AI-Driven Optimization for Solar Energy Systems - Couverture souple

Islam, Mohammad Shariful

9786202452588: AI-Driven Optimization for Solar Energy Systems

Couverture souple

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Editeur : LAP Lambert Academic Publishing, 2025

Afficher les exemplaires de cette �dition comportant l'ISBN

0 D'occasion

11 Neuf

De EUR 83,02

D�tails de l'�dition

�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2025
Langue: anglais
ISBN 10: 6202452587
ISBN 13: 9786202452588
Reliure: Broch�
Nombre de pages: 176
Coordonn�es du fabricant: non disponible
Personne responsable: KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Allemagne

R�sultats de recherche pour AI-Driven Optimization for Solar Energy Systems

Image d'archives

AI-Driven Optimization for Solar Energy Systems

Islam, Mohammad Shariful

Edit� par LAP LAMBERT Academic Publishing, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf PAP

Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis

�valuation du vendeur 5 sur 5 �toiles

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N� de r�f. du vendeur L2-9786202452588

Contacter le vendeur

Acheter neuf

EUR 83,02

Livraison gratuite
Exp�dition nationale�: Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

AI-Driven Optimization for Solar Energy Systems

Islam, Mohammad Shariful

Edit� par LAP LAMBERT Academic Publishing, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf PAP

Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N� de r�f. du vendeur L2-9786202452588

Contacter le vendeur

Acheter neuf

EUR 81,68

Exp�dition �EUR 3,84
Exp�dition depuis Royaume-Uni vers Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

AI-Driven Optimization for Solar Energy Systems

Islam, Mohammad Shariful

Edit� par LAP Lambert Academic Publishing, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf Couverture souple

Vendeur : California Books, Miami, FL, Etats-Unis

�valuation du vendeur 4 sur 5 �toiles

Etat : New. N� de r�f. du vendeur I-9786202452588

Contacter le vendeur

Acheter neuf

EUR 87,27

Livraison gratuite
Exp�dition nationale�: Etats-Unis

Quantit� disponible : Plus de 20 disponibles

Ajouter au panier

Image d'archives

AI-Driven Optimization for Solar Energy Systems

Mohammad Shariful Islam

Edit� par Omniscriptum, LAP Lambert Academic Publishing, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf Taschenbuch

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 176 pp. Englisch. N� de r�f. du vendeur 9786202452588

Contacter le vendeur

Acheter neuf

EUR 79,90

Exp�dition �EUR 23
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 2 disponible(s)

Ajouter au panier

Image d'archives

AI-Driven Optimization for Solar Energy Systems (Paperback)

Mohammad Shariful Islam

Edit� par LAP Lambert Academic Publishing, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf Paperback

impression � la demande

Vendeur : CitiRetail, Stevenage, Royaume-Uni

�valuation du vendeur 5 sur 5 �toiles

Paperback. Etat : new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9786202452588

Contacter le vendeur

Acheter neuf

EUR 87,76

Exp�dition �EUR 43,19
Exp�dition depuis Royaume-Uni vers Etats-Unis

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image fournie par le vendeur

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

Mohammad Shariful Islam

Edit� par LAP LAMBERT Academic Publishing, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf Taschenbuch

Vendeur : preigu, Osnabr�ck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. AI-Driven Optimization for Solar Energy Systems | AI-Driven Optimization for Solar Energy Systems: Nature-Inspired Algorithms for Solar Energy System | Mohammad Shariful Islam | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786202452588 | Verantwortliche Person f�r die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. N� de r�f. du vendeur 134204788

Contacter le vendeur

Acheter neuf

EUR 67,20

Exp�dition �EUR 70
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 5 disponible(s)

Ajouter au panier

Image fournie par le vendeur

AI-Driven Optimization for Solar Energy Systems

Mohammad Shariful Islam

Edit� par LAP LAMBERT Academic Publishing Okt 2025, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf Taschenbuch

impression � la demande

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 176 pp. Englisch. N� de r�f. du vendeur 9786202452588

Contacter le vendeur

Acheter neuf

EUR 79,90

Exp�dition �EUR 60
Exp�dition depuis Allemagne vers Etats-Unis

Quantit� disponible : 1 disponible(s)

Ajouter au panier

Image d'archives

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

Mohammad Shariful Islam

Edit� par Omniscriptum, LAP Lambert Academic Publishing, 2025

ISBN 10 : 6202452587 ISBN 13 : 9786202452588

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Taschenbuch. Etat : Neu. Neuware - 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. N� de r�f. du vendeur 9786202452588

Contacter le vendeur