IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v275y2023ics0360544223007259.html
   My bibliography  Save this article

An infiltration load calculation model of large-space buildings based on the grand canonical ensemble theory

Author

Listed:
  • Lin, Xiaojie
  • Zhang, Junwei
  • Du-Ikonen, Liuliu
  • Zhong, Wei

Abstract

Air infiltration significantly impacts building energy consumption and the indoor thermal environment. The proportion of infiltration load in large-space buildings is higher than in normal buildings. Due to the particularity of building structure and function, the existing building infiltration load calculation methods are unsuitable for large-space buildings. This paper proposed a new method for analyzing the building infiltration process from a thermodynamic perspective, enabling large-space buildings' infiltration load to be calculated more accurately and quickly. Compared with the existing method, it was found that a fixed proportional difference existed, and the proposed method relied on certain assumptions. The correction factor η was introduced to correct these limitations. The proposed method was verified to be accurate and universal in actual buildings. The paper also presents the results of η in different building scenarios and discusses the influence of mechanical ventilation and ideal gas assumption on η. Compared with the air change method and the gap method, the correction factor is stable in the range of 0.257–0.278 and 0.327–0.353, respectively. Furthermore, the correction factor for a monatomic ideal gas is 40% higher than that for a diatomic ideal gas. The new method provides a more accurate and efficient way to calculate the infiltration load, which can help improve the energy efficiency of buildings and the indoor thermal environment.

Suggested Citation

  • Lin, Xiaojie & Zhang, Junwei & Du-Ikonen, Liuliu & Zhong, Wei, 2023. "An infiltration load calculation model of large-space buildings based on the grand canonical ensemble theory," Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:energy:v:275:y:2023:i:c:s0360544223007259
    DOI: 10.1016/j.energy.2023.127331
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223007259
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127331?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
    3. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    4. Shan, Kui & Wang, Jiayuan & Hu, Maomao & Gao, Dian-ce, 2019. "A model-based control strategy to recover cooling energy from thermal mass in commercial buildings," Energy, Elsevier, vol. 172(C), pages 958-967.
    5. Liu, Xiaochen & Zhang, Tao & Liu, Xiaohua & Li, Lingshan & Lin, Lin & Jiang, Yi, 2021. "Energy saving potential for space heating in Chinese airport terminals: The impact of air infiltration," Energy, Elsevier, vol. 215(PB).
    6. Jiying Liu & Mohammad Heidarinejad & Saber Khoshdel Nikkho & Nicholas W. Mattise & Jelena Srebric, 2019. "Quantifying Impacts of Urban Microclimate on a Building Energy Consumption—A Case Study," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    7. Samuel Domínguez-Amarillo & Jesica Fernández-Agüera & Miguel Ángel Campano & Ignacio Acosta, 2019. "Effect of Airtightness on Thermal Loads in Legacy Low-Income Housing," Energies, MDPI, vol. 12(9), pages 1-14, May.
    8. Andrius Jurelionis & Demetri G. Bouris, 2016. "Impact of Urban Morphology on Infiltration-Induced Building Energy Consumption," Energies, MDPI, vol. 9(3), pages 1-13, March.
    9. Zhao, Kang & Liu, Xiao-Hua & Jiang, Yi, 2016. "Application of radiant floor cooling in large space buildings – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 1083-1096.
    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. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    2. 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).
    3. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
    4. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
    5. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
    6. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    7. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang, 2020. "Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting," Applied Energy, Elsevier, vol. 270(C).
    8. Artur Nowoświat & Iwona Pokorska-Silva & Mateusz Konewecki, 2021. "Tightness of Single-Family Buildings Made in Prefabricated Wood Frame Technology," Energies, MDPI, vol. 14(15), pages 1-14, July.
    9. Du, Kun & Calautit, John & Eames, Philip & Wu, Yupeng, 2021. "A state-of-the-art review of the application of phase change materials (PCM) in Mobilized-Thermal Energy Storage (M-TES) for recovering low-temperature industrial waste heat (IWH) for distributed heat," Renewable Energy, Elsevier, vol. 168(C), pages 1040-1057.
    10. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    11. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    12. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    13. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    14. Liu, Xiaochen & Zhang, Tao & Liu, Xiaohua & Li, Lingshan & Lin, Lin & Jiang, Yi, 2021. "Energy saving potential for space heating in Chinese airport terminals: The impact of air infiltration," Energy, Elsevier, vol. 215(PB).
    15. Juan Rojas-Fernández & Carmen Galán-Marín & Jorge Roa-Fernández & Carlos Rivera-Gómez, 2017. "Correlations between GIS-Based Urban Building Densification Analysis and Climate Guidelines for Mediterranean Courtyards," Sustainability, MDPI, vol. 9(12), pages 1-26, December.
    16. 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).
    17. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    18. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    19. Demetri Bouris & Athanasios G. Triantafyllou & Athina Krestou & Elena Leivaditou & John Skordas & Efstathios Konstantinidis & Anastasios Kopanidis & Qing Wang, 2021. "Urban-Scale Computational Fluid Dynamics Simulations with Boundary Conditions from Similarity Theory and a Mesoscale Model," Energies, MDPI, vol. 14(18), pages 1-22, September.
    20. Severinsen, A. & Myrland, Ø., 2022. "ShinyRBase: Near real-time energy saving models using reactive programming," Applied Energy, Elsevier, vol. 325(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:eee:energy:v:275:y:2023:i:c:s0360544223007259. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.