CN112818458B - Building green performance design optimization method and system - Google Patents
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Abstract
The invention discloses a building green performance design optimization method and system, comprising the steps of selecting an evaluation index for evaluating the green performance of a building, and determining independent variable parameters influencing the green performance of the building; building a building model to be optimized, and importing the building model to be optimized into modeFRONTIER software; according to the selected evaluation index for evaluating the green performance of the building, a multi-objective optimization algorithm is executed by using modeFRONTIER software to obtain a Pareto optimization solution set; screening the Pareto optimal solution set to obtain a building green performance design optimization result; the invention utilizes modularized and visual programming languages, has simple operation process, and effectively improves the optimizing efficiency and the accuracy of optimizing solution sets; through cluster analysis or multi-criterion decision analysis on the Pareto optimal solution set, the screening of the Pareto optimal solution set is realized, the decision efficiency and the precision of multi-objective optimal design of the building are improved, the design decision is made for an aided designer, and the objectivity and the scientificity of the decision are improved.
Description
Technical Field
The invention belongs to the technical field of building design optimization, and particularly relates to a building green performance design optimization method and system.
Background
In the process of building design, the optimization of the design scheme to balance the green performance of the building is the original purpose of designing the green building by a designer; with the development of scientific technology, a method for optimizing green performance of a building by utilizing performance simulation and algorithm optimization is applied to building design; however, since the Pareto optimal solution of the multi-objective optimization is high-dimensional data having multiple design parameters (independent variables) and multiple green performances (dependent variables), and it is difficult to artificially compare the merits of a set of Pareto optimal solutions, the difficulty of making design decisions is great. The existing research and practice pay more attention to the optimization process for obtaining the Pareto optimal solution set, and neglect to screen the final building optimization design scheme from the Pareto optimal solution set and mine the data characteristics of the Pareto optimal solution set; with the development of discipline intersection, methods for integrating performance simulation and optimization algorithms are more and more diversified, and corresponding requirements on the operability and functions of the methods are met, including operation difficulty, algorithm suitability, pareto optimization solution set potential mechanism, whether a clear and objective design decision can be provided for a user or not.
Performance simulation is carried out on the related building design elements and performance evaluation indexes; the multi-objective optimization is to mine a potential scheme by utilizing a multi-objective optimization algorithm, and balance and solve a multi-variable multi-objective optimization problem. The building green performance optimization design method integrating the performance simulation and optimization algorithm mainly comprises two types, namely a building energy-saving optimization design method based on mathematical software and a building green performance optimization design method based on a parameterized design platform.
The building energy-saving optimization design method based on mathematical software MATLAB is based on interaction of the mathematical software MATLAB and energy consumption simulation software (such as energy plus/TRNSYS), and is to automatically search for a scheme solution with the lowest energy consumption by utilizing various optimization algorithms; whereas MATLAB uses programming languages such as C, C ++, java, etc.; the method has higher requirements on programming languages and higher operation difficulty; cross-platform interaction of mathematical software and energy consumption simulation software requires joint simulation interfaces such as BCVTB, jEPlus, MLE+ and the like; can only be related to energy consumption simulation software, and the optimization performance is limited to energy conservation.
Functional plug-ins such as a parameterized design platform Rhino integrated geometric modeling, performance simulation, evaluation, optimization and the like; although the method has algorithm plug-ins such as Octopus, the optional optimization algorithms are limited such as SPEA-2 and HypE algorithms; the optimization result is displayed by a data table and a scatter diagram, the optimal solution needs to be selected manually, namely the subjectivity and the uncertainty of the optimal solution are large, and the analysis function of the optimization result is lost.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a building green performance design optimization method and system, so as to solve the technical problems of large optimization performance limitation, complex operation process, manual selection of optimal solution and large uncertainty of the existing building green performance design method.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention provides a building green performance design optimization method, which comprises the following steps:
selecting an evaluation index for evaluating the green performance of the building, and determining independent variable parameters affecting the green performance of the building;
building a building model to be optimized, and importing the building model to be optimized into modeFRONTIER software;
according to the selected evaluation index for evaluating the green performance of the building, a multi-objective optimization algorithm is executed by using modeFRONTIER software to obtain a Pareto optimization solution set;
and screening the Pareto optimal solution set by using a cluster analysis method or a multi-criterion decision analysis method to obtain a multi-objective optimal solution, and obtaining a building green performance design optimization result.
Further, the evaluation indexes for evaluating the green performance of the building include the energy consumption EUI per unit building area, the percentage of dissatisfaction with thermal environment PPD, the percentage of space full natural lighting time sDA and the annual light exposure ASE.
Further, the optimization process of the multi-objective optimization algorithm is adopted, and the minimum value of the unit building area energy consumption EUI, the percentage of dissatisfied thermal environment PPD and the annual light exposure ASE and the maximum value of the spatial total natural lighting time percentage sDA are adopted as optimization objectives.
Further, independent parameters affecting green performance of the building include building orientation, window wall ratio, floor height, standard floor area, aspect ratio, window SHGC, window heat transfer coefficient, exterior wall heat transfer coefficient, air conditioning heating temperature, air conditioning cooling temperature, and illumination power density.
