A service area near zero energy consumption building design optimization method based on NSGA-II algorithm
By optimizing the architectural design of the service area using the NSGA-II algorithm and taking into account various factors, near-zero energy consumption technology was selected, which solved the problem of low energy utilization efficiency in traditional design and achieved a low-energy and economically reasonable design scheme.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHANGAN UNIV
- Filing Date
- 2026-05-06
- Publication Date
- 2026-07-10
AI Technical Summary
Traditional building design methods fail to comprehensively consider various influencing factors, resulting in low energy efficiency and high energy consumption in service area buildings during actual operation.
The NSGA-II algorithm is used for multi-objective optimization. The heat distribution and environmental factors of the service area are comprehensively analyzed to screen near-zero energy building technologies, establish an energy consumption analysis model, form feasible solutions through the NSGA-II genetic algorithm model, and select the optimal design scheme.
It achieves the goal of reducing energy consumption while avoiding excessive increases in construction costs, satisfying the complex relationship between energy consumption and cost input, and meeting the standards for near-zero energy buildings.
Smart Images
Figure CN122365677A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of architectural design technology, specifically to a method, system, medium, equipment, and program for optimizing near-zero energy consumption building design in service areas based on the NSGA-II algorithm. Background Technology
[0002] Service areas, as indispensable key nodes in transportation networks, play a vital role in providing rest, refueling, and catering services to passing vehicles and personnel. Due to their complex functions, frequent passenger flow, and numerous pieces of equipment, energy consumption is a significant issue in service areas. From lighting systems to air conditioning and ventilation equipment, from catering facilities to various office appliances, every link continuously consumes a large amount of energy. Moreover, the energy consumption patterns and characteristics of service areas vary across different regions and of different sizes, further increasing the difficulty of energy conservation and emission reduction.
[0003] Traditional building design methods have revealed numerous limitations in addressing the complex energy consumption issues of service areas. Traditional designs often focus on meeting basic functional and structural safety requirements, with a relatively one-sided and isolated consideration of energy consumption. The design process typically involves simply selecting equipment and materials based on standards and specifications, lacking a comprehensive analysis and optimization of various influencing factors. For example, the impact of local climate conditions, sunlight, wind direction, and wind speed on building energy consumption is not fully considered, nor are in-depth studies and accurate simulations of equipment operation patterns and occupant activity patterns within the building conducted. This results in low energy efficiency and high energy consumption during actual operation, increasing operating costs and placing significant pressure on the environment. Summary of the Invention
[0004] To address the problem that existing building design methods often consider energy consumption in isolation without comprehensively analyzing various influencing factors, resulting in low energy efficiency and high energy consumption in service area buildings during actual operation, this invention provides a near-zero energy consumption building design optimization method for service areas based on the NSGA-II algorithm. This method leverages the efficient multi-objective optimization capabilities of the NSGA-II algorithm to comprehensively analyze multiple influencing factors, thereby improving the energy efficiency of service area buildings during actual operation.
[0005] To achieve the above objectives, the present invention provides the following technical solution.
[0006] In a first aspect, the present invention provides a service area near-zero energy building design optimization method based on the NSGA-II algorithm, comprising: Based on the heat distribution and environmental factors of the service area, a set of near-zero energy building technologies was selected, and an energy consumption analysis model of the benchmark building and the building after adopting the candidate technologies was established. Using the energy consumption analysis models of the benchmark building and the building after adopting candidate technologies, we obtain energy consumption simulation results and calculate the building's comprehensive energy consumption and incremental costs. Based on the building's overall energy consumption and incremental costs, the NSGA-II genetic algorithm model is used to form feasible solutions, and multi-dimensional indicators are formed based on these feasible solutions. By using multi-dimensional indicators, the optimal near-zero energy building design optimization scheme for the service area is selected.
[0007] As a further improvement of the present invention, the step of selecting a set of near-zero energy building technologies based on the heat distribution and environmental factors of the service area location, and establishing an energy consumption analysis model of a benchmark building and a building using candidate technologies, includes: Based on the heat distribution and environmental factors of the service area location, a set of near-zero energy building technologies was selected. ; Based on near-zero energy building technologies We used DesignBuilder to build energy consumption analysis models of the baseline building and the building after adopting candidate technologies.
