A machine learning-based prediction model of lcco2 for building envelope renovation in Taiwan

Yaw Shyan Tsay, Chiu Yu Yeh, Yu Han Chen, Mei Chen Lu, Yu Chen Lin

研究成果: Article同行評審


In 2015, Taiwan’s government announced the “Greenhouse Gas Reduction and Management Act”, the goal of which was a 50% reduction in carbon emissions by 2050, compared with 2005. The residential and commercial sectors produce approximately one third of all carbon emissions in Taiwan, and the number of construction renovation projects is much larger than that of new construction projects. In this paper, we considered the life-cycle CO2 (LCCO2 ) of a building envelope renovation project in Tainan and focused on local construction methods for typical row houses. The LCCO2 of 744 cases with various climate zones, orientations, and insulation and glazing types was calculated via EnergyPlus, SimaPro, and a local database (LCBA database), and the results were then used to develop a machine learning model. Our findings showed that the machine learning model was capable of predicting annual energy consumption and LCCO2 . With regard to annual energy consumption, the RMSE was 227.09 kW·h (per year) and the R2 was 0.992. For LCCO2, the RMSE was 2792.47 kgCO2 eq and the R2 was 0.989, which indicates a high-confidence process for decision making in the early stages of building design and renovation.

期刊Sustainability (Switzerland)
出版狀態Published - 2021 八月 1

All Science Journal Classification (ASJC) codes

  • 地理、規劃與發展
  • 可再生能源、永續發展與環境
  • 環境科學(雜項)
  • 能源工程與電力技術
  • 管理、監督、政策法律


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