Further, the building model to be optimized comprises a three-dimensional model of a building structure, building structure parameters, active equipment control parameters and meteorological data of an area where the active equipment control parameters are located.
Further, a multi-objective optimization algorithm process is executed by using modeFRONTIER software, and a Latin hypercube sampling method is adopted for sampling, so that an initial sample is obtained; optimizing the initial sample by using an optimization module of modeFRONTIER software to obtain a Pareto optimization solution set; wherein, the pilOPT algorithm is built in the optimization module of the modeFRONTIER software.
Further, a cluster analysis method is adopted to carry out a screening process on the Pareto optimal solution set:
the data source is a Pareto optimal solution set, and the constraint condition is a key design independent variable parameter affecting the green performance of the building; the sensitivity analysis is carried out on the independent variable parameters affecting the green performance of the building by adopting modeFRONTIER software, so as to obtain key design independent variable parameters affecting the green performance of the building; utilizing a cluster analysis module in modeFRONTIER software to perform cluster analysis on the Pareto optimal solution set; wherein, the cluster analysis module of the modeFRONTIER software is internally provided with a K-Means algorithm.
Further, a multi-criterion decision analysis method is adopted to carry out a screening process on the Pareto optimal solution set:
the data source is a Pareto optimal solution set, the constraint condition is the minimum value of unit building area energy consumption EUI, percentage of dissatisfaction with thermal environment PPD and annual light exposure ASE, and the maximum value of spatial total natural lighting time percentage sDA; utilizing a multi-criterion analysis module in modeFRONTIER software to carry out multi-criterion decision analysis on the Pareto optimal solution set; the multi-criterion analysis module of the modeFRONTIER software is internally provided with a Linear MCDM algorithm.
The invention also provides a building green performance design optimization system, which comprises a variable module, a model module, an optimizing module and an analyzing module;
the variable module is used for selecting an evaluation index for evaluating the green performance of the building and determining independent variable parameters affecting the green performance of the building;
the model module is used for constructing a building model to be optimized, and importing the building model to be optimized into modeFRONTIER software;
the optimizing module is used for executing a multi-objective optimizing algorithm by utilizing modeFRONTIER software according to the selected evaluation index for evaluating the green performance of the building to obtain a Pareto optimizing solution set;
and the analysis module is used for screening the Pareto optimal solution set by using a cluster analysis method or a multi-criterion decision analysis method to obtain a multi-objective optimal solution, namely obtaining a building green performance design optimization result.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a building green performance design optimization method and system, which are characterized in that indexes and independent variable parameters for evaluating the green performance of a building are determined, and modeFRONTIER software is utilized for multi-objective optimization to obtain a Pareto optimization solution set; the modularized and visual programming language is utilized, the operation process is simple, and the optimizing efficiency and the accuracy of optimizing solution sets are effectively improved; through cluster analysis or multi-criterion decision analysis on the Pareto optimal solution set, the screening of the Pareto optimal solution set is realized, the decision efficiency and the precision of multi-objective optimization design of the building are improved, and the objectivity and the scientificity of the decision are improved for assisting a designer to make a design decision; the optimization method has the advantages that the optimization performance limitation is small, the selection of the optimal solution is determined by the preference of the user, namely, the optimal solution can be automatically generated according to the preference of the user, and the accuracy is higher.
Furthermore, the energy consumption EUI of a unit building area is used as an energy consumption evaluation index, the percentage PPD of unsatisfied thermal environment is used as a thermal performance evaluation index, the solar radiation heat obtaining and the visible light influence the indoor photo-thermal environment, and the natural environment is reasonably utilized to meet the comfort requirement without increasing the energy consumption; the percentage sDA of the total natural lighting time and the annual light exposure ASE ensure that the natural light is sufficient and the glare problem is avoided.
Furthermore, the Latin hypercube sampling can meet the probability distribution covering the full parameter space by utilizing the modeFRONTIER software optimizing process; the pilOPT algorithm can automatically stop optimizing, so that the operability of the algorithm is obviously enhanced, and the applicability of the method is greatly improved; the optimization algorithm pilOPT has the characteristics of global and local search, and ensures the uniform distribution and better convergence of the Pareto optimal solution set.
Furthermore, by adopting a cluster analysis method or a multi-criterion decision analysis method for the Pareto optimal solution set, the influence degree of building parameters on the green performance is quantized, so that a designer can conveniently mine the mapping relation between building elements and the green performance, a user can be assisted in making a design decision, and an optimal solution meeting the green performance of the building can be selected.
Drawings
FIG. 1 is a schematic workflow diagram of a building green performance design optimization method according to an embodiment;
FIG. 2 is a schematic diagram of a performance optimization framework in an embodiment;
FIG. 3 is a graph showing the results of sensitivity analysis of the effect space annual light exposure ASE in the example.
