IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i3p1797-d1038958.html
   My bibliography  Save this article

A Suggestion of the Alternatives Evaluation Method through IFC-Based Building Energy Performance Analysis

Author

Listed:
  • Jungsik Choi

    (Department of Architecture, Kangwon National University, Samcheok-si 25913, Republic of Korea)

  • Sejin Lee

    (Department of Architecture, Kyung Hee University, Yongin-si 17104, Republic of Korea)

Abstract

In a rapidly changing modern society, the construction industry is facing various issues, including the Fourth Industrial Revolution and climate change. Research on convergence between technologies such as artificial intelligence, AR/VR, IoT, and metaverse, and sustainable technologies such as green buildings and eco-friendly energy is being attempted in each field. The most important thing in the development of these technologies will be the interoperability of data. BIM is a technology that can effectively store data regardless of the size of a building or the amount of information and can be shared and stored without loss of data through an open format called IFC (industry foundation classes). This study aims to present a plan to generate alternatives and evaluate energy performance by analyzing the shape of the envelope for amorphous buildings through IFC. Design elements were derived through analysis of previous studies, and alternatives were automated by developing interfaces that can generate shapes according to the derived design elements. The generated alternatives can be compared and analyzed through the analysis of building energy by developing an evaluation system based on IFC. Based on the quantitative results in the initial design stage, the reliability of the design proposal considering the performance of the building is improved, and the process and cost can be predicted in advance; thus, it is expected to be an efficient decision support tool.

