TY - GEN
T1 - Identification of vapour compression air conditioning system behaviour using Bayesian regularization neural network
AU - Sholahudin,
AU - Ohno, Keisuke
AU - Yamaguchi, Seiichi
AU - Saito, Kiyoshi
N1 - Funding Information:
The author would like to acknowledge the financial support provided by LPDP Endowment Fund for Education).
Publisher Copyright:
© 2019 International Institute of Refrigeration. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Identification for system dynamic behaviour is necessary to develop control strategy. In this paper, the dynamic performance of air conditioning (AC) system is predicted using artificial neural network (ANN) approach. The ANN is developed to predict exergy efficiency, coefficient of performance (COP), and cooling capacity. The controllable parameters including compressor speed and evaporator and condenser fan speed are considered as the input. The datasets for prediction are generated by AC system simulator. The system was simulated by randomly varying compressor speed and evaporator and condenser fan speed with N-sample signal input. The dynamic ANN configuration with Bayesian regularization is proposed to predict one-step ahead of system performance behaviour. The results show that the developed ANN in present study yields good prediction accuracy for all outputs. Accordingly, ANN can be further applied for predictive control application in AC system to control cooling capacity while maintaining system efficiency.
AB - Identification for system dynamic behaviour is necessary to develop control strategy. In this paper, the dynamic performance of air conditioning (AC) system is predicted using artificial neural network (ANN) approach. The ANN is developed to predict exergy efficiency, coefficient of performance (COP), and cooling capacity. The controllable parameters including compressor speed and evaporator and condenser fan speed are considered as the input. The datasets for prediction are generated by AC system simulator. The system was simulated by randomly varying compressor speed and evaporator and condenser fan speed with N-sample signal input. The dynamic ANN configuration with Bayesian regularization is proposed to predict one-step ahead of system performance behaviour. The results show that the developed ANN in present study yields good prediction accuracy for all outputs. Accordingly, ANN can be further applied for predictive control application in AC system to control cooling capacity while maintaining system efficiency.
KW - Air Conditioning
KW - Exergy
KW - Neural Network
KW - System Identification
UR - http://www.scopus.com/inward/record.url?scp=85082675421&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082675421&partnerID=8YFLogxK
U2 - 10.18462/iir.icr.2019.1244
DO - 10.18462/iir.icr.2019.1244
M3 - Conference contribution
AN - SCOPUS:85082675421
T3 - Refrigeration Science and Technology
SP - 4061
EP - 4068
BT - ICR 2019 - 25th IIR International Congress of Refrigeration
A2 - Minea, Vasile
PB - International Institute of Refrigeration
T2 - 25th IIR International Congress of Refrigeration, ICR 2019
Y2 - 24 August 2019 through 30 August 2019
ER -