FIG. 4 is a graph of the results of the Davies-Bouldin index for partitional clustering analysis in the examples;
FIG. 5 is a graph of the results of cluster analysis in an embodiment;
FIG. 6 is a diagram of the results of ranking Pareto optimal solution sets using a multi-criteria decision analysis method in an embodiment.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects solved by the invention more clear, the following specific embodiments are used for further describing the invention in detail. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides a building green performance design optimization method, which comprises the following steps:
step 1, selecting an evaluation index for evaluating the green performance of a building, and determining independent variable parameters influencing the green performance of the building; the evaluation indexes for evaluating the green performance of the building comprise energy consumption EUI of unit building area, percentage PPD of unsatisfied thermal environment, percentage sDA of all-natural lighting time of space and annual light exposure ASE; independent variable parameters affecting the green performance of the building include building orientation, window wall ratio, layer height, standard layer area, aspect ratio, window SHGC, window heat transfer coefficient, outer wall heat transfer coefficient, air conditioning heating temperature, air conditioning cooling temperature, and illumination power density; the window wall ratio comprises a southbound window wall ratio, a northbound window wall ratio, a westbound window wall ratio and an eastbound window wall ratio.
Step 2, building a building model to be optimized, and importing the building model to be optimized into modeFRONTIER software; the building model to be optimized comprises a three-dimensional model of a building structure, building structure parameters, active equipment control parameters and meteorological data of an area where the active equipment control parameters are located; the three-dimensional model comprises morphological parameters (such as length-width ratio), functional partitions and window wall ratio information of the building structure; the building structure parameters comprise an outer wall heat transfer coefficient, a window heat transfer coefficient, a roof heat transfer coefficient, window visible light transmittance and window light transmittance; active plant control parameters include hvac type, hvac system integrated coefficient of performance, personnel density, zone load, heating design temperature, fresh air volume, cooling design temperature, lighting power density, and plant power density.
Step 3, according to the selected evaluation index for evaluating the green performance of the building, a multi-objective optimization algorithm is executed by using modeFRONTIER software to obtain a Pareto optimization solution set; in the invention, a multi-objective optimization algorithm optimizing process is utilized, and the minimum value of the percentage of energy consumption EUI, thermal environment dissatisfaction, PPD and annual light exposure ASE in unit building area and the maximum value of the percentage of space total natural lighting time sDA are adopted as optimizing targets.
Specifically, a modeFRONTIER software is utilized to execute a multi-objective optimization algorithm process, and a Latin hypercube sampling method is adopted to sample, so as to obtain an initial sample; optimizing the initial sample by using an optimization module of modeFRONTIER software to obtain a Pareto optimization solution set; wherein, the pilOPT algorithm is built in the optimization module of the modeFRONTIER software.
Step 4, screening the Pareto optimal solution set by using a cluster analysis method or a multi-criterion decision analysis method to obtain a multi-objective optimal solution, namely obtaining a building green performance design optimization result; and the building green performance design optimization result is used as a building green performance optimal solution for a user to make design decisions.
In the invention, the process of screening the Pareto optimal solution set by using a cluster analysis method comprises the following steps:
dividing a plurality of optimization solutions in a Pareto optimization solution set into a plurality of groups of solution clusters which are mutually disjoint according to the principle that independent variables in the clusters are homogeneous and dependent variables among the clusters are heterogeneous; according to designer preference, presetting the weight of an optimization target, carrying out cluster-by-cluster screening on a plurality of solution clusters to obtain optimal solutions with preset preference, obtaining a cluster analysis result, and obtaining a multi-target optimal solution, namely obtaining a building green performance design optimization result which is used as a building green performance oriented optimal design result.
The clustering analysis process specifically comprises the following steps:
the solution sets are optimized with Pareto as the data source.
And carrying out sensitivity analysis on the independent variable parameters influencing the green performance of the building by using a sensitivity analysis module of modeFronier software to obtain the key design independent variable parameters influencing the green performance of the building.
Taking key design independent variable parameters and optimization targets which influence the green performance of the building as constraint conditions; utilizing a cluster analysis module in modeFRONTIER software, adopting a K-Means algorithm to perform cluster analysis on the Pareto optimal solution set, and randomly decomposing the Pareto optimal solution set into a group of disjoint clusters; wherein the K-Means algorithm settings include: maximum iteration number, maximum cluster number of clusters, random generator seed and cluster numbering mode.
Creating a partitional clustering model by using modeFRONTIER software; the K-Means algorithm presets the distance in the reduced cluster when each iteration is performed, and the evaluation index of the cluster model quality is measured by using the Dyson Bobber index DBI; davidsenburg Ding Zhishu DBI is the ratio of the sum of intra-cluster variance and inter-cluster distance, with lower davidsenburg index DBI leading to better cluster quality for cluster partitioning.
Selecting a partitional clustering model of the DBI with the lowest Dyson Babbing index, and performing partitional clustering analysis on the Pareto optimal solution set to obtain a clustering data table; and obtaining a multi-objective optimal solution from the clustering data table to obtain a building green performance design optimization result.
Ordering and selecting among multiple alternatives is a relatively common but often difficult task, and multi-criterion decision analysis methods refer to making decisions for selecting among multiple alternatives with conflicting, non-co-ordinatable degrees; the multi-criterion decision analysis method is to carry out structural analysis on the Pareto optimal solution set according to a pre-designated decision rule, and obtain a suggestion with basis for a decision maker to refer.
