Mathematical and neural network modeling for predicting and analyzing of nanofluid-nano PCM photovoltaic thermal systems performance

Abstract This paper aims to enhance the power production performance of the PV/T based on three cooling models using nanofluid, SiC-water and nano-PCM. The effect of solar irradiance and ambient temperature on the power productivity performance has been investigated based on sensitivity analysis that shows that electrical current and solar irradiance has more impact on power prediction than voltage and temperature. Also, three mathematical linear prediction models were developed and compared with the prediction of ANN models, and the results were verified and fit the experimental results. The comparison with published literature is made based on three common evaluation criteria that include the coefficient of determination R2, MSE and RSME. The proposed predicting models attained an excellent R2 result of 0.99 and MSE value of 0.006 and RSME of 0.009 for both P-M1 and P-M2 models. Besides, the P-M3 obtained an MSE value of 0.022. Finally, the proposed linear prediction models help to reduce the error in furcating future results and determine the best conditions for any solar system in an easy and fast way.

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