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The Power Consumption Model of Chiller with Elman Neural Networks for On-line Prediction and Control

  • Zhiyang Jia
  • Tianyi ZhaoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)

Abstract

In this paper, a new steady-state power consumption model using the Elman Neural Network (ENN) is proposed. The model is dependent on the external parameters of chiller, which are easily monitored and which are related to the global optimization of an air-conditioning water system. The simulation results show that the model can complete the training process within 3 s. In addition, it can be seen that the results of the model are in good agreement with the experimental values with the majority of the RE values within ±3%. Therefore, this model is suitable for on-line prediction of the power consumption of chiller in on-field engineering.

Keywords

Elman neural network On-Line prediction Chiller Global optimization Power consumption 

Notes

Acknowledgements

The work is supported by National Key Research and Development Project of China (Grant No. 2017YFC0704100, entitled “New Generation Intelligent Building Platform Techniques”) and “the Fundamental Research Funds for the Central Universities” (Grant No. DUT17ZD232).

References

  1. 1.
    Ng, K.C., Chua, H.T., Ong, W., Lee, S.S., Gordon, J.M.: Diagnostics and optimization of reciprocating chillers: theory and experiment. Appl. Therm. Eng. 17(3), 263–276 (1997)Google Scholar
  2. 2.
    Lee, T.S.: Thermodynamic modeling and experimental validation of screw liquid chillers. Ashrae Trans. 110, 206–216 (2004)Google Scholar
  3. 3.
    Ma, Z., Wang, S.: Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm. Appl. Energy 88(1), 198–211 (2011)CrossRefGoogle Scholar
  4. 4.
    Misenheimer, C., Terry, S.D.: The development of a dynamic single effect, lithium bromide absorption chiller model with enhanced generator fidelity. Energy Convers. Manag. 150, 574–587 (2017)CrossRefGoogle Scholar
  5. 5.
    Liu, Z., Tan, H., Luo, D., et al.: Optimal chiller sequencing control in an office building considering the variation of chiller maximum cooling capacity. Energy Build. 140, 430–442 (2017)CrossRefGoogle Scholar
  6. 6.
    Swider, D.J., Browne, M.W., Bansal, P.K., et al.: Modelling of vapour-compression liquid chillers with neural networks. Appl. Therm. Eng. 21(3), 311–329 (2001)CrossRefGoogle Scholar
  7. 7.
    Zhou, X., Cai, P., Lian, S., et al.: Research on COP prediction model of chiller based on PSO-SVR. J. Refrig. (2015)Google Scholar
  8. 8.
    Hydeman, M., Sreedharan, P., Webb, N., Blanc, S.: Development and testing of a reformulated regression-based electric chiller model. Ashrae Trans. 108(2), 1118–1127 (2002)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Infrastructure EngineeringDalian University of TechnologyDalianChina

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