CN114969950A - Residence comprehensive performance optimization design method and system based on genetic algorithm - Google Patents
Residence comprehensive performance optimization design method and system based on genetic algorithm Download PDFInfo
- Publication number
- CN114969950A CN114969950A CN202210743100.8A CN202210743100A CN114969950A CN 114969950 A CN114969950 A CN 114969950A CN 202210743100 A CN202210743100 A CN 202210743100A CN 114969950 A CN114969950 A CN 114969950A
- Authority
- CN
- China
- Prior art keywords
- building
- target technical
- genetic algorithm
- carbon emission
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/84—Greenhouse gas [GHG] management systems
Abstract
The invention relates to the technical field of building design, and provides a residence comprehensive performance optimization design method based on a genetic algorithm, which takes the carbon emission of a unit area full life cycle, the building operation energy consumption of a unit area and the building indoor health time ratio performance improvement as objective functions; then determining target technical parameters to be optimized; determining a value range of a target technical parameter; and establishing a genetic algorithm model, inputting the objective function, the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes. The invention also discloses a system which optimizes the comprehensive performance of the building by the genetic algorithm, reduces excessive dependence of the building design process on engineering experience and software simulation trial and error, and can output a plurality of alternative feasible schemes, thereby improving the building optimization design efficiency, reducing the workload and the strength of the building design process, and being simple and convenient to operate.
Description
Technical Field
The invention relates to the technical field of building design, in particular to a residence comprehensive performance optimization design method and a residence comprehensive performance optimization design system based on a genetic algorithm.
Background
The energy consumption and carbon emission of residential buildings occupy a large proportion in the whole energy-saving and carbon-reducing link, and with the prominent energy crisis and global warming, the technical scheme of low energy consumption and low carbon emission is adopted in the building design stage, so that the method is very important in the building design process.
CN114444778A discloses a method and a device for judging an optimization scheme of architectural design, the method includes: acquiring the distance between a pre-construction project and a road; acquiring a garage design scheme aiming at an underground parking garage in each alternative building design scheme; selecting a first building design scheme with parking efficiency meeting preset conditions from the alternative building design schemes according to the road distance and the garage design scheme; acquiring evaluation parameters of other building design sub-schemes except the garage design scheme in the first building design scheme; determining the first building design plan with the highest evaluation parameter score as the second building design plan for final use according to the evaluation parameters.
In the prior art, the comprehensive performance of the building is usually adjusted based on engineering experience and software simulation trial and error, so that the dependence on experience is high, the workload is large, and the consideration target is relatively single.
Disclosure of Invention
Through long-term research practice, in the face of multi-objective optimization design problems of comprehensive performance, through manual or single-objective computer optimization, local optimization of a design scheme is often caused, but the optimization of the comprehensive performance is difficult to realize, and the technical problem which is difficult to solve in the field of building design at present is solved.
In view of the above, the present invention is directed to a method for optimizing and designing comprehensive performance of a home based on a genetic algorithm, which comprises,
step S1, taking the carbon emission of the whole life cycle of unit area, the building operation energy consumption of unit area and the building indoor health time ratio performance improvement as objective functions; wherein the objective function Max (f (x)) is,
min F carbon (x) The calculation function of the carbon emission of the building in the whole life period is used as the minimum target;
min F energy (x) The calculation function of the building operation energy consumption is minimized;
max F health (x) Calculating a function of building indoor health performance with a maximum target;
step S2, determining target technical parameters to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size;
step S3, determining the value range of the target technical parameter;
and step S4, establishing a genetic algorithm model, inputting the objective function Max (f (x)), the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes, wherein N is a positive integer greater than or equal to 1.
Preferably, in step S2, including,
step S21, determining the target technical parameter types to be optimized, including the external wall construction form, the window structure, the window-wall ratio, the window aspect ratio, the sunshade form and the sunshade size;
and step S22, screening target technical parameter types which have influences on the operation energy consumption of the building, the carbon emission of the whole life cycle of the building and the indoor health performance of the building according to the selected target technical parameter types, and taking the target technical parameter types as variable parameters which are subsequently input into the genetic algorithm model for optimization.
