Zero-carbon building multi-objective optimization method

Abstract

The invention provides a zero-carbon building multi-objective optimization method. The zero-carbon building multi-objective optimization method comprises the steps of obtaining a researched building and a plurality of design parameters corresponding to the researched building; the design parameters, matched with the physical characteristics of the researched building, in the multiple design parameters are defined as physical design parameters, and the design parameters except the physical design parameters are defined as technical design parameters; according to the shape design parameters, obtaining a shape design optimal solution of the researched building; and setting a plurality of technical design stage optimization objectives, and obtaining a zero-carbon building multi-objective optimization optimal solution of the researched building according to the shape design optimal solution and the technical design parameters. According to the method, a zero-carbon building design stage is divided into a body design stage and a technical design stage, and an optimal zero-carbon building body design and technical design scheme is obtained based on a multi-objective optimization module.

Description

Technical Field

[0001] The present invention relates to the technical field of building design, and in particular to a zero-carbon building multi-objective optimization method, electronic equipment, storage medium and program product. Background Art

[0003] Traditional building energy conservation and carbon reduction design often focuses on the thermal performance of the envelope structure and the performance of the electromechanical equipment system. It mainly relies on electromechanical and green building consulting engineers in the later stage of design to make partial modifications to the plan to achieve performance goals, while ignoring the important role of passive form design led by architects in the early stage of design in energy conservation and carbon reduction. In addition, traditional energy conservation and carbon reduction design relies heavily on the guidance of standards or specifications. To improve performance, designers usually rely on experience to superimpose various energy-saving design techniques or technologies, which makes it difficult to fully consider their economic efficiency. Conventional experience-oriented design and simulation trial and error design methods have certain limitations due to their strong randomness and excessive reliance on subjective judgment. Summary of the invention

[0004] The purpose of the present invention is to provide a zero-carbon building multi-objective optimization method, electronic equipment, storage medium and program product, which divides the zero-carbon building design stage into a form design stage and a technical design stage, and obtains the optimal zero-carbon building form design and technical design scheme based on a multi-objective optimization module and a multi-factor decision-making module.

[0005] To achieve the above-mentioned purpose, the present invention provides a zero-carbon building multi-objective optimization method, comprising the following steps: obtaining a plurality of design parameters of a studied building and corresponding to the studied building; defining the design parameters that match the shape characteristics of the studied building among the plurality of design parameters as shape design parameters, and defining the design parameters other than the shape design parameters as technical design parameters; obtaining the optimal solution for the shape design of the studied building according to the shape design parameters; setting a plurality of optimization targets for the technical design stage, and obtaining the optimal solution for the zero-carbon building multi-objective optimization of the studied building according to the optimal solution for the shape design and the technical design parameters.

[0006] Optionally, obtaining the optimal solution for multi-objective optimization of the zero-carbon building of the studied building according to the optimal solution for the shape design and the technical design parameters specifically includes: performing energy consumption simulation according to the optimal solution for the shape design and the technical design parameters to obtain multiple first simulation arrays corresponding to the optimization objectives of the technical design stage; based on a genetic algorithm, screening out multiple first Pareto arrays from the multiple first simulation arrays; performing multi-factor decision analysis according to the multiple first Pareto arrays, and taking the arrays ranked top several in the analysis results as the optimal solution for multi-objective optimization of the zero-carbon building of the studied building.

[0007] Optionally, before performing a multi-factor decision analysis based on a plurality of the first Pareto arrays, the first Pareto arrays that do not meet the design standard specifications are eliminated.

[0008] Optionally, obtaining the optimal solution for the shape design of the building under study based on the shape design parameters specifically includes: setting multiple optimization targets in the shape design stage, and performing energy consumption simulation according to the shape design parameters to obtain multiple second simulation arrays corresponding to the optimization targets in the shape design stage; based on a genetic algorithm, screening out multiple second Pareto arrays from the multiple second simulation arrays; performing a multi-factor decision analysis based on the multiple second Pareto arrays, and taking the arrays ranked top several in the analysis results as the optimal solution for the shape design.

