Artificial Neural Network and Genetic Algorithm-based prediction of photovoltaic panel performance with porous foam gradient and nano-enhanced phase change material

Abstract

Photovoltaic panels (PV) become less efficient as the temperature rises. The use of porous foam gradient, phase change materials (PCMs), and nanoparticle-PCM (nePCM) can effectively lower the temperature of PV panels. This study employed numerical analysis using the finite volume method to investigate the effects of different types of PCMs (n-octadecane, decanoic acid natural, lauric acid, paraffin wax, and RT-42), mono and hybrid nanoparticles/PCM, and the position of PCM in the design system. The results indicate that the highest electrical efficiency of 9 % is achieved when the PCM is placed under the PV panel and the thickness of the PCM layer is 0.6 times the height of the microchannel. Furthermore, the use of a porous medium with a variable porosity coefficient that increases along the y-direction can significantly improve thermal and electrical efficiency (approximately 5–42 % and 5–48 % respectively). Additionally, incorporating PCM and hybrid nanoparticles-PCM enhances PV performance compared to the absence of nanoparticles. For TiO2, Ag, Al2O3, GO, and MWCNT nanoparticles, the thermal efficiency, electrical efficiency, and exergetic efficiency increase by approximately 24.18 %, 26.15 %, 26.28 %, 26.30 %, and 26.33 % respectively. Similarly, for hybrid nanoparticles such as GO-Al2O3, GO-Al2O3-MWCNT, MWCNT-GO, and MWCNT-Al2O3, these values increase by approximately 26.32 %, 26.3419 %, 26.3432 %, and 26.335 % respectively for thermal efficiency, and 5.16 %, 5.1702 %, 5.1704 %, and 5.1689 % respectively for electrical efficiency. Moreover, n-octadecane exhibits the highest melting fraction, while paraffin wax has the lowest melting fraction. Lastly, the genetic algorithm-neural network method was employed to estimate the PV electrical efficiency.