Preferably, in step S1, the building operation energy consumption per unit area is,
wherein Q is equivalent power consumption, kWh/(m) 2 ·a)，η i The equivalent electrical method conversion coefficient of the ith energy source; q i The calorific value corresponding to the ith energy source; q i,j Consumption of class i energy for class j systems; a is the building area.
Preferably, the unit area full life cycle carbon emission in step S1 includes the sum of greenhouse gas emissions generated during the production and transportation, construction and demolition, and operation of the building material, expressed as carbon dioxide equivalent.
Preferably, the carbon emission in the building material production and transportation stage is,
wherein, C JC For carbon emission in the transportation stage of building material production, the unit kgCO 2 /m 2 ；C sc For carbon emission in the building material production stage, the unit kgCO 2 ；C ys Carbon emission for building material transportation process, kgCO 2 ；M i The consumption of the ith main building material; f i A carbon emission factor, kgCO, of the ith major building material 2 Per unit number of building materials; d i The average transport distance is the average transport distance of the ith building material, and the unit is km; t is i A carbon emission factor, kgCO, per unit weight of transport distance in the i-th building material transport mode 2 /(t·km)。
Preferably, the carbon emission in the construction and demolition stages of the building is,
wherein, C JZ Carbon emission for building construction stage, kgCO 2 /m 2 ；C CC For carbon emission at the demolition stage of the building, kgCO 2 /m 2 ；E jz,i The total energy consumption in the ith energy consumption of the building construction stage; e cc,i The total energy consumption in the ith stage of the building demolition is calculated; EF i A carbon emission factor that is a class i energy source; a is the building area, m 2 。
Preferably, the carbon emissions during the construction operation are,
wherein, C YX For carbon emission in the construction operating phase, kgCO 2 /m 2 ；E i The year consumption of the ith type of energy of the building, unit/a; EF i A carbon emission factor for the ith energy of the building; c p Reducing carbon content of the green land carbon sink system year by kgCO 2 A; y is the building design life, unit/a; a is the building area, m 2 ；E i,j Is the i-th energy consumption of the j-system, unit/a; ER i,j The amount of class i energy provided by the renewable energy system for class j systems, in units/a.
The invention also discloses a system for implementing the residence comprehensive performance optimization design method based on the genetic algorithm, which comprises,
the objective function unit is used for taking the carbon emission of the whole life cycle of a unit area, the building operation energy consumption of the unit area and the building indoor health time ratio performance improvement as objective functions; wherein the objective function Max (f (x)) is,
min F carbon (x) The calculation function of the carbon emission of the building in the whole life period is used as the minimum target;
min F energy (x) The calculation function of the building operation energy consumption is minimized;
max F health (x) Calculating a function of building indoor health performance with a maximum target;
the technical parameter determining unit is used for determining a target technical parameter to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size;
the value range unit is used for determining the value range of the target technical parameter;
and the optimization unit is used for establishing a genetic algorithm model, inputting the target function Max (f (x)), the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes, wherein N is a positive integer greater than or equal to 1.
Preferably, the system comprises, in combination,
the parameter type determining module is used for determining the target technical parameter types to be optimized, and comprises an outer wall construction form, a window structure, a window-wall ratio, a window aspect ratio, a sunshade form and a sunshade size;
and the parameter screening module is used for screening target technical parameter types which have influences on the operation energy consumption of the building, the carbon emission of the whole life cycle of the building and the indoor health performance of the building in the target technical parameter types according to the selected target technical parameter types, and taking the target technical parameter types as variable parameters which are subsequently input into the genetic algorithm model for optimization.
According to another aspect of the embodiments of the present invention, there is provided a storage medium, the storage medium including a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the above method.