[0008] As a further improvement of the present invention, the step of using a benchmark building and a building energy consumption analysis model after adopting candidate technologies to obtain energy consumption simulation results and calculate the building's comprehensive energy consumption and incremental cost includes: Using a benchmark building and a building energy consumption analysis model with candidate technologies, the annual heating energy consumption, annual cooling energy consumption, annual average equipment energy consumption, electricity purchase price, and electricity sold to the grid are calculated to obtain energy consumption simulation results. Based on the energy consumption simulation results, calculate the building's overall energy consumption and incremental costs.
[0009] As a further improvement of the present invention, the building's overall energy consumption is:
[0010] The incremental cost is:
[0011] In the formula, It is an energy consumption function; It refers to the energy-saving technology used; It is the number of energy-saving technologies used; It is the building's annual heating energy consumption; It is the building's annual cooling energy consumption; N is the building's average annual energy consumption; N is the building's design lifespan. It is an incremental cost function; It is the incremental cost per unit area of near-zero energy building technology.
[0012] As a further improvement of the present invention, the step of forming feasible solutions using the NSGA-II genetic algorithm model based on the building's comprehensive energy consumption and incremental costs, and calculating multi-dimensional indicators based on the feasible solutions, includes: Based on the building's overall energy consumption and incremental costs, the NSGA-II genetic algorithm model is used to iteratively solve the Pareto front. Each scheme in the Pareto front is evaluated, and schemes that meet the set conditions are selected to form feasible schemes. Calculate multidimensional indicators for each feasible solution.
[0013] As a further improvement of the present invention, the step of selecting the optimal near-zero energy building design optimization scheme for the service area through multi-dimensional indicators includes: The multidimensional indicators include payback period, carbon reduction amount, carbon reduction rate, and carbon reduction cost; Based on the requirements of energy-saving templates, economic templates, and balanced templates, the optimal near-zero energy consumption building design optimization scheme for the service area was selected using multi-dimensional indicators.
[0014] Secondly, the present invention provides a service area near-zero energy building design optimization system based on the NSGA-II algorithm, comprising: The analysis model building module is used to screen out a set of near-zero energy building technologies based on the heat distribution and environmental factors of the service area, and to establish an energy consumption analysis model of the benchmark building and the building after adopting the candidate technologies. The Building Cost Calculation Module is used to obtain energy consumption simulation results and calculate the building's overall energy consumption and incremental costs using a benchmark building and a building energy consumption analysis model with candidate technologies. Multi-dimensional index module: Used to generate feasible solutions based on building energy consumption and incremental costs using the NSGA-II genetic algorithm model, and generate multi-dimensional indexes based on the feasible solutions; Optimal Design Scheme Module: Used to select the optimal near-zero energy building design optimization scheme for the service area through multi-dimensional indicators.
[0015] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the service area near-zero energy consumption building design optimization method based on the NSGA-II algorithm.
[0016] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the service area near-zero energy building design optimization method based on the NSGA-II algorithm.
[0017] Fifthly, the present invention provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the service area near-zero energy consumption building design optimization method based on the NSGA-II algorithm.
[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention comprehensively considers the heat distribution and environmental factors of the service area location to select a set of near-zero energy building technologies. It then establishes an energy consumption analysis model for a benchmark building and buildings using candidate technologies, overcoming the limitations of traditional methods by incorporating multiple key influencing factors into the analysis, thus avoiding energy waste caused by one-sided considerations. Secondly, after obtaining energy consumption simulation results and calculating the building's overall energy consumption and incremental costs, this invention leverages the powerful and efficient multi-objective optimization capabilities of the NSGA-II genetic algorithm model to form feasible solutions and construct multi-dimensional indicators. The NSGA-II algorithm can simultaneously handle multiple objective functions, efficiently balancing and optimizing between the two key objectives of overall building energy consumption and incremental costs, avoiding the one-sidedness of traditional single-objective optimization methods. Through this multi-objective optimization, the resulting feasible solutions fully consider the complex relationship between energy consumption and cost input, ensuring that while pursuing low energy consumption, construction costs are not excessively increased. Attached Figure Description
[0019] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. In the drawings: Figure 1 This is a flowchart illustrating a near-zero energy building design optimization method for service areas based on the NSGA-II algorithm according to the present invention. Figure 2 This is a flowchart of the NSGA-II genetic algorithm in this invention; Figure 3 This is a schematic diagram of the framework of the near-zero energy building optimization design method in this invention; Figure 4 This is a schematic diagram of the structure of a service area near-zero energy building design optimization system based on the NSGA-II algorithm according to the present invention. Figure 5 This is a schematic diagram of an electronic device in an embodiment of the present invention. Detailed Implementation
[0020] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.
