Optimizing Control of Total Heat Supply Based on Machine Learning

Authors

Li Qi, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China;
Xingqi Hu, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China;
Jianmin Zhao, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China;

Abstract

Abstract: The central heating system has complex structure, along with the characteristics of hysteresis, strong coupling and nonlinear. Contraposing the problem that the process is difficult to be identified and controlled by the mechanism modeling, an optimal control method of heat source total heat production based on machine learning is proposed. The heat source model of central heating system is established by BP neural network and long short-term memory neural network. Under the premise of meeting the demand of heating quality, with the total energy consumption as the optimization objective, the optimal control sequence of water supply temperature and water flow at heat source is obtained by the action-dependent dual heuristic programming (ADDHP) algorithm. The simulation analysis shows that, the established heat source model can effectively identify the heat source production process, and the ADDHP control method can achieve the optimal control of total heat production of heat source.

Recommended Citation

Qi, Li; Hu, Xingqi; and Zhao, Jianmin (2019) "Optimizing Control of Total Heat Supply Based on Machine Learning," Journal of System Simulation: Vol. 30: Iss. 3, Article 46.
DOI: 10.16182/j.issn1004731x.joss.201803046
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss3/46

First Page

1134

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201803046

Last Page

1143

CLC

TP301

Recommended Citation

Li Qi, Hu Xingqi, Zhao Jianmin. Optimizing Control of Total Heat Supply Based on Machine Learning[J]. Journal of System Simulation, 2018, 30(3): 1134-1143.

DOI

10.16182/j.issn1004731x.joss.201803046