IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3361-d1688255.html
   My bibliography  Save this article

Optimizing Energy and Cost Performance in Residential Buildings: A Multi-Objective Approach Applied to the City of Patras, Greece

Author

Listed:
  • Dionyssis Makris

    (Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece)

  • Anastasia Antzoulatou

    (Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece)

  • Alexandros Romaios

    (Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece)

  • Sonia Malefaki

    (Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece)

  • John A. Paravantis

    (Department of International and European Studies, University of Piraeus, 80 Karaoli and Dimitriou Street, 18534 Piraeus, Greece)

  • Athanassios Giannadakis

    (Department of Mechanical Engineering, University of Peloponnese, M. Alexandrou 1, 26334 Patras, Greece)

  • Giouli Mihalakakou

    (Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece)

Abstract

Improving the energy efficiency of buildings is a critical pathway in mitigating greenhouse gas emissions and fostering sustainable urban development. This study introduces a simulation-based multi-objective optimization framework designed to enhance both the thermal and economic performance of residential buildings. A representative single-family dwelling located in Patras, Greece, served as a case study to demonstrate the application and scalability of the proposed methodology. The optimization simultaneously minimized two conflicting objectives: the building’s annual thermal energy demand and the cost of construction materials. The computational process was implemented using MATLAB’s Multi-Objective Genetic Algorithm, supported by a modular Excel interface that enables the dynamic customization of design parameters and climatic inputs. A parametric analysis across four optimization scenarios was conducted by systematically varying the key algorithmic hyperparameters—population size, mutation rate, and number of generations—to assess their impact on convergence behavior, Pareto front resolution, and solution diversity. The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, while also highlighting the sensitivity of optimization outcomes to hyperparameter tuning. The proposed framework is a flexible, reproducible, and computationally tractable approach to supporting early-stage, performance-driven building design under realistic constraints.

Suggested Citation

  • Dionyssis Makris & Anastasia Antzoulatou & Alexandros Romaios & Sonia Malefaki & John A. Paravantis & Athanassios Giannadakis & Giouli Mihalakakou, 2025. "Optimizing Energy and Cost Performance in Residential Buildings: A Multi-Objective Approach Applied to the City of Patras, Greece," Energies, MDPI, vol. 18(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3361-:d:1688255
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3361/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/13/3361/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heiselberg, Per & Brohus, Henrik & Hesselholt, Allan & Rasmussen, Henrik & Seinre, Erkki & Thomas, Sara, 2009. "Application of sensitivity analysis in design of sustainable buildings," Renewable Energy, Elsevier, vol. 34(9), pages 2030-2036.
    2. Chwieduk, Dorota A., 2017. "Towards modern options of energy conservation in buildings," Renewable Energy, Elsevier, vol. 101(C), pages 1194-1202.
    3. Huda I. Ahmed & Eman T. Hamed & Hamsa Th. Saeed Chilmeran, 2020. "A Modified Bat Algorithm with Conjugate Gradient Method for Global Optimization," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2020, pages 1-14, June.
    4. Chelouah, Rachid & Siarry, Patrick, 2000. "Tabu Search applied to global optimization," European Journal of Operational Research, Elsevier, vol. 123(2), pages 256-270, June.
    5. Chanas, Stefan & Zielinski, Pawel, 2000. "On the equivalence of two optimization methods for fuzzy linear programming problems," European Journal of Operational Research, Elsevier, vol. 121(1), pages 56-63, February.
    6. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    7. 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.
    8. Chantrelle, Fanny Pernodet & Lahmidi, Hicham & Keilholz, Werner & Mankibi, Mohamed El & Michel, Pierre, 2011. "Development of a multicriteria tool for optimizing the renovation of buildings," Applied Energy, Elsevier, vol. 88(4), pages 1386-1394, April.
    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. Kangji Li & Lei Pan & Wenping Xue & Hui Jiang & Hanping Mao, 2017. "Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study," Energies, MDPI, vol. 10(2), pages 1-23, February.
    2. 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.
    3. 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.
    4. 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.
    5. Diana Manjarres & Lara Mabe & Xabat Oregi & Itziar Landa-Torres, 2019. "Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level," Sustainability, MDPI, vol. 11(5), pages 1-24, March.
    6. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    7. Niemelä, Tuomo & Kosonen, Risto & Jokisalo, Juha, 2016. "Cost-optimal energy performance renovation measures of educational buildings in cold climate," Applied Energy, Elsevier, vol. 183(C), pages 1005-1020.
    8. Lin, Yu-Hao & Tsai, Kang-Ting & Lin, Min-Der & Yang, Ming-Der, 2016. "Design optimization of office building envelope configurations for energy conservation," Applied Energy, Elsevier, vol. 171(C), pages 336-346.
    9. 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.
    10. Rabani, Mehrdad & Bayera Madessa, Habtamu & Mohseni, Omid & Nord, Natasa, 2020. "Minimizing delivered energy and life cycle cost using Graphical script: An office building retrofitting case," Applied Energy, Elsevier, vol. 268(C).
    11. 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.
    12. Prada, A. & Gasparella, A. & Baggio, P., 2018. "On the performance of meta-models in building design optimization," Applied Energy, Elsevier, vol. 225(C), pages 814-826.
    13. Karmellos, M. & Kiprakis, A. & Mavrotas, G., 2015. "A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies," Applied Energy, Elsevier, vol. 139(C), pages 131-150.
    14. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    15. 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.
    16. Ciardiello, Adriana & Rosso, Federica & Dell'Olmo, Jacopo & Ciancio, Virgilio & Ferrero, Marco & Salata, Ferdinando, 2020. "Multi-objective approach to the optimization of shape and envelope in building energy design," Applied Energy, Elsevier, vol. 280(C).
    17. 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.
    18. Ceballos-Fuentealba, Irlanda & Álvarez-Miranda, Eduardo & Torres-Fuchslocher, Carlos & del Campo-Hitschfeld, María Luisa & Díaz-Guerrero, John, 2019. "A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings," Applied Energy, Elsevier, vol. 256(C).
    19. Torres-Rivas, Alba & Palumbo, Mariana & Haddad, Assed & Cabeza, Luisa F. & Jiménez, Laureano & Boer, Dieter, 2018. "Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk," Applied Energy, Elsevier, vol. 224(C), pages 602-614.
    20. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:18:y:2025:i:13:p:3361-:d:1688255. 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.