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Modeling of Multiple Heating Substations Based on Long Short-Term Memory Networks

  • Qi LiEmail author
  • Bingcheng Han
  • Mingwei Yu
  • Jianglan Shang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)

Abstract

The central heating is a complex nonlinear system. It is difficult to establish an accurate model based on multiple heating substations. In this paper, the Long Short-Term Memory (LSTM) algorithm is proposed to solve this problem. Heating substations generate data with the time series characteristics. The algorithm not only reflects the characteristics of time sequence of heating substations, but also solves the problem of long-term dependence. And, the necessary information can be saved in a limited memory capacity. Based on a large amount of historical data of the heating system of a Baotou heating company, ensuring that the total heat source is sufficient, the simulation results of the LSTM model in multiple substations show the validity, which provides the basis for the optimization of the central heating system, and a reference for LSTM to solve the complex time series modeling and prediction problems.

Keywords

Time sequence Long-term dependence LSTM Modeling of multiple heating substations 

Notes

Acknowledgements

The work was supported by National Natural Science Foundation of China (61463040).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Qi Li
    • 1
    Email author
  • Bingcheng Han
    • 1
  • Mingwei Yu
    • 1
  • Jianglan Shang
    • 1
  1. 1.College of Information EngineeringInner Mongolia University of Science and TechnologyBaotouChina

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