AI-Driven Optimization for Solar Energy Systems

The rising global demand for sustainable energy has accelerated solar PV adoption yet efficiency is limited by challenges such as variable irradiance partial shading storage and grid integration. This study explores 15 nature-inspired AI optimization algorithms-ABC PSO PIO DIO PDO SMO RIOA ACO TIOA OIOA EIO CIO OOA PIOA and MLO-that mimic biological behaviors to solve nonlinear multi-objective problems in solar systems. Using theoretical models and case studies the research shows how these methods improve MPPT tilt/orientation storage scheduling microgrid dispatch and reliability. Results highlight ABC ACO TIOA CIO RIOA and OOA as top performers achieving 98-99% MPPT efficiency 6-9% annual yield gains and major reductions in storage losses and diesel reliance. Lightweight approaches like PDO and simplified ABC excel in embedded MPPT while CIO OIOA and EIO deliver high-accuracy offline tilt and layout optimization. Specialized roles include MLO for power quality and PIOA/OOA for resource scheduling. Collectively these algorithms provide adaptive scalable solutions that boost efficiency cut costs and enhance sustainability in solar energy.