Multi-Objective Optimization of Building Environmental Performance: An Integrated Parametric Design Method Based on Machine Learning Approaches

Yijun Lu, Wei Wu, Xuechuan Geng, Yanchen Liu, Hao Zheng and Miaomiao Hou ()
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Yijun Lu: Department of Architecture, College of Design and Engineering, National University of Singapore, Singapore 119077, Singapore
Wei Wu: A. Alfred Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, MI 48103, USA
Xuechuan Geng: College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266000, China
Yanchen Liu: Department of Architecture, The University of Tokyo, Tokyo 113-8654, Japan
Hao Zheng: Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, PA 19104, USA
Miaomiao Hou: Stuart Weitzman School of Design, University of Pennsylvania, Philadelphia, PA 19104, USA

Energies, 2022, vol. 15, issue 19, 1-23

Abstract: Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved through the fast evolution of the genetic algorithm binding with the database. The test case used in this paper demonstrates that this approach can solve the optimization problem with less time and computational cost, and it provides architects with a fast and easily implemented tool to optimize design strategies based on specific environmental objectives.

Keywords: building performance simulation; machine learning; multi-objective optimization; parametric design; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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