[0009] Optionally, the optimization target in the shape design stage includes the maximum photovoltaic installed area.

[0010] Optionally, the genetic algorithm includes a NSGA-II algorithm.

[0011] Optionally, the multi-factor decision analysis includes TOPSIS analysis.

[0012] To achieve the above-mentioned purpose, the present invention also provides an electronic device, which includes: a memory storing a computer program; a processor, which is communicatively connected to the memory and executes any one of the zero-carbon building multi-objective optimization methods described above when the computer program is called; and a display, which is communicatively connected to the processor and the memory and is used to display a GUI interactive interface related to the zero-carbon building multi-objective optimization method.

[0013] To achieve the above objectives, the present invention further provides a readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned zero-carbon building multi-objective optimization methods.

[0014] To achieve the above objectives, the present invention also provides a computer program product, which, when executed by a processor, implements any of the above-mentioned zero-carbon building multi-objective optimization methods.

[0015] The zero-carbon building multi-objective optimization method, electronic device, storage medium and program product provided by the present invention have the following beneficial effects:

[0016] The present invention provides a zero-carbon building multi-objective optimization method, comprising the following steps: obtaining the shape design parameters of the studied building according to the shape characteristics of the studied building; obtaining the technical design parameters of the studied building according to the heat generation and heat transfer characteristics of the studied building other than the shape characteristics; obtaining the shape design optimal solution of the studied building according to the shape design parameters; setting multiple technical design stage optimization targets, performing energy consumption simulation according to the shape design optimal solution and the technical design parameters, so as to obtain multiple first simulation arrays corresponding to the technical design stage optimization targets; based on a genetic algorithm, screening out multiple first Pareto arrays from the multiple first simulation arrays; performing a multi-factor decision analysis according to the multiple first Pareto arrays, and taking the arrays ranked top several in the analysis results as the zero-carbon building multi-objective optimization optimal solutions.

[0017] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0018] 1. The present invention divides the design stage into a form design stage and a technical design stage, and carries out targeted multi-objective optimization design according to the design content and related performance, which helps to integrate the zero-carbon concept throughout the entire process of architectural design, improve the contribution of form design to the goal of building energy conservation and carbon reduction, and promote the implementation of zero-carbon goals in different professional divisions of architectural design.

[0019] 2. In the face of multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithm is adopted. Compared with the conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, and can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable.

[0020] The present invention also provides an electronic device, because the electronic device is used to execute any of the above-mentioned zero-carbon building multi-objective optimization methods. Therefore, the electronic device can divide the design stage into a form design stage and a technical design stage, and perform targeted multi-objective optimization design according to the design content and related performance, which is helpful to integrate the zero-carbon concept throughout the entire process of building design, improve the contribution of form design to the building energy conservation and carbon reduction goals, and promote the implementation of zero-carbon goals in different building design professional divisions. And to achieve the multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithms is adopted. Compared with the conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, and can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable. .

[0021] The present invention also provides a readable storage medium, because the readable storage medium is used to execute any of the above-mentioned zero-carbon building multi-objective optimization methods. Therefore, the readable storage medium can divide the design stage into a form design stage and a technical design stage, and carry out targeted multi-objective optimization design according to the design content and related performance, which is helpful to integrate the zero-carbon concept throughout the entire process of architectural design, improve the contribution of form design to the goal of building energy conservation and carbon reduction, and promote the implementation of zero-carbon goals in different architectural design professional divisions. And to achieve the multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithms is adopted. Compared with the conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable.

[0022] The present invention also provides a computer program product, because the computer program product is used to execute any of the above-mentioned zero-carbon building multi-objective optimization methods. Therefore, the computer program product can divide the design stage into a form design stage and a technical design stage, and carry out targeted multi-objective optimization design according to the design content and related performance, which is helpful to integrate the zero-carbon concept throughout the entire process of architectural design, improve the contribution of form design to the goal of building energy conservation and carbon reduction, and promote the implementation of zero-carbon goals in different architectural design professional divisions. And to achieve the multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithms is adopted. Compared with conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, and can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A schematic diagram of a process flow of a zero-carbon building multi-objective optimization method provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a framework of a zero-carbon building multi-objective optimization method provided by an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of a block structure of an electronic device provided by an embodiment of the present invention;

[0026] The accompanying drawings are denoted as follows:

[0027] 101 - processor; 102 - communication interface; 103 - memory; 104 - communication bus; 105 - display. DETAILED DESCRIPTION

[0028] In order to make the purpose, advantages and features of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the drawings are all in a very simplified form and are not drawn to scale, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In addition, the structure shown in the drawings is often a part of the actual structure. In particular, the emphasis of each drawing is different, and sometimes different scales are used.