In the invention, a multi-criterion decision analysis method is adopted to carry out a screening process on the Pareto optimal solution set;
the data source is a Pareto optimal solution set, and the constraint condition is an optimization target; utilizing a multi-criterion analysis module in modeFRONTIER software to carry out multi-criterion decision analysis on the Pareto optimal solution set; the multi-criterion analysis module of the modeFRONTIER software is internally provided with a Linear MCDM algorithm.
The method specifically comprises the following steps:
the data sources are selected to create a new multi-criteria decision model.
In the invention, pareto optimal solution sets are used as data sources; the constraint condition is an optimization target; namely, the minimum value of the building energy density EUI, the percentage of dissatisfaction with hot environment PPD and the annual light exposure ASE, and the maximum value of the percentage of space full natural lighting time sDA.
Selecting a Linear multi-criterion decision algorithm (Linear MCDM) to carry out multi-criterion decision by utilizing a multi-criterion analysis module of modeFRONTIER software; the Linear MCDM calculation utility function of the Linear multi-criterion decision algorithm is a basis for ordering Pareto optimization solutions; the utility function considers the weight, and the weight of the optimization target is set, so that the utility function can be integrated into a preference control decision process of a designer; the algorithm settings include whether to exclude false design solutions, priority magnitudes, and no-difference magnitudes.
Presetting preference and indifferent amplitude, and running a multi-accurate measurement decision model.
In the invention, compared with the existing method that the program automatically weights the mean and variance of the target as the target, the method can directly operate the mean and variance of the design target in the modeFRONTIER software, and has high flexibility and operability for selecting the optimal solution; designer preferences, i.e., corresponding to variables such as inputs, outputs, constraints, and goals, are considered in making design decisions.
The most reasonable solution is selected from a set of available solutions, i.e. the optimal solution with the highest evaluation value is selected. Evaluating each alternative to order the available alternatives; and reflecting the preference and the non-difference margin in the color of the ranking map, wherein green represents the best ranking, red represents the worst ranking, and the optimal design solution with the largest evaluation value of green has the best target performance.
The invention also provides a building green performance design optimization system, which comprises a variable module, a model module, an optimizing module and an analyzing module; the variable module is used for selecting an evaluation index for evaluating the green performance of the building and determining independent variable parameters affecting the green performance of the building; the model module is used for constructing a building model to be optimized, and importing the building model to be optimized into modeFRONTIER software; the optimizing module is used for executing a multi-objective optimizing algorithm by utilizing modeFRONTIER software according to the selected evaluation index for evaluating the green performance of the building to obtain a Pareto optimizing solution set; and the analysis module is used for screening the Pareto optimal solution set by using a cluster analysis method or a multi-criterion decision analysis method to obtain a multi-objective optimal solution, namely obtaining a building green performance design optimization result.
The building green performance design optimization method and system provided by the invention provide a building green performance optimization design method based on modeFRONTIER, so that the problem that multiple green performances are difficult to balance is effectively solved; the accuracy of simulation optimization can be improved, a group of multi-objective optimal solutions with balanced green performance can be obtained, the data characteristics of the multi-objective optimal solutions can be mined, and the mapping relation between independent variables and dependent variables can be quantized; building energy is generally consumed in order to provide good indoor light environment and thermal comfort; the indexes of energy consumption EUI, percentage PPD of dissatisfaction in thermal environment, percentage sDA of space full natural lighting time, annual light exposure ASE and the like are mutually conflicting; the invention utilizes a multi-criterion decision analysis method and a cluster analysis method to evaluate the Pareto optimal solution set according to the preference of a designer and sort or cluster a plurality of optimal solutions, thereby being convenient for the designer to make design decisions.
Examples
As shown in fig. 1-2, the present embodiment provides a building green performance design optimization method and system, which integrates a Grasshopper/Ladybug+Honeybee building green performance optimization design method based on modeFRONTIER software, and follows the building design flow of design, simulation, optimization, data mining and decision:
firstly, analyzing initial design conditions and building performance optimization targets, determining independent variables and dependent variables, and establishing a green performance optimization framework in modeFRONTIER software; calling Grasshopper/Ladybug+Honeybees to perform performance simulation, and calculating the unit building area energy consumption EUI, the percentage PPD of unsatisfied thermal environment, the percentage sDA of all-natural lighting time of space, the annual light exposure ASE and other dependent variables; then, sampling by using an optimization module of modeFRONTIER software to generate an initial sample, and automatically optimizing by using an optimization algorithm to obtain a group of Pareto optimization solution sets; finally, data mining is carried out on the Pareto optimal solution set, and a designer is assisted in making design decisions; the method specifically comprises the following steps:
and step 1, constructing a building green performance optimization framework on a modeFRONTIER software platform by utilizing modules such as independent variables, sampling, optimization, grasshop interfaces, dependent variables, optimization targets, data post-processing, optimal solutions and the like.
Step 2, determining independent variable parameters influencing building energy consumption and photo-thermal performance based on existing research and practice; in this embodiment, the independent parameters affecting the green performance of the building include building orientation, southbound window wall ratio, northbound window wall ratio, westbound window wall ratio, eastbound window wall ratio, building layer height, standard layer area, aspect ratio, window SHGC, window heat transfer coefficient, exterior wall heat transfer coefficient, air conditioning heating temperature, air conditioning cooling temperature, and illumination power density.
