A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps
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.
A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps
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.
A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps
Jun Kwon Hwang (author) / Patrick Nzivugira Duhirwe (author) / Geun Young Yun (author) / Sukho Lee (author) / Hyeongjoon Seo (author) / Inhan Kim (author) / Mat Santamouris (author)
2020
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Hybrid model using Bayesian neural network for variable refrigerant flow system
| Taylor & Francis Verlag | 2022
The use of endothermic refrigerant/absorbent systems in absorption heat pumps
| British Library Conference Proceedings | 1984
| American Institute of Physics | 2022
Deep Neural Network (DNN) Model to Predict Close-In Blast Load
| TIBKAT | 2022