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AI-Driven Optimization for Solar Energy Systems
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ISBN10: 6202452587
ISBN13: 9786202452588
Publisher: LAP Lambert Academic Publishing
Published: Oct 9 2025
Pages: 176
Weight: 0.54
Height: 0.41 Width: 6.00 Depth: 9.00
Language: English
ISBN13: 9786202452588
Publisher: LAP Lambert Academic Publishing
Published: Oct 9 2025
Pages: 176
Weight: 0.54
Height: 0.41 Width: 6.00 Depth: 9.00
Language: English
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.
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