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Article

Design Optimization of a Passive Building with Green Roof through Machine Learning and Group Intelligent Algorithm

by 1,*, 2, 3,4,*, 5,*, 6 and 7
1
School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
2
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
3
School of Architecture, University of South China, Hengyang 421001, China
4
Hunan University Design and Research Institute Co., Ltd., Changsha 410012, China
5
Faculty of Architecture, Building and Planning, The University of Melbourne, Melbourne 3010, Australia
6
College of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
7
School of Engineering, RMIT University, Melbourne 3000, Australia
*
Authors to whom correspondence should be addressed.
Academic Editor: Natasa Nord
Buildings 2021, 11(5), 192; https://doi.org/10.3390/buildings11050192
Received: 26 March 2021 / Revised: 23 April 2021 / Accepted: 28 April 2021 / Published: 2 May 2021
(This article belongs to the Special Issue Advanced Building Performance Analysis)
This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive building in Shanghai, China. A total of 35 design parameters relating to building form, envelope properties, thermostat settings, and green roof configurations were considered. First, the Latin hypercube sampling method (LHSM) was used to generate a set of design samples, and the energy consumption and visual discomfort of the samples were obtained through computer simulation and calculation. Second, four machine learning prediction models, including stepwise linear regression (SLR), back-propagation neural networks (BPNN), support vector machine (SVM), and random forest (RF) models, were developed. It was found that the BPNN model performed the best, with average absolute relative errors of 3.27% and 1.25% for energy consumption and visual comfort, respectively. Third, six optimization algorithms were selected to couple with the BPNN models to find the optimal design solutions. The multi-objective ant lion optimization (MOALO) algorithm was found to be the best algorithm. Finally, optimization with different groups of design variables was conducted by using the MOALO algorithm with the associated outcomes being analyzed. Compared with the reference building, the optimal solutions helped reduce energy consumption up to 34.8% and improved visual discomfort up to 100%. View Full-Text
Keywords: design optimization; green roof; passive building; energy consumption; machine learning; visual comfort design optimization; green roof; passive building; energy consumption; machine learning; visual comfort
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MDPI and ACS Style

Lin, Y.; Zhao, L.; Liu, X.; Yang, W.; Hao, X.; Tian, L. Design Optimization of a Passive Building with Green Roof through Machine Learning and Group Intelligent Algorithm. Buildings 2021, 11, 192. https://doi.org/10.3390/buildings11050192

AMA Style

Lin Y, Zhao L, Liu X, Yang W, Hao X, Tian L. Design Optimization of a Passive Building with Green Roof through Machine Learning and Group Intelligent Algorithm. Buildings. 2021; 11(5):192. https://doi.org/10.3390/buildings11050192

Chicago/Turabian Style

Lin, Yaolin, Luqi Zhao, Xiaohong Liu, Wei Yang, Xiaoli Hao, and Lin Tian. 2021. "Design Optimization of a Passive Building with Green Roof through Machine Learning and Group Intelligent Algorithm" Buildings 11, no. 5: 192. https://doi.org/10.3390/buildings11050192

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