AI-Driven Optimization for Solar Energy Systems: AI-Driven Optimization for Solar Energy Systems: Nature-Inspired Algorithms for Solar Energy System - Softcover

Islam, Mohammad Shariful

 
9786202452588: AI-Driven Optimization for Solar Energy Systems: AI-Driven Optimization for Solar Energy Systems: Nature-Inspired Algorithms for Solar Energy System
Softcover
ISBN 10: 6202452587 ISBN 13: 9786202452588
Verlag: LAP LAMBERT Academic Publishing, 2025

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Mohammad Shariful Islam, Ph.D. researcher at UKM, received the Excellence in R&D Leadership Award at The Solar Week Malaysia 2025. With a B.Sc. (UIU, 2010), M.Sc. (UiTM, 2024), and diverse R&D roles, he has authored books and papers on solar and power electronics. His research spans nano/micro-grids, NZE homes, battery systems, and high-efficiency.

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Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2025
Sprache
Englisch
ISBN 10 
6202452587
ISBN 13 
9786202452588
Einband
Taschenbuch
Anzahl der Seiten
176
Kontakt zum Hersteller
preigu GmbH & Co. KG
mail@preigu.de

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