IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i10p5739-d558514.html
   My bibliography  Save this article

Building Optimization through a Parametric Design Platform: Using Sensitivity Analysis to Improve a Radial-Based Algorithm Performance

Author

Listed:
  • Nayara R. M. Sakiyama

    (Materials Testing Institute (MPA), University of Stuttgart, Pfaffenwaldring 2b, 70569 Stuttgart, Germany
    Institute for Science, Engineering and Technology (ICET), Federal University of the Jeq. and Muc. Valleys (UFVJM), R. Cruzeiro, 01-Jardim São Paulo, Teófilo Otoni 39803-371, Brazil)

  • Joyce C. Carlo

    (Architecture and Urbanism Department (DAU), Federal University of Vicosa (UFV), Av P. H. Rolfs, Viçosa 36570-900, Brazil)

  • Leonardo Mazzaferro

    (Laboratory of Energy Efficiency in Buildings (LabEEE), Federal University of Santa Catarina (UFSC), Caixa Postal 476, Florianópolis 88040-970, Brazil)

  • Harald Garrecht

    (Materials Testing Institute (MPA), University of Stuttgart, Pfaffenwaldring 2b, 70569 Stuttgart, Germany)

Abstract

Performance-based design using computational and parametric optimization is an effective strategy to solve the multiobjective problems typical of building design. In this sense, this study investigates the developing process of parametric modeling and optimization of a naturally ventilated house located in a region with well-defined seasons. Its purpose is to improve its thermal comfort during the cooling period by maximizing Natural Ventilation Effectiveness (NVE) and diminishing annual building energy demand, namely Total Cooling Loads (TCL) and Total Heating Loads (THL). Following a structured workflow, divided into (i) model setting, (ii) Sensitivity Analyses (SA), and (iii) Multiobjective Optimization (MOO), the process is straightforwardly implemented through a 3D parametric modeling platform. After building set up, the input variables number is firstly reduced with SA, and the last step runs with an innovative model-based optimization algorithm (RBFOpt), particularly appropriate for time-intensive performance simulations. The impact of design variables on the three-performance metrics is comprehensively discussed, with a direct relationship between NVE and TCL. MOO results indicate a great potential for natural ventilation and heating energy savings for the residential building set as a reference, showing an improvement between 14–87% and 26–34% for NVE and THL, respectively. The approach meets the current environmental demands related to reducing energy consumption and CO 2 emissions, which include passive design implementations, such as natural or hybrid ventilation. Moreover, the design solutions and building orientation, window-to-wall ratio, and envelope properties could be used as guidance in similar typologies and climates. Finally, the adopted framework configures a practical and replicable approach for studies aiming to develop high-performance buildings through MOO.

