IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i5p1495-d213126.html
   My bibliography  Save this article

Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

Author

Listed:
  • Diana Manjarres

    (Tecnalia Research and Innovation, Parque Tecnológico de Bizkaia, 48160 Derio, Spain)

  • Lara Mabe

    (Tecnalia Research and Innovation, Parque Tecnológico de Bizkaia, 48160 Derio, Spain)

  • Xabat Oregi

    (Architecture Department, University of the Basque Country (UPV/EHU), 20018 Donostia, Spain)

  • Itziar Landa-Torres

    (Petronor Innovación S.L, 48550 Muskiz, Spain)

Abstract

Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1495-:d:213126
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/5/1495/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/5/1495/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Ballarini, Ilaria & Corrado, Vincenzo & Madonna, Francesco & Paduos, Simona & Ravasio, Franco, 2017. "Energy refurbishment of the Italian residential building stock: energy and cost analysis through the application of the building typology," Energy Policy, Elsevier, vol. 105(C), pages 148-160.
    5. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    6. Jennings, Mark & Fisk, David & Shah, Nilay, 2014. "Modelling and optimization of retrofitting residential energy systems at the urban scale," Energy, Elsevier, vol. 64(C), pages 220-233.
    7. 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.
    8. 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.
    9. Lidberg, T. & Gustafsson, M. & Myhren, J.A. & Olofsson, T. & Ödlund (former Trygg), L., 2018. "Environmental impact of energy refurbishment of buildings within different district heating systems," Applied Energy, Elsevier, vol. 227(C), pages 231-238.
    10. Xabat Oregi & Maxime Pousse & Lara Mabe & Alexandre Escudero & Iker Mardaras, 2016. "Sustainability assessment of three districts in the city of Donostia through the NEST simulation tool," Natural Resources Forum, Blackwell Publishing, vol. 40(4), pages 156-168, November.
    11. 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)

    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. Wu, Raphael & Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2017. "Multiobjective optimisation of energy systems and building envelope retrofit in a residential community," Applied Energy, Elsevier, vol. 190(C), pages 634-649.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Napolitano, Davide Ferdinando, 2019. "Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness," Applied Energy, Elsevier, vol. 254(C).
    11. 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.
    12. 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).
    13. 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.
    14. 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).
    15. 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.
    16. 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.
    17. Ferrara, Maria & Rolfo, Andrea & Prunotto, Federico & Fabrizio, Enrico, 2019. "EDeSSOpt – Energy Demand and Supply Simultaneous Optimization for cost-optimized design: Application to a multi-family building," Applied Energy, Elsevier, vol. 236(C), pages 1231-1248.
    18. 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.
    19. Chi, Fang'ai & Xu, Ying, 2022. "Building performance optimization for university dormitory through integration of digital gene map into multi-objective genetic algorithm," Applied Energy, Elsevier, vol. 307(C).
    20. Germán Campos Gordillo & Germán Ramos Ruiz & Yves Stauffer & Stephan Dasen & Carlos Fernández Bandera, 2020. "EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB ® and C#," Sustainability, MDPI, vol. 12(2), pages 1-14, January.

    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:11:y:2019:i:5:p:1495-:d:213126. 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.