IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i11p915-d82184.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/11/915/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/11/915/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giuliano Dall'O' & Elisa Bruni & Angela Panza, 2013. "Improvement of the Sustainability of Existing School Buildings According to the Leadership in Energy and Environmental Design (LEED) ® Protocol: A Case Study in Italy," Energies, MDPI, vol. 6(12), pages 1-21, December.
    2. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
    3. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    4. Arnette, Andrew & Zobel, Christopher W., 2012. "An optimization model for regional renewable energy development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4606-4615.
    5. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    6. Fabio Magrassi & Adriana Del Borghi & Michela Gallo & Carlo Strazza & Michela Robba, 2016. "Optimal Planning of Sustainable Buildings: Integration of Life Cycle Assessment and Optimization in a Decision Support System (DSS)," Energies, MDPI, vol. 9(7), pages 1-15, June.
    7. Dongjun Suh & Seongju Chang, 2014. "A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings," Energies, MDPI, vol. 7(11), pages 1-33, October.
    8. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    9. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
    2. Tronchin, Lamberto & Manfren, Massimiliano & James, Patrick AB., 2018. "Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building," Energy, Elsevier, vol. 165(PA), pages 26-40.
    3. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
    4. Li, Hong Xian & Li, Yan & Jiang, Boya & Zhang, Limao & Wu, Xianguo & Lin, Jingyi, 2020. "Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis," Renewable Energy, Elsevier, vol. 149(C), pages 1414-1423.
    5. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    6. Ramos Ruiz, Germán & Fernández Bandera, Carlos, 2017. "Analysis of uncertainty indices used for building envelope calibration," Applied Energy, Elsevier, vol. 185(P1), pages 82-94.
    7. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    8. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    9. Enghok Leang & Pierre Tittelein & Laurent Zalewski & Stéphane Lassue, 2020. "Design Optimization of a Composite Solar Wall Integrating a PCM in a Individual House: Heating Demand and Thermal Comfort Considerations," Energies, MDPI, vol. 13(21), pages 1-29, October.
    10. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    11. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    12. Benedek Kiss & Jose Dinis Silvestre & Rita Andrade Santos & Zsuzsa Szalay, 2021. "Environmental and Economic Optimisation of Buildings in Portugal and Hungary," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    13. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    14. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    15. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    16. García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design," Energy, Elsevier, vol. 128(C), pages 244-263.
    17. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    18. Eleftheria Touloupaki & Theodoros Theodosiou, 2017. "Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review," Energies, MDPI, vol. 10(5), pages 1-18, May.
    19. Forde, Joe & Hopfe, Christina J. & McLeod, Robert S. & Evins, Ralph, 2020. "Temporal optimization for affordable and resilient Passivhaus dwellings in the social housing sector," Applied Energy, Elsevier, vol. 261(C).
    20. Harkouss, Fatima & Fardoun, Farouk & Biwole, Pascal Henry, 2018. "Passive design optimization of low energy buildings in different climates," Energy, Elsevier, vol. 165(PA), pages 591-613.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:9:y:2016:i:11:p:915-:d:82184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.