Journal of System Simulation
Abstract
Abstract: Based on the model predictive control method, this paper uses the discrete controlled autoregressive model to establish the dynamic heat transfer delay model of the secondary network and the thermal station model. The polynomial fitting method of machine learning algorithm is applied to identify and calibrate the parameters of the secondary network model and the thermal station model. The primary flow rate of the heating station under future operating conditions is predicted based on the model results, which provides a basis for the quality-based regulation of heating system. The model is verified by measured data, and the actual deviation is less than 5%, which provides a good guide for the engineering practice of heating system flow regulation.
Recommended Citation
Li, Zhongbo; Meng, Jia; Yan, Kang; Wang, Haihong; Miao, Li; Qing, Lü; Xie, Jingjing; and Fang, Dajun
(2021)
"Real-Time Prediction of Primary Flow by MPC Method in Heating System,"
Journal of System Simulation: Vol. 33:
Iss.
1, Article 19.
DOI: 10.16182/j.issn1004731x.joss.19-0148
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss1/19
First Page
180
Revised Date
2019-08-31
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-0148
Last Page
188
CLC
TP18
Recommended Citation
Li Zhongbo, Jia Meng, Kang Yan, Wang Haihong, Li Miao, Lü Qing, Xie Jingjing, Fang Dajun. Real-Time Prediction of Primary Flow by MPC Method in Heating System[J]. Journal of System Simulation, 2021, 33(1): 180-188.
DOI
10.16182/j.issn1004731x.joss.19-0148
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