[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0022] To address the problem that existing building design methods often consider energy consumption in isolation without comprehensively analyzing various influencing factors, resulting in low energy efficiency and high energy consumption in service area buildings during actual operation, this invention provides a near-zero energy building design optimization method for service areas based on the NSGA-II algorithm. Figure 1 As shown, it includes: S100: Based on the heat distribution and environmental factors of the service area, a set of near-zero energy building technologies is selected, and an energy consumption analysis model is established for the benchmark building and the building after adopting the candidate technologies. S200: Using a benchmark building and a building energy consumption analysis model after adopting candidate technologies, obtain energy consumption simulation results and calculate the building's overall energy consumption and incremental costs; S300: Based on the building's comprehensive energy consumption and incremental costs, use the NSGA-II genetic algorithm model to form feasible solutions, and then form multi-dimensional indicators based on the feasible solutions. S400: Select the optimal near-zero energy building design optimization scheme for the service area through multi-dimensional indicators.
[0023] This method utilizes the efficient multi-objective optimization capability of the NSGA-II algorithm to comprehensively analyze various influencing factors and provide energy utilization efficiency of service area buildings in actual operation.
[0024] The present invention will be further explained and described below with reference to specific solutions.
[0025] A service area near-zero energy building design optimization method based on the NSGA-II algorithm, specifically including: S1: Non-dominated sorting (NSGA-II) genetic algorithm Building energy consumption often exhibits nonlinear and nonstationary characteristics. The Non-Dominated Sorting (NSGA-II) genetic algorithm is an efficient multi-objective optimization algorithm with strong robustness, suitable for handling complex optimization problems involving high dimensionality, nonlinearity, and uncertainty. Compared to other genetic algorithms, its advantages lie in reducing computational complexity and ensuring the diversity of individuals.
[0026] Its algorithm flow is as follows Figure 2 As shown.
[0027] The basic process of the NSGA-II genetic algorithm is as follows: 1) Population initialization: In single-objective problems, the genetic local search (GLS) method is usually used to initialize the master process (MS) layer after encoding.
[0028] There are three methods for GLS initialization: global selection, local selection, and random selection. The Operating System (OS) layer uses random initialization. In multi-objective problems, the GLS method is improved after encoding. At the MS layer, initialization can be performed based on minimum machine load or minimum energy consumption, while the OS layer's initialization method is the same as for single-objective problems.
[0029] 2) Crossover: Partial Process Exchange (POX) Crossover: First, the job is randomly divided into two sets, J1 and J2. During the crossover process, both offspring retain their positions in set J1. The positions of other elements in the first offspring are determined by the elements in set J2, and the second offspring are processed in the same way. Machine Crossover: Machine codes are obtained by selecting machines for the process, which can be achieved using a random 0-1 method. This results in a machine chromosome of the same length as the process code. The genes on this chromosome consist of 0s and 1s. The crossover method is determined based on the numbers on the two chromosomes: if the corresponding number is 1, the number remains unchanged; if the corresponding number is 0, the numbers on the two chromosomes are swapped. If the swapped number is greater than the total number of machines available for the process in the machine set, a new sequence number is generated randomly for the process to select the corresponding machine.
[0030] 3) Mutation: NSGA-II employs mutation operations primarily to escape the current search region and avoid getting trapped in local optima. A good mutation method can increase chromosome perturbation, allowing it to escape the current search region as much as possible. Here, an insertion mutation method is used to operate on the process, that is, selecting a gene segment from the chromosome and inserting it into another position on the chromosome. The mutation method uses a spaced-out exchange. After the exchange, if the gene number is greater than the total number of machines that can be selected for that process, the machine selection for that process is randomly generated. A machine is randomly selected from the set of available machines for that process, and the chromosome number is changed to the position of that machine in the set of available machines.
[0031] 4) Non-dominated sorting: Based on the dominance relationships between individuals, the population is divided into different non-dominated layers (Front). For each individual in the population, the number of individuals it dominates and the number of individuals it is dominated by are calculated. All non-dominated individuals (i.e., individuals with a dominance count of 0) are assigned to the first layer (Front 1). Individuals in the first layer are removed from the population, and the above process is repeated to sequentially divide the population into the second layer, the third layer, and so on, until all individuals are assigned to a non-dominated layer.
