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Evaluation of cost-effective building retrofit strategies through soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method

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  • Yuan, Jun
  • Nian, Victor
  • Su, Bin

Abstract

The building sector contributes a major proportion of the global energy consumptions and carbon emissions. The energy performance or efficiency of buildings can be improved through a wide range of retrofitting measures which can have very different costs. Under budget, time and other resource constraints, it is not practical to apply all energy saving measures to a given retrofitting project. As such, there is a need to rank and select the most cost-effective measures to meet efficiency improvement goals. Traditionally, energy efficiency improvement measures and their costs are evaluated separately which makes prioritising among the measures difficult. In response, an integrated approach by soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method is proposed to rank and select the most cost-effective retrofitting measures. The metamodel-based method is used to compute building energy consumptions before and after retrofit; and the cost-assessment method is used to evaluate the life cycle cost of implementing each measure. A selection of nine retrofitting measures are ranked according to life cycle energy savings, life cycle cost, and cost-effectiveness (measured by cost per unit energy saved). Findings from the Singapore case study suggest that retrofitting building envelop is the third least cost-effective measure although it can lead to highest energy savings. Lighting replacement has the least life cycle energy savings, but it is the most cost-effective measure. Electricity price has little influence on the cost-effectiveness ranking of all nine measures but discount rates (tested for 4%, 7% and 12%) can influence the ranking of home appliances. Based on the findings from the case study, the proposed integrated approach can help identify an optimum retrofit strategy and the cost of achieving energy efficiency targets for existing buildings.

Suggested Citation

  • Yuan, Jun & Nian, Victor & Su, Bin, 2019. "Evaluation of cost-effective building retrofit strategies through soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:77
    DOI: 10.1016/j.apenergy.2019.113573
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    References listed on IDEAS

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    Cited by:

    1. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    2. Anna Musz-Pomorska & Marcin K. Widomski & Justyna Gołębiowska, 2024. "Financial Aspects of Sustainable Rainwater Management in Small-Scale Urban Housing Communities," Sustainability, MDPI, vol. 16(2), pages 1-21, January.
    3. He, Qiong & Hossain, Md. Uzzal & Ng, S. Thomas & Augenbroe, Godfried, 2021. "Identifying practical sustainable retrofit measures for existing high-rise residential buildings in various climate zones through an integrated energy-cost model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    4. Bragolusi, Paolo & D'Alpaos, Chiara, 2022. "The valuation of buildings energy retrofitting: A multiple-criteria approach to reconcile cost-benefit trade-offs and energy savings," Applied Energy, Elsevier, vol. 310(C).
    5. Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
    6. Jun Yuan & Haowei Wang & Szu Hui Ng & Victor Nian, 2020. "Ship Emission Mitigation Strategies Choice Under Uncertainty," Energies, MDPI, vol. 13(9), pages 1-20, May.
    7. Skiba, Marta & Mrówczyńska, Maria & Sztubecka, Małgorzata & Bazan-Krzywoszańska, Anna & Kazak, Jan K. & Leśniak, Agnieszka & Janowiec, Filip, 2021. "Probability estimation of the city’s energy efficiency improvement as a result of using the phase change materials in heating networks," Energy, Elsevier, vol. 228(C).
    8. Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).

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