[0029] It should be understood that when an element or layer is referred to as "on...", "connected to" other elements or layers, it can be directly on other elements or layers, connected to other elements or layers, or there can be intervening elements or layers. On the contrary, when an element is referred to as "directly on...", "directly connected to" other elements or layers, there is no intervening element or layer. Although the terms first, second, third, etc. can be used to describe various elements, components, regions, layers and / or parts, these elements, components, regions, layers and / or parts should not be limited by these terms. These terms are only used to distinguish an element, component, region, layer or part from another element, component, region, layer or part. Therefore, without departing from the teachings of the present invention, the first element, component, region, layer or part discussed below can be expressed as a second element, component, region, layer or part. Spatial relationship terms such as "under...", "below", "below", "above...", "above", "above", etc., can be used here for the convenience of description to describe the relationship between an element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, the spatial relationship terms are intended to include different orientations of the devices in use and operation. For example, if the device in the accompanying drawings is turned over, then, the elements or features described as "under...", "below", "below" will be oriented to be "on" other elements or features. The device can be oriented in addition (rotated 90 degrees or other orientations) and the spatial descriptors used herein are interpreted accordingly. The purpose of the terms used herein is only to describe specific embodiments and is not intended to be a limitation of the present invention. When used herein, the singular forms of "one", "one" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the term "include" is used to determine the presence of features, steps, operations, elements and / or parts, but does not exclude the presence or addition of one or more other features, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0030] The purpose of the present invention is to provide a zero-carbon building multi-objective optimization method, electronic equipment, storage medium and program product, which divides the zero-carbon building design stage into a form design stage and a technical design stage, and obtains the optimal zero-carbon building form design and technical design scheme based on a multi-objective optimization module and a multi-factor decision-making module.

[0031] Please refer to Figure 1 , Figure 1 A flowchart of a multi-objective optimization method for a zero-carbon building provided by an embodiment of the present invention. To achieve the above-mentioned purpose, the present invention provides a multi-objective optimization method for a zero-carbon building, comprising the following steps:

[0032] Acquire a studied building and a plurality of design parameters corresponding to the studied building;

[0033] Among the plurality of design parameters, the design parameters that match the shape characteristics of the studied building are defined as shape design parameters, and the design parameters other than the shape design parameters are defined as technical design parameters;

[0034] According to the shape design parameters, obtaining the optimal solution of the shape design of the building under study;

[0035] A plurality of optimization objectives in the technical design stage are set, and the multi-objective optimization optimal solution of the zero-carbon building of the studied building is obtained according to the optimal solution of the shape design and the technical design parameters.

[0036] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0037] 1. The present invention divides the design stage into a form design stage and a technical design stage, and carries out targeted multi-objective optimization design according to the design content and related performance, which helps to integrate the zero-carbon concept throughout the entire process of architectural design, improve the contribution of form design to the goal of building energy conservation and carbon reduction, and promote the implementation of zero-carbon goals in different professional divisions of architectural design.

[0038] 2. In the face of multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithm is adopted. Compared with the conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, and can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable.

[0039] Specifically, obtaining the zero-carbon building multi-objective optimization optimal solution of the studied building according to the optimal solution of the shape design and the technical design parameters specifically includes:

[0040] Performing energy consumption simulation according to the optimal solution of the shape design and the technical design parameters to obtain a plurality of first simulation arrays corresponding to the optimization objectives of the technical design stage;

[0041] Based on a genetic algorithm, selecting a plurality of first Pareto arrays from a plurality of the first simulation arrays;

[0042] A multi-factor decision analysis is performed based on the plurality of the first Pareto arrays, and the arrays with the top several rankings in the analysis results are used as the optimal solutions for the multi-objective optimization of the zero-carbon building of the building under study.

