Multi-step ahead prediction of vapor compression air conditioning system behaviour using neural networks

S. Sholahudin, K. Ohno, S. Yamaguchi, K. Saito

Research output: Contribution to journalConference article

Abstract

Cooling capacity and super heat temperature control for air conditioning (AC) system operation is necessary to ensure that the system operates efficiently. In this paper, multi-step-ahead prediction of AC system behaviour is presented using backpropagation neural network model as the first effort to develop the effective control strategy. Several step-ahead cooling capacity and superheat temperature performance are predicted under modulation of compressor speed and expansion valve opening. The prediction is proposed to capture the dynamic behaviour of system that can be applied in predictive control purpose. The configuration of ANN model is developed based on nonlinear autoregressive network with exogenous input (NARX) structure. Input and output data for training and validation of ANN model are generated by AC simulator. The ANN model is optimized by investigating the effect of number of neuron and time delay input on prediction accuracy. The results show that the ANN model developed in present study has good accuracy in predicting several step-ahead of cooling capacity and superheat temperature. Accordingly, this ANN model is applicable for predictive control in future study.

Original languageEnglish
Article number012003
JournalIOP Conference Series: Materials Science and Engineering
Volume539
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1
Event1st International Conference on Design, Energy, Materials and Manufacture, ICDEMM 2018 - Bali, Indonesia
Duration: 2018 Oct 242018 Oct 25

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Air conditioning
Vapors
Neural networks
Cooling
Nonlinear networks
Backpropagation
Temperature control
Neurons
Compressors
Time delay
Simulators
Modulation
Temperature

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

Cite this

Multi-step ahead prediction of vapor compression air conditioning system behaviour using neural networks. / Sholahudin, S.; Ohno, K.; Yamaguchi, S.; Saito, K.

In: IOP Conference Series: Materials Science and Engineering, Vol. 539, No. 1, 012003, 01.01.2019.

Research output: Contribution to journalConference article

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