IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i9p4640-d540848.html
   My bibliography  Save this article

Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

Author

Listed:
  • Seung Yeoun Choi

    (Hanil Mechanical & Electrical Consultant, Seoul 07271, Korea)

  • Sean Hay Kim

    (School of Architecture, Seoul National University of Science and Technology, Seoul 01811, Korea)

Abstract

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

Suggested Citation

  • Seung Yeoun Choi & Sean Hay Kim, 2021. "Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System," Sustainability, MDPI, vol. 13(9), pages 1-36, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4640-:d:540848
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/9/4640/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/9/4640/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
    2. César Benavente-Peces & Nisrine Ibadah, 2020. "Buildings Energy Efficiency Analysis and Classification Using Various Machine Learning Technique Classifiers," Energies, MDPI, vol. 13(13), pages 1-24, July.
    3. Sean Hay Kim & Jungmin Nam, 2020. "Can Both the Economic Value and Energy Performance of Small- and Mid-Sized Buildings Be Satisfied? Development of a Design Expert System in the Context of Korea," Sustainability, MDPI, vol. 12(12), pages 1-29, June.
    4. Homaei, Shabnam & Hamdy, Mohamed, 2020. "A robustness-based decision making approach for multi-target high performance buildings under uncertain scenarios," Applied Energy, Elsevier, vol. 267(C).
    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. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    2. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    4. Roberta Moschetti & Shabnam Homaei & Ellika Taveres-Cachat & Steinar Grynning, 2022. "Assessing Responsive Building Envelope Designs through Robustness-Based Multi-Criteria Decision Making in Zero-Emission Buildings," Energies, MDPI, vol. 15(4), pages 1-27, February.
    5. Binju P Raj & Chandan Swaroop Meena & Nehul Agarwal & Lohit Saini & Shabir Hussain Khahro & Umashankar Subramaniam & Aritra Ghosh, 2021. "A Review on Numerical Approach to Achieve Building Energy Efficiency for Energy, Economy and Environment (3E) Benefit," Energies, MDPI, vol. 14(15), pages 1-26, July.
    6. ChungYeon Won & SangTae No & Qamar Alhadidi, 2019. "Factors Affecting Energy Performance of Large-Scale Office Buildings: Analysis of Benchmarking Data from New York City and Chicago," Energies, MDPI, vol. 12(24), pages 1-17, December.
    7. Chunwang Xiaogeng LiRen & Xiaojun Ma & Fuxiang Chen & Zhicheng Yang & Sandeep Panchal, 2022. "Simulation and inspection of fault arc in building energy-saving distribution system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 331-339, March.
    8. Xunzhi Yin & Jiaqi Yu & Qi Dong & Yongheng Jia & Cheng Sun, 2020. "Energy Sustainability of Rural Residential Buildings with Bio-Based Building Fabric in Northeast China," Energies, MDPI, vol. 13(21), pages 1-14, November.
    9. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.
    10. 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.
    11. Attia, Shady & Shadmanfar, Niloufar & Ricci, Federico, 2020. "Developing two benchmark models for nearly zero energy schools," Applied Energy, Elsevier, vol. 263(C).
    12. Salah Vaisi & Saleh Mohammadi & Benedetto Nastasi & Kavan Javanroodi, 2020. "A New Generation of Thermal Energy Benchmarks for University Buildings," Energies, MDPI, vol. 13(24), pages 1-18, December.
    13. Sana Sayadi & Jan Akander & Abolfazl Hayati & Mattias Gustafsson & Mathias Cehlin, 2023. "Comparison of Space Cooling Systems from Energy and Economic Perspectives for a Future City District in Sweden," Energies, MDPI, vol. 16(9), pages 1-22, April.
    14. Gaspars-Wieloch Helena, 2021. "On some analogies between one-criterion decision making under uncertainty and multi-criteria decision making under certainty," Economics and Business Review, Sciendo, vol. 7(2), pages 17-36, June.
    15. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
    16. Yong-Joon Jun & Seung-ho Ahn & Kyung-Soon Park, 2021. "Improvement Effect of Green Remodeling and Building Value Assessment Criteria for Aging Public Buildings," Energies, MDPI, vol. 14(4), pages 1-28, February.
    17. Mohammed Hammam Mohammed Al-Madani & Yudi Fernando & Ming-Lang Tseng, 2022. "Assuring Energy Reporting Integrity: Government Policy’s Past, Present, and Future Roles," Sustainability, MDPI, vol. 14(22), pages 1-24, November.
    18. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    19. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).
    20. George M. Stavrakakis & Dimitris Bakirtzis & Korina-Konstantina Drakaki & Sofia Yfanti & Dimitris Al. Katsaprakakis & Konstantinos Braimakis & Panagiotis Langouranis & Konstantinos Terzis & Panagiotis, 2024. "Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece," Energies, MDPI, vol. 17(3), pages 1-30, 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:13:y:2021:i:9:p:4640-:d:540848. 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.