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A New Method of Building Envelope Thermal Performance Evaluation Considering Window–Wall Correlation

Author

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  • Zhengrong Li

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Yang Si

    (School of Mechanical Engineering, Tongji University, Shanghai 201804, China)

  • Qun Zhao

    (College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China)

  • Xiwen Feng

    (School of Civil Engineering, Guangzhou University, Guangzhou 510206, China)

Abstract

This study proposes a new method to accurately evaluate the overall building envelope thermal performance considering the window–wall correlation, providing a new tool for building thermal design. Firstly, a non-stationary room heat transfer model is established based on the Resistance-Capacity Network method. The influence of solar heat gain through the windows on the heat transfer process of the walls in the actual environment is considered, and the room’s integrated thermal resistance and integrated heat capacity indexes describing the overall room thermal resilience performance are proposed. Then, a field research test is conducted around Lhasa to obtain the local dwelling information, climate conditions, and indoor thermal environment. Numerical simulations using EnergyPlus are made to verify the effectiveness of the indexes in describing the overall building (maximum difference within 3.67% MBE and 2.92% CVRMSE) based on the field test results. Finally, the proposed envelope thermal performance index is used to analyze the local residential buildings around Lhasa. The results show that the lack of consideration of window–wall correlation has led to the failure of a local newly built building’s actual envelope performance to meet the design requirements. These findings could help to develop the thermal design method of the building envelope.

Suggested Citation

  • Zhengrong Li & Yang Si & Qun Zhao & Xiwen Feng, 2023. "A New Method of Building Envelope Thermal Performance Evaluation Considering Window–Wall Correlation," Energies, MDPI, vol. 16(19), pages 1-25, October.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6927-:d:1252689
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    References listed on IDEAS

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    1. 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.
    2. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    3. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    4. Mazzone, Antonella, 2020. "Thermal comfort and cooling strategies in the Brazilian Amazon. An assessment of the concept of fuel poverty in tropical climates," Energy Policy, Elsevier, vol. 139(C).
    5. Georgios E. Arnaoutakis & Dimitris A. Katsaprakakis, 2021. "Energy Performance of Buildings with Thermochromic Windows in Mediterranean Climates," Energies, MDPI, vol. 14(21), pages 1-14, October.
    6. George M. Stavrakakis & Dimitris Al. Katsaprakakis & Markos Damasiotis, 2021. "Basic Principles, Most Common Computational Tools, and Capabilities for Building Energy and Urban Microclimate Simulations," Energies, MDPI, vol. 14(20), pages 1-41, October.
    7. Wang, Yang & Kuckelkorn, Jens & Zhao, Fu-Yun & Spliethoff, Hartmut & Lang, Werner, 2017. "A state of art of review on interactions between energy performance and indoor environment quality in Passive House buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1303-1319.
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