[0043] Similarly, obtaining the optimal solution of the shape design of the studied building according to the shape design parameters specifically includes:

[0044] Setting a plurality of optimization targets in the shape design stage, and performing energy consumption simulation according to the shape design parameters to obtain a plurality of second simulation arrays corresponding to the optimization targets in the shape design stage;

[0045] Based on a genetic algorithm, selecting a plurality of second Pareto arrays from a plurality of the second simulation arrays;

[0046] Based on multiple second Pareto arrays, a multi-factor decision analysis is performed, and the arrays ranked in the top several positions in the analysis results are taken as the optimal solutions for the shape design. For intuitive reflection, a building model corresponding to the shape design parameters can be established and visually displayed at any stage of the whole process.

[0047] Please refer to Figure 2 , Figure 2A schematic diagram of the framework of a multi-objective optimization method for a zero-carbon building provided in one embodiment of the present invention. In an exemplary embodiment, the shape design parameters include plane shape factor (the ratio of the square of the perimeter of the plane shape to the plane area), building height, number of floors, window-to-wall ratio, and orientation; the technical design parameters include exterior wall heat transfer coefficient, exterior window heat transfer coefficient, roof heat transfer coefficient, exterior window comprehensive shading coefficient, air conditioning system type, air conditioning system energy efficiency, lighting power density, gas water heater thermal efficiency, elevator drive mode, and photovoltaic installed capacity per unit area. By confirming the value range of each design parameter as the range of variation of the multi-objective optimization variable, based on the value range of the shape design parameter, a plurality of first simulation arrays corresponding to the optimization objectives of the technical design stage are calculated to form the calculation material of the genetic algorithm. The value range of the shape design parameter is set according to the design conditions and engineering experience, and the value range of the technical design parameter is set according to the current specifications and product parameters. The parameter value range refers to the range of variation of the parameter value. The lower limit can be set according to the "General Specification for Energy Conservation and Renewable Energy Utilization in Buildings" (GB 55015-2021) and the "Technical Standard for Nearly Zero Energy Buildings" (GBT51350-2019), and the upper limit can be set according to the energy-saving equipment and product parameters currently on the market.

[0048] In an exemplary embodiment, the optimization goal of the physical design stage is the minimum heating and air-conditioning load per unit area of ​​the building and the maximum photovoltaic installed area, and the optimization goal of the technical design stage is the minimum annual operating carbon emissions per unit area of ​​the building and the minimum global cost increment per unit area, but it is not limited to this.

[0049] It should be noted that the genetic algorithm may include the NSGA-II algorithm. When performing multi-objective optimization based on a genetic algorithm such as NSGA-II, the data in the same array as the material should be complex and non-linear, so as to finally screen out the Pareto array. For example, a two-dimensional array is expressed as a Pareto curve in a two-dimensional coordinate system, and the Pareto array is located on the Pareto curve. Similarly, a three-dimensional array is expressed as a Pareto surface in a three-dimensional coordinate system.

[0050] The optimization targets of the shape design stage proposed by the present invention, namely, the minimum heating and air-conditioning load per unit area of ​​the building and the maximum photovoltaic installed area, can form multiple two-dimensional arrays. Each two-dimensional array has two numbers, one is the minimum heating and air-conditioning load per unit area of ​​the building, and the other is the maximum photovoltaic installed area. Different design parameter combinations can calculate different minimum heating and air-conditioning loads per unit area of ​​the building, and also calculate different maximum photovoltaic installed areas correspondingly, thereby forming multiple two-dimensional arrays. With such a setting, the difference from the prior art is further reflected in that the present invention proposes for the first time to take the maximum photovoltaic installed area as the optimization target of the shape design stage. In the shape design not involving photovoltaics, generally, the smaller the building surface area, the smaller the heat exchange area with the outside world, and the better the building energy-saving effect. However, photovoltaics are devices that use light to generate energy. The larger the building surface area, the larger its installation area, the more renewable energy is generated, and the more helpful it is to achieve the zero-carbon target of the building. This shows that when photovoltaics are taken into consideration, the influence of the building surface area on the zero-carbon target is complex and nonlinear. Therefore, when the optimization target of the shape design stage includes the maximum photovoltaic installed area, it can be effectively applied to the genetic algorithm to obtain the Pareto array. It should be understood that other forms of optimization objectives may also be set as arrays to be applied to genetic algorithms such as NSGA-II, which will not be described in detail here.