Step 3, utilizing natural resources is an effective measure for reducing building energy consumption, and the building energy consumption and the photo-thermal performance are closely related; therefore, building energy consumption and photo-thermal performance are used as dependent variables; the evaluation indexes for evaluating the green performance of the building comprise energy consumption E UI of unit building area, percentage PPD of unsatisfied thermal environment, percentage sDA of all-natural lighting time of space and annual light exposure ASE; the optimization process of the multi-objective optimization algorithm is utilized, and the minimum value of the unit building area energy consumption EUI, the thermal environment dissatisfaction percentage PPD and the annual light exposure ASE and the maximum value of the space total natural lighting time percentage sDA are adopted as optimization targets.
And 4, automatically calling Grasshop/Lady bug+Honeybees (L+H) by using a Grasshop interface of a modeFRONTIER software platform to perform building green performance simulation, and calculating green performance corresponding to independent variable parameters, namely four performance indexes including unit building area energy consumption EUI, thermal environment dissatisfaction percentage PPD, space full natural lighting time percentage sDA and annual light exposure AS E.
Step 5, optimizing by using an optimization module of modeFRONTIER software; sampling by using a Latin hypercube sampling method to obtain an initial sample; optimizing the initial sample by using an optimization module of modeFRONTIER software to obtain a Pareto optimization solution set; wherein, the pilOPT algorithm is built in the optimization module of the modeFRONTIER software.
And 6, screening the Pareto optimal solution set by using a cluster analysis method or a multi-criterion decision analysis method to obtain a multi-objective optimal solution, and obtaining a building green performance design optimization result.
Description of the examples
The following details are given by way of example of a design of an office building in the western security market, and the specific steps are as follows:
step 1, building a building green performance optimization framework on a modeFRONTIER software platform, and adopting modularized and visual programming languages to optimize the green performance of the building, wherein the building green performance optimization required modules comprise: independent variable module group, grasshop interface module, dependent variable module, optimization objective module, optimization module, completion module, etc.; the following optimization flow is input independent variables, running an external program Grasshopper, outputting dependent variables, inputting new variables according to an optimization algorithm, optimizing by using the algorithm, generating a calculation result, extracting a Pareto optimization solution set, analyzing the optimization result and selecting an optimal solution to make a design decision.
Step 2, determining building independent variables affecting green performance of the building comprises the following steps: building orientation, southbound window wall ratio, northbound window wall ratio, eastbound window wall ratio, westbound window wall ratio, layer height, standard layer area, aspect ratio, window SHGC, window heat transfer coefficient, exterior wall heat transfer coefficient, air conditioning heating temperature, refrigeration temperature, and illumination power density, and setting the name, unit, and range of values of the independent variables, respectively, as described in table 1 below.
TABLE 1 independent variable parameter Table for influencing building Green Performance
Step 3, inputting Grasshoper files to be coupled and calculated, and automatically generating a Grasshoper coupling calculation model, namely automatically calling Grasshoper/Ladybug+Honeybees to perform building green performance simulation; the Ladybug+Honeybe is a Grasshoper-based parameterized performance simulation plug-in module, which calls energy consumption simulation software energy plus and light performance simulation software radius and the like, and has the simulation functions of coupling light environment, heat environment, wind environment, energy consumption and the like.
Western security belongs to cold regions, latitude 34.3 ° N, longitude 108.9 ° E; acquiring the data of a standard weather database-epw of western security from an energy plus official network, and selecting the data as weather data used by the model of the embodiment; and calculating the green performance index corresponding to the independent variable parameter at Grasshopper/L+H.
And 4, selecting four indexes for evaluating the green performance of the building, taking total energy consumption EUI, percentage PPD of unsatisfied thermal environment, percentage sDA of all-natural lighting time of space and annual light exposure ASE as dependent variables, and taking the minimum value of the energy consumption EUI, percentage PPD of unsatisfied thermal environment and annual light exposure ASE of unit building area and the maximum value of percentage sDA of all-natural lighting time of space as an optimization target.
And 5, performing algorithm optimization by using an optimization module of modeFRONTIER software.
S51, generating an initial sample by utilizing Latin hypercube sampling, wherein the Latin hypercube sampling can not only meet the probability distribution covering a full parameter space, but also has the sample size which is 5 times of the number of the self-variables, namely 70; the initial samples are shown in table 2 below.
Table 2 initial sample table
S52, the pilOPT optimization algorithm has the characteristics of global and local search, and can automatically stop optimizing, and green performance-oriented optimizing is performed by using the pilOPT algorithm.
In this embodiment, the total optimization times is 1400 times, that is, 1400 optimization solutions, wherein the Pareto optimization solution set is 141; as shown in the following table 3, table 3 shows Pareto optimal solution sets and index values thereof calculated by the building performance optimization design method according to the present embodiment.
TABLE 3 optimization calculation of this embodiment to Pareto optimal solution set
| Numbering device | EUI(kWh/m 2 ) | PPD(％) | sDA(％) | ASE(％) |
| 1081 | 114.5 | 28.0 | 26.0 | 22.1 |
| 815 | 72.8 | 31.1 | 31.8 | 13.9 |
| … | … | … | … | … |
Meanwhile, the optimization algorithm adopted by the Octopus is NSGA2 instead of the pilOPT algorithm used in the step 5 by using the Octopus based on Grasshopper commonly used in the prior art as the performance optimization. And calculating a Pareto optimal solution set by using an NSGA2 algorithm, and selecting an optimal solution from the Pareto optimal solution set.