[0032] 5) Crowding Distance Calculation: Within each non-dominated layer, assess the distribution of individuals to avoid overly concentrated solutions. For each individual in a non-dominated layer, calculate its crowding distance in each target dimension. Specifically, for each target dimension, sort individuals by target value, calculate the difference in target value between adjacent individuals, and sum these differences as the individual's crowding distance. A larger crowding distance indicates a more "spacious" environment around the individual, meaning the individual is more unique in the target space.
[0033] S2: Near-zero energy building optimization design Based on the NSGA-II genetic algorithm, the optimization design of near-zero energy buildings is carried out, and a framework for the optimization design is built. The framework consists of a technology selection module, an effect calculation module, a technology optimization module, a technology screening module, a multi-dimensional evaluation module, and a template determination module. A schematic diagram of the framework is attached. Figure 3 As shown.
[0034] In the technology selection module, considering the heat distribution and environmental factors of the building's location, appropriate near-zero energy building technologies are chosen. For example, in areas with lower temperatures, high-efficiency insulation materials should be selected; while for buildings in hot summers, outdoor shading facilities can be added to regulate indoor temperature. In the effect calculation and technology optimization modules, DesignBuilder is used to build a building energy consumption analysis model and calculate energy consumption, and then the NSGA-II genetic algorithm tool is used for optimization design. The fitness function of the optimization calculation minimizes the building's overall energy consumption and incremental cost, as shown below: (1) (2) (3) In the formula, It is an energy consumption function (building total energy consumption). ); It refers to the energy-saving technology used; It is the number of energy-saving technologies used; It is the building's annual heating energy consumption (J); It is the building's annual cooling energy consumption (J); It is the building's average annual equipment energy consumption (J); N is the building's service life at the time of design, which is set at 50 years; It is an incremental cost function (the incremental cost of the initial investment in the building, in yuan / m2); It is the incremental cost per unit area (yuan / m2) of near-zero energy building technology.
[0035] (4) (5) (6) (7) (8) In the formula, K is the total annual cost; L is the initial investment cost; L is the investment recovery coefficient. It is the cost of operation; It is the cost of equipment maintenance; 'c' is the annual interest rate, set at 0.1%; 'c' is the lifespan of the energy equipment, set at 20 years. It is the capacity of the l-th device in the system; This is the unit price of the l-th device; It is the ratio between maintenance costs and initial investment costs; It represents the electricity purchased by the power grid at time t; It is the electricity purchase price at time t; It is the amount of electricity sold to the grid at time t; It is the electricity price at time t.
[0036] (9) (10) In the formula, Q is the annual carbon emissions; It is the carbon dioxide emission coefficient; It is the annual carbon emission reduction rate; It is the annual carbon emissions of the benchmark system.
[0037] (11) (12) In the formula, R is the total amount of power grid interaction; It is the rate of reduction in grid interaction; It represents the total amount of grid interaction in the reference system.
[0038] In the screening module, the Pareto frontier obtained from the technology optimization module is used as the optimal solution set. The Pareto solution set is then screened based on net present value (NPV) and energy saving rate. The results must satisfy the following conditions: the NPV is greater than zero during the project's calculation period; and the ratio of the energy savings of the target building to the energy intensity of the benchmark building should meet the requirements of GB / T51350-2019 "Technical Standard for Near-Zero Energy Buildings". The NPV formula is shown below: (13) In the formula, V is the net present value; It is the cash inflow in year y (the cost saved each year after energy conservation). y is the cash outflow in year y (except for the initial investment, this item is 0 in all other years); x is the benchmark rate of return.
[0039] In the multi-dimensional evaluation module, the feasibility of a solution is assessed using the payback period of dynamic investment and the carbon reduction effect. The payback period of dynamic investment refers to the time it takes for the cumulative net value of future net cash inflows to equal the initial net value of the investment. Compared to the payback period of static investment, it primarily considers the impact of time on monetary value and is more closely aligned with reality. The assessment of the carbon reduction effect mainly includes the amount of carbon reduction, the carbon reduction rate, and the carbon reduction cost, calculated using the following formula: (14) (15) (16) (17) In the formula, It is the payback period for dynamic investments; It is the carbon reduction per unit area ( ); It is the carbon reduction rate (%). The cost of carbon reduction per unit area (yuan / ); It is the energy consumption per unit area of the benchmark building ( ); It is the energy consumption per unit area in the Pareto solution set. ); It is a carbon emission factor of the local power grid; The unit area cost (yuan / ) of using near-zero energy building technologies ).
