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A Simplified Calculation Method for Building Envelope Cooling Loads in Central South China

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  • Ping Wang

    (Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
    Faculty of Building Environment and Energy Application Engineering, College of Civil Engineering and Mechanics, Xiangtan University, Xiangtan, Hunan 411105, China)

  • Guangcai Gong

    (Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China)

  • Yan Zhou

    (Faculty of Architectural, Civil Engineering and Environment, NingBo University, Ningbo, Zhejiang 315211, China)

  • Bin Qin

    (Hunan KURBON Curtain Wall Ltd. Co., Changsha, Hunan 410100, China)

Abstract

The cooling load calculation of building envelopes is important for building design to realize building energy efficiency. A simplified way to predict the building envelope cooling loads is desperately needed to predict the cooling load of building envelopes with various construction layouts and to predict the energy-saving effect of various reconstruction measures for buildings. In light of this need a simplified calculation model for building envelope cooling loads is proposed in this paper. The model is based on dynamic hourly calculations using EnergyPlus. It considers almost all the thermal factors about the building envelope which may affect the building cooling load and it is studied by dimensional analysis. The equivalent window to wall ratio ( EWWR ) and building orientation factor are defined to study the building envelope cooling load. The cooling loads of twenty hypothetical buildings with various envelopes are predicated by EnergyPlus. With the results from EnergyPlus the simplified calculation model is developed by MATLAB. Then the newly developed model is validated by two typical actual buildings located in Central-South China. The results show the model is accurate enough to predict the building envelope cooling loads.

Suggested Citation

  • Ping Wang & Guangcai Gong & Yan Zhou & Bin Qin, 2018. "A Simplified Calculation Method for Building Envelope Cooling Loads in Central South China," Energies, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1708-:d:155509
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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. Mao, Ning & Pan, Dongmei & Song, Mengjie & Li, Zhao & Xu, Yingjie & Deng, Shiming, 2017. "Operating optimization for improved energy consumption of a TAC system affected by nighttime thermal loads of building envelopes," Energy, Elsevier, vol. 133(C), pages 491-501.
    3. Babak Raji & Martin J. Tenpierik & Andy Van den Dobbelsteen, 2017. "Early-Stage Design Considerations for the Energy-Efficiency of High-Rise Office Buildings," Sustainability, MDPI, vol. 9(4), pages 1-28, April.
    4. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    5. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    6. Kontoleon, K.J. & Eumorfopoulou, E.A., 2008. "The influence of wall orientation and exterior surface solar absorptivity on time lag and decrement factor in the Greek region," Renewable Energy, Elsevier, vol. 33(7), pages 1652-1664.
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    Cited by:

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