TABLE 4 optimization of Performance of Grasshopper-based Octopus commonly used in the prior art
| Existing methods | EUI(kWh/m 2 ) | PPD(％) | sDA(％) | ASE(％) |
| Optimal solution | 75.2 | 34.8 | 49.8 | 75.1 |
And 7, performing sensitivity analysis on the Pareto optimal solution set to obtain key design independent variable parameters affecting the green performance of the building: as shown in fig. 3, sensitivity analysis is performed with the annual light exposure ASE of the affected space in the present embodiment, and the sensitivity analysis result is shown in fig. 3; in this embodiment, the determined key design argument parameters affecting the green performance of the building include east-to-west wall ratio, aspect ratio, north-to-north wall ratio, south-to-south wall ratio, west-to-west wall ratio, heating temperature, and cooling temperature;
step 8, performing cluster analysis on the Pareto optimal solution set, and assisting a user in selecting a multi-objective optimal solution to obtain a building green performance design optimization result; the method specifically comprises the following steps:
fourteen independent variables and four dependent variables in the Pareto optimal solution set are taken as data sources.
Setting key design independent variable parameters and performance targets which influence green performance of the building as variables of a cluster analysis model; the scale function is designed to be random, and the distance type is Euclidean distance.
The method comprises the steps of performing partition cluster analysis on a Pareto optimal solution set by adopting a K-Means algorithm, wherein the algorithm is set as follows: the maximum iteration number is 5, the maximum cluster number of clusters is 10, the random generator seed is 1, and the cluster numbers are automatically selected.
Setting modeFRONTIER software to create a partitional clustering model, and reducing the intra-cluster distance by the K-Means algorithm at each iteration. As shown in FIG. 4, the davison burg index DBI gave an optimal cluster number of 3, comparing clusters of 1-10 clusters of Pareto optimal solution.
And clustering the calculated clusters to assist a user in selecting an optimal solution.
Because the Pareto optimal solution set has good diversity and distribution, cluster analysis is carried out on the Pareto optimal solution set, the Pareto optimal solution set is divided into three clusters, and the number of solutions in each cluster is 52, 24 and 65 respectively; the influence of the design elements on the building performance is regular and circulated, the mapping relation between the design variables and the performance targets is constructed by utilizing cluster analysis, and the user is assisted to flexibly select the optimal solution according to a certain area value of the design elements of the user by the cluster result.
As shown in fig. 5, it is assumed that the user prefers lower energy consumption and thermal comfort dissatisfaction and better light environment; i.e., cluster 0, the corresponding design parameters within the range of cluster 0 can be selected; the ratio of the southbound window wall of the cluster 0 optimal solution is 50% -60% and the ratio is the largest, the window SHGC is 0.1-0.2, and the standard layer area is 1005-1555m 2 The length-width ratio is 4-5, the east-direction window wall ratio is 30% -60%, the north-direction window wall ratio is 50% -90%, the west-direction window wall ratio is 10% -20%, and the window heat transfer coefficient is 1-1.2W/m 2 K, the heat transfer coefficient of the outer wall is 0.33-0.38, the layer height is 3.3-4m, the refrigerating temperature is 25-27 ℃, the heating temperature is 20-21 ℃, and the illumination power density is 10.5-12W/m 2 。
In another embodiment of the invention, a multi-criterion decision analysis method is adopted to screen a Pareto optimal solution set to obtain a multi-objective optimal solution, namely, a building green performance design optimization result is obtained, and a designer is assisted in making a design decision; the specific process is as follows:
independent variables and dependent variables of the Pareto optimal solution set are taken as source data.
And selecting a Linear MCDM (Multi-criterion decision algorithm) to calculate a utility function, wherein the algorithm setting comprises error-eliminating design solution, priority amplitude of 0.05 and indiscriminate amplitude of 0.02.
Setting the weight proportion of the energy consumption EUI of unit building area, the percentage PPD of unsatisfied thermal environment, the percentage sDA of all-natural lighting time of space and the annual light exposure ASE to be the same, namely, the weight proportion of each weight proportion is 25%; evaluating and sequencing the optimized solutions by using a multi-criterion decision analysis method, and selecting the optimized solution with the largest evaluation value; as shown in FIG. 5, a diagram of the results of ranking the optimization solutions using the multi-criterion decision analysis method is shown in FIG. 5.
In this embodiment, the maximum evaluation value is 0.71, the corresponding design solution number is 858, and as shown in table 5, it can be seen from table 5 that the corresponding optimization target unit building area energy consumption EUI, the percentage of dissatisfied thermal environment PPD, and the spatial all-natural lightingPercentage of time sDA and annual light exposure ASE of 68.5kWh/m respectively 2 33.4%, 66.1% and 53.5%.
