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

Jun Kwon Hwang, Patrick Nzivugira Duhirwe, Geun Young Yun, Sukho Lee, Hyeongjoon Seo, Inhan Kim and Mat Santamouris
Additional contact information
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

Sustainability, 2020, vol. 12, issue 7, 1-23

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.

Keywords: building energy; energy use; energy efficiency; prediction model; deep neural network; electric heat pump; refrigerant charge amount (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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