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A new design exploring framework based on sensitivity analysis and Gaussian process regression in the early design stage
A new design exploring framework based on sensitivity analysis and Gaussian process regression in...
Gao, Yun; Mae, Masayuki; Taniguchi, Keiichiro
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 3, 326–339 https://doi.org/10.1080/13467581.2020.1783271 ENVIRONMENTAL ENGINEERING A new design exploring framework based on sensitivity analysis and Gaussian process regression in the early design stage Yun Gao, Masayuki Mae and Keiichiro Taniguchi Department of Architecture, Graduated School of Engineering, University of Tokyo, Tokyo, Japan ABSTRACT ARTICLE HISTORY Received 12 February 2020 With building energy codes getting strict, quantitative analysis is necessary in the early design Accepted 29 May 2020 stage of high-energy-performance buildings. To fully explore the design space, a highly efficient method is necessary. In the research, a new design exploring framework was pro- KEYWORDS posed. Parameters are screened and separated into groups based on the sensitivity analysis, to Sensitivity analysis; reduce the dimension of inputs. Gaussian process regression, able to deal with the uncertainty interaction analysis; Gaussian in inputs, was used to make metamodels to reduce the calculation cost and fully explore the process; uncertainty; design design space. Dashboards were made to visualize the data interactively, to help designers dashboard make decisions and make communications smooth. This framework was demonstrated with a case study and showed its efficiency. 1. Introduction process, the possibilities of the design are often not fully explored (Attia 2018). Meanwhile the communica- Buildings are responsible for more than 40% of global tion cost, within the design team and between energy use and one-third of greenhouse gas emissions designers and clients is considerable. Automatic opti- globally (Hirst 2013). In Japan, since the oil crisis in the mization, with Genetic algorithm, Particle Swarming 1970s, the energy consumption by building sector Optimization, etc., is another way to efficiently improve considerably increased by about 250%, which the energy performance of the design (Wang, Rivard, accounts for 34.5% of the total energy use in the and Zmeureanu 2006). However, it is not very suitable country (Ministry of Economy, Trade and Industry, JP in the early stage, as there are a lot of factors and 2015). With. the progress of urbanization, the energy criteria that cannot quantify be such as aesthetical or use of building sector will continuously increase by historical things. The optimized results might be unde- about 30% in the coming 20 years (U.S. Energy sirable to designers. It requires thousands of iterations Information Administration 2017). of simulation to run a successful optimization, which is On the other hand, building sector has the largest quite calculation costly and takes too much time. On potential for a reduction in energy consumption and the other hand, in the early stage, it is better to explore greenhouse gas emission. It has been proved that the the design possibility as much as possible, rather than architecture design, especially in the early stage, has get accurate analysis or an optimized result. a significant impact on the energy performance The interactions between parameters are rarely ana- (Naboni et al. 2015), while the cost of changing design lyzed in the design process. When the designers study will get higher and higher in the later stage (WBDG a certain parameter, they usually assign constant 2019). However, the most important decisions are values to other parameters based on some assump- made in the early design stage by architects tions. The decisions made in earlier phases are often (Schlueter and Thesseling 2009), usually with some influenced by those made in the latter stages and rules of thumbs. With the energy codes getting more become no more optimal, due to the interactions and more strict in recent years and Zero Energy House between the parameters. As a result, the designers gradually becoming mandatory in Japan, it has been have to move back and repeat the studies in previous appointed out by some researchers that guild lines or phases. rule of thumbs are not enough to ensure the energy This study focuses on four questions, (1) How to performance (Attia 2018). Integrated Design Process is explore the design possibilities, (2) how to keep the required to achieve ultra-low-energy design (Ferrara, calculation cost at a low level at the same time, (3) how Sirombo, and Fabrizio 2018). Quantitative energy ana- to utilize the data to help the designers when making lysis should be carried out in the early stage. However, decisions and (4) how to avoid duplicated work. This limited by the low efficiency of the trial and error paper has six sections, including this introduction. CONTACT Yun Gao firstname.lastname@example.org The University of Tokyo, Tokyo, Japan Supplemental data for this article can be accessed here. © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 327 Related works, including published tools and extracted the information from the BIM model to carry researches, are reviewed in Section 2. Section 3 out energy, daylight and structure analysis. The design describes the framework of design exploring, as well would be optimized by Optimp, an