TABLE 5 optimal solutions for automatic searching with this embodiment
| Numbering device | 14 independent variable values | EUI(kWh/m 2 ) | PPD(％) | sDA(％) | ASE(％) |
| 858 | (0，70，60，10，10，3.6，1625，2.5，0.2，1，0.35，20，25，12) | 68.5 | 33.4 | 66.1 | 53.5 |
Further, by comparing the existing method with the optimizing result of the present invention, table 6 shows the optimal solution searched by the building performance optimizing design method of the present invention and the existing building performance optimizing design method based on Grasshopper/Octopus; compared with the optimal solution searched by the existing method, the optimal solution searched by the automatic searching method is better in performance, and the energy saving rate is improved by 8.9%, the PPD is reduced by 1.4%, the sDA is improved by 16.3%, and the ASE is reduced by 21.6%.
Table 6 compares the optimization design method of the performance of the present embodiment with the optimization results of the existing method
According to the building green performance design optimization method, the Latin hypercube sampling is adopted to improve random sampling, so that the global property and the optimization quality of a sample are ensured; the pilOPT algorithm has the characteristics of global and local search, so the design method of the invention has the advantages of improved optimizing efficiency, high accuracy of the optimized solution and the like; the invention simultaneously considers four performance indexes of energy consumption EUI of unit building area, percentage PPD of dissatisfaction of thermal environment, percentage sDA of all-natural lighting time of space and annual light exposure ASE, and automatically searches a group of Pareto optimal solution sets by utilizing an optimization algorithm; however, the optimal solutions are difficult to balance by a user, the modeFRONTI ER has a multi-criterion decision function, the Pareto optimal solution sets are evaluated and ordered according to the preference of the user, a designer is assisted in making design decisions, and the objectivity and scientificity of the decisions are improved; according to the invention, the pilOPT algorithm is utilized to automatically stop optimizing, so that the operability of the algorithm is obviously enhanced, and the applicability of the method is greatly improved; the method has the data post-processing function, utilizes a cluster analysis method to carry out quantization processing on the Pareto optimal solution set, and utilizes a multi-criterion decision analysis method to sort the Pareto optimal solution set so as to assist a user in selecting an optimal solution.
The above embodiment is only one of the implementation manners capable of implementing the technical solution of the present invention, and the scope of the claimed invention is not limited to the embodiment, but also includes any changes, substitutions and other implementation manners easily recognized by those skilled in the art within the technical scope of the present invention.
Claims (5)
1. The green performance design optimization method for the building is characterized by comprising the following steps of:
selecting an evaluation index for evaluating the green performance of the building, and determining independent variable parameters affecting the green performance of the building;
building a building model to be optimized, and importing the building model to be optimized into modeFRONTIER software;
according to the selected evaluation index for evaluating the green performance of the building, a multi-objective optimization algorithm is executed by using modeFRONTIER software to obtain a Pareto optimization solution set; executing a multi-objective optimization algorithm process by using modeFRONTIER software, sampling by using a Latin hypercube sampling method, and obtaining an initial sample; optimizing the initial sample by using an optimization module of modeFRONTIER software to obtain a Pareto optimization solution set; wherein, a pilOPT algorithm is built in an optimization module of the modeFRONTIER software;
screening the Pareto optimal solution set by using a cluster analysis method or a multi-criterion decision analysis method to obtain a multi-objective optimal solution, namely obtaining a building green performance design optimization result;
the evaluation indexes for evaluating the green performance of the building comprise the energy consumption EUI of unit building area, the percentage PPD of unsatisfied thermal environment, the percentage sDA of all-natural lighting time of space and the annual light exposure ASE;
and screening the Pareto optimal solution set by adopting a multi-criterion decision analysis method:
the data source is a Pareto optimal solution set, the constraint condition is the minimum value of unit building area energy consumption EUI, percentage of dissatisfaction with thermal environment PPD and annual light exposure ASE, and the maximum value of spatial total natural lighting time percentage sDA; selecting a Linear multi-criterion decision algorithm (Linear MCDM) to carry out multi-criterion decision by utilizing a multi-criterion analysis module of modeFRONTIER software; the Linear MCDM calculation utility function of the Linear multi-criterion decision algorithm is a basis for ordering Pareto optimization solutions; the utility function considers the weight, and the weight of the optimization target is set, so that the utility function can be integrated into a preference control decision process of a designer; the algorithm setting comprises whether to exclude wrong design solutions, priority amplitude and non-difference amplitude; presetting preference and indifferent amplitude, and running a multi-accurate measurement decision model;
and (3) adopting a cluster analysis method to carry out a screening process on the Pareto optimal solution set:
collecting a data source by using a Pareto optimal solution;
the sensitivity analysis module of modeFRONTIER software is utilized to carry out sensitivity analysis on the independent variable parameters influencing the green performance of the building, and key design independent variable parameters influencing the green performance of the building are obtained;
taking key design independent variable parameters and optimization targets which influence the green performance of the building as constraint conditions; utilizing a cluster analysis module in modeFRONTIER software, adopting a K-Means algorithm to perform cluster analysis on the Pareto optimal solution set, and randomly decomposing the Pareto optimal solution set into a group of disjoint clusters; wherein the K-Means algorithm settings include: maximum iteration times, maximum cluster number of clusters, random generator seeds and cluster numbering mode;
creating a partitional clustering model by using modeFRONTIER software; the K-Means algorithm presets the distance in the reduced cluster when each iteration is performed, and the evaluation index of the cluster model quality is measured by using the Dyson Bobber index DBI; davison burg Ding Zhishu DBI is the ratio of the sum of intra-cluster variance and inter-cluster distance, with lower davison burg index DBI leading to better cluster quality for cluster division;
selecting a partitional clustering model of the DBI with the lowest Dyson Babbing index, and performing partitional clustering analysis on the Pareto optimal solution set to obtain a clustering data table; and obtaining a multi-objective optimal solution from the clustering data table to obtain a building green performance design optimization result.
