Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex

Kwangbok Jeong, Taehoon Hong, Jimin Kim, Kyuman Cho

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1 Citation (Scopus)

Abstract

Measures to improve the building energy efficiency of deteriorated multi-family housing complexes (MFHCs) require trade-offs between economic and environmental feasibility, and therefore these aspects should be considered simultaneously. Towards this end, this study aimed to develop a multi-objective optimization model for determining the optimal CO2 emission reduction (CER) strategies for MFHCs that can be used not only by experts but also by non-experts. This study used integrated multi-objective optimization with a genetic algorithm as an optimization methodology. The developed model, which considers the five optimization objectives (i.e., initial investment cost, net present value, savings-to-investment cost, CER, and marginal abatement cost) at the same time, can review a total of 31,200 scenarios combined with four energy saving measures (i.e., insulation, window, lighting, and shading systems). To verify the feasibility of the developed model, this study conducted a case study targeting ‘D’ MFHC in South Korea. First, the accuracy of the calibration of the energy simulation model for ‘D’ MFHC (coefficient of variation of the root mean square error: 12.84%; mean bias error: 0.39%) satisfied the criteria of ASHRAE Guideline 14; second, I8 (expanded polystyrene board - type 1 (No. 4))-L3 (LED lighting installed in louvered ceiling) and I8 (expanded polystyrene board - type 1 (No. 4)) - L4 (ceiling-mounted light) were determined to be the optimal CER strategy (integrated multi-objective optimization score: 0.2252). The developed model can help building owners make the optimal decision on green modeling by entering simple information (e.g., region, total floor area, etc.).

Original languageEnglish
Pages (from-to)118-131
Number of pages14
JournalRenewable and Sustainable Energy Reviews
Volume110
DOIs
Publication statusPublished - 2019 Aug

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Multiobjective optimization
Ceilings
Polystyrenes
Lighting
Costs
Mean square error
Light emitting diodes
Energy efficiency
Insulation
Energy conservation
Genetic algorithms
Calibration
Economics

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment

Cite this

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title = "Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex",
abstract = "Measures to improve the building energy efficiency of deteriorated multi-family housing complexes (MFHCs) require trade-offs between economic and environmental feasibility, and therefore these aspects should be considered simultaneously. Towards this end, this study aimed to develop a multi-objective optimization model for determining the optimal CO2 emission reduction (CER) strategies for MFHCs that can be used not only by experts but also by non-experts. This study used integrated multi-objective optimization with a genetic algorithm as an optimization methodology. The developed model, which considers the five optimization objectives (i.e., initial investment cost, net present value, savings-to-investment cost, CER, and marginal abatement cost) at the same time, can review a total of 31,200 scenarios combined with four energy saving measures (i.e., insulation, window, lighting, and shading systems). To verify the feasibility of the developed model, this study conducted a case study targeting ‘D’ MFHC in South Korea. First, the accuracy of the calibration of the energy simulation model for ‘D’ MFHC (coefficient of variation of the root mean square error: 12.84{\%}; mean bias error: 0.39{\%}) satisfied the criteria of ASHRAE Guideline 14; second, I8 (expanded polystyrene board - type 1 (No. 4))-L3 (LED lighting installed in louvered ceiling) and I8 (expanded polystyrene board - type 1 (No. 4)) - L4 (ceiling-mounted light) were determined to be the optimal CER strategy (integrated multi-objective optimization score: 0.2252). The developed model can help building owners make the optimal decision on green modeling by entering simple information (e.g., region, total floor area, etc.).",
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AU - Cho, Kyuman

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