[0051] As for the optimization goals in the technical design phase, the setting of optimization goals in the technical design phase is mainly based on the following two points:

[0052] 1. Different technical strategies adopted in the technical design stage directly affect the building energy consumption and carbon emissions. Therefore, the annual operating carbon emissions per unit area of ​​the building are taken as the optimization target.

[0053] 2. The incremental costs involved in different technical strategies vary greatly, which plays a key role in determining the economic feasibility of zero-carbon building projects. Therefore, the global incremental cost per unit area is taken as the second optimization objective.

[0054] Other ways of setting the optimization target in the technical design phase of the present invention are not limited to the above two points.

[0055] Furthermore, the multi-factor decision analysis includes TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) analysis, and the calculation material of the TOPSIS analysis is the first Pareto array or the second Pareto array.

[0056] It should be understood that in the technical design stage, each parameter and its calculation results should be subject to the constraints of design standards and specifications. If they are violated, they should be eliminated midway and not participate in the final calculation. Based on this, the present invention further proposes the following technical solution: before performing a multi-factor decision analysis based on multiple first Pareto arrays, the first Pareto arrays that do not meet the design standards and specifications are eliminated.

[0057] According to a zero-carbon building multi-objective optimization method provided by the present invention, based on the Pareto solution set of building form design, a multi-factor decision analysis method TOPSIS under multiple objectives is used to calculate the optimal solution of form design under different objective weights. The multi-factor decision analysis method calculates the Euclidean distance between the Pareto solution and the ideal solution to rank them, and the calculation steps are as follows:

[0058] Step 1: Establish a multi-factor decision matrix:

[0059]

[0060] Among them, n represents the evaluation object and m represents the evaluation index.

[0061] Step 2: Normalize each element in the matrix according to formula (1):

[0062]

[0063] Among them, X ij is the multi-factor decision matrix value; is the normalized value.

[0064] Step 3: Calculate the weighted normalized matrix:

[0065]

[0066] Among them, V ij is the weighted normalized value; W j is the weight coefficient.

[0067] Step 4: According to the pros and cons of each evaluation index, obtain positive and negative ideal solutions respectively. If the evaluation index is energy consumption, the lowest value is the positive ideal solution and the highest value is the negative ideal solution.

[0068] Step 5: Calculate the distance between the evaluation solution and the positive and negative ideal solutions.

[0069] According to formula (4), the Euclidean distance between the evaluation solution and the positive ideal solution is calculated

[0070]

[0071] According to formula (5), the Euclidean distance between the evaluation solution and the negative ideal solution is calculated

[0072]

[0073] Step 6: Calculate the relative closeness between the evaluation solution and the ideal solution. The best evaluated solution should be closest to the positive ideal solution and farthest from the negative ideal solution. The calculation formula is (6):

[0074]

[0075] in, and is the distance between the evaluation solution and the positive ideal solution and the negative ideal solution.

[0076] According to a zero-carbon building multi-objective optimization method provided by the present invention, the annual operating carbon emissions per unit area of ​​the building are calculated using formulas (7) and (8):

[0077]

[0078] Among them, E i E represents the annual energy consumption of the i-th type of building; i,j represents the energy consumption of the i-th type of system; ER i,j represents the amount of energy of the i-th type provided by the renewable energy system consumed by the j-th type system; i represents the type of terminal energy consumed by the building, including electricity, gas, etc.; j represents the type of building energy system, including heating and air conditioning, lighting, domestic hot water system, etc.; C M Indicates the annual operating carbon emissions per unit area of ​​the building; EF i represents the carbon emission factor of the i-th type of energy; C p represents the annual carbon reduction of the building green space carbon sink system; y represents the design life of the building; A represents the building area.