[0040] In the template determination module, the top 33% of the schemes with the lowest overall building energy consumption are selected as the optimal energy-saving templates, the top 33% of the schemes with the lowest incremental costs are selected as the optimal economic templates, and the rest are selected as the optimal equilibrium templates.
[0041] In summary, this invention proposes a near-zero energy building design optimization method for service areas based on the NSGA-II algorithm. This method addresses the shortcomings of traditional building design in addressing energy crises and environmental protection requirements. It constructs an optimization design framework encompassing modules such as technology selection, effect calculation, technology optimization, technology screening, multi-dimensional evaluation, and template determination. Using the minimization of overall building energy consumption and incremental cost as the fitness function, it leverages the efficient multi-objective optimization capabilities of the NSGA-II algorithm to solve for the Pareto front solution set, providing decision-makers with optimal design options under multiple objectives. Furthermore, the Pareto solution set is screened using net present value and energy saving rate to ensure the selected scheme is economically feasible and meets national energy-saving standards. Simultaneously, multi-dimensional evaluation is conducted using indicators such as dynamic payback period and carbon reduction effect to ensure the environmental benefits and practical feasibility of the scheme. Finally, by ranking overall energy consumption and incremental cost, the optimal energy-saving template, economic template, and equilibrium template are determined, providing specific guidance and reference for the design of near-zero energy buildings in service areas.
[0042] The second objective of this invention is to propose a service area near-zero energy building design optimization system based on the NSGA-II algorithm, such as... Figure 4 As shown, it includes: Module 100 for constructing analysis models: Based on the heat distribution and environmental factors of the service area, it is used to screen out a set of near-zero energy building technologies and establish an energy consumption analysis model of the benchmark building and the building after adopting candidate technologies. Building Cost Calculation Module 200: Used to obtain energy consumption simulation results and calculate the building's overall energy consumption and incremental costs using a benchmark building and a building energy consumption analysis model after adopting candidate technologies; Multi-dimensional index module 300: It is used to form feasible solutions based on the building's comprehensive energy consumption and incremental costs, using the NSGA-II genetic algorithm model, and then form multi-dimensional indexes based on the feasible solutions; Optimal Design Scheme Module 400: Used to select the optimal near-zero energy building design optimization scheme for the service area through multi-dimensional indicators.
[0043] like Figure 5 As shown, a third objective of this invention is to provide an electronic device comprising a processor 501, a memory 502, and a display screen 503. The memory 502 and the display screen 503 are both connected to the processor 501, such as via a bus 504. Optionally, the electronic device may further include a transceiver 505. It should be noted that in practical applications, the transceiver 505 is not limited to one type, and the structure of this electronic device does not constitute a limitation on the embodiments of this application.
[0044] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 501 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
[0045] Bus 504 may include a pathway for transmitting information between the aforementioned components. Bus 504 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 504 can be divided into address bus, data bus, control bus, etc.
[0046] The memory 502 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.
[0047] The memory 502 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 501. The processor 501 is used to execute the application code stored in the memory 502 to implement the content shown in the foregoing method embodiments.
[0048] Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.
[0049] A fourth objective of this invention is to provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the aforementioned functions. Figure 1 The illustrated method embodiments include various processes. For example, a memory may include instructions that can be executed by a processor of an electronic device to perform the described method.
[0050] A computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.
[0051] A fifth objective of this invention is to provide a computer program product comprising computer instructions that, when executed by a processor, implement the above-described... Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.
[0052] Many embodiments and applications beyond the examples provided will be apparent to those skilled in the art upon reading the foregoing description. Therefore, the scope of this teaching should not be determined by reference to the foregoing description, but rather by reference to the foregoing claims and the full scope of their equivalents. For purposes of completeness, all articles and references, including patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the foregoing claims is not intended as a waiver of that subject matter, nor should it be construed as an indication that the applicant has not considered that subject matter as part of the disclosed inventive subject matter.
[0053] The above content provides a further detailed description of the present invention. It should not be construed that the specific embodiments of the present invention are limited to this. For those skilled in the art, several simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered as falling within the scope of protection of the present invention as defined by the submitted claims.