Compared with the prior art, the residence comprehensive performance optimization design method based on the genetic algorithm provided by the invention takes the carbon emission of the whole life cycle of a unit area, the building operation energy consumption of the unit area and the building indoor health time ratio performance improvement as objective functions; then determining target technical parameters to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size; determining the value range of the target technical parameter; and establishing a genetic algorithm model, inputting the objective function, the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes. The invention also discloses a system for executing the residence comprehensive performance optimization design method based on the genetic algorithm. The method and the system provided by the invention can comprehensively consider the aspects of the whole life cycle carbon emission per unit area, the building operation energy consumption per unit area and the improvement of the indoor health time ratio performance of the building, and can optimize the comprehensive performance of the building by means of a genetic algorithm, reduce the excessive dependence of the building design process on engineering experience and software simulation trial and error, and output a plurality of alternative feasible schemes, thereby improving the building optimization design efficiency, reducing the workload and the strength of the building design process, and being simple and convenient to operate.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of an embodiment of the method for optimizing and designing comprehensive performance of a residence based on genetic algorithm of the present invention;
FIG. 2 is a flow chart of the method for optimizing and designing comprehensive performance of a residence based on genetic algorithm.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," "third," "fourth," and the like in the description and in the claims of the invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged under appropriate circumstances in order to facilitate the description of the embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The method aims to solve the problems that in the prior art, the comprehensive performance of the building is usually adjusted by trial and error based on engineering experience and software simulation, the experience dependence is high, the workload is large, and the considered target is relatively single; in the face of the multi-objective optimization design problem of comprehensive performance, a series of problems that a design scheme is locally optimal but the optimal comprehensive performance is difficult to realize are often caused by manual or single-objective computer optimization. The invention provides a residence comprehensive performance optimization design method based on a genetic algorithm, which comprises the following steps of as shown in figures 1-2,
step S1, taking the carbon emission of the whole life cycle of unit area, the building operation energy consumption of unit area and the building indoor health time ratio performance improvement as objective functions; wherein the objective function Max (f (x)) is,
min F carbon (x) The calculation function of the carbon emission of the building in the whole life period is used as the minimum target;
min F energy (x) The calculation function of the building operation energy consumption is minimized;
max F health (x) Calculating a function of the indoor health performance of the building with the maximization as a target;
step S2, determining target technical parameters to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size;
step S3, determining the value range of the target technical parameter;
and step S4, establishing a genetic algorithm model, inputting the target function Max (f (x)), the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes, wherein N is a positive integer greater than or equal to 1.
The method for optimizing and designing the comprehensive performance of the residence based on the genetic algorithm takes the carbon emission of the whole life cycle of a unit area, the building operation energy consumption of the unit area and the improvement of the indoor health time ratio performance of the building as objective functions; then determining target technical parameters to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size; determining a value range of a target technical parameter; and establishing a genetic algorithm model, inputting the objective function, the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes. The method provided by the invention can comprehensively consider three aspects of the whole life cycle carbon emission per unit area, the building operation energy consumption per unit area and the building indoor health time ratio performance improvement, and can optimize the comprehensive performance of the building house by means of a genetic algorithm, reduce excessive dependence of the building design process on engineering experience and software simulation trial and error, and output a plurality of alternative feasible schemes, thereby improving the building optimization design efficiency, reducing the workload and the strength of the building design process, and being simple and convenient to operate.
In order to better determine the types of target technical parameters that have an impact on the building operating energy consumption, the building life-cycle carbon emissions and the building indoor health performance, in a preferred case of the present invention, in step S2,
step S21, determining the target technical parameter types to be optimized, including the external wall construction form, the window structure, the window-wall ratio, the window aspect ratio, the sunshade form and the sunshade size;
and step S22, screening target technical parameter types which have influences on the operation energy consumption of the building, the carbon emission of the whole life cycle of the building and the indoor health performance of the building according to the selected target technical parameter types, and taking the target technical parameter types as variable parameters which are subsequently input into the genetic algorithm model for optimization.
