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Modeling Method of Heat Pump System Based on Recurrent Neural Network

  • Yin Zheng
  • Guiqiang WangEmail author
  • Guohui Feng
  • Zhiqiang Kang
Conference paper
  • 214 Downloads
Part of the Environmental Science and Engineering book series (ESE)

Abstract

Heat pump system is a complex interaction system of multiple mechanical, electrical, and control systems. Traditional modeling methods based on physical laws have large computational complexity and poor precision, which make it not suitable for control strategy optimization. To solve these problems, this paper presents a modeling method based on recurrent neural network (RNN). The network structure and training algorithm were determined according to actual needs. The RNN model was tested and verified on a ground source heat pump system in an office building of a university in Northeast China. The heat pump operation data were continuously monitored and collected, and input into the neural network with three layers. Part of the data set is used for training and the rest is used for testing. The results show that the model has high precision, indicating that this modeling method is effective. This method is considered to be repeatable and can be applied to other heat pump systems.

Keywords

Heat pump system Recurrent neural network Modeling RMSprop Adam 

Notes

Acknowledgements

The authors wish to acknowledge the support of the Program of National Science and Technology of China during the Thirteenth Five-year Plan (2017YFB0604004-03).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yin Zheng
    • 1
  • Guiqiang Wang
    • 1
    Email author
  • Guohui Feng
    • 1
  • Zhiqiang Kang
    • 1
  1. 1.School of Municipal and Environmental EngineeringShenyang Jianzhu UniversityShenyangChina

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