Claims
1. A method for optimizing near-zero energy consumption building design in service areas based on the NSGA-II algorithm, characterized in that, include: Based on the heat distribution and environmental factors of the service area, a set of near-zero energy building technologies was selected, and an energy consumption analysis model of the benchmark building and the building after adopting the candidate technologies was established. Using the energy consumption analysis models of the benchmark building and the building after adopting candidate technologies, we obtain energy consumption simulation results and calculate the building's comprehensive energy consumption and incremental costs. Based on the building's overall energy consumption and incremental costs, the NSGA-II genetic algorithm model is used to form feasible solutions, and multi-dimensional indicators are formed based on these feasible solutions. By using multi-dimensional indicators, the optimal near-zero energy building design optimization scheme for the service area is selected.
2. The service area near-zero energy building design optimization method based on the NSGA-II algorithm according to claim 1, characterized in that, Based on the heat distribution and environmental factors of the service area location, a set of near-zero energy building technologies is selected, and an energy consumption analysis model is established for the benchmark building and the building after adopting candidate technologies, including: Based on the heat distribution and environmental factors of the service area location, a set of near-zero energy building technologies was selected. ; Based on near-zero energy building technologies We used DesignBuilder to build energy consumption analysis models of the baseline building and the building after adopting candidate technologies.
3. The service area near-zero energy building design optimization method based on the NSGA-II algorithm according to claim 1, characterized in that, The energy consumption analysis model using a benchmark building and a building with candidate technologies is used to obtain energy consumption simulation results, calculate the building's overall energy consumption and incremental costs, including: Using a benchmark building and a building energy consumption analysis model with candidate technologies, the annual heating energy consumption, annual cooling energy consumption, annual average equipment energy consumption, electricity purchase price, and electricity sold to the grid are calculated to obtain energy consumption simulation results. Based on the energy consumption simulation results, calculate the building's overall energy consumption and incremental costs.
4. The service area near-zero energy building design optimization method based on the NSGA-II algorithm according to claim 3, characterized in that, The building's overall energy consumption is: The incremental cost is: In the formula, It is an energy consumption function; It refers to the energy-saving technology used; It is the number of energy-saving technologies used; It is the building's annual heating energy consumption; It is the building's annual cooling energy consumption; N is the building's average annual energy consumption; N is the building's design lifespan. It is an incremental cost function; It is the incremental cost per unit area of near-zero energy building technology.
5. The service area near-zero energy building design optimization method based on the NSGA-II algorithm according to claim 1, characterized in that, Based on the building's overall energy consumption and incremental costs, the NSGA-II genetic algorithm model is used to generate feasible solutions. Multidimensional indicators are then calculated based on these feasible solutions, including: Based on the building's overall energy consumption and incremental costs, the NSGA-II genetic algorithm model is used to iteratively solve the Pareto front. Each scheme in the Pareto front is evaluated, and schemes that meet the set conditions are selected to form feasible schemes. Calculate multidimensional indicators for each feasible solution.
6. The service area near-zero energy building design optimization method based on the NSGA-II algorithm according to claim 1, characterized in that, The selection of the optimal near-zero energy building design optimization scheme for the service area through multi-dimensional indicators includes: The multidimensional indicators include payback period, carbon reduction amount, carbon reduction rate, and carbon reduction cost; Based on the requirements of energy-saving templates, economic templates, and balanced templates, the optimal near-zero energy consumption building design optimization scheme for the service area was selected using multi-dimensional indicators.
7. A service area near-zero energy building design optimization system based on the NSGA-II algorithm, comprising a service area near-zero energy building design optimization method based on the NSGA-II algorithm as described in any one of claims 1-6, characterized in that, include: The analysis model building module is used to screen out a set of near-zero energy building technologies based on the heat distribution and environmental factors of the service area, and to establish an energy consumption analysis model of the benchmark building and the building after adopting the candidate technologies. The Building Cost Calculation Module is used to obtain energy consumption simulation results and calculate the building's overall energy consumption and incremental costs using a benchmark building and a building energy consumption analysis model with candidate technologies. Multi-dimensional index module: Used to generate feasible solutions based on building energy consumption and incremental costs using the NSGA-II genetic algorithm model, and generate multi-dimensional indexes based on the feasible solutions; Optimal Design Scheme Module: Used to select the optimal near-zero energy building design optimization scheme for the service area through multi-dimensional indicators.
8. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the service area near-zero energy building design optimization method based on the NSGA-II algorithm as described in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the service area near-zero energy consumption building design optimization method based on the NSGA-II algorithm as described in any one of claims 1-6.
10. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of a service area near-zero energy building design optimization method based on the NSGA-II algorithm as described in any one of claims 1-6.