In the process of optimizing the comprehensive performance of the building, firstly, the type of target technical parameters to be optimized, such as the external wall construction form and the window technical parameters, is determined. As another example, the basic technical parameters of a typical building determine the exterior wall construction form and the window technical parameters, as shown in fig. 2. And screening the parameter types which have influences on the operation energy consumption, the carbon emission in the whole life period and the indoor health performance of the building in the target technical parameter types according to the selected target technical parameter types. Finally inputting the technical parameters into a genetic algorithm model for optimization, for example, selecting the technical parameters related to the side window as the target technical type of the optimization of the comprehensive performance of the building, and searching the technical parameters related to the side window in a large range, such as window-wall ratio, window aspect ratio, sunshade form, sunshade size, comprehensive heat transfer coefficient of a window component and visible light transmittance of window glass; and analyzing whether obvious relation exists between the technical parameters and the building operation energy consumption, the whole life carbon emission and the indoor health performance one by one, and keeping the relevant target technical parameter types.
The building operation energy consumption refers to energy input into a device system in a building, namely energy in different forms such as natural gas and electric power obtained at an entrance of the building. For example, in order to obtain the maximum ratio of the power consumption to the energy consumption, and to determine the value range of the target technical parameter, and to optimize the value range for the genetic algorithm model with the minimization as the target, in a preferred case of the present invention, in step S1, the energy consumption for the building operation per unit area is,
wherein Q is equivalent power consumption, kWh/(m) 2 ·a)，η i The equivalent electrical method conversion coefficient of the ith energy source; q i The calorific value corresponding to the ith energy source; q i,j Consumption of class i energy for class j systems; a is the building area.
If the energy source is electric energy, its mass coefficient eta i If other forms of energy are used, 1,
wherein eta is i The equivalent electrical method conversion coefficient of the ith energy source;
T 0 is ambient temperature, in kelvin, K;
T 1 the working temperature, the temperature at which the energy source applies work externally, in kelvin, K.
Building full life cycleCarbon emission is the sum of greenhouse gas emissions generated in the production, transportation, construction, demolition and operation stages of building materials, and in order to better obtain the carbon emission of the whole life cycle of the building, the carbon emission is minimized for the purpose of optimizing the comprehensive performance of the genetic algorithm model, in the preferred case of the invention, the carbon emission of the whole life cycle per unit area in the step S1 comprises the sum of greenhouse gas emissions generated in the production, transportation, construction, demolition and operation stages of building materials, expressed in terms of carbon dioxide equivalent, and expressed in units of kg CO 2 。
Because the carbon emission of the building is different due to different production and transportation processes related to the use of different building materials, in order to more comprehensively obtain the carbon emission in the building material production and transportation stages in the carbon emission of the whole life cycle of the building, under the preferable condition of the invention, the carbon emission in the building material production and transportation stages is,
wherein, C JC For carbon emission in the transportation stage of building material production, the unit kgCO 2 /m 2 ；C sc The unit of kgCO is carbon emission in the building material production stage 2 ；C ys Carbon emission for building material transportation process, kgCO 2 ；M i The consumption of the ith main building material; f i A carbon emission factor, kgCO, of the ith major building material 2 Per unit number of building materials; d i The average transport distance is the average transport distance of the ith building material, and the unit is km; t is i A carbon emission factor, kgCO, per unit weight of transport distance in the i-th building material transport mode 2 /(t·km)。
Because the building has different forms of energy obtained and used in the construction and demolition stages and has different carbon emission, under the preferred condition of the invention, the carbon emission in the construction and demolition stages of the building is,
wherein, C JZ Carbon emission for building construction stage, kgCO 2 /m 2 ；C CC kgCO for carbon emission at the stage of building demolition 2 /m 2 ；E jz,i The total energy consumption in the ith energy consumption of the building construction stage; e cc,i The total energy consumption in the ith stage of the building demolition is calculated; EF i A carbon emission factor that is a class i energy source; a is the building area, m 2 。
In order to better obtain the carbon emission in the building operation stage, the carbon emission in the building operation stage is optimized by a genetic algorithm model with the aim of minimization,
wherein, C YX For carbon emission in the construction operating phase, kgCO 2 /m 2 ；E i The year consumption of the ith type of energy of the building, unit/a; EF i The carbon emission factor of the ith energy of the building; c p Reducing carbon content of the green land carbon sink system year by kgCO 2 A; y is the building design life, unit/a; a is the building area, m 2 ；E i,j Is the i-th energy consumption of the j-system, unit/a; ER i,j Provided by renewable energy systems for class j systemsEnergy of type i, units/a.
