Name: AI-Driven Optimization for Solar Energy Systems (en Inglés)
SKU: 66200730
Price: 100.30 USD
Availability: InStock
Author: Islam, Mohammad Shariful

portada AI-Driven Optimization for Solar Energy Systems (en Inglés)

AI-Driven Optimization for Solar Energy Systems (en Inglés)

Formato

Libro Físico

Editorial

LAP Lambert Academic Publishing

Autor

Islam, Mohammad Shariful

Idioma

Inglés

N° páginas

176

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x1 cm

ISBN13

9786202452588

AI-Driven Optimization for Solar Energy Systems (en Inglés)

Islam, Mohammad Shariful (Autor) · LAP Lambert Academic Publishing · Tapa Blanda

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Reseña del libro "AI-Driven Optimization for Solar Energy Systems (en Inglés)"

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.