[0079] The global cost increment per unit area represents the difference between the global cost of the zero-carbon building and the global cost of the reference building. The global cost represents the incremental cost of the initial investment brought by the energy-saving technology and the benefits brought by the energy savings during the calculation period, and is converted into present value. This indicator only calculates the cost of energy-saving technology-related components, such as the cost of energy-saving technology components such as insulation layers, exterior windows, and equipment systems, and does not calculate the cost of basic components such as building structures. The global cost increment per unit area is expressed by formula (9):

[0080] dC NPV =C NPV (zcb)-C NPV (ref) (9)

[0081] Among them, dC NPV : Global cost increment per unit area of ​​zero-carbon building; C NPV(zcb): global cost per unit area of ​​zero-carbon buildings; C NPV (ref): Reference to the global cost per unit area of ​​the building.

[0082] To achieve the above object, the present invention further provides a zero-carbon building multi-objective optimization system, which is applied to any of the above-mentioned zero-carbon building multi-objective optimization methods, comprising:

[0083] A first execution module, wherein the first execution module is configured to obtain shape design parameters of the studied building according to the shape characteristics of the studied building;

[0084] A second execution module, the second execution module is configured to obtain technical design parameters of the studied building according to the heat generation and heat transfer characteristics of the studied building;

[0085] A third execution module, wherein the third execution module is configured to obtain an optimal solution for the shape design of the studied building according to the shape design parameters;

[0086] a fourth execution module, the fourth execution module being configured to set a plurality of technical design stage optimization targets, and perform energy consumption simulation according to the shape design optimal solution and the technical design parameters, so as to obtain a plurality of first simulation arrays corresponding to the technical design stage optimization targets;

[0087] a fifth execution module, wherein the fifth execution module is configured to select a plurality of first Pareto arrays from a plurality of the first simulation arrays based on a genetic algorithm;

[0088] The sixth execution module is configured to perform a multi-factor decision analysis based on the plurality of the first Pareto arrays, and use the arrays ranked top several in the analysis results as the optimal solution for the multi-objective optimization of the zero-carbon building.

[0089] Since the zero-carbon building multi-objective optimization system is used to execute any of the above-mentioned zero-carbon building multi-objective optimization methods. Therefore, the zero-carbon building multi-objective optimization system can divide the design stage into the form design stage and the technical design stage, and carry out targeted multi-objective optimization design according to the design content and related performance, which is helpful to integrate the zero-carbon concept throughout the entire process of architectural design, improve the contribution of form design to the building energy conservation and carbon reduction goals, and promote the implementation of zero-carbon goals in different architectural design professional divisions. And to achieve the multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithms is adopted. Compared with the conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, and can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable.

[0090] Since the zero-carbon building multi-objective optimization system provided by the present invention and the zero-carbon building multi-objective optimization method described above belong to the same inventive concept, the zero-carbon building multi-objective optimization system provided by the present invention has all the advantages of the zero-carbon building multi-objective optimization method described above, and therefore the beneficial effects of the zero-carbon building multi-objective optimization system provided by the present invention will not be described one by one here.

[0091] To achieve the above object, the present invention also provides an electronic device, please refer to Figure 3 , Figure 3 FIG. 1 is a block diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, the electronic device includes:

[0092] A memory 103 storing a computer program;

[0093] A processor 101 is connected to the memory for executing any one of the above-mentioned zero-carbon building multi-objective optimization methods when calling the computer program;

[0094] The display 105 is communicatively connected with the processor and the memory, and is used to display a GUI interaction interface related to the zero-carbon building multi-objective optimization method.

[0095] like Figure 3 As shown, the electronic device also includes a communication interface 102 and a communication bus 104, wherein the processor 101, the communication interface 102, and the memory 103 communicate with each other through the communication bus 104. The communication bus 104 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 104 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface 102 is used for communication between the above-mentioned electronic device and other devices.

[0096] The processor 101 referred to in the present invention may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor 101 is the control center of the electronic device, and various interfaces and lines are used to connect various parts of the entire electronic device.

