JACIII Vol.28 No.2 pp. 296-302
doi: 10.20965/jaciii.2024.p0296

Research Paper:

Research on Boiler Energy Saving Technology Based on Internet of Things Data

Ming Jiang*,†, Haihan Yu* ORCID Icon, Minghui Jin*, Ichiro Nakamoto*, Guo Tai Tang**, and Yan Guo*

*School of Internet Economics and Business, Fujian University of Technology
Fuzhou, Fujian 350118, China

Corresponding author

**Hanxin (Fujian) Energy Technology Development Co., Ltd.
First Floor, Building D, Science and Technology Financial Services Center, High-tech Zone, Licheng District, Quanzhou 362005, China

February 20, 2023
October 23, 2023
March 20, 2024
boiler, energy-saving, Internet of Things data

This paper proposes a heat demand prediction model that analyzes the heat behavior of heat users, and analyzes the heat behavior data of heat users through a clustering algorithm to predict their heat demand. This paper is based on the supply-demand balance strategy to reduce the heat loss during the transmission of heat medium, and then improve the energy-saving efficiency of the boiler. The traditional boiler energy-saving and consumption reduction method is to optimize the boiler combustion parameters, improve the fuel combustion efficiency and waste heat recovery technology through the Internet of Things and big data technology. The method of balancing the heat-using end with the load of the boiler at low usage frequency is seldom considered. Therefore, this paper predicts the heat demand of the heat-using end by analyzing its thermal behavior, and balances the heat demand and boiler heat supply under the condition of meeting the heat demand. Finally, through simulation experiments, the validity of the model is verified, and the trend and data can be well predicted in the short-term heat demand prediction.

Diagram of the distribution of thermal energy

Diagram of the distribution of thermal energy

Cite this article as:
M. Jiang, H. Yu, M. Jin, I. Nakamoto, G. Tang, and Y. Guo, “Research on Boiler Energy Saving Technology Based on Internet of Things Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 296-302, 2024.
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Last updated on Jul. 19, 2024