] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0190] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0191] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0192] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A multi-objective optimization method for thermal performance of residential building envelope structures, characterized in that: The specific steps include: Step 1: Design an energy consumption simulation calculation plan using the uniform design method, calculate building heating energy consumption data using 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 and the building heating energy consumption. The thermal performance parameters of the residential building envelope 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 building heating energy consumption and annual building heating operating costs, as well as the relationship between initial insulation investment costs and thermal performance parameters of residential building envelopes, and establish a relationship between the thermal performance parameters of residential building envelopes and the full life cycle costs to obtain a first relationship function; the full life cycle costs are calculated from the annual building heating operating costs and the initial insulation investment costs; Step 3: Obtain the relationship between building heating energy consumption and carbon emissions during the building operation phase through emission factors. Establish a relationship between the thermal performance parameters of the building envelope and the carbon emissions during the entire life cycle through this relationship to obtain a second relationship function. The carbon emissions during the entire life cycle include carbon emissions during the building construction phase, carbon emissions during the building demolition phase, and carbon emissions during the building material production and transportation phase. Step 4: Taking the minimization of the building's life cycle cost and life cycle carbon emissions 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 based on the closeness; 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 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 full life cycle cost and the carbon emission constitute a joint matrix, each element in the joint matrix is ​​standardized, the index characteristic proportion of the joint matrix is ​​calculated, the index entropy value of the joint matrix is ​​calculated by the index characteristic proportion, and the index weight is calculated by the index entropy value of the joint matrix. The joint matrix is ​​weighted by the index weight, and 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 parameter of the residential building envelope structure with the closeness closest to 1 is defined as the optimal thermal performance parameter of the residential building envelope structure. The joint matrix is ​​expressed as: in, is the joint matrix, is the Pareto optimal solution set The life cycle cost corresponding to each solution is is the Pareto optimal solution set The full life cycle carbon emissions corresponding to each solution are: 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: in, is the characteristic weight of the indicator, The first element after the joint matrix is ​​normalized Rank Matrix elements of columns; The specific formula for calculating the index entropy value is: in, is the index entropy value; The specific formula for calculating indicator weights is: in, is the indicator weight; The specific formula for weighting the joint matrix by indicator weight is: in, After weighting Rank Matrix elements of columns; The specific formula for calculating the positive ideal solution distance and the negative ideal solution distance is: in, for The ideal solution distance is for The distance between the solution and the ideal solution is is the maximum positive ideal solution, is the minimum negative ideal solution; The specific formula for calculating closeness is: in, For the The closeness of the plan.

2. The multi-objective optimization method for thermal performance of residential building envelope structures according to claim 1, characterized in that: The specific logic for obtaining the first relational function is: The relationship between building heating energy consumption and annual building heating operating costs, the relationship between initial insulation investment cost and thermal performance parameters of residential building envelope structures, and the relationship between thermal performance parameters of residential building envelope structures and life cycle costs were established to obtain the first relationship function; the relationship between building heating energy consumption and annual building heating operating costs is: in, The annual operating cost of heating the building, Energy consumption for heating buildings, is the energy price, The fuel has low calorific value. is the boiler operating efficiency, For outdoor pipe network transmission efficiency, is the thickness of the exterior wall insulation layer, is the thickness of the roof insulation layer, 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: in, The initial investment cost for building insulation, is the exterior wall area, is the roof area, is the basement ceiling area, The unit price of exterior wall insulation materials is The unit price of roof insulation materials is The unit price of basement roof insulation material; For the exterior wall construction costs, For roof construction costs, Cost of basement roof construction; The first relation function is expressed as: in, is the total life cycle cost, is the present value factor of total expenditure within the economic analysis period, It is the ratio of the total amount of funds spent during the analysis period to the initial investment.

