Neural network and polynomial model to improve the coefficient of performance prediction for solar intermittent refrigeration system

•A novel neural network and polynomial model was developed.•The model was used to predict COP of a solar intermittent refrigeration system.•The model presented an excellent agreement between experimental and simulated values of COP.•The proposed methodology could be used to simulate and optimize dif...

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Bibliographic Details
Published in:Solar energy 2016-05, Vol.129, p.28-37
Main Authors: Escobedo-Trujillo, B.A., Colorado, D., Rivera, W., Alaffita-Hernández, F.A.
Format: Article
Language:English
Subjects:
Ammonia
Correlation matrix
Gaussian distribution
Lithium bromide solution
Multivariable fitting
Neural networks
Polynomials
Refrigeration
Residual analysis
Solar energy
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Summary:•A novel neural network and polynomial model was developed.•The model was used to predict COP of a solar intermittent refrigeration system.•The model presented an excellent agreement between experimental and simulated values of COP.•The proposed methodology could be used to simulate and optimize different kind of solar systems. This study presents a novel hybrid methodology to estimate the coefficient of performance in an absorption intermittent cooling system; the system is for ice production and operates with an ammonia/lithium nitrate mixture. The hybrid model integrates a polynomial fitting method and an artificial neural network model to improve the network performance and the estimation of the COPs. The improvement uses fewer hidden neurons without sacrificing accuracy in the prediction. The proposed hybrid model has two neurons in the input and two in the hidden layers and shows better results than those obtained through polynomial fitting or artificial neural networks separately. The developed model presents an excellent agreement between experimental and simulated values of the coefficient of performance with a determination coefficient R2>0.9978.
ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2016.01.041