Artificial Neural Network–Based Control of a Variable Refrigerant Flow System in the Cooling Season

Insung Kang, Kwang Ho Lee, Je Hyeon Lee and Jin Woo Moon
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Insung Kang: Department of Architectural Engineering, Chung-Ang University, Seoul 06974, Korea
Kwang Ho Lee: Department of Architectural Engineering, Hanbat National University, Daejeon 34158, Korea
Je Hyeon Lee: Department of Digital Appliance R&D Team, Samsung Electronics, Suwon 16677, Korea
Jin Woo Moon: School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea

Energies, 2018, vol. 11, issue 7, 1-15

Abstract: This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next control cycle, was embedded into the control algorithm. By comparing the predicted energy for the different set-point combinations of the control variables, the control algorithm can determine the most energy-effective set-points to optimally operate the cooling system. Two major processes were conducted in the development process. The first process was to develop the predictive control algorithm which embedded the ANN model. The second process involved performance tests of the control algorithm in terms of prediction accuracy and energy efficiency in computer simulation programs. The results revealed that the prediction accuracy between simulated and predicted outcomes proved to have a low coefficient of variation root mean square error (CVRMSE) value (10.30%). In addition, the predictive control algorithm markedly saved the cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings suggest that the ANN model and the control algorithm showed potential for the prediction accuracy and energy-effectiveness of VRF cooling systems.

Keywords: variable refrigerant flow (VRF) cooling systems; artificial neural network (ANN); predictive control algorithm; optimal set-points of system variables (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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