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Investigations on optimal discharge pressure in CO2 heat pumps using the GMDH and PSO-BP type neural network—Part A: Theoretical modeling
Citation
Yin, X and Cao, F and Wang, J and Li, M and Wang, X, Investigations on optimal discharge pressure in CO2 heat pumps using the GMDH and PSO-BP type neural network Part A: Theoretical modeling, International Journal of Refrigeration, 106 pp. 549-557. ISSN 0140-7007 (2019) [Refereed Article]
Copyright Statement
Copyright 2019 Elsevier Ltd and IIR
DOI: doi:10.1016/j.ijrefrig.2019.04.027
Abstract
Discharge pressure is an important factor that heavily affects the system COP in the transcritical CO2 heat pump. In most cases, it is commonly confirmed by the empirical correlations or calculated by the mathematical model according to a single operation condition, thus leading to the prediction error or lengthy time. In this paper, a novel model using the statistical method known as the group method of data handling-type (GMDH) and PSO-BP-type (Particle-Swarm-Optimization and Back-Propagation) neural network was developed to predict the optimal discharge pressure. The relevance of all the parameters to the optimal discharge pressure was investigated orderly. Results showed that the new model had the highest accuracy compared to the current correlations. The relative error was around 1.6% while the error of traditional methods ranged from 11.1% to 44.9%. Therefore, the CO2 heat pump could work better in the optimal COP operation condition with the novel statistical model.
Item Details
Item Type: | Refereed Article |
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Keywords: | optimal discharge pressure, CO2 heat pump, GMDH, PSO-BP neural network |
Research Division: | Engineering |
Research Group: | Mechanical engineering |
Research Field: | Energy generation, conversion and storage (excl. chemical and electrical) |
Objective Division: | Energy |
Objective Group: | Energy efficiency |
Objective Field: | Residential energy efficiency |
UTAS Author: | Wang, X (Professor Xiaolin Wang) |
ID Code: | 134415 |
Year Published: | 2019 |
Web of Science® Times Cited: | 11 |
Deposited By: | Engineering |
Deposited On: | 2019-08-12 |
Last Modified: | 2020-01-14 |
Downloads: | 0 |
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