Suggested Citation

  • Nayara R. M. Sakiyama & Joyce C. Carlo & Leonardo Mazzaferro & Harald Garrecht, 2021. "Building Optimization through a Parametric Design Platform: Using Sensitivity Analysis to Improve a Radial-Based Algorithm Performance," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5739-:d:558514
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/10/5739/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/10/5739/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Méndez Echenagucia, Tomás & Capozzoli, Alfonso & Cascone, Ylenia & Sassone, Mario, 2015. "The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis," Applied Energy, Elsevier, vol. 154(C), pages 577-591.
    2. Piselli, Cristina & Prabhakar, Mohit & de Gracia, Alvaro & Saffari, Mohammad & Pisello, Anna Laura & Cabeza, Luisa F., 2020. "Optimal control of natural ventilation as passive cooling strategy for improving the energy performance of building envelope with PCM integration," Renewable Energy, Elsevier, vol. 162(C), pages 171-181.
    3. Shi, Xing & Tian, Zhichao & Chen, Wenqiang & Si, Binghui & Jin, Xing, 2016. "A review on building energy efficient design optimization rom the perspective of architects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 872-884.
    4. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.
    5. Fabrizio Maria Amoruso & Udo Dietrich & Thorsten Schuetze, 2018. "Development of a Building Information Modeling-Parametric Workflow Based Renovation Strategy for an Exemplary Apartment Building in Seoul, Korea," Sustainability, MDPI, vol. 10(12), pages 1-30, November.
    6. 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.
    7. Krzysztof Grygierek & Joanna Ferdyn-Grygierek, 2018. "Multi-Objective Optimization of the Envelope of Building with Natural Ventilation," Energies, MDPI, vol. 11(6), pages 1-17, May.
    8. Xiaodong Xu & Chenhuan Yin & Wei Wang & Ning Xu & Tianzhen Hong & Qi Li, 2019. "Revealing Urban Morphology and Outdoor Comfort through Genetic Algorithm-Driven Urban Block Design in Dry and Hot Regions of China," Sustainability, MDPI, vol. 11(13), pages 1-19, July.
    9. Müller, Liana & Berker, Thomas, 2013. "Passive House at the crossroads: The past and the present of a voluntary standard that managed to bridge the energy efficiency gap," Energy Policy, Elsevier, vol. 60(C), pages 586-593.
    10. Marini, Dashamir, 2013. "Optimization of HVAC systems for distributed generation as a function of different types of heat sources and climatic conditions," Applied Energy, Elsevier, vol. 102(C), pages 813-826.
    11. Ibrahim, Mohamad & Biwole, Pascal Henry & Achard, Patrick & Wurtz, Etienne & Ansart, Guillaume, 2015. "Building envelope with a new aerogel-based insulating rendering: Experimental and numerical study, cost analysis, and thickness optimization," Applied Energy, Elsevier, vol. 159(C), pages 490-501.
    12. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    13. Tian, Wei, 2013. "A review of sensitivity analysis methods in building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 411-419.
    14. Nayara Rodrigues Marques Sakiyama & Jurgen Frick & Timea Bejat & Harald Garrecht, 2021. "Using CFD to Evaluate Natural Ventilation through a 3D Parametric Modeling Approach," Energies, MDPI, vol. 14(8), pages 1-27, April.
    15. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    16. 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.
    17. Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2018. "Simulation-based approach to optimize passively designed buildings: A case study on a typical architectural form in hot and humid climates," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P2), pages 1712-1725.
    18. 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.
    19. Nari Yoon & Mary Ann Piette & Jung Min Han & Wentao Wu & Ali Malkawi, 2020. "Optimization of Window Positions for Wind-Driven Natural Ventilation Performance," Energies, MDPI, vol. 13(10), pages 1-25, May.
    Full references (including those not matched with items on IDEAS)

    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. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    2. 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.
    3. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "Impact of adjustment strategies on building design process in different climates oriented by multiple performance," Applied Energy, Elsevier, vol. 266(C).
    4. Shaoxiong Li & Le Liu & Changhai Peng, 2020. "A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges," Sustainability, MDPI, vol. 12(4), pages 1-36, February.
    5. 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.
    6. Fernandes, Marco S. & Rodrigues, Eugénio & Gaspar, Adélio Rodrigues & Costa, José J. & Gomes, Álvaro, 2019. "The impact of thermal transmittance variation on building design in the Mediterranean region," Applied Energy, Elsevier, vol. 239(C), pages 581-597.
    7. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    8. 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.
    9. 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.
    10. 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.
    11. Si, Binghui & Tian, Zhichao & Jin, Xing & Zhou, Xin & Tang, Peng & Shi, Xing, 2016. "Performance indices and evaluation of algorithms in building energy efficient design optimization," Energy, Elsevier, vol. 114(C), pages 100-112.
    12. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    13. Sakiyama, N.R.M. & Carlo, J.C. & Frick, J. & Garrecht, H., 2020. "Perspectives of naturally ventilated buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    14. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    15. Huang, Junchao & Chen, Xi & Yang, Hongxing & Zhang, Weilong, 2018. "Numerical investigation of a novel vacuum photovoltaic curtain wall and integrated optimization of photovoltaic envelope systems," Applied Energy, Elsevier, vol. 229(C), pages 1048-1060.
    16. de Almeida Rocha, Ana Paula & Reynoso-Meza, Gilberto & Oliveira, Ricardo C.L.F. & Mendes, Nathan, 2020. "A pixel counting based method for designing shading devices in buildings considering energy efficiency, daylight use and fading protection," Applied Energy, Elsevier, vol. 262(C).
    17. Østergård, Torben & Jensen, Rasmus L. & Maagaard, Steffen E., 2016. "Building simulations supporting decision making in early design – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 187-201.
    18. 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.
    19. Binghui Si & Zhichao Tian & Wenqiang Chen & Xing Jin & Xin Zhou & Xing Shi, 2018. "Performance Assessment of Algorithms for Building Energy Optimization Problems with Different Properties," Sustainability, MDPI, vol. 11(1), pages 1-22, December.
    20. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.

    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:jsusta:v:13:y:2021:i:10:p:5739-:d:558514. 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.