A Machine Learning-Based Prediction Model of LCCO 2 for Building Envelope Renovation in Taiwan

Yaw-Shyan Tsay, Chiu-Yu Yeh, Yu-Han Chen, Mei-Chen Lu and Yu-Chen Lin
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Yaw-Shyan Tsay: Department of Architecture, National Cheng Kung University, Tainan 701, Taiwan
Chiu-Yu Yeh: Department of Architecture, National Cheng Kung University, Tainan 701, Taiwan
Yu-Han Chen: Department of Architecture, National Cheng Kung University, Tainan 701, Taiwan
Mei-Chen Lu: Department of Architecture, National Cheng Kung University, Tainan 701, Taiwan
Yu-Chen Lin: Department of Architecture, National Cheng Kung University, Tainan 701, Taiwan

Sustainability, 2021, vol. 13, issue 15, 1-18

Abstract: 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 CO 2 (LCCO 2 ) of a building envelope renovation project in Tainan and focused on local construction methods for typical row houses. The LCCO 2 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 LCCO 2 . With regard to annual energy consumption, the RMSE was 227.09 kW·h (per year) and the R 2 was 0.992. For LCCO 2 , the RMSE was 2792.47 kgCO 2 eq and the R 2 was 0.989, which indicates a high-confidence process for decision making in the early stages of building design and renovation.

Keywords: life-cycle CO 2; energy consumption; building envelope renovation; row houses; machine learning; supervised learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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