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

구분 : 논문  |  저자 : Jun Kwon Hwang, Patrick Nzivugira Duhirwe , Geun Young Yun , Sukho Lee , Hyeongjoon Seo , Inhan Kim, Mat Santamouris

성과코드 2-양-A-11-1 연구기관 경희대학교
성과명 A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps 버전
성과형식 연구기관 연락처 jungsikchoi@hanyang.ac.kr
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성과구성 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 su_er 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
di_erent 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 di_erence,
indicating its reliable generalization capabilities. To summarize, the hybrid DNN model improves
data-driven approaches and can be used for designing e_cient and energy-saving EHP systems.


Keywords: building energy; energy use; energy efficiency; prediction model; deep neural network; electric heat pump; refrigerant charge amount
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