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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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.
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