2. The method for optimizing green performance design of building according to claim 1, wherein the optimizing process of the multi-objective optimizing algorithm is performed by using the minimum values of the energy consumption EUI per building area, the percentage PPD of dissatisfied with thermal environment and the annual light exposure ASE, and the maximum value of the percentage sDA of the total natural lighting time of the space as the optimizing objective.
3. The method of claim 1, wherein the independent parameters affecting the green performance of the building include building orientation, window wall ratio, floor height, standard floor area, aspect ratio, window SHGC, window heat transfer coefficient, exterior wall heat transfer coefficient, air conditioning heating temperature, air conditioning cooling temperature, and illumination power density.
4. The method for optimizing green performance design of building according to claim 1, wherein the building model to be optimized comprises a three-dimensional model of a building structure, building structure parameters, active equipment control parameters and meteorological data of an area where the active equipment control parameters are located.
5. The building green performance design optimization system is characterized by comprising a variable module, a model module, an optimizing module and an analyzing module;
the variable module is used for selecting an evaluation index for evaluating the green performance of the building and determining independent variable parameters affecting the green performance of the building;
the model module is used for constructing a building model to be optimized, and importing the building model to be optimized into modeFRONTIER software;
the optimizing module is used for executing a multi-objective optimizing algorithm by utilizing modeFRONTIER software according to the selected evaluation index for evaluating the green performance of the building to obtain a Pareto optimizing solution set; executing a multi-objective optimization algorithm process by using modeFRONTIER software, sampling by using a Latin hypercube sampling method, and obtaining an initial sample; optimizing the initial sample by using an optimization module of modeFRONTIER software to obtain a Pareto optimization solution set; wherein, a pilOPT algorithm is built in an optimization module of the modeFRONTIER software;
the analysis module is used for screening the Pareto optimal solution set by using a cluster analysis method or a multi-criterion decision analysis method to obtain a multi-objective optimal solution, namely obtaining a building green performance design optimization result;
the evaluation indexes for evaluating the green performance of the building comprise the energy consumption EUI of unit building area, the percentage PPD of unsatisfied thermal environment, the percentage sDA of all-natural lighting time of space and the annual light exposure ASE;
and screening the Pareto optimal solution set by adopting a multi-criterion decision analysis method:
the data source is a Pareto optimal solution set, the constraint condition is the minimum value of unit building area energy consumption EUI, percentage of dissatisfaction with thermal environment PPD and annual light exposure ASE, and the maximum value of spatial total natural lighting time percentage sDA; selecting a Linear multi-criterion decision algorithm (Linear MCDM) to carry out multi-criterion decision by utilizing a multi-criterion analysis module of modeFRONTIER software; the Linear MCDM calculation utility function of the Linear multi-criterion decision algorithm is a basis for ordering Pareto optimization solutions; the utility function considers the weight, and the weight of the optimization target is set, so that the utility function can be integrated into a preference control decision process of a designer; the algorithm setting comprises whether to exclude wrong design solutions, priority amplitude and non-difference amplitude; presetting preference and indifferent amplitude, and running a multi-accurate measurement decision model;
and (3) adopting a cluster analysis method to carry out a screening process on the Pareto optimal solution set:
collecting a data source by using a Pareto optimal solution;
the sensitivity analysis module of modeFRONTIER software is utilized to carry out sensitivity analysis on the independent variable parameters influencing the green performance of the building, and key design independent variable parameters influencing the green performance of the building are obtained;
taking key design independent variable parameters and optimization targets which influence the green performance of the building as constraint conditions; utilizing a cluster analysis module in modeFRONTIER software, adopting a K-Means algorithm to perform cluster analysis on the Pareto optimal solution set, and randomly decomposing the Pareto optimal solution set into a group of disjoint clusters; wherein the K-Means algorithm settings include: maximum iteration times, maximum cluster number of clusters, random generator seeds and cluster numbering mode;
creating a partitional clustering model by using modeFRONTIER software; the K-Means algorithm presets the distance in the reduced cluster when each iteration is performed, and the evaluation index of the cluster model quality is measured by using the Dyson Bobber index DBI; davison burg Ding Zhishu DBI is the ratio of the sum of intra-cluster variance and inter-cluster distance, with lower davison burg index DBI leading to better cluster quality for cluster division;
selecting a partitional clustering model of the DBI with the lowest Dyson Babbing index, and performing partitional clustering analysis on the Pareto optimal solution set to obtain a clustering data table; and obtaining a multi-objective optimal solution from the clustering data table to obtain a building green performance design optimization result.
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