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A hybrid Decision Support System for Generation of Holistic Renovation Scenarios—Cases of Energy Consumption, Investment Cost, and Thermal Indoor Comfort

Author

Listed:
  • Aliakbar Kamari

    (Department of Engineering, Aarhus University, 8000 Aarhus, Denmark)

  • Stefan Jensen

    (Department of Engineering, Aarhus University, 8000 Aarhus, Denmark)

  • Maria Leonhard Christensen

    (Department of Engineering, Aarhus University, 8000 Aarhus, Denmark)

  • Steffen Petersen

    (Department of Engineering, Aarhus University, 8000 Aarhus, Denmark)

  • Poul Henning Kirkegaard

    (Department of Engineering, Aarhus University, 8000 Aarhus, Denmark)

Abstract

Future building renovations must rely on a holistic perspective in relation to sustainability. This paper presents a Decision Support Systems (DSS) that can be used by architects and engineering consultants to generate and evaluate the sustainability of renovation scenarios in a holistic manner during the early design stage of renovation projects. Firstly, this paper discusses both the notion of a sustainable renovation, together with various renovation approaches, towards the appreciation of the developing DSS for the generation of holistic scenarios. Secondly, it provides details about the mechanism and types of Multiple Criteria Decision Making methods to be exploited in the main body of the DSS. As such, a hybrid approach including a search algorithm with the Genetic Algorithm is used to combine and develop countless optimal scenarios. The performance of the generated scenarios is simulated and evaluated in terms criteria for Energy Consumption , Investment Cost , and Thermal Indoor Comfort . The trade-off between the criteria is addressed using the Pareto-front approach, and subsequently, the optimal scenarios are determined and selected using MCDM-based rating methods. The outcome is verified discussing a case study about an actual [recently] renovated dwelling and the top ranked generated scenarios using the DSS in this paper.

Suggested Citation

  • Aliakbar Kamari & Stefan Jensen & Maria Leonhard Christensen & Steffen Petersen & Poul Henning Kirkegaard, 2018. "A hybrid Decision Support System for Generation of Holistic Renovation Scenarios—Cases of Energy Consumption, Investment Cost, and Thermal Indoor Comfort," Sustainability, MDPI, vol. 10(4), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1255-:d:142073
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    References listed on IDEAS

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    Cited by:

    1. Giovanna Acampa & Lorenzo Diana & Giorgia Marino & Rossella Marmo, 2021. "Assessing the Transformability of Public Housing through BIM," Sustainability, MDPI, vol. 13(10), pages 1-24, May.
    2. Fang-Jye Shiue & Meng-Cong Zheng & Hsin-Yun Lee & Akhmad F.K. Khitam & Pei-Ying Li, 2019. "Renovation Construction Process Scheduling for Long-Term Performance of Buildings: An Application Case of University Campus," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
    3. Aliakbar Kamari & Ashwin Paari & Henrik Øien Torvund, 2020. "BIM-Enabled Virtual Reality (VR) for Sustainability Life Cycle and Cost Assessment," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    4. Arturas Kaklauskas & Gintautas Dzemyda & Laura Tupenaite & Ihar Voitau & Olga Kurasova & Jurga Naimaviciene & Yauheni Rassokha & Loreta Kanapeckiene, 2018. "Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment," Energies, MDPI, vol. 11(8), pages 1-20, August.
    5. Antonio Artino & Riccardo Caponetto & Gianpiero Evola & Giuseppe Margani & Edoardo Michele Marino & Emanuele Murgano, 2020. "Decision Support System for the Sustainable Seismic and Energy Renovation of Buildings: Methodological Layout," Sustainability, MDPI, vol. 12(24), pages 1-21, December.

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