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Investigation of occupied/unoccupied period on thermal comfort in Guangzhou: Challenges and opportunities of public buildings with high window-wall ratio

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  • Xie, Xing
  • Chen, Xing-ni
  • Xu, Bin
  • Pei, Gang

Abstract

Public buildings generally suffer from many problems due to their high window-wall ratio (WWR), but the unique advantages of this feature are rarely explored. Therefore, indoor thermal comfort during occupancy is studied using WWR as the variable. The thermal environment during unoccupied period is also observed to obtain more key information for improving thermal comfort. In Guangzhou, it is more reasonable for WWR to be 30% without using energy-saving methods or active measures. However, if only the thermal comfort during occupancy is considered, the WWR may be selected smaller, because it ignores heat dissipation and cool storage effect during unoccupied period. In summer, high WWR (>0.7) has more application advantages than the low one (＜0.3). Due to high solar irradiation in winter, high WWR is easy to cause daytime overheating, which can be solved by reasonable shading. When WWR exceeds 0.2, it will cause undercooling at the beginning of occupancy. When WWR is 100%, the time proportion of cool storage zone can reach 31%, and the cooling capacity can be transferred by cool storage materials. Therefore, if reasonable improvement can be adopted, buildings with high WWR will have great potential to achieve year-round thermal comfort.

Suggested Citation

  • Xie, Xing & Chen, Xing-ni & Xu, Bin & Pei, Gang, 2022. "Investigation of occupied/unoccupied period on thermal comfort in Guangzhou: Challenges and opportunities of public buildings with high window-wall ratio," Energy, Elsevier, vol. 244(PB).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pb:s0360544222000895
    DOI: 10.1016/j.energy.2022.123186
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    2. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).

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