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Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK

Author

Listed:
  • Zheng, Lin
  • Mueller, Markus
  • Luo, Chunbo
  • Yan, Xiaoyu

Abstract

Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability approaches within the building sector.

Suggested Citation

  • Zheng, Lin & Mueller, Markus & Luo, Chunbo & Yan, Xiaoyu, 2024. "Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK," Applied Energy, Elsevier, vol. 357(C).
    • Handle: RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018366
    DOI: 10.1016/j.apenergy.2023.122472
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    References listed on IDEAS

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    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
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    4. Xiang-Li, Li & Zhi-Yong, Ren & Lin, Duanmu, 2015. "An investigation on life-cycle energy consumption and carbon emissions of building space heating and cooling systems," Renewable Energy, Elsevier, vol. 84(C), pages 124-129.
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    Cited by:

    1. Xue, Qingwen & Gu, Mei & Yang, Yingxia & Bai, Pengyun & Wang, Zhichao & Jiang, Sihang & Duan, Pengfei, 2025. "Calibration study of uncertainty parameters for nearly-zero energy buildings based on a novel approximate Bayesian approach," Energy, Elsevier, vol. 322(C).
    2. Chen, Haoyu & Huang, Hai & Zheng, Yong & Yang, Bing, 2024. "A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model," Applied Energy, Elsevier, vol. 375(C).
    3. Luo, Wenhong & Liu, Weicheng & Liu, Wenlong & Xia, Lingyu & Zheng, Junjun & Liu, Yang, 2025. "Analysis of influencing factors and carbon emission scenario prediction during building operation stage," Energy, Elsevier, vol. 316(C).

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