A multi-objective optimization strategy for building carbon emission from the whole life cycle perspective

Ruijun Chen, Yaw Shyan Tsay, Ting Zhang

研究成果: Article同行評審

摘要

This research proposed an integrated strategy for building performance optimization from the whole life cycle perspective to explore the optimal building scheme. After the feature elimination, the ensemble learning model (ELM) was trained to obtain a high-precision model for predicting life cycle carbon emissions (LCCE), life cycle costs (LCC), and indoor discomfort hours (IDH). Then, the optimal optimization algorithm was selected among three different optimization algorithms. Finally, the best building scheme was chosen according to the newly proposed solution. The results showed that the ELM could achieve high prediction efficiency by combining input feature evaluation and screening, multi-sampling methods, and hyperparameter optimization. The R2 value of ELM can reach 0.980, while the Two-Archive Evolutionary Algorithm for Constrained multi-objective optimization (C-TAEA) was the optimal optimization algorithm. The best equilibrium solution proposed in this study solved the problem of different optimization ranges of different objectives and maximized the optimization value. Finally, the best equilibrium scheme reduced the LCCE by 34.7%, the LCC by 13.9%, and the IDH by 26.6%. Therefore, this strategy can efficiently optimize building objectives and produce a more balanced and optimal building scheme, thus making it widely applicable in building performance optimization.

原文English
文章編號125373
期刊Energy
262
DOIs
出版狀態Published - 2023 1月 1

All Science Journal Classification (ASJC) codes

  • 土木與結構工程
  • 建模與模擬
  • 可再生能源、永續發展與環境
  • 建築與營造
  • 燃料技術
  • 能源工程與電力技術
  • 污染
  • 機械工業
  • 能源(全部)
  • 管理、監督、政策法律
  • 工業與製造工程
  • 電氣與電子工程

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