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Analysis of influencing factors on heat consumption of large residential buildings with different occupancy rates-Tianjin case study

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  • Yuan, Jianjuan
  • Huang, Ke
  • Lu, Shilei
  • Zhang, Ji
  • Han, Zhao
  • Zhou, Zhihua

Abstract

In-depth studies on the supply-side and demand-side are required to realize the on-demand heating of the heating system. At present, a lot of researches have conducted on the supply-side for obtaining the accurate heat consumption prediction, and researches on the demand-side are mainly concentrated on the single-family. For the large residential buildings, occupancy rate is an important factor affecting building heat consumption, but it is often ignored. In this paper, taking buildings in Tianjin as an example, the heat transfer relationship between households was analyzed firstly, and obtained that the heating condition of up household had a greater influence on object household than left, right and down households. Secondly, the influencing factors of heat consumption of household in different locations were analyzed, the number of classes for households in buildings with and without basement were determined, and the numerical relationship of heat consumption of classes for building with different energy-saving standard were further determined. Finally, the building heat consumption calculation method and household heat consumption distribution method with different occupancy rates were determined, and the correctness and necessity of their application in practical engineering were verified, that will provide theoretical guidance for refined heating.

Suggested Citation

  • Yuan, Jianjuan & Huang, Ke & Lu, Shilei & Zhang, Ji & Han, Zhao & Zhou, Zhihua, 2022. "Analysis of influencing factors on heat consumption of large residential buildings with different occupancy rates-Tianjin case study," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s036054422102082x
    DOI: 10.1016/j.energy.2021.121834
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    References listed on IDEAS

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    3. Wang, Zhijin & Liu, Xiufeng & Huang, Yaohui & Zhang, Peisong & Fu, Yonggang, 2023. "A multivariate time series graph neural network for district heat load forecasting," Energy, Elsevier, vol. 278(PA).

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