Predicting the performance of thermal, electrical and overall efficiencies of a nanofluid-based photovoltaic/thermal system using Elman recurrent neural network methodology. (May 2023)

• Record Type: Journal Article
• Authors: Hai, Tao; Zhou, Jincheng
• Is Part Of: Engineering analysis with boundary elements. Volume 150(2023)
• Page Start: 394
• Page End: 399
• Publication Date: 2023-05
• Subjects: Nanofluid -- Neural network -- Overall efficiency -- PV/T system -- Serpentine tube; Boundary element methods -- Periodicals; Engineering mathematics -- Periodicals; Équations intégrales de frontière, Méthodes des -- Périodiques; Mathématiques de l'ingénieur -- Périodiques; Boundary element methods; Engineering mathematics; Periodicals; 620.00151
• DOI: 10.1016/j.enganabound.2023.02.013
• Languages: English
• ISSNs: 0955-7997

Abstract

Highlights: A PV/T device with sheet-and-serpentine tube collector is analyzed. PV panels are cooled using the water-magnetite nanofluid. Elman recurrent neural network methodology is used to develop predictive models. Thermal, electrical and overall efficiencies are utilized as objective functions Mass flow rate and concentration of nanofluid are considered as input parameters. The best accuracy is obtained for the thermal efficiency. Abstract: In this research, the Elman recurrent neural network methodology is used to develop a relationship to predict the performance of a photovoltaic/thermal device with sheet-and-serpentine tube collector. In this system, PV panels are cooled using the water-magnetite nanofluid. The thermal (η TH ), electrical (η EL ) and overall (η OV ) efficiencies of the PV/T device are utilized as objective functions, and the mass flow rate ( m ˙ ) and concentration (φ) of nanofluid are considered as input parameters. The range considered for the m ˙ and φ is 20-80 kg/hr and 0-2% respectively. To measure the accuracy of the models developed, the mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R) were used. It was found that the best accuracy is obtained for the η TH in terms of (Training: R=0.9978 and RMSE=0.2045; Testing: R=0.9973 and RMSE=0.1973) followed by η OV in terms of (Testing: R=0.9959 and RMSE=0.1797), and η EL in terms of (Testing: R=0.9825 and RMSE=0.0037), respectively.