3. The multi-objective optimization method for thermal performance of residential building envelope structures according to claim 2, characterized in that: The specific logic for obtaining the second relationship function is: The relationship between building heating energy consumption and carbon emissions during the building operation phase is obtained through the emission factor. The thermal performance parameters of the building envelope structure are linked to the carbon emissions during the entire life cycle through the relationship between building heating energy consumption and carbon emissions during the building operation phase, and the second relationship function is obtained. The relationship between building heating energy consumption and carbon emissions during the building operation phase is: in, is the carbon emissions during the building operation phase, Energy consumption for heating buildings, is the carbon emission factor of heating energy, The annual carbon reduction of the building green space carbon sink system, For the analysis period; The second relationship function is expressed as: in, For the whole life cycle carbon emissions, Carbon emissions during the building construction phase, Carbon emissions from the building demolition phase, Carbon emissions from the production and transportation of building materials.

4. The multi-objective optimization method for thermal performance of residential building envelope structures according to claim 3, characterized in that: The specific logic for obtaining carbon emissions during the construction phase, the demolition phase, and the production and transportation of building materials is as follows: calculate the carbon emissions during the production of building materials, use 4% of the carbon emissions during the production of building materials as the carbon emissions during the construction phase, and use 10% of the carbon emissions during the construction phase as the carbon emissions during the demolition phase. The specific formula for obtaining carbon emissions during the production of building materials is as follows: in, is the carbon emissions during the building materials production stage, For the The amount of building materials used, For the Carbon emission factors of various building materials, is the number of building material types, Index of building material types; The specific logic for calculating carbon emissions from the production and transportation of building materials is as follows: in, For the Average transportation distance of various building materials, For the Carbon emission factors per unit weight of the transportation distance of various building materials.

5. The multi-objective optimization method for thermal performance of residential building envelope structures according to claim 1, characterized in that: 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 and second relationship functions, 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: in, is the thermal performance optimization function of residential building envelope structure, is the total life cycle cost, For the whole life cycle carbon emissions, is the constraint condition of the thermal performance optimization function of residential building envelope structure, is the thickness of the exterior wall insulation layer, is the thickness of the roof insulation layer, The thickness of the basement roof insulation layer.

6. The multi-objective optimization method for thermal performance of residential building envelope structures according to claim 1, characterized in that: The specific logic behind using the multi-objective genetic algorithm NSGA-II to solve the thermal performance optimization function of residential building envelope structures and 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 in the population are non-dominated sorted by the optimization function value, and the crowding degree of each individual is calculated, 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, the initial population of thermal performance parameters of residential building envelope structures after the iteration is 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 after the iteration is used as the Pareto optimal solution set of the thermal performance optimization function of the residential building envelope structure. ; The initial population is expressed as: in, is the initial population of thermal performance parameters of residential building envelope structures, is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the exterior wall insulation layer of each individual is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the roof insulation layer of each individual is the first in the population of thermal performance parameters of the initial residential building envelope structure The thickness of the basement roof insulation layer for each individual; The non-dominated sorting result can be expressed as: is the non-dominated sorting result, is the Pareto frontier, is the number of non-dominated sorting levels; The specific formula for calculating congestion is: in, For the The crowding of individuals, To optimize the target number, and For the Individual Optimize the objective function value of the target adjacent individuals, where , For the The maximum value of the optimization objectives, For the The minimum value of the optimization objectives; 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 the 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 the larger crowding level as the parent individual. The specific logic of the crossover operation is: randomly select two parent individuals and in, is the thickness of the exterior wall insulation layer after crossing, is the thickness of the roof insulation layer after crossing, is the thickness of the basement roof insulation layer after crossing, The crossover ratio is randomly generated in the range [0,1]; The specific formula on which the mutation operation is based is: in, is the thickness of the exterior wall insulation layer after variation, is the thickness of the roof insulation layer after variation, is the thickness of the basement roof insulation layer after crossing, , and is a random disturbance; The specific logic for updating the population is: 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: start 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 higher congestion in the same non-dominated sorting layer.

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