[0097] The memory 103 may be used to store the computer program. The processor 101 implements various functions of the electronic device by running or executing the computer program stored in the memory 103 and calling the data stored in the memory 103 .

[0098] The memory 103 may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0099] Since the electronic device is used to execute any of the above-mentioned multi-objective optimization methods for zero-carbon buildings. Therefore, the electronic device can divide the design stage into a form design stage and a technical design stage, and carry out targeted multi-objective optimization design according to the design content and related performance, which is helpful to integrate the zero-carbon concept throughout the entire process of architectural design, improve the contribution of form design to the goal of building energy conservation and carbon reduction, and promote the implementation of zero-carbon goals in different professional divisions of architectural design. And to achieve the multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithms is adopted. Compared with the conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, and can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable.

[0100] Since the electronic device provided by the present invention and the zero-carbon building multi-objective optimization method described above belong to the same inventive concept, the electronic device provided by the present invention has all the advantages of the zero-carbon building multi-objective optimization method described above, and therefore the beneficial effects of the electronic device provided by the present invention will not be described one by one here.

[0101] To achieve the above purpose, the present invention provides a readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the multi-objective optimization method of zero-carbon building as described in any of the above. Since the readable storage medium is used to execute any of the multi-objective optimization methods of zero-carbon building described above. Therefore, the readable storage medium can divide the design stage into a form design stage and a technical design stage, and carry out targeted multi-objective optimization design according to the design content and related performance, which is helpful to run the zero-carbon concept through the entire process of building design, improve the contribution of form design to the goal of building energy conservation and carbon reduction, and promote the implementation of zero-carbon goals in different professional divisions of building design. And to achieve the multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithm is adopted. Compared with the conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, can automatically find the optimal solution for zero-carbon building design that takes into account economy, significantly improves the efficiency of optimizing building performance, and makes the entire design process more objective and controllable.

[0102] Since the readable storage medium provided by the present invention and the zero-carbon building multi-objective optimization method described above belong to the same inventive concept, the readable storage medium provided by the present invention has all the advantages of the zero-carbon building multi-objective optimization method described above, and therefore the beneficial effects of the readable storage medium provided by the present invention will not be described one by one here.

[0103] The readable storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer hard disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this article, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0104] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0105] The present invention also provides a computer program product, which, when executed by a processor, implements any of the above-mentioned zero-carbon building multi-objective optimization methods.

[0106] The present invention also provides a computer program product, because the computer program product is used to execute any of the above-mentioned zero-carbon building multi-objective optimization methods. Therefore, the computer program product can divide the design stage into a form design stage and a technical design stage, and carry out targeted multi-objective optimization design according to the design content and related performance, which is helpful to integrate the zero-carbon concept throughout the entire process of architectural design, improve the contribution of form design to the goal of building energy conservation and carbon reduction, and promote the implementation of zero-carbon goals in different architectural design professional divisions. And to achieve the multi-variable and multi-objective optimization problems faced by zero-carbon building design, a multi-objective optimization design method based on genetic algorithms is adopted. Compared with conventional experience-oriented and simulation trial and error design methods, it breaks through the single-objective scheme evaluation and optimization logic, and can automatically find the optimal zero-carbon building design solution that takes into account economy, significantly improve the efficiency of optimizing building performance, and make the entire design process more objective and controllable.

[0107] Since the computer program product provided by the present invention and the zero-carbon building multi-objective optimization method described above belong to the same inventive concept, the computer program product provided by the present invention has all the advantages of the zero-carbon building multi-objective optimization method described above, and therefore the beneficial effects of the computer program product provided by the present invention will not be described one by one here.

[0108] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0109] It should also be noted that, although the present invention has been disclosed as a preferred embodiment, the above embodiment is not intended to limit the present invention. For any technician familiar with the art, without departing from the scope of the technical solution of the present invention, the technical content disclosed above can be used to make many possible changes and modifications to the technical solution of the present invention, or modified into equivalent embodiments of equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of protection of the technical solution of the present invention.

