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Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method

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  • Sun, Chunhua
  • Liu, Yiting
  • Cao, Shanshan
  • Chen, Jiali
  • Xia, Guoqiang
  • Wu, Xiangdong

Abstract

Efficient operation and fine control are the keys of heating stations (HSs) to reduce energy consumption and carbon emission. Identifying control regularity of HSs is significant to realize fine control. This paper firstly defines control regularity, regulation time step (RTS) and regulation time node (RTN) of HSs. Then identify HSs’ type based on insulation performance of buildings. Secondary loop supply and return temperature are chosen as characteristic parameters to determine pipe network thermal delay, while indoor temperature and the comprehensive outdoor temperature are used to determine building thermal inertia delay. Cross-correlation function dynamic time delay method is used to identify the delay time, and RTS of HS is determined. The regression model of RTS is obtained to decide daily RTNs. It is found that RTS of buildings with floor heating are larger than that with radiators, energy-saving buildings are larger than non-energy-saving buildings, and the high cold period is greater than the early and last cold period. The proposed method is applied to three HSs. Results show that average indoor temperature is maintained within the target range of 20 ± 1 °C, varies within a narrow range. Indoor temperature unevenness coefficient reduction indicates that indoor thermal comfort has been improved.

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  • Sun, Chunhua & Liu, Yiting & Cao, Shanshan & Chen, Jiali & Xia, Guoqiang & Wu, Xiangdong, 2022. "Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method," Energy, Elsevier, vol. 246(C).
  • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222002328
    DOI: 10.1016/j.energy.2022.123329
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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Seal, Sayani & Boulet, Benoit & Dehkordi, Vahid R., 2020. "Centralized model predictive control strategy for thermal comfort and residential energy management," Energy, Elsevier, vol. 212(C).
    3. Saloux, Etienne & Candanedo, José A., 2021. "Model-based predictive control to minimize primary energy use in a solar district heating system with seasonal thermal energy storage," Applied Energy, Elsevier, vol. 291(C).
    4. Yuan, Jianjuan & Wang, Chendong & Zhou, Zhihua, 2019. "Study on refined control and prediction model of district heating station based on support vector machine," Energy, Elsevier, vol. 189(C).
    5. Gustafsson, Jonas & Delsing, Jerker & van Deventer, Jan, 2011. "Experimental evaluation of radiator control based on primary supply temperature for district heating substations," Applied Energy, Elsevier, vol. 88(12), pages 4945-4951.
    6. Jing Zhao & Yu Shan, 2019. "An Influencing Parameters Analysis of District Heating Network Time Delays Based on the CFD Method," Energies, MDPI, vol. 12(7), pages 1-19, April.
    7. Jie, Pengfei & Tian, Zhe & Yuan, Shanshan & Zhu, Neng, 2012. "Modeling the dynamic characteristics of a district heating network," Energy, Elsevier, vol. 39(1), pages 126-134.
    8. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    9. Aoun, Nadine & Bavière, Roland & Vallée, Mathieu & Aurousseau, Antoine & Sandou, Guillaume, 2019. "Modelling and flexible predictive control of buildings space-heating demand in district heating systems," Energy, Elsevier, vol. 188(C).
    10. Kontoleon, K.J. & Giarma, C., 2016. "Dynamic thermal response of building material layers in aspect of their moisture content," Applied Energy, Elsevier, vol. 170(C), pages 76-91.
    11. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    12. Lund, Henrik & Østergaard, Poul Alberg & Nielsen, Tore Bach & Werner, Sven & Thorsen, Jan Eric & Gudmundsson, Oddgeir & Arabkoohsar, Ahmad & Mathiesen, Brian Vad, 2021. "Perspectives on fourth and fifth generation district heating," Energy, Elsevier, vol. 227(C).
    13. Verbeke, Stijn & Audenaert, Amaryllis, 2018. "Thermal inertia in buildings: A review of impacts across climate and building use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2300-2318.
    14. Shamshirband, Shahaboddin & Petković, Dalibor & Enayatifar, Rasul & Hanan Abdullah, Abdul & Marković, Dušan & Lee, Malrey & Ahmad, Rodina, 2015. "Heat load prediction in district heating systems with adaptive neuro-fuzzy method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 760-767.
    15. Sartor, K. & Dewalef, P., 2017. "Experimental validation of heat transport modelling in district heating networks," Energy, Elsevier, vol. 137(C), pages 961-968.
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