The building indoor health performance at least comprises indoor hot and humid environment, light environment, sound environment and air quality; in order to better reduce the workload and the strength in the building design process, the operation is simple and convenient; index requirements proposed in standards, documents and basic technical parameters of typical buildings are collected, as shown in table 1, quantitative evaluation requirements of indoor health performance are obtained, and then required evaluation indexes are increased according to requirements in actual use. And calculating the sum of hours for which each index in the house meets the requirement through simulation software, such as a Rhino platform and Grasshopper software built in the Rhino platform, so that the sum is the indoor health performance of a certain building.
TABLE 1 indoor health Performance quantitative evaluation requirements
In order to better implement the above-mentioned method for optimizing the overall performance of a home based on genetic algorithm, the present invention also provides a system for implementing the above-mentioned method, the system comprising,
the objective function unit is used for taking the carbon emission of the whole life cycle of a unit area, the building operation energy consumption of the unit area and the building indoor health time ratio performance improvement as objective functions; wherein the objective function Max (f (x)) is,
min F carbon (x) The calculation function of the carbon emission of the building in the whole life period is used as the minimum target;
min F energy (x) The calculation function of the building operation energy consumption is minimized;
max F health (x) Calculating a function of the indoor health performance of the building with the maximization as a target;
the technical parameter determining unit is used for determining a target technical parameter to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size;
the value range unit is used for determining the value range of the target technical parameter;
and the optimization unit is used for establishing a genetic algorithm model, inputting the target function Max (f (x)), the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes, wherein N is a positive integer greater than or equal to 1.
The system for implementing the residence comprehensive performance optimization design method based on the genetic algorithm provided by the invention takes the improvement of the carbon emission per unit area full life cycle, the building operation energy consumption per unit area and the building indoor health time ratio performance in the objective function unit as an objective function; determining a target technical parameter to be optimized from a technical parameter determining unit; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size; determining a value range of the target technical parameter in a value range unit; and finally, establishing a genetic algorithm model in the optimization unit, inputting the objective function, the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes. The system provided by the invention can comprehensively consider three aspects of the whole life cycle carbon emission per unit area, the building operation energy consumption per unit area and the building indoor health time ratio performance improvement, and can optimize the comprehensive performance of the building house by means of a genetic algorithm, reduce excessive dependence of the building design process on engineering experience and software simulation trial and error, and output a plurality of alternative feasible schemes, thereby improving the building optimization design efficiency, reducing the workload and the strength of the building design process, and being simple and convenient to operate.
In order to better determine the type of target technical parameters that have an impact on the building operating energy consumption, the building's total life carbon emissions and the building's indoor health performance, in a preferred aspect of the invention, the system comprises,
the parameter type determining module is used for determining the target technical parameter types to be optimized, and comprises an outer wall construction form, a window structure, a window-wall ratio, a window aspect ratio, a sunshade form and a sunshade size;
and the parameter screening module is used for screening target technical parameter types which have influences on the operation energy consumption of the building, the carbon emission of the whole life cycle of the building and the indoor health performance of the building in the target technical parameter types according to the selected target technical parameter types, and taking the target technical parameter types as variable parameters which are subsequently input into the genetic algorithm model for optimization.
