IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3432-d1379061.html
   My bibliography  Save this article

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Author

Listed:
  • Salah Alghamdi

    (Department of Building Engineering, College of Architecture and Planning, Imam Abdulrahman Bin Faisal University, Dammam 31451, Saudi Arabia)

  • Waiching Tang

    (School of Architecture and Built Environment, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia)

  • Sittimont Kanjanabootra

    (School of Architecture and Built Environment, The University of Newcastle, University Drive, Callaghan, NSW 2308, Australia)

  • Dariusz Alterman

    (School of Science, Technology and Engineering, The University of the Sunshine Coast, 90 Sippy Downs Drive, Sippy Downs, QLD 4556, Australia)

Abstract

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R 2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

Suggested Citation

  • Salah Alghamdi & Waiching Tang & Sittimont Kanjanabootra & Dariusz Alterman, 2024. "Optimising Building Energy and Comfort Predictions with Intelligent Computational Model," Sustainability, MDPI, vol. 16(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3432-:d:1379061
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3432/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/8/3432/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yıldız, Yusuf & Arsan, Zeynep Durmuş, 2011. "Identification of the building parameters that influence heating and cooling energy loads for apartment buildings in hot-humid climates," Energy, Elsevier, vol. 36(7), pages 4287-4296.
    2. Sun, Yanyi & Wu, Yupeng & Wilson, Robin, 2018. "A review of thermal and optical characterisation of complex window systems and their building performance prediction," Applied Energy, Elsevier, vol. 222(C), pages 729-747.
    3. Alwosheel, Ahmad & van Cranenburgh, Sander & Chorus, Caspar G., 2018. "Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis," Journal of choice modelling, Elsevier, vol. 28(C), pages 167-182.
    4. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
    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. 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).
    2. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    3. 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.
    4. Saurbayeva, Assemgul & Memon, Shazim Ali & Kim, Jong, 2023. "Integrated multi-stage sensitivity analysis and multi-objective optimization approach for PCM integrated residential buildings in different climate zones," Energy, Elsevier, vol. 278(PB).
    5. 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.
    6. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    7. Tao, Yao & Yan, Yihuan & Tu, Jiyuan & Shi, Long, 2024. "Impact of wind on solar-induced natural ventilation through double-skin facade," Applied Energy, Elsevier, vol. 364(C).
    8. Seok-Hyun Kim & Hakgeun Jeong & Soo Cho, 2019. "A Study on Changes of Window Thermal Performance by Analysis of Physical Test Results in Korea," Energies, MDPI, vol. 12(20), pages 1-17, October.
    9. Yildiz, Yusuf & Korkmaz, Koray & Göksal Özbalta, Türkan & Durmus Arsan, Zeynep, 2012. "An approach for developing sensitive design parameter guidelines to reduce the energy requirements of low-rise apartment buildings," Applied Energy, Elsevier, vol. 93(C), pages 337-347.
    10. Koffi Dumor & Li Yao, 2019. "Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis," Sustainability, MDPI, vol. 11(5), pages 1-22, March.
    11. Chen, Xi & Yang, Hongxing & Wang, Yuanhao, 2017. "Parametric study of passive design strategies for high-rise residential buildings in hot and humid climates: miscellaneous impact factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 442-460.
    12. Su, Ziyi & Li, Xiaofeng, 2022. "Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations," Energy, Elsevier, vol. 261(PA).
    13. Jie Li & Qichao Ban & Xueming (Jimmy) Chen & Jiawei Yao, 2019. "Glazing Sizing in Large Atrium Buildings: A Perspective of Balancing Daylight Quantity and Visual Comfort," Energies, MDPI, vol. 12(4), pages 1-14, February.
    14. Chen, Xia & Geyer, Philipp, 2022. "Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty," Applied Energy, Elsevier, vol. 307(C).
    15. Premrov, Miroslav & Žigart, Maja & Žegarac Leskovar, Vesna, 2018. "Influence of the building shape on the energy performance of timber-glass buildings located in warm climatic regions," Energy, Elsevier, vol. 149(C), pages 496-504.
    16. Singh, Manav Mahan & Singaravel, Sundaravelpandian & Geyer, Philipp, 2021. "Machine learning for early stage building energy prediction: Increment and enrichment," Applied Energy, Elsevier, vol. 304(C).
    17. Tao, Yao & Fang, Xiang & Chew, Michael Yit Lin & Zhang, Lihai & Tu, Jiyuan & Shi, Long, 2021. "Predicting airflow in naturally ventilated double-skin facades: theoretical analysis and modelling," Renewable Energy, Elsevier, vol. 179(C), pages 1940-1954.
    18. Yingying Zhou & Christiane Margerita Herr, 2023. "A Review of Advanced Façade System Technologies to Support Net-Zero Carbon High-Rise Building Design in Subtropical China," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    19. Zhao, Haitao & Ezeh, Collins I. & Ren, Weijia & Li, Wentao & Pang, Cheng Heng & Zheng, Chenghang & Gao, Xiang & Wu, Tao, 2019. "Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials," Applied Energy, Elsevier, vol. 254(C).
    20. 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.

    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:16:y:2024:i:8:p:3432-:d:1379061. 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.