[0110] It should also be understood that, unless otherwise specified or indicated, the terms "first", "second", "third", etc. in the specification are merely used to distinguish between the various components, elements, steps, etc. in the specification, and are not used to indicate the logical relationship or sequential relationship between the various components, elements, steps, etc.

[0111] It should also be recognized that the terms described herein are used only to describe specific embodiments and are not intended to limit the scope of the invention. It should be noted that the singular forms "a" and "an" used herein and in the appended claims include plural references unless the context clearly indicates otherwise. For example, a reference to "a step" or "a device" means a reference to one or more steps or devices, and may include secondary steps and secondary devices. All conjunctions used should be understood in the broadest sense. And, the word "or" should be understood to have the definition of a logical "or", rather than a logical "exclusive or", unless the context clearly indicates otherwise. In addition, the implementation of embodiments of the present invention may include performing selected tasks manually, automatically, or in combination.

Claims

1. A multi-objective optimization method for zero-carbon buildings, characterized in that: The following steps are involved: Acquire a studied building and a plurality of design parameters corresponding to the studied building; Among the plurality of design parameters, the design parameters that match the shape characteristics of the studied building are defined as shape design parameters, and the design parameters other than the shape design parameters are defined as technical design parameters; According to the shape design parameters, obtaining the optimal solution of the shape design of the building under study; A plurality of optimization objectives in the technical design stage are set, and the multi-objective optimization optimal solution of the zero-carbon building of the studied building is obtained according to the optimal solution of the shape design and the technical design parameters.

2. The zero-carbon building multi-objective optimization method according to claim 1, characterized in that: The step of obtaining the zero-carbon building multi-objective optimization optimal solution of the studied building according to the optimal solution of the shape design and the technical design parameters specifically includes: Performing energy consumption simulation according to the optimal solution of the shape design and the technical design parameters to obtain a plurality of first simulation arrays corresponding to the optimization objectives of the technical design stage; Based on a genetic algorithm, selecting a plurality of first Pareto arrays from a plurality of the first simulation arrays; A multi-factor decision analysis is performed based on the plurality of the first Pareto arrays, and the arrays with the top several rankings in the analysis results are used as the optimal solutions for the multi-objective optimization of the zero-carbon building of the building under study.

3. The zero-carbon building multi-objective optimization method according to claim 1, characterized in that: Before performing a multi-factor decision analysis based on a plurality of the first Pareto arrays, the first Pareto arrays that do not meet the design standard specifications are eliminated.

4. The zero-carbon building multi-objective optimization method according to claim 1, characterized in that: The step of obtaining the optimal solution for the shape design of the building under study according to the shape design parameters specifically includes: Setting a plurality of optimization targets in the shape design stage, and performing energy consumption simulation according to the shape design parameters to obtain a plurality of second simulation arrays corresponding to the optimization targets in the shape design stage; Based on a genetic algorithm, selecting a plurality of second Pareto arrays from a plurality of the second simulation arrays; A multi-factor decision analysis is performed based on the plurality of the second Pareto arrays, and the arrays with the top several rankings in the analysis results are taken as the optimal solutions for the shape design.

5. The zero-carbon building multi-objective optimization method according to claim 4, characterized in that: The optimization objectives in the shape design stage include the maximum photovoltaic installed area.

6. The zero-carbon building multi-objective optimization method according to claim 2 or 4, characterized in that: The genetic algorithm includes the NSGA-II algorithm.

7. The zero-carbon building multi-objective optimization method according to claim 1 or 2, characterized in that: The multi-factor decision analysis includes TOPSIS analysis.

8. An electronic device, characterized in that: The electronic device comprises: a memory storing a computer program; a processor, communicatively connected to the memory, and configured to execute the zero-carbon building multi-objective optimization method according to any one of claims 1 to 7 when calling the computer program; A display is communicatively connected to the processor and the memory, and is used to display a GUI interaction interface related to the zero-carbon building multi-objective optimization method.

9. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the zero-carbon building multi-objective optimization method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that When the computer program product is executed by a processor, the zero-carbon building multi-objective optimization method according to any one of claims 1 to 7 is implemented.

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