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A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings

Author

Listed:
  • Cristina Brunelli

    (Department of Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy)

  • Francesco Castellani

    (Department of Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy)

  • Alberto Garinei

    (Department of Sustainability Engineering, Guglielmo Marconi University, Via Plinio 44, 00193 Roma, Italy)

  • Lorenzo Biondi

    (Department of Sustainability Engineering, Guglielmo Marconi University, Via Plinio 44, 00193 Roma, Italy)

  • Marcello Marconi

    (Department of Sustainability Engineering, Guglielmo Marconi University, Via Plinio 44, 00193 Roma, Italy)

Abstract

When dealing with sustainable design concepts in new construction or in retrofitting existing buildings, it is useful to define both economic and environmental performance indicators, in order to select the optimal technical solutions. In most of the cases, the definition of the optimal strategy is not trivial because it is necessary to solve a multi-objective problem with a high number of the variables subjected to nonlinear constraints. In this study, a powerful multi-objective optimization genetic algorithm, NSGAII (Non-dominated Sorting Genetic Algorithm-II), is used to derive the Pareto optimal solutions, which can illustrate the whole trade-off relationship between objectives. A method is then proposed, to introduce uncertainty evaluation in the optimization procedure. A new university building is taken as a case study to demonstrate how each step of the optimization process should be performed. The results achieved turn out to be reliable and show the suitableness of this procedure to define both economic and environmental performance indicators. Similar analysis on a set of buildings representatives of a specific region might be used to assist local/national administrations in the definition of appropriate legal limits that will permit a strategic optimized extension of renewable energy production. Finally, the proposed approach could be applied to similar optimization models for the optimal planning of sustainable buildings, in order to define the best solutions among non-optimal ones.

Suggested Citation

  • Cristina Brunelli & Francesco Castellani & Alberto Garinei & Lorenzo Biondi & Marcello Marconi, 2016. "A Procedure to Perform Multi-Objective Optimization for Sustainable Design of Buildings," Energies, MDPI, vol. 9(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:915-:d:82184
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    References listed on IDEAS

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    Cited by:

    1. Hanze Yu & Wei Yang & Qiyuan Li & Jie Li, 2022. "Optimizing Buildings’ Life Cycle Performance While Allowing Diversity in the Early Design Stage," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    2. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    3. Mohamed Hamdy & Gerardo Maria Mauro, 2017. "Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO 2 -eq vs. Discounted Payback Time," Energies, MDPI, vol. 10(7), pages 1-26, July.
    4. Małgorzata Basińska & Dobrosława Kaczorek & Halina Koczyk, 2020. "Building Thermo-Modernisation Solution Based on the Multi-Objective Optimisation Method," Energies, MDPI, vol. 13(6), pages 1-19, March.
    5. Ali Sadollah & Mohammad Nasir & Zong Woo Geem, 2020. "Sustainability and Optimization: From Conceptual Fundamentals to Applications," Sustainability, MDPI, vol. 12(5), pages 1-34, March.
    6. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.

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