Design of a metaheuristic artificial intelligence (AI) model for an optimal photovoltaic module cooling system

Abstract

<title>Abstract</title> The absorption and heat management processes of the PV module are very diverse and in constant development. The advantages and disadvantages of each process imply an effective method of optimal choice. This paper therefore aims to design a multi-objective particle swarm optimization (MOPSO) model to search for a better configuration of cooled PV/T. Seven objective functions were implemented. The Cost of Energy (COE), Net Present Value (NPV), Internal Rate of Return (IRR, Ergonomic Factor (EF) and Payback Time (CPBT) revealed that photovoltaic /thermal systems (PV/T) with hybrid cooling (Passive/Active) with forced convection PCM/Air (phase change materials) are better. Likewise, the evaluation of the total annual cost (TAC) shows that air cooling systems are more economical. On the other hand, the evaluation of the \(\:{\text{C}\text{O}}_{2}\) cost shows that hybrid cooling systems with NanoPCM /TNF (Ternary Nanofluids) are less polluting. However, the return on investment time of the cooled PV module unit is less than 4 years and the sensitivity of savings of more than $20 in just 5 years of life. The MOPSO method deduced that PV/T systems with hybrid PCM/Air cooling are optimal compared to hybrid TEG/NF (thermoelectric / Nanofluid) systems which are the least efficient. The developed algorithm is very precise for choosing an optimal solar system.