The embodiment of the invention also provides a storage medium, which comprises a stored program, wherein when the program runs, the device where the storage medium is located is controlled to execute the method.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a mobile terminal, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A residence comprehensive performance optimization design method based on genetic algorithm is characterized in that the residence comprehensive performance optimization design method based on the genetic algorithm comprises the following steps,
step S1, taking the carbon emission of the whole life cycle of unit area, the building operation energy consumption of unit area and the building indoor health time ratio performance improvement as objective functions; wherein the objective function Max (f (x)) is,
min F carbon (x) The calculation function of the carbon emission of the building in the whole life period is used as the minimum target;
min F energy (x) The calculation function of the building operation energy consumption is minimized;
max F health (x) Calculating a function of the indoor health performance of the building with the maximization as a target;
step S2, determining target technical parameters to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size;
step S3, determining the value range of the target technical parameter;
and step S4, establishing a genetic algorithm model, inputting the objective function Max (f (x)), the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes, wherein N is a positive integer greater than or equal to 1.
2. The method for optimizing the comprehensive performance of a dwelling based on a genetic algorithm as claimed in claim 1, wherein the step S2 includes,
step S21, determining the target technical parameter types to be optimized, including the external wall construction form, the window structure, the window-wall ratio, the window aspect ratio, the sunshade form and the sunshade size;
and step S22, screening target technical parameter types which have influences on the operation energy consumption of the building, the carbon emission of the whole life cycle of the building and the indoor health performance of the building according to the selected target technical parameter types, and taking the target technical parameter types as variable parameters which are subsequently input into the genetic algorithm model for optimization.
3. The method of claim 1, wherein in step S1, the building operation energy consumption per unit area is,
wherein Q is equivalent power consumption, kWh/(m) 2 ·a)，η i The equivalent electrical method conversion coefficient of the ith energy source; q i The calorific value corresponding to the ith energy source; q i,j Consumption of class i energy for class j systems; a is the building area.
4. The method of claim 1, wherein the carbon emissions per unit area for the full life cycle in step S1 includes the sum of greenhouse gas emissions generated during the production and transportation, construction and demolition, and operation of building materials, expressed as carbon dioxide equivalent.
5. The method of claim 4, wherein the carbon emissions during the building material production and transportation stages are,
wherein, C JC For carbon emission in the transportation stage of building material production, the unit kgCO 2 /m 2 ；C sc For carbon emission in the building material production stage, the unit kgCO 2 ；C ys Carbon emission for building material transportation process, kgCO 2 ；M i The consumption of the ith main building material; f i A carbon emission factor, kgCO, of the ith major building material 2 Per unit number of building materials; d i The average transport distance is the average transport distance of the ith building material, and the unit is km; t is a unit of i A carbon emission factor, kgCO, per unit weight of transport distance in the i-th building material transport mode 2 /(t·km)。
6. The method of claim 4, wherein the carbon emissions during the construction and demolition stages are,
wherein, C JZ Carbon emission for building construction stage, kgCO 2 /m 2 ；C CC For carbon emission at the demolition stage of the building, kgCO 2 /m 2 ；E jz,i The total energy consumption in the ith energy consumption of the building construction stage; e cc,i The total energy consumption in the ith stage of the building demolition is calculated; EF i A carbon emission factor that is a class i energy source; a is the building area, m 2 。
7. The method of claim 4, wherein the carbon emissions during the building operation are,
wherein, C YX For carbon emission in the construction operating phase, kgCO 2 /m 2 ；E i The year consumption of the ith type of energy of the building, unit/a; EF i The carbon emission factor of the ith energy of the building; c p Reducing carbon content of the green land carbon sink system year by kgCO 2 A; y is the building design life, unit/a; a is the building area, m 2 ；E i,j Is the i-th energy consumption of the j-system, unit/a; ER i,j The amount of class i energy provided by the renewable energy system for class j systems, in units/a.
8. A system for implementing the method of genetic algorithm based optimized design of home performance according to any of claims 1-7, characterized in that the system comprises,
the objective function unit is used for taking the carbon emission of the whole life cycle of a unit area, the building operation energy consumption of the unit area and the building indoor health time ratio performance improvement as objective functions; wherein the objective function Max (f (x)) is,
min F carbon (x) The calculation function of the carbon emission of the building in the whole life period is used as the minimum target;
min F energy (x) The calculation function of the building operation energy consumption is minimized;
max F health (x) Calculating a function of building indoor health performance with a maximum target;
the technical parameter determining unit is used for determining a target technical parameter to be optimized; wherein the target technical parameters at least comprise an external wall construction form, a window body structure, a window-wall ratio, a window height-width ratio, a sun-shading form and a sun-shading size;
the value range unit is used for determining the value range of the target technical parameter;
and the optimization unit is used for establishing a genetic algorithm model, inputting the target function Max (f (x)), the target technical parameters and the value range of the target technical parameters into the genetic algorithm model for optimization, and then obtaining N alternative building design schemes, wherein N is a positive integer greater than or equal to 1.
9. The system of claim 8, comprising,
the parameter type determining module is used for determining the target technical parameter types to be optimized, and comprises an outer wall construction form, a window structure, a window-wall ratio, a window aspect ratio, a sunshade form and a sunshade size;
and the parameter screening module is used for screening target technical parameter types which have influences on the operation energy consumption of the building, the carbon emission of the whole life cycle of the building and the indoor health performance of the building in the target technical parameter types according to the selected target technical parameter types, and taking the target technical parameter types as variable parameters which are subsequently input into the genetic algorithm model for optimization.
10. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, the storage medium is controlled to execute a home comprehensive performance optimization design method based on a genetic algorithm according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210743100.8A CN114969950A (en) | 2022-06-27 | 2022-06-27 | Residence comprehensive performance optimization design method and system based on genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210743100.8A CN114969950A (en) | 2022-06-27 | 2022-06-27 | Residence comprehensive performance optimization design method and system based on genetic algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114969950A true CN114969950A (en) | 2022-08-30 |
Family
ID=82964594
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210743100.8A Pending CN114969950A (en) | 2022-06-27 | 2022-06-27 | Residence comprehensive performance optimization design method and system based on genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114969950A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115392792A (en) * | 2022-10-25 | 2022-11-25 | 南方电网数字电网研究院有限公司 | New energy potential carbon reduction equivalent calculation method based on carbon emission intensity |
| CN115600283A (en) * | 2022-09-30 | 2023-01-13 | 广西大学（Cn） | Target performance oriented multi-layer point type residential parameterization generation method |
| CN115796458A (en) * | 2023-02-07 | 2023-03-14 | 四川省建筑设计研究院有限公司 | Computing system and computing method for building carbon emission |
| CN116446518A (en) * | 2023-03-17 | 2023-07-18 | 中电建路桥集团有限公司 | Assembled steel structure building system and energy-saving method thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113297660A (en) * | 2021-06-05 | 2021-08-24 | 西北工业大学 | Multi-objective-based construction scheme stage energy-saving optimization design mode construction method |
| US20220058556A1 (en) * | 2020-08-18 | 2022-02-24 | Johnson Controls Tyco IP Holdings LLP | Building system with building health analysis |
| CN114611201A (en) * | 2022-05-12 | 2022-06-10 | 北京中建协认证中心有限公司 | Multi-objective energy-saving optimization method and system for future climate building |
-
2022
- 2022-06-27 CN CN202210743100.8A patent/CN114969950A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220058556A1 (en) * | 2020-08-18 | 2022-02-24 | Johnson Controls Tyco IP Holdings LLP | Building system with building health analysis |
| CN113297660A (en) * | 2021-06-05 | 2021-08-24 | 西北工业大学 | Multi-objective-based construction scheme stage energy-saving optimization design mode construction method |
| CN114611201A (en) * | 2022-05-12 | 2022-06-10 | 北京中建协认证中心有限公司 | Multi-objective energy-saving optimization method and system for future climate building |
Non-Patent Citations (3)
| Title |
|---|
| 喻伟;王迪;李百战;: "居住建筑室内热环境低能耗营造的多目标设计方法", 土木建筑与环境工程, no. 04, pages 17 - 23 * |
| 朱春雁，梁秀英，白雪等: "《公共机构能源管理共性方法及关键技术研究》", 中国质检出版社, pages: 44 - 45 * |
| 郑锐锋等: "健康建筑标准评价体系分析", 《浙江科技学院学报》, pages 225 - 229 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115600283A (en) * | 2022-09-30 | 2023-01-13 | 广西大学（Cn） | Target performance oriented multi-layer point type residential parameterization generation method |
| CN115600283B (en) * | 2022-09-30 | 2023-11-28 | 广西大学 | Multi-layer point type residence parameterization generating method guided by target performance |
| CN115392792A (en) * | 2022-10-25 | 2022-11-25 | 南方电网数字电网研究院有限公司 | New energy potential carbon reduction equivalent calculation method based on carbon emission intensity |
| CN115796458A (en) * | 2023-02-07 | 2023-03-14 | 四川省建筑设计研究院有限公司 | Computing system and computing method for building carbon emission |
| CN116446518A (en) * | 2023-03-17 | 2023-07-18 | 中电建路桥集团有限公司 | Assembled steel structure building system and energy-saving method thereof |
| CN116446518B (en) * | 2023-03-17 | 2023-11-17 | 中电建路桥集团有限公司 | Assembled steel structure building system and energy-saving method thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114969950A (en) | Residence comprehensive performance optimization design method and system based on genetic algorithm | |
| Ascione et al. | Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness | |
| Best et al. | Modeling and optimization of building mix and energy supply technology for urban districts | |
| CN103324862B (en) | A kind of coal-burning boiler optimization method based on improving neural network and genetic algorithm | |
| Kerdan et al. | An exergy-based multi-objective optimisation model for energy retrofit strategies in non-domestic buildings | |
| Camporeale et al. | Towards nearly Zero Energy Buildings: Shape optimization of typical housing typologies in Ibero-American temperate climate cities from a holistic perspective | |
| Kristiansen et al. | Perspectives on industrialized transportable solar powered zero energy buildings | |
| CN111429301B (en) | Collaborative optimization processing method and device for capacity configuration and operation strategy | |
| Lin et al. | Application of multi-objective genetic algorithm based simulation for cost-effective building energy efficiency design and thermal comfort improvement | |
| Wang et al. | Collaborative optimization between passive design measures and active heating systems for building heating in Qinghai-Tibet plateau of China | |
| CN112348276A (en) | Comprehensive energy system planning optimization method based on multiple elements and three levels | |
| Agbodjan et al. | Bibliometric analysis of zero energy building research, challenges and solutions | |
| Manjarres et al. | Two-stage multi-objective meta-heuristics for environmental and cost-optimal energy refurbishment at district level | |
| Liu et al. | A multi-objective optimization framework for designing urban block forms considering daylight, energy consumption, and photovoltaic energy potential | |
| Liu et al. | Optimization of intelligent heating ventilation air conditioning system in urban building based on BIM and artificial intelligence technology | |
| Ren et al. | Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers | |
| Yousefi et al. | Determining the optimal size of a ground source heat pump within an air-conditioning system with economic and emission considerations | |
| CN116992548A (en) | Design optimization method and system for building and regional comprehensive energy system | |
| Bayraktar | A methodology for energy optimization of buildings considering simultaneously building envelope HVAC and renewable system parameters | |
| Dakyen et al. | Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus | |
| Brown | Toward zero net energy buildings: optimized for energy use and cost | |
| Rashid et al. | A Review of Using Solar Energy for Cooling Systems: Applications, Challenges, and Effects | |
| Yan et al. | An overview of an integrated building simulation tool-designer’s simulation toolkit (DeST) | |
| Qu et al. | Optimal design of building integrated energy systems by combining two-phase optimization and a data-driven model | |
| CN111340267A (en) | Optimization control method and equipment of regional comprehensive energy system based on block data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |