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Decision support model for establishing the optimal energy retrofit strategy for existing multi-family housing complexes

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  • Hong, Taehoon
  • Koo, Choongwan
  • Kim, Hyunjoong
  • Seon Park, Hyo

Abstract

The number of multi-family housing complexes (MFHCs) over 15yr old in South Korea is expected to exceed 5 million by 2015. Accordingly, the demand for energy retrofit in the deteriorating MFHCs is rapidly increasing. This study aimed to develop a decision support model for establishing the optimal energy retrofit strategy for existing MFHCs. It can provide clear criteria for establishing the carbon emissions reduction target (CERT) and allow efficient budget allocation for conducting the energy retrofit. The CERT for “S” MFHC, one of MFHCs located in Seoul, as a case study, was set at 23.0% (electricity) and 27.9% (gas energy). In the economic and environmental assessment, it was determined that scenario #12 was the optimal scenario (ranked second with regard to NPV40 (net present value at year 40) and third with regard to SIR40 (saving to investment ratio at year 40). The proposed model could be useful for owners, construction managers, or policymakers in charge of establishing energy retrofit strategy for existing MFHCs. It could allow contractors in a competitive bidding process to rationally establish the CERT and select the optimal energy retrofit strategy. It can be also applied to any other country or sector in a global environment.

Suggested Citation

  • Hong, Taehoon & Koo, Choongwan & Kim, Hyunjoong & Seon Park, Hyo, 2014. "Decision support model for establishing the optimal energy retrofit strategy for existing multi-family housing complexes," Energy Policy, Elsevier, vol. 66(C), pages 157-169.
    • Handle: RePEc:eee:enepol:v:66:y:2014:i:c:p:157-169
    DOI: 10.1016/j.enpol.2013.10.057
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    Cited by:

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    5. Murto, Pekka & Jalas, Mikko & Juntunen, Jouni & Hyysalo, Sampsa, 2019. "The difficult process of adopting a comprehensive energy retrofit in housing companies: Barriers posed by nascent markets and complicated calculability," Energy Policy, Elsevier, vol. 132(C), pages 955-964.
    6. Murto, P. & Jalas, M. & Juntunen, J. & Hyysalo, S., 2019. "Devices and strategies: An analysis of managing complexity in energy retrofit projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
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    9. Becchio, Cristina & Bottero, Marta Carla & Corgnati, Stefano Paolo & Dell’Anna, Federico, 2018. "Decision making for sustainable urban energy planning: an integrated evaluation framework of alternative solutions for a NZED (Net Zero-Energy District) in Turin," Land Use Policy, Elsevier, vol. 78(C), pages 803-817.
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    15. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    16. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Koo, Choongwan & Jeong, Kwangbok, 2016. "An optimization model for selecting the optimal green systems by considering the thermal comfort and energy consumption," Applied Energy, Elsevier, vol. 169(C), pages 682-695.
    17. Koo, Choongwan & Hong, Taehoon & Kim, Jimin & Kim, Hyunjoong, 2015. "An integrated multi-objective optimization model for establishing the low-carbon scenario 2020 to achieve the national carbon emissions reduction target for residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 410-425.

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