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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
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 Engineering
Objective Division:Energy
Objective Group:Energy Conservation and Efficiency
Objective Field:Residential Energy Conservation and Efficiency
UTAS Author:Wang, X (Associate Professor Xiaolin Wang)
ID Code:134415
Year Published:2019
Deposited By:Engineering
Deposited On:2019-08-12
Last Modified:2020-01-14
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