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A new feedback predictive model for improving the operation efficiency of heating station based on indoor temperature

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

Abstract

Most of the existing predictive models for heating station, which are based on outdoor meteorological parameters, are feed-forward adjustment, without considering the influence of building thermal inertia on heating parameters. Most importantly, indoor temperature is not taken into account as a influence factor or a feedback adjustment factor, resulting in high heating consumption and low thermal comfort. In this paper, firstly, the secondary supply temperature predictive model based on building thermal inertia was established, and cross-correlation analysis method was used to determine the adjustment cycle and time. Then, the modified model of the solar radiation, the uncertainty of outdoor temperature and the heat consumer behavior on the heating parameters were established respectively, which were used to correct the supply temperature and achieve closed-loop control. Finally, the proposed model was applied to a heating station, the results show that after adopting the model, the fluctuation range of the opening valve is small, the standard deviation is significantly reduced, i.e. good stability of pipe network. The difference between the maximum and minimum indoor temperature is small, i.e. high thermal comfort. The energy-saving rate is 5.8 ± 0.1%, and the lower the target indoor temperature is, the higher the energy-saving rate is, i.e remarkable energy-saving effect.

Suggested Citation

  • Yuan, Jianjuan & Huang, Ke & Han, Zhao & Zhou, Zhihua & Lu, Shilei, 2021. "A new feedback predictive model for improving the operation efficiency of heating station based on indoor temperature," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221002103
    DOI: 10.1016/j.energy.2021.119961
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    7. Daniel Olsson & Peter Filipsson & Anders Trüschel, 2023. "Feedback Control in Swedish Multi-Family Buildings for Lower Energy Demand and Assured Indoor Temperature—Measurements and Interviews," Energies, MDPI, vol. 16(18), pages 1-14, September.
    8. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    9. Tunzi, Michele & Benakopoulos, Theofanis & Yang, Qinjiang & Svendsen, Svend, 2023. "Demand side digitalisation: A methodology using heat cost allocators and energy meters to secure low-temperature operations in existing buildings connected to district heating networks," Energy, Elsevier, vol. 264(C).
    10. Ding, Shixing & Gu, Wei & Lu, Shuai & Yu, Ruizhi & Sheng, Lina, 2022. "Cyber-attack against heating system in integrated energy systems: Model and propagation mechanism," Applied Energy, Elsevier, vol. 311(C).

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