optimization tool as the sensitivity analysis (SA), metamodeling and data integrated in Dynamo. The whole process was highly visualization methods used in this study. Section 4 integrated, nevertheless, the calculation was still very provides a case study as a demonstration of this frame- costly. Jalaei and Jrade (2015) integrated BIM and LEED work. Conclusion and future work are presented in certification system for the conceptual stage. This inte- Section 5. grated tool could generate LEED certification point using the information from BIM and help the designer selects the proper materials. However, LEED certifica - 2. Related works tion required a highly completed design. A lot of infor- mation necessary for LEED was lacked in the 2.1. Dashboard tools Dashboards, able to visualize the building parameters conceptual stage. As a result, it was still a question and the energy performance, are useable when that whether LEED provides good criteria in the early stage. designers make design decisions. Insight 360 (Autodesk 2019) is a building energy performance assessment tool published by Autodesk. 2.3. Sensitivity analysis This designer-oriented dashboard tool is intuitive and SA has been adopted by many researchers to reduce easy to use. Good data visualization helps the designer the number of parameters and calculation cost. understands the energy performances well. Heiselberg et al. (2009) introduced Morris method Parameters can be modified, and the feedback is real- into the design of sustainable buildings. They screened time. However, there are still some limitations. The the parameters by their importance and dropped less parameters and their range that can be studied are important parameters in the latter stage. They com- limited to the preset. Only the average value of energy mented that SAshould be performed in the early stage consumption of all cases is displayed. Other criteria, when important factors were not decided. Garcia such as thermal comfort, are not included or Sanchez et al. (2014) performed both first- extendable. and second -order analysis in building energy simula- Designer Explorer (Tomasetti 2019) is another tool tions. They commented that higher-order effects could under the concept of design dashboard. It consists of help better understanding the results of SA. Østergård, two parts, Grasshopper3D components and online Jensen, and Maagaard (2017) described an approach data visualization. Designers can do the analysis in to explore the multi-dimensional design space. They Grasshopper3D and output the results into a CSV file. used Morris method to screen parameters. The impor- The online data visualization tool visualizes the data tance of each parameter helped designers to make and makes dashboards once this CSV file is uploaded. decisions in the latter stage. Nguyen and Reiter This process takes quite a long time to execute the (2015), Rivalin et al. (2018) and Gagnon, Gosselin, and simulations if the designer wants to explore the possi- Decker (2018) compared several SAtechnics applied to bilities of design widely. building energy performance assessment. Linear regression was reported not good enough by most researchers. Morris method showed both good effi - 2.2. BIM-based tools ciency and enough reliability. Variance-based meth- The authors have made several interviews with profes- sional architects. They said that they prefer to stick to ods, though had the best reliability, were very BIM tools from the very beginning, even though they calculation costing. It was also pointed out that Latin hypercube outperformed the standard Monte Carlo in know that Grasshopper3D might be more convenient Morris method (Figure 1). in the early stage, so that they can push the work more smoothly to latter stage. Several Building Information Modeling (BIM)-based tools, aiming at the early design 2.4. Metamodeling stage, have been developed by researchers. Schlueter Metamodeling is a very effective way to reduce calcu- and Thesseling (2009) developed a plug-in for lation cost. Autodesk Revit called Design Performance Viewer, Hygh et al. (2012) presented a Monte Carlo frame- which could evaluate the energy performance of the work to develop a multivariate linear regression model building, using the information from BIM. Once the based on 27 parameters. The coefficient of each para- designers modified the model, they could get nearly meter in the regressed model could be used as the real-time feedbacks. Though modeling and analyzing sensitivity. Østergård, Jensen, and Maagaard (2017) were integrated, designers still had to work in the trial- also made metamodels with multivariate linear regres- and-error process. It was difficult to compare a lot of sion. They made a “what-if” dashboard, with the meta- alternatives simultaneously. Rahmani Asl et al. (2015) models running in background, to help the designers integrated BIM and multi-objective optimization. They making decisions. Gossard, Lartigue, and Thellier 328 Y. GAO ET AL. Figure 1. Comparison between sampling methods with 25 sample points. (2013) trained artificial neural network (ANN) with the simulation results of energy performance of the build- ing. They used this trained ANN as a part of objective function in GA optimizations. Asadi et al. (2014) also combined ANN and GA in their optimization process and applied it in retrofit projects. Rivalin et al. (2018), Wei et al. (2015) and Østergård, Jensen, and Maagaard (2018) compared several techs of metamodeling applied to building energy perfor- mance assessment. Gaussian process was reported to be the most robust and easy-to use, neural network and multivariate adaptive regression splines also had good performance. In some cases, polynomial chaos showed better accuracy than Gaussian process did. 3. Methodology 3.1. Targeted parameter and non-targeted parameter In order to make this research more understandable, we defined two concepts, targeted parameters and non-targeted parameters. Target parameters, which is actually an alternative way to say parameters of inter- est, means the parameters that can be decided by the design team, such as window to wall ratio (WWR), insulation, etc. Other parameters influencing the Figure 2. The proposed framework of design exploring. energy performance but can hardly be decided or controlled by the design team, such as climate, occu- pancy, etc., are called non-targeted parameters in this research. We think that both targeted and non- volume or the topology of the functional spaces. targeted parameters should be considered, as well as Based on the conceptual design, the design team, the interactions between them. including architects and experts from different disci- plines, will list the targeted and non-targeted para- 3.2. Framework meters and their ranges or distributions. The second In this research, we proposed a framework of design step is to analyze the sensitivity of each parameter. exploring based on SA (Figure 2). Samples will be made with Latin Hyper-cube. An IDF Architects will firstly work on the conceptual file will be made for each sample parametrically and schema of the design, decide the rough shape, the simulated with EnergyPlus. Morris method will be used JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 329 Figure 3. The movement of a sample in design space, Morris method. Figure 4. The movement of a sample, Expanded Morris method. to get the sensitivity of each parameter based on the each phase, those non-targeted parameters, which simulation results. Parameters will be screened based interact with the targeted parameters in this phase, on their sensitivities, or importance in another word. will also be studied together. The parameters will be The third step is to analyze the interactions between sampled with Monte Carlo method and simulated with parameters with Expanded Morris method, by quanti- EnergyPlus. A meta-model will be made with Gaussian fying the higher-order effects. An interaction matrix process. The targeted parameters and results from will be made. simulations will be used to train the model. Non- The design team can separate the targeted para- targeted parameters will be regarded as noise of the meters into groups based on their interactions, so that model. one problem is divided into several problems and the With a trained meta-model, the design team is able dimension of each problem is reduced. The design to get real-time feedback when any parameter is mod- team can study the problems phase by phase. In ified, and test thousands of cases in minutes. A design 330 Y. GAO ET AL. Figure 5. The movement of a sample, Expanded Morris method. dashboard, visualizing the data, can help the design after moving, divided by the distance is used to repre- team understand the energy performance, make deci- sent the local sensitivity of the jth parameter (Eq.4), sions and communicate in teamwork or with clients. which can be regarded as an approximation of the After decision made, the design team can move into absolute value of the partial derivative. The local sen- the next phase, do simulations, train a metamodel, sitivity of each parameter will be calculated, and yðXÞ expand the design dashboard and make decisions on will be calculated kþ 1 time for each row. another group of targeted parameters. This procedure repeats r times for all the rows in the input matrix, so the calculation of yðXÞ will be executed totally rðkþ 1Þ times. The average value of local sensi- 3.3. Sensitivity and interaction analysis tivity of jth parameter in all the rows will be used as its 3.3.1. Morris method. global sensitivity (Eq. 5). The SA can be roughly divided into variance-based 2 3 method and one-step-at-a-time method (OAT). As in 1 k x x ��� x ��� 1 1 the early stage of design, efficiency was more impor- 6 7 . . 6 . . . . . 7 . . . . . 6 7 tant than accuracy, we employed Morris method, . . . 6 7 1 j k 6 7 a global OAT for global SA, to analyze the sensitivity Mo ¼ x x # (1) ��� x ��� i i 6 i 7 6 7 . . of each parameter. 6 7 . . . . . . . . . . 4 5 . . . The first step is to generate the input matrix in the 1 j k x x ��� x ��� r r r range of (0,1]. In this research, Latin hypercube sam- pling (LHS) was used to generate the input matrix Mo � � (Eq.1), as LHS can better fill the exploring space than 1 k X ¼ Mo ¼ x ; . . . ; x ; . . . ; x # (2) i i i i i random sampling. The shape of Mo is r in row and k in column. Column count k is equal to the number of � � parameters of interest. Row count r represents the j j 1 j jþ1 1 k X ¼ x þ Δ ; . . . ; x þ Δ ; x þ Δ ; x ; . . . ; x 1 j 1 j i i i i i i number of times that OAT will be repeated. j 1 ¼ X þ Δ þ . . .þ Δ i 1 One row in the input matrix can be regarded as j¼X þΔ # a k-dimensional vector, X . (Eq. 2) An element in the i (3) vector, x , will move a small distance of equal size Δ in j j 1 its dimension (Eq. 3). The absolute value of the differ - jyðXÞ