Suggested Citation

  • Jungsik Choi & Sejin Lee, 2023. "A Suggestion of the Alternatives Evaluation Method through IFC-Based Building Energy Performance Analysis," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1797-:d:1038958
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/3/1797/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/3/1797/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abanda, F.H. & Byers, L., 2016. "An investigation of the impact of building orientation on energy consumption in a domestic building using emerging BIM (Building Information Modelling)," Energy, Elsevier, vol. 97(C), pages 517-527.
    2. Mona Abouhamad & Metwally Abu-Hamd, 2021. "Life Cycle Assessment Framework for Embodied Environmental Impacts of Building Construction Systems," Sustainability, MDPI, vol. 13(2), pages 1-21, January.
    3. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
    4. Zoran Pučko & Damjan Maučec & Nataša Šuman, 2020. "Energy and Cost Analysis of Building Envelope Components Using BIM: A Systematic Approach," Energies, MDPI, vol. 13(10), pages 1-24, May.
    5. Eleftheriadis, Stathis & Mumovic, Dejan & Greening, Paul, 2017. "Life cycle energy efficiency in building structures: A review of current developments and future outlooks based on BIM capabilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 811-825.
    6. Zhaoxi Zhan & Wenna Xu & Lin Xu & Xinyue Qi & Wenjie Song & Chen Wang & Ziye Huang, 2022. "BIM-Based Green Hospital Building Performance Pre-Evaluation: A Case Study," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    7. Zhenmin Yuan & Jianliang Zhou & Yaning Qiao & Yadi Zhang & Dandan Liu & Hui Zhu, 2020. "BIM-VE-Based Optimization of Green Building Envelope from the Perspective of both Energy Saving and Life Cycle Cost," Sustainability, MDPI, vol. 12(19), pages 1-16, September.
    8. Vyshali Simhachalam & Tong Wang & Yan Liu & Hans Wamelink & Lorena Montenegro & Geert van Gorp, 2021. "Accelerating Building Energy Retrofitting with BIM-Enabled BREEAM-NL Assessment," Energies, MDPI, vol. 14(24), pages 1-18, December.
    9. Mohammad S. Albdour & Mohammad Shalby & Ahmad A. Salah & Fadi Alhomaidat, 2022. "Evaluating and Enhancing the Energy Efficiency of Representative Residential Buildings by Applying National and International Standards Using BIM," Energies, MDPI, vol. 15(20), pages 1-23, October.
    10. Antonio Galiano-Garrigós & María Domenech-Mataix & Ángel Benigno González-Avilés & Carlos Rizo-Maestre, 2021. "Evaluation of Energy Performance and Comfort: Case-Study of University Buildings with Design Adapted to Local Climate," Sustainability, MDPI, vol. 13(13), pages 1-28, June.
    11. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    12. Guofeng Ma & Ying Liu & Shanshan Shang, 2019. "A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space," Sustainability, MDPI, vol. 11(18), pages 1-26, September.
    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. José Pedro Carvalho & Fernanda Schmitd Villaschi & Luís Bragança, 2021. "Assessing Life Cycle Environmental and Economic Impacts of Building Construction Solutions with BIM," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    2. Rafiee, A. & Dias, E. & Koomen, E., 2019. "Analysing the impact of spatial context on the heat consumption of individual households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 461-470.
    3. Kropotin, P. & Marchuk, I., 2024. "Analytical and quantitative assessment of capital expenditures for construction of an aboveground suspended weight energy storage," Renewable Energy, Elsevier, vol. 220(C).
    4. Zhenmin Yuan & Jianliang Zhou & Yaning Qiao & Yadi Zhang & Dandan Liu & Hui Zhu, 2020. "BIM-VE-Based Optimization of Green Building Envelope from the Perspective of both Energy Saving and Life Cycle Cost," Sustainability, MDPI, vol. 12(19), pages 1-16, September.
    5. Tomasz Szul & Stanisław Kokoszka, 2020. "Application of Rough Set Theory (RST) to Forecast Energy Consumption in Buildings Undergoing Thermal Modernization," Energies, MDPI, vol. 13(6), pages 1-17, March.
    6. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    7. Xun Liu & Zhenhan Ding & Xiaobo Li & Zhiyuan Xue, 2023. "Research Progress, Hotspots, and Trends of Using BIM to Reduce Building Energy Consumption: Visual Analysis Based on WOS Database," IJERPH, MDPI, vol. 20(4), pages 1-21, February.
    8. Ding, Zhikun & Chen, Weilin & Hu, Ting & Xu, Xiaoxiao, 2021. "Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building," Applied Energy, Elsevier, vol. 288(C).
    9. Sanhudo, Luís & Ramos, Nuno M.M. & Poças Martins, João & Almeida, Ricardo M.S.F. & Barreira, Eva & Simões, M. Lurdes & Cardoso, Vítor, 2018. "Building information modeling for energy retrofitting – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 249-260.
    10. Gatt, Damien & Yousif, Charles & Cellura, Maurizio & Camilleri, Liberato & Guarino, Francesco, 2020. "Assessment of building energy modelling studies to meet the requirements of the new Energy Performance of Buildings Directive," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    11. José Pedro Carvalho & Ismael Alecrim & Luís Bragança & Ricardo Mateus, 2020. "Integrating BIM-Based LCA and Building Sustainability Assessment," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    12. Chen, Yibo & Wu, Jianzhong, 2018. "Distribution patterns of energy consumed in classified public buildings through the data mining process," Applied Energy, Elsevier, vol. 226(C), pages 240-251.
    13. Muhammad Altaf & Wesam Salah Alaloul & Muhammad Ali Musarat & Abdul Hannan Qureshi, 2023. "Life cycle cost analysis (LCCA) of construction projects: sustainability perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12071-12118, November.
    14. Shen, Yuxuan & Pan, Yue, 2023. "BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, Elsevier, vol. 333(C).
    15. Linyan Chen & Xin Gao & Shitao Gong & Zhou Li, 2020. "Regionalization of Green Building Development in China: A Comprehensive Evaluation Model Based on the Catastrophe Progression Method," Sustainability, MDPI, vol. 12(15), pages 1-22, July.
    16. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    17. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    18. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    19. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    20. Rongrong Yu & Ning Gu & Michael J. Ostwald, 2022. "Architects’ Perceptions about Sustainable Design Practice and the Support Provided for This by Digital Tools: A Study in Australia," Sustainability, MDPI, vol. 14(21), pages 1-18, October.

    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:15:y:2023:i:3:p:1797-:d:1038958. 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.