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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Insung Kang & Kwang Ho Lee & Je Hyeon Lee & Jin Woo Moon, 2018. "Artificial Neural Network–Based Control of a Variable Refrigerant Flow System in the Cooling Season," Energies, MDPI, vol. 11(7), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1643-:d:154099
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    References listed on IDEAS

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    1. Joanna Ferdyn-Grygierek & Krzysztof Grygierek, 2017. "Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms," Energies, MDPI, vol. 10(10), pages 1-20, October.
    2. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    3. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
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    Cited by:

    1. Zhang, Zi-Yang & Zhang, Chun-Lu & Xiao, Fu, 2020. "Energy-efficient decentralized control method with enhanced robustness for multi-evaporator air conditioning systems," Applied Energy, Elsevier, vol. 279(C).
    2. Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.

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