Abstract
This book describes describes applications of machine learning techniques to maximum power point tracking (MPPT), PV-cell design and sizing, and hybridmodule design and modeling. PV-cell parameter extraction and estimation are proposed using moth–flame and chaotic optimization or deterministic and metaheuristic global optimization algorithms. Neuro-fuzzy inference systems (ANFