CN114764521A - Garden building shape optimization method and system based on genetic algorithm - Google Patents
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Abstract
The invention relates to the technical field of building energy conservation, and provides a garden building shape optimization method based on a genetic algorithm, which comprises the steps of obtaining peripheral shielding information of a garden building, wherein the peripheral shielding information of the garden building comprises a shielding object type and a shielding interval; determining the optimized variable and the constrained variable types of the building shapes of all categories and the value ranges of the optimized variable and the constrained variable according to the park building database; inputting optimization variables and a value range of constraint variables, establishing a building body parameterized model, and constructing building body models to be optimized under different peripheral shielding conditions by taking net energy consumption as a target function; and (4) executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body. The invention comprehensively considers the influence of energy conservation and photovoltaic utilization potential of the building body in the design of the building shape, can realize energy conservation to the maximum extent, and is beneficial to the building to realize the aim of reducing the carbon emission of the building.
Description
Technical Field
The invention relates to the technical field of building energy conservation, in particular to a garden building shape optimization method and system based on a genetic algorithm.
Background
Energy consumption and carbon emission of the Building industry account for 36% and 39% of the total Energy consumption and carbon emission of the world respectively, and near Zero Energy consumption buildings (near Zero Energy Building) and Zero Energy consumption buildings (Net Zero Energy Building) have become the development directions of Energy conservation of the world buildings. When energy conservation of a building body is pursued, balance and replacement of building energy consumption by utilization of renewable energy sources need to be considered. The energy consumption of the building in the garden is large, the scale growth is rapid, the building is mainly low and multi-layer office buildings, the shape is simple, and the application potential and the energy-saving potential of renewable energy sources are large.
CN106951611B discloses a building energy-saving design optimization method for severe cold regions based on user behaviors, aiming at the special climatic conditions of severe cold regions, utilizing investigation data to analyze and obtain a building design parameter group for promoting energy-saving behaviors; learning the actually measured behavior data by applying a machine learning algorithm in the data mining technology to obtain a more accurate random behavior pattern of the user; optimizing a traditional prediction model through a behavior mode, and correcting the prediction model by utilizing a Gaussian process based on a Bayesian theory to obtain a design parameter set for optimizing energy-saving performance; coupling the energy-saving behavior actuation and the energy-saving performance optimization parameter set to obtain an optimized parameter set; and finally, establishing a new building energy-saving design process in the severe cold region by combining the optimized design parameter set and the energy consumption prediction model.
The design of the building shape is in the early stage of concept design, and the load requirement and the application potential of renewable energy of the building are determined to a certain extent. The intervention of physical design is key to achieving the near zero energy consumption goal efficiently. The park building shape optimization method comprehensively considering the body energy conservation and the photovoltaic utilization potential improvement can effectively promote the building practice with near zero energy consumption and reduce the energy consumption and the carbon emission of the building industry.
Disclosure of Invention
The existing building shape energy-saving research park buildings mostly use common energy-saving buildings as research objects, only the energy conservation of a building body is considered in the building design process, the influence of park building shape design on the application potential of building renewable energy sources is not considered, and the building design scheme is difficult to meet the building requirement of near-zero energy consumption. In order to achieve the aim of near-zero energy consumption, the design of the building body needs to comprehensively consider the energy conservation of the building body and the improvement of the photovoltaic utilization potential.
In view of the above, the present invention is directed to a method for optimizing a physical form of a park building based on a genetic algorithm, which comprises,
step S1, acquiring peripheral shielding information of the garden building, wherein the peripheral shielding information of the garden building comprises the type of a shielding object and the shielding distance;
Step S2, extracting the building body types, and determining the optimized variable and the constrained variable types and the value ranges of the optimized variable and the constrained variable of the building bodies of each type;
step S3, inputting the value ranges of the optimization variables and the constraint variables, and constructing building body optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function;
and step S4, setting optimization parameters such as population size, maximum iteration times, cross variation probability and the like, executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body.
Preferably, step S2 includes,
step S21, calculating the shape index of the sample building based on the drawing;
step S22, dividing the sample buildings into N building body categories by using a K-means clustering algorithm, wherein N is a positive integer;
and step S23, statistically determining the value ranges of the optimization variables and the constraint variables of the building shapes of each category according to the existing building shape data, wherein the optimization variables and the constraint variables at least comprise a plane form, a layer number, a depth, an aspect ratio, a shape coefficient, a standard layer area and an orientation.
Preferably, step S3 includes,
step S31, selecting the building shape to be optimized, inputting the value ranges of optimization variables and constraint variables according to actual research data, and completing the parametric definition of the shape in Rhino and Grasshopper;
Step S32, building a simulation model of the energy consumption and the photovoltaic power generation amount of the building body under the condition that different peripheral shelters are completed in the Honeybee and Ladybug plug-in units; wherein, the energy consumption of the building body comprises heating, refrigerating and lighting energy consumption; the photovoltaic power generation comprises a roof and four photovoltaic power generation total amounts facing to the vertical surface;
step S33, connecting the building body optimization variables and the building net energy consumption simulation result data to Wallace plug-in units, and constructing building body optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function, wherein the building body optimization variables comprise the number of layers, depth, aspect ratio and orientation; the data of the building net energy consumption simulation result refers to the energy consumption value of the building body minus the photovoltaic power generation value.
Preferably, step S4 includes,
step S41, setting optimization parameters such as population size, maximum iteration times, cross variation probability and the like;
step S42, Grasshopper randomly generates an initial building body, Honeybee and Ladybug plug-ins execute building body energy consumption and photovoltaic power generation amount simulation, and the output net energy consumption value is used as a fitness function value of a genetic algorithm;
step S43, optimizing by using a genetic algorithm NSGA-II carried by Wallace plug-in, generating a new building body parameter combination, returning to Grasshopper to generate a new building body, and completing one iterative computation;
And step S44, when the set maximum optimization iteration number is reached, stopping the optimization step S42, and outputting a preferred design scheme of the body.
Preferably, in step S1, at least M garden building samples are randomly sampled according to existing garden building data in the area, where M is a positive integer and is greater than or equal to 100 and less than or equal to 300; determining the type of a shelter in the shelter information around the building in the park; the types of the shelters at least comprise no shelter, four-side shelter, north-south shelter and east-west shelter; and calculating east-west spacing and south-north aspect ratio between the M garden building samples and the shielding buildings, and taking the average value as the input data of the step S3.
The invention also discloses a system for implementing the method for optimizing the shape of the park building based on the genetic algorithm, which comprises,
the data acquisition module is used for acquiring peripheral shielding information of the garden building, wherein the peripheral shielding information of the garden building comprises the type of a shielding object and shielding intervals;
the constraint boundary module is used for counting the park buildings according to the park building characteristic database to determine the optimized variables and the constraint variable types of the building shapes of all categories and the value ranges of the optimized variables and the constraint variables;
the model parameterization module is used for inputting an optimization variable and a constraint variable value range, establishing a building body parameterization model, and constructing building body models to be optimized under different peripheral shielding conditions by taking net energy consumption as a target function;
The optimization calculation module is used for setting optimization parameters such as the population size, the maximum iteration times, the cross mutation probability and the like; and (4) executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body.
Preferably, the constraint boundary module further comprises,
the body index generating unit is used for calculating the building body index of the sample based on the drawing;
the clustering unit is used for dividing the sample buildings into N building body categories by using a K-means clustering algorithm, wherein N is a positive integer;
and the variable calculation unit is used for statistically determining the value ranges of the optimization variables and the constraint variables of the building bodies of all categories according to the existing building body data, wherein the value ranges of the optimization variables and the constraint variables at least comprise a plane form, a layer number, a depth, an aspect ratio, a body shape coefficient, a standard layer area and an orientation.
Preferably, the shape optimization module comprises a plurality of shape optimization modules,
the parameterization definition unit is used for selecting the building body to be optimized, inputting the value ranges of body optimization variables and constraint variables according to actual research data, and completing the parameterization definition of the body in Rhino and Grasshopper;
the energy consumption model building unit is used for building an energy consumption and photovoltaic power generation simulation model of the building body under the condition that different peripheral shelters are finished in the Honeybee and Ladybug plug-in units; the energy consumption of the building body comprises heating, refrigerating and lighting energy consumption; the photovoltaic power generation comprises a roof and four photovoltaic power generation total amounts facing to the vertical surface;
The optimization model construction unit is used for connecting the building body optimization variables and the building net energy consumption simulation result data to the Wallace plug-in unit, and constructing the building body optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function, wherein the building body optimization variables comprise the number of layers, depth, aspect ratio and orientation; the net energy consumption of the building means that the photovoltaic power generation value is subtracted from the energy consumption value of the building body.
The optimization calculation module comprises a plurality of optimization calculation modules,
the optimization setting unit is used for setting optimization parameters such as the size of a population, the maximum iteration times, the cross variation probability and the like;
an energy consumption calculation unit: randomly generating an initial building body by using Grasshopper, executing building body energy consumption and photovoltaic power generation simulation by using Honeybee and Ladybug plug-in units, and taking an output net energy consumption value as a fitness function value of a genetic algorithm;
the optimization iteration unit is used for performing optimization by using a genetic algorithm NSGA-II carried by Wallace plug-in, generating a new building body parameter combination, returning to Grasshopper to generate a new building body, and finishing one-time iterative computation;
and a stop optimizing condition unit for stopping the optimizing step S42 when the set maximum optimizing iteration number is reached and outputting the preferred design scheme of the shape.
Preferably, the data acquisition module comprises a basic data acquisition unit, which is used for randomly sampling at least M garden building samples according to the existing garden building data in the area, wherein M is a positive integer and is more than or equal to 100 and less than or equal to 300; determining the type of a shelter in the shelter information around the building in the park; the types of the shelters at least comprise no shelter, four-side shelter, north-south shelter and east-west shelter; and calculating the east-west distance and the south-north aspect ratio between the M garden building samples and the shielding buildings, and averaging.
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 method for optimizing the shape of the garden building based on the genetic algorithm comprises the steps of obtaining peripheral shielding information of the garden building, wherein the peripheral shielding information of the garden building comprises the type of a shelter and shielding intervals; determining an optimized variable type, a constrained variable type, an optimized variable value range and a constrained variable value range of the building according to the peripheral shielding information of the building in the park; inputting optimization variables and a value range of constraint variables, establishing a building body parameterized model, and constructing building body models to be optimized under different peripheral shielding conditions by taking net energy consumption as a target function; setting optimization parameters such as population size, maximum iteration times, cross variation probability and the like; executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body; the method and the system provided by the invention can comprehensively consider two aspects of energy conservation and photovoltaic utilization potential improvement of the building body, can realize energy conservation to the maximum extent, and are more beneficial to realizing a near-zero energy consumption target of a building compared with the prior art only considering the energy conservation of the building body; the campus building shape optimization method based on the genetic algorithm is used for automatically simulating, calculating and optimizing through a computer, and selecting a shape optimization design scheme from massive shape optimization variable combinations, so that the reliability of results can be improved, the working efficiency of building optimization design can be improved, and the building design process and energy-saving optimization can be better integrated.
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 one embodiment of the method for optimizing the physical form of a park building based on a genetic algorithm according to the present invention;
FIG. 2 is a schematic diagram of the system architecture for performing the genetic algorithm based campus building profile optimization method of the present invention;
fig. 3 is a logic diagram of an embodiment of the method for optimizing the shape of a building in a park based on a 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 claims of the present 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 shape optimization of a common energy-saving building is only relied on, the influence of the shape design of a park building on the application potential of renewable energy sources of the building is not considered in the building design process, and the comprehensive consideration of energy-saving and carbon emission systems of the park building is lacked, so that the integral energy-saving requirement and the carbon emission requirement of the building cannot be met; in the optimization process, various constraint cross influences exist, so that the problems of low building optimization design efficiency and the like are caused. The invention provides a method for optimizing the figure of a garden building based on a genetic algorithm, which comprises the following steps of as shown in figures 1 and 3,
Step S1, acquiring peripheral shielding information of the garden building, wherein the peripheral shielding information of the garden building comprises the type of a shielding object and the shielding distance;
step S2, refining the building shape categories, and determining the optimized variable and the constrained variable types and the value ranges of the optimized variable and the constrained variable of the building shapes of each category;
step S3, inputting the value ranges of the optimization variables and the constraint variables, and constructing building shape optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function;
and step S4, setting optimization parameters such as population size, maximum iteration times, cross mutation probability and the like, executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body.
The garden building shape optimization method based on the genetic algorithm comprises the steps of obtaining peripheral shielding information of a garden building, wherein the peripheral shielding information of the garden building comprises the type of a shelter and shielding intervals; determining an optimized variable type, a constrained variable type, an optimized variable value range and a constrained variable value range of the building according to the peripheral shielding information of the building in the park; inputting optimization variables and a value range of constraint variables, establishing a building body parameterized model, and constructing building body models to be optimized under different peripheral shielding conditions by taking net energy consumption as a target function; setting optimization parameters such as population size, maximum iteration times, cross variation probability and the like; executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body; the method provided by the invention can comprehensively consider two aspects of energy conservation and photovoltaic utilization potential improvement of the building body, can realize energy conservation to the greatest extent, and is more favorable for realizing the near-zero energy consumption target of the building compared with the prior art which only considers the energy conservation of the building body; the park building body optimization method based on the genetic algorithm screens out a body optimization design scheme from massive body optimization variable combinations through automatic simulation calculation optimization of a computer, can improve the reliability of results and improve the working efficiency of building optimization design, enables the building design process to be better integrated with energy-saving optimization, comprehensively considers the influence of body design on the application potential of building renewable energy sources, and achieves the final requirements of building energy saving and carbon emission.
In order to better address the performance of building shapes within a region, for the same natural environment, for generating building shapes that meet the needs of the region, in a more preferred aspect of the invention, step S2 includes,
step S21, calculating the shape index of the sample building based on the drawing;
step S22, dividing the sample buildings into N building body categories by using a K-means clustering algorithm, wherein N is a positive integer;
and step S23, statistically determining the value ranges of the optimization variables and the constraint variables of the building shapes of each category according to the existing building shape data, wherein the optimization variables and the constraint variables at least comprise a plane form, a layer number, a depth, an aspect ratio, a shape coefficient, a standard layer area and an orientation.
And combing the commonly used body indexes of the buildings according to the construction drawing data in the area, and acquiring and calculating the body indexes of the sample buildings based on drawing statistics. For example, based on drawing information of more than 200 garden building samples, calculating each physical index; secondly, after highly relevant indexes are removed through the autocorrelation analysis of the body indexes, the remaining indexes are used as clustering indexes; then, carrying out Z-score standardization processing on the data to avoid that variables with large variation range influence clustering results; after the abnormal samples are removed, dividing the park building samples into N typical body categories by using a K-means clustering algorithm; and respectively counting the value ranges of the optimized variables and the constrained variables of the typical body types, namely taking the minimum value and the maximum value of the abnormal values of the typical building body indexes after the abnormal values are eliminated as threshold values.
In order to prepare the building model of the garden building for parameterization and further optimization under the boundary constraint conditions determined in step S2, in a more preferred aspect of the present invention, step S3 includes,
step S31, selecting the building shape to be optimized, inputting the value ranges of optimization variables and constraint variables according to actual research data, and completing the parametric definition of the shape in Rhino and Grasshopper;
step S32, building a simulation model of building body energy consumption and photovoltaic power generation capacity under the condition that different peripheral shelters are completed in Honeybee and Ladybug plugins; wherein, the energy consumption of the building body comprises heating, refrigerating and lighting energy consumption; the photovoltaic power generation comprises a roof and four photovoltaic power generation total amounts facing to the vertical surface;
step S33, connecting the building body optimization variables and the building net energy consumption simulation result data to Wallace plug-in units, and constructing building body optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function, wherein the building body optimization variables comprise the number of layers, depth, aspect ratio and orientation; the data of the building net energy consumption simulation result refers to the energy consumption value of the building body minus the photovoltaic power generation value.
The system comprises a Rhino, an Openstudio, a Grasshopper, a Honeybee plug-in, a Ladybug plug-in, a building performance analysis plug-in and a Wallacei plug-in, wherein the Rhino is 3D modeling software of Robert McNeel company, the Openstudio is integrated EnergyPlus building energy consumption simulation software, the Grasshopper is a simulation generation plug-in used for parameter design in the Rhino modeling process, the Honeybee and Ladybug plug-in is a building performance analysis plug-in the Rhino modeling process, and the Wallacei is a Grasshopper plug-in.
For example, a typical shape to be optimized is selected, and the parameterized definition of the shape is completed in Rhino and Grasshopper according to the value ranges of the actual survey data input shape optimization variables and constraint variables. Building body energy consumption, e.g., heating, cooling, and lighting energy consumption, under different perimeter shading conditions accomplished in the Honeybee and Ladybug plug-ins; and building a simulation model of the photovoltaic power generation amount, such as the total photovoltaic power generation amount of the roof and the four facing vertical surfaces. The window-wall ratio, the thermal parameters of the building enclosure, the efficiency of an air conditioning system, indoor thermal disturbance, indoor environmental parameters, the running mode and other simulation parameters are set according to GB/T51350-2019 near-zero energy consumption building technical standard; the usable area of the building surface and the necessary access channel, the photovoltaic laying area of the roof and the vertical surface are 80% of the surface area; the photovoltaic material is a monocrystalline silicon photovoltaic component, the power generation efficiency is 18%, and the system efficiency is 0.75.
And connecting the simulation results of the building body optimization variables such as the number of layers, depth, aspect ratio and orientation and the building net energy consumption such as the body energy consumption-photovoltaic power generation amount into the Wallace plug-in, and constructing body optimization models under different peripheral occlusion conditions by taking the minimum net energy consumption as a target.
In order to optimize the heuristic algorithm for the building model to be optimized, so that the energy saving of the building body and the photovoltaic utilization potential are improved, and the energy saving can be realized to the maximum extent, in the preferred case of the invention, the step S4 includes,
step S41, setting optimization parameters such as population size, maximum iteration times, cross mutation probability and the like;
step S42, Grasshopper randomly generates an initial building body, Honeybee and Ladybug plug-ins execute building body energy consumption and photovoltaic power generation amount simulation, and the output net energy consumption value is used as a fitness function value of a genetic algorithm;
step S43, optimizing by using a genetic algorithm NSGA-II carried by Wallace plug-in, generating a new building body parameter combination, returning to Grasshopper to generate a new building body, and completing one iteration calculation;
and step S44, when the set maximum optimization iteration number is reached, stopping the optimization step S42 and outputting a preferred design scheme of the body.
For example, the setting of the optimized parameters such as the population size, the maximum number of iterations, and the cross mutation probability is shown in table 1.
TABLE 1 optimized parameter settings
Wherein, Grasshopper is applied to randomly generate an initial building shape; the Honeybee and Ladybug plug-in unit executes simulation of body energy consumption and photovoltaic power generation amount and outputs a net energy consumption value; the simulation result is used as a fitness function value of the genetic algorithm, and optimization is performed by utilizing the genetic algorithm NSGA-II carried by Wallace plug-in; and generating a new body parameter combination after optimization, returning to Grasshopper to generate a new building body, and finishing one-time iterative computation. When the set maximum optimization iteration times are reached, optimizing is stopped; if the result meets the requirement that the change rate of the average adaptability of the front generation and the rear generation is less than 0.1%, the optimization result is considered to be effective, and a preferable design scheme of the body is output.
Because the shielding of the surrounding buildings can generate great influence on the natural lighting and surface solar radiation quantity of the designed buildings, so that the illumination, heating, refrigerating energy consumption and solar photovoltaic application potential of the buildings are influenced, the surrounding shielding conditions of a typical building, such as whether shielding exists in the east-west direction and the south-north direction or not and the shielding distance, need to be determined before optimization, in the preferable condition of the invention, at least M garden building samples are randomly sampled according to the existing garden building data in the area in step S1, wherein M is a positive integer, and M is more than or equal to 100 and less than or equal to 300; determining the type of a shelter in the shelter information around the building in the park; the types of the shelters at least comprise no shelter, four-side shelter, south-north shelter and east-west shelter; and calculating east-west spacing and south-north aspect ratio between the M garden building samples and the shielding buildings, and taking the average value as the input data of the step S3.
The east-west distance between buildings and the north-south aspect ratio, namely the ratio of the height of the south shielding building to the distance between the two buildings, are important measurement factors of the shielding influence degree and are obtained through the existing building drawing data in the region.
For example, in a hot-summer and cold-winter area, selecting a typical city of the climate area, establishing a park building database of each city based on geographic mapping data of a planning office of the corresponding city, selecting M200 park building samples by adopting a random sampling method, and combing peripheral shielding conditions, such as no shielding, four-side shielding, only north-south shielding, only east-west shielding and the like; and calculating the east-west spacing and the south-north aspect ratio between each building sample and the shielding building, and taking the average value of the calculation results as the input data of the step S2.
In order to better perform the above described genetic algorithm based campus building physique optimization method, the present invention further provides a system, as shown in fig. 2 and 3, comprising,
the data acquisition module is used for acquiring peripheral shielding information of the garden building, wherein the peripheral shielding information of the garden building comprises the type of a shielding object and shielding intervals;
the constraint boundary module is used for counting the park buildings according to the park building characteristic database to determine the optimized variables and the constraint variable types of the building shapes of all categories and the value ranges of the optimized variables and the constraint variables;
the model parameterization module is used for inputting an optimization variable and a constraint variable value range, establishing a building body parameterization model, and constructing building body models to be optimized under different peripheral shielding conditions by taking net energy consumption as a target function;
the optimization calculation module is used for setting optimization parameters such as the size of a population, the maximum iteration times, the cross variation probability and the like; and (4) executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body.
According to the system provided by the invention, the peripheral shielding information of the garden building is acquired through the data acquisition module, and the peripheral shielding information of the garden building comprises the type of a shielding object and the shielding distance; determining peripheral shielding information of the building in the park according to the constraint boundary module, and determining an optimized variable type, a constraint variable type, an optimized variable value range and a constraint variable value range of the building; inputting an optimization variable and a constraint variable value range; building a building body parameterized model in the model parameterized module, and constructing a building body model to be optimized under different peripheral occlusion conditions by taking net energy consumption as an objective function; setting optimization parameters such as the population size, the maximum iteration times, the cross variation probability and the like in an optimization calculation module; executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body; the system provided by the invention can comprehensively consider two aspects of energy conservation and photovoltaic utilization potential improvement of the building body, can realize energy conservation to the greatest extent, and is more favorable for realizing the near-zero energy consumption target of the building compared with the prior art which only considers the energy conservation of the building body; the optimization is calculated through automatic simulation of a computer, a body optimization design scheme is screened from massive body optimization variable combinations, the reliability of results can be improved, the working efficiency of building optimization design is improved, the building design process and energy-saving optimization are better integrated, the influence of body design on the application potential of building renewable energy sources is comprehensively considered, and the final requirements of building energy saving and carbon emission are met.
In order to determine the multiple constraint boundary conditions for the building model to be optimized, so that the building design optimization process is more efficient, in a more preferred aspect of the present invention, the constraint boundary module further comprises,
the body index generating unit is used for calculating the body index of the building of the sample based on the drawing;
the clustering unit is used for dividing the sample buildings into N building body categories by using a K-means clustering algorithm, wherein N is a positive integer;
and the variable calculation unit is used for statistically determining the value ranges of the optimization variables and the constraint variables of the building bodies of all categories according to the existing building body data, wherein the value ranges of the optimization variables and the constraint variables at least comprise a plane form, a layer number, a depth, an aspect ratio, a body shape coefficient, a standard layer area and an orientation.
And (3) combing the physical indexes commonly used by the buildings according to historical building drawing data in the region, and acquiring and calculating the physical indexes of the sample buildings in a physical index generation unit based on drawing statistics. For example, based on drawing information of more than 200 garden building samples, calculating each physical index; secondly, analyzing the autocorrelation of the body indexes through a clustering unit, and taking the residual indexes as clustering indexes after eliminating highly correlated indexes; then, carrying out Z-score standardization processing on the data to avoid that variables with large variation range influence clustering results; after the abnormal samples are removed, dividing the park building samples into N typical body categories by using a K-means clustering algorithm; in the variable calculation unit, for each typical body type, the value ranges of the optimization variables and the constraint variables are respectively counted, that is, the minimum value and the maximum value of each body index of the typical building after the abnormal value is removed are used as threshold values.
In order to more vividly perform further optimization aiming at the building model and perform heuristic algorithm optimization aiming at the building model to be optimized, so that the energy conservation and the photovoltaic utilization potential of the building body are improved, and the energy conservation can be realized to the greatest extent, under the preferable condition of the invention, the shape optimization module comprises,
the parameterization definition unit is used for selecting the building shape to be optimized, inputting the value ranges of the shape optimization variables and the constraint variables according to actual research data, and completing the parameterization definition of the shape in the Rhino and the Grasshopper;
the energy consumption model building unit is used for building an energy consumption and photovoltaic power generation simulation model of the building body under the condition that different peripheral shelters are finished in the Honeybee and Ladybug plug-in units; wherein, the energy consumption of the building body comprises heating, refrigerating and lighting energy consumption; the photovoltaic power generation comprises a roof and four photovoltaic power generation total amounts facing to the vertical surface;
the optimization model construction unit is used for connecting the building body optimization variables and the building net energy consumption simulation result data to the Wallace plug-in unit, and constructing the building body optimization models under different peripheral shielding conditions by taking net energy consumption as an objective function, wherein the building body optimization variables comprise the number of layers, depth, aspect ratio and orientation; the net building energy consumption is the value of the energy consumption of the building body minus the value of the photovoltaic power generation.
The optimization calculation module comprises a plurality of optimization calculation modules,
the optimization setting unit is used for setting optimization parameters such as the size of a population, the maximum iteration times, the cross variation probability and the like;
an energy consumption calculation unit: randomly generating an initial building body by using Grasshopper, executing the simulation of the energy consumption and the photovoltaic power generation amount of the building body by using the Honeybee and Ladybug plug-in units, and taking the output net energy consumption value as a fitness function value of a genetic algorithm;
the optimization iteration unit is used for performing optimization by using a genetic algorithm NSGA-II carried by Wallace plug-in, generating a new building body parameter combination, returning to Grasshopper to generate a new building body, and finishing one-time iterative computation;
and a stop optimizing condition unit for stopping the optimizing step S42 when the set maximum optimizing iteration number is reached and outputting the preferred design scheme of the shape.
For example, a typical shape to be optimized is selected in a parameterization definition unit, and the parameterization definition of the shape is completed in Rhino and Grasshopper according to the value ranges of the actual survey data input shape optimization variables and constraint variables. Under the condition that different peripheral shelters are completed in Honeybee and Ladybug plugins in the energy consumption calculation unit, the energy consumption of the building body, such as heating, refrigerating and lighting energy consumption; and building a simulation model of the photovoltaic power generation amount, such as the total photovoltaic power generation amount of the roof and the four facing vertical surfaces. The window-wall ratio, the thermal parameters of the enclosure structure, the efficiency of an air conditioning system, indoor thermal disturbance, indoor environmental parameters, the operation mode and other simulation parameters are set according to the GB/T51350-plus 2019 near-zero energy consumption building technical standard; the usable area of the building surface and the necessary access channel, the photovoltaic laying area of the roof and the vertical surface are 80% of the surface area; the photovoltaic material is a monocrystalline silicon photovoltaic component, the power generation efficiency is 18%, and the system efficiency is 0.75.
And building body optimization variables such as the number of layers, depth, aspect ratio and orientation and the net building energy consumption, for example, in a model building unit, a simulation result of the body energy consumption-photovoltaic power generation amount is connected to a Wallace plug-in, and a body optimization model under different peripheral occlusion conditions is built with the aim of minimum net energy consumption.
Because the shielding of the surrounding buildings can generate great influence on the natural lighting and the surface solar radiation quantity of the designed buildings, so that the illumination, heating, refrigerating energy consumption and the solar photovoltaic application potential of the buildings are influenced, the surrounding shielding conditions of typical buildings, such as whether shielding exists in the east-west direction and the south-north direction or not and the shielding distance, need to be determined before optimization, and under the preferable condition of the invention, the data acquisition module comprises a basic data acquisition unit which is used for randomly sampling at least M garden building samples according to the existing garden building data in the area, wherein M is a positive integer and is more than or equal to 100 and less than or equal to 300; determining the type of a shelter in the shelter information around the building in the park; the types of the shelters at least comprise no shelter, four-side shelter, north-south shelter and east-west shelter; and calculating the east-west distance and the south-north aspect ratio between the M garden building samples and the shielding buildings, and averaging.
For example, in a hot-summer and cold-winter area, the basic data acquisition unit in the data acquisition module is used for selecting a typical city of the climate zone, establishing a campus building database of each city based on geographic mapping data of a planning bureau of the corresponding city, selecting M to 200 campus building samples by using a random sampling method, and combing peripheral occlusion conditions, such as no occlusion, four-side occlusion, only north-south occlusion, only east-west occlusion and the like; and calculating the east-west distance and the north-south aspect ratio between each building sample and the shielding building, and taking the average value of the calculation results as the input data of the step S2.
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 shown as a series of combinations 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 will appreciate that the embodiments described in this specification are presently preferred and that no acts or modules are 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 the 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 position, or may be distributed on multiple 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 may be implemented in the form of hardware, or may also be implemented in the 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 substantially or partially contribute to the prior art, or all or part of the technical solution 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, etc.) 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 disk, or an optical disk, and 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 garden building shape optimization method based on genetic algorithm is characterized in that the garden building shape optimization method based on genetic algorithm comprises the following steps,
step S1, acquiring peripheral shielding information of the garden building, wherein the peripheral shielding information of the garden building comprises the type of a shielding object and shielding intervals;
step S2, extracting the building body types, and determining the optimized variable and the constrained variable types and the value ranges of the optimized variable and the constrained variable of the building bodies of each type;
step S3, inputting the value ranges of the optimization variables and the constraint variables, and constructing building body optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function;
and step S4, setting optimization parameters such as population size, maximum iteration times, cross variation probability and the like, executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body.
2. The genetic algorithm-based campus building configuration optimization method of claim 1, wherein the step S2 comprises,
step S21, calculating the shape index of the sample building based on the drawing;
step S22, dividing the sample buildings into N building body categories by using a K-means clustering algorithm, wherein N is a positive integer;
and step S23, statistically determining the value ranges of the optimization variables and the constraint variables of the building shapes of each category according to the existing building shape data, wherein the optimization variables and the constraint variables at least comprise a plane form, a layer number, a depth, an aspect ratio, a shape coefficient, a standard layer area and an orientation.
3. The genetic algorithm-based campus building configuration optimization method of claim 1, wherein the step S3 comprises,
step S31, selecting the building shape to be optimized, inputting the value ranges of optimization variables and constraint variables according to actual research data, and completing the parametric definition of the shape in Rhino and Grasshopper;
step S32, building a simulation model of building body energy consumption and photovoltaic power generation capacity under the condition that different peripheral shelters are completed in Honeybee and Ladybug plugins; wherein, the energy consumption of the building body comprises heating, refrigerating and lighting energy consumption; the photovoltaic power generation comprises a roof and four photovoltaic power generation total amounts facing to the vertical face;
Step S33, connecting the building body optimization variables and the building net energy consumption simulation result data to Wallace plug-in units, and constructing building body optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function, wherein the building body optimization variables comprise the number of layers, depth, aspect ratio and orientation; the building net energy consumption simulation result data refers to the value of the photovoltaic power generation quantity subtracted from the value of the building body energy consumption.
4. The method for optimizing the physical shape of the campus based on the genetic algorithm as claimed in claim 1, wherein the step S4 comprises,
step S41, setting optimization parameters such as population size, maximum iteration times, cross mutation probability and the like;
step S42, Grasshopper randomly generates an initial building body, Honeybee and Ladybug plug-ins execute building body energy consumption and photovoltaic power generation amount simulation, and the output net energy consumption value is used as a fitness function value of a genetic algorithm;
step S43, optimizing by using a genetic algorithm NSGA-II carried by Wallace plug-in, generating a new building body parameter combination, returning to Grasshopper to generate a new building body, and completing one iteration calculation;
and step S44, when the set maximum optimization iteration number is reached, stopping the optimization step S42, and outputting a preferred design scheme of the body.
5. The method for optimizing the physical form of a garden building based on a genetic algorithm as claimed in any one of claims 1 to 4, wherein in step S1, at least M garden building samples are randomly sampled according to the existing garden building data in the area, M is a positive integer, M is greater than or equal to 100 and less than or equal to 300; determining the type of a shelter in the shelter information around the building in the park; the types of the shelters at least comprise no shelter, four-side shelter, north-south shelter and east-west shelter; and calculating east-west spacing and south-north aspect ratio between the M garden building samples and the shielding buildings, and taking the average value as the input data of the step S3.
6. A system for implementing the genetic algorithm based campus building physique optimization method according to any one of claims 1 to 5, wherein the system comprises,
the data acquisition module is used for acquiring peripheral shielding information of the garden building, wherein the peripheral shielding information of the garden building comprises the type of a shielding object and shielding intervals;
the constraint boundary module is used for counting the park buildings according to the park building characteristic database to determine the optimized variables and the constraint variable types of the building shapes of all categories and the value ranges of the optimized variables and the constraint variables;
the model parameterization module is used for inputting an optimization variable and a constraint variable value range, establishing a building body parameterization model, and constructing building body models to be optimized under different peripheral shielding conditions by taking net energy consumption as a target function;
The optimization calculation module is used for setting optimization parameters such as the population size, the maximum iteration times, the cross mutation probability and the like; and (4) executing an optimization process by adopting a genetic algorithm, and outputting a preferred design scheme of the body.
7. The system of claim 6, wherein the constraint boundary module further comprises,
the body index generating unit is used for calculating the body index of the building of the sample based on the drawing;
the clustering unit is used for dividing the sample buildings into N building body categories by using a K-means clustering algorithm, wherein N is a positive integer;
and the variable calculation unit is used for statistically determining the value ranges of the optimization variables and the constraint variables of the building bodies of all categories according to the existing building body data, wherein the value ranges of the optimization variables and the constraint variables at least comprise a plane form, a layer number, a depth, an aspect ratio, a body shape coefficient, a standard layer area and an orientation.
8. The system of claim 6, wherein the shape optimization module comprises,
the parameterization definition unit is used for selecting the building shape to be optimized, inputting the value ranges of the shape optimization variables and the constraint variables according to actual research data, and completing the parameterization definition of the shape in the Rhino and the Grasshopper;
The energy consumption model building unit is used for building an energy consumption and photovoltaic power generation simulation model of the building body under the condition that different peripheral shelters are finished in the Honeybee and Ladybug plug-in units; the energy consumption of the building body comprises heating, refrigerating and lighting energy consumption; the photovoltaic power generation comprises a roof and four photovoltaic power generation total amounts facing to the vertical surface;
the optimization model construction unit is used for connecting the building body optimization variables and the building net energy consumption simulation result data to the Wallace plug-in unit, and constructing the building body optimization models under different peripheral occlusion conditions by taking net energy consumption as an objective function, wherein the building body optimization variables comprise the number of layers, depth, aspect ratio and orientation; the building net energy consumption is that the photovoltaic power generation value is subtracted from the building body energy consumption value;
the optimization calculation module comprises a plurality of optimization calculation modules,
the optimization setting unit is used for setting optimization parameters such as the size of a population, the maximum iteration times, the cross variation probability and the like;
an energy consumption calculation unit: randomly generating an initial building body by using Grasshopper, executing building body energy consumption and photovoltaic power generation simulation by using Honeybee and Ladybug plug-in units, and taking an output net energy consumption value as a fitness function value of a genetic algorithm;
the optimization iteration unit is used for performing optimization by using a genetic algorithm NSGA-II carried by Wallace plug-in, generating a new building body parameter combination, returning to Grasshopper to generate a new building body, and finishing one-time iterative computation;
And a stop optimizing condition unit for stopping the optimizing step S42 when the set maximum optimizing iteration number is reached and outputting the preferred design scheme of the shape.
9. The system according to any one of claims 6 to 8, wherein the data acquisition module comprises a basic data acquisition unit, which is used for randomly sampling at least M garden building samples according to the existing garden building data in the area, wherein M is a positive integer, and M is more than or equal to 100 and less than or equal to 300; determining the type of a shelter in the shelter information around the building in the park; the types of the shelters at least comprise no shelter, four-side shelter, north-south shelter and east-west shelter; and calculating the east-west distance and the south-north aspect ratio between the M garden building samples and the shielding buildings, and averaging.
10. A storage medium comprising a stored program, wherein the program, when executed, controls a device on the storage medium to perform the method for optimizing a physical property of a campus building based on genetic algorithms according to any one of claims 1 to 5.
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