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Study on refined control and prediction model of district heating station based on support vector machine

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  • Yuan, Jianjuan
  • Wang, Chendong
  • Zhou, Zhihua

Abstract

The realization of refined management in the heating station can not only meet the comfortable indoor, but also improve the energy efficiency, reduce the heating consumption, and alleviate air pollution. Previous studies ignored the indoor temperature and building thermal inertia (BC), as a result, the prediction models of secondary supply temperature have poor energy saving and thermal comfort. This paper adopts the support vector machine to compare and analyze the influence when adding BC and indoor temperature as input parameters. The results show that with temperature automatic monitor indoor, and when Tout, Tin, Th are taken as input parameters, the maximum error between the actual and predicted is 3% with BC, and 4% without. When there is no temperature monitor indoor and only Tout, Th are taken as input parameters, the BC can be calculated by manual indoor temperature measurement, and the maximum error between the actual and predicted is 3.5% when considering BC, and 4.75% without. To validate the universality of this proposed model, four models are applied to the heat stations in different cities, their performance all show that the BC and indoor temperature has a great impact on the accuracy of predict models, and BC has the greater.

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  • 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).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219318882
    DOI: 10.1016/j.energy.2019.116193
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    Cited by:

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    5. 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).
    6. Zhong, Wei & Feng, Encheng & Lin, Xiaojie & Xie, Jinfang, 2022. "Research on data-driven operation control of secondary loop of district heating system," Energy, Elsevier, vol. 239(PB).
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    9. Wang, Chendong & Yuan, Jianjuan & Zhang, Ji & Deng, Na & Zhou, Zhihua & Gao, Feng, 2020. "Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing," Energy, Elsevier, vol. 202(C).
    10. 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).
    11. Sun, Chunhua & Liu, Yanan & Gao, Xiaoyu & Wang, Jinda & Yang, Lan & Qi, Chengyong, 2022. "Research on control strategy integrated with characteristics of user's energy-saving behavior of district heating system," Energy, Elsevier, vol. 245(C).
    12. Wang, Yanmin & Li, Zhiwei & Liu, Junjie & Pei, Mingzhe & Zhao, Yan & Lu, Xuan, 2023. "Data-driven analysis and prediction of indoor characteristic temperature in district heating systems," Energy, Elsevier, vol. 282(C).
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