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A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps

Author

Listed:
  • Jun Kwon Hwang

    (Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Patrick Nzivugira Duhirwe

    (Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Geun Young Yun

    (Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Sukho Lee

    (HVAC Solutions R&D Lab2, Samsung Electronics, Suwon 16677, Korea)

  • Hyeongjoon Seo

    (HVAC Solutions R&D Lab2, Samsung Electronics, Suwon 16677, Korea)

  • Inhan Kim

    (Department of Architecture, Kyung Hee University, Yongin 17104, Korea)

  • Mat Santamouris

    (Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea
    Faculty of Built Environment, University of New South Wales, Sydney 2052, Australia)

Abstract

Improper refrigerant charge amount (RCA) is a recurring fault in electric heat pump (EHP) systems. Because EHP systems show their best performance at optimum charge, predicting the RCA is important. There has been considerable development of data-driven techniques for predicting RCA; however, the current data-driven approaches for estimating RCA suffer from poor generalization and overfitting. This study presents a hybrid deep neural network (DNN) model that combines both a basic DNN model and a thermodynamic model to counter the abovementioned challenges of existing data-driven approaches. The data for designing models were collected from two EHP systems with different specifications, which were used for the training and testing of models. In addition to the data obtained using the basic DNN model, the hybrid DNN model uses the thermodynamic properties as a thermodynamic model. The testing results show that the hybrid DNN model has a prediction performance of 93%, which is 21% higher than that of the basic DNN model. Furthermore, for model training and model testing, the hybrid DNN model has a 6% prediction performance difference, indicating its reliable generalization capabilities. To summarize, the hybrid DNN model improves data-driven approaches and can be used for designing efficient and energy-saving EHP systems.

Suggested Citation

  • Jun Kwon Hwang & Patrick Nzivugira Duhirwe & Geun Young Yun & Sukho Lee & Hyeongjoon Seo & Inhan Kim & Mat Santamouris, 2020. "A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps," Sustainability, MDPI, vol. 12(7), pages 1-23, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2914-:d:342036
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    References listed on IDEAS

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    Cited by:

    1. Viorica Rozina Chifu & Tudor Cioara & Cristina Bianca Pop & Ionut Anghel & Andrei Pelle, 2024. "Physics-Informed Neural Networks for Heat Pump Load Prediction," Energies, MDPI, vol. 18(1), pages 1-20, December.

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