IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i16p9318-d617597.html
   My bibliography  Save this article

Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings

Author

Listed:
  • Linda Barelli

    (Department of Engineering, University of Perugia, 06125 Perugia, Italy)

  • Elisa Belloni

    (Department of Engineering, University of Perugia, 06125 Perugia, Italy)

  • Gianni Bidini

    (Department of Engineering, University of Perugia, 06125 Perugia, Italy)

  • Cinzia Buratti

    (Department of Engineering, University of Perugia, 06125 Perugia, Italy)

  • Emilia Maria Pinchi

    (Department of Engineering, University of Perugia, 06125 Perugia, Italy)

Abstract

This paper concerns the development of an automatic tool, based on Fuzzy Logic, which is able to identify the proper solutions for the energy retrofitting of existing buildings. Regarding winter heating, opaque and glazing surfaces are considered in order to reduce building heat dispersions. Starting from energy diagnosis, it is possible to formulate retrofitting proposals and to evaluate the effectiveness of the intervention considering several aspects (energy savings, costs, intervention typology). The innovation of this work is represented by the application of a fuzzy logic expert system to obtain an indication about the proper interventions for building energy retrofitting, providing as inputs only few parameters, with a strong reduction in time and effort with respect to the software tools and methodologies currently applied by experts. The novelty of the paper is the easy handling properties of the developed tool, which requires only a few data about the buildings: not many such methods were developed in the last years. The energy requirements for winter heating before and after particular interventions were evaluated for a consistent set of buildings in order to produce the required knowledge base for the tool’s development. The identified appropriate inputs and outputs, their domains of discretization, the membership functions associated to each fuzzy set, and the linguistic rules were deduced on the basis of the knowledge determined in this was. Therefore, the system was successfully validated with reference to further buildings characterized by different design and architecture features, showing a good agreement with the intervention opportunities evaluated.

Suggested Citation

  • Linda Barelli & Elisa Belloni & Gianni Bidini & Cinzia Buratti & Emilia Maria Pinchi, 2021. "Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:9318-:d:617597
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/16/9318/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/16/9318/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
    2. Choi, Jun-Ki & Morrison, Drew & Hallinan, Kevin P. & Brecha, Robert J., 2014. "Economic and environmental impacts of community-based residential building energy efficiency investment," Energy, Elsevier, vol. 78(C), pages 877-886.
    3. García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation," Applied Energy, Elsevier, vol. 192(C), pages 33-58.
    4. Aktacir, Mehmet Azmi & Büyükalaca, Orhan & YIlmaz, Tuncay, 2010. "A case study for influence of building thermal insulation on cooling load and air-conditioning system in the hot and humid regions," Applied Energy, Elsevier, vol. 87(2), pages 599-607, February.
    5. Wada, Kenichi & Akimoto, Keigo & Sano, Fuminori & Oda, Junichiro & Homma, Takashi, 2012. "Energy efficiency opportunities in the residential sector and their feasibility," Energy, Elsevier, vol. 48(1), pages 5-10.
    6. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    7. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    8. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
    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. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
    2. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    3. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    4. 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.
    5. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    6. Hu, Jingfan & Zheng, Wandong & Zhang, Sirui & Li, Hao & Liu, Zijian & Zhang, Guo & Yang, Xu, 2021. "Thermal load prediction and operation optimization of office building with a zone-level artificial neural network and rule-based control," Applied Energy, Elsevier, vol. 300(C).
    7. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    8. Soutullo, S. & Giancola, E. & Heras, M.R., 2018. "Dynamic energy assessment to analyze different refurbishment strategies of existing dwellings placed in Madrid," Energy, Elsevier, vol. 152(C), pages 1011-1023.
    9. Bordbari, Mohammad Javad & Seifi, Ali Reza & Rastegar, Mohammad, 2018. "Probabilistic energy consumption analysis in buildings using point estimate method," Energy, Elsevier, vol. 142(C), pages 716-722.
    10. Sun, Ying & Haghighat, Fariborz & Fung, Benjamin C.M., 2022. "Trade-off between accuracy and fairness of data-driven building and indoor environment models: A comparative study of pre-processing methods," Energy, Elsevier, vol. 239(PD).
    11. Beaudreau, Bernard C. & Lightfoot, H. Douglas, 2015. "The physical limits to economic growth by R&D funded innovation," Energy, Elsevier, vol. 84(C), pages 45-52.
    12. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    13. Qi Dong & Kai Xing & Hongrui Zhang, 2017. "Artificial Neural Network for Assessment of Energy Consumption and Cost for Cross Laminated Timber Office Building in Severe Cold Regions," Sustainability, MDPI, vol. 10(1), pages 1-15, December.
    14. García Kerdan, Iván & Morillón Gálvez, David, 2020. "Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building," Applied Energy, Elsevier, vol. 280(C).
    15. 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.
    16. Ünal, Berat Berkan & Onaygil, Sermin & Acuner, Ebru & Cin, Rabia, 2022. "Application of energy efficiency obligation scheme for electricity distribution companies in Turkey," Energy Policy, Elsevier, vol. 163(C).
    17. Weiss, Mariana & Chueca, J. Enrique & Jacob, Jorge & Gonçalves, Felipe & Azevedo, Marina & Gouvêa, Adriana & Ravillard, Pauline & Carvalho Metanias Hallack, Michelle, 2022. "Empowering Electricity Consumers through Demand Response Approach: Why and How," IDB Publications (Working Papers) 12133, Inter-American Development Bank.
    18. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    19. Re Cecconi, F. & Moretti, N. & Tagliabue, L.C., 2019. "Application of artificial neutral network and geographic information system to evaluate retrofit potential in public school buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 266-277.
    20. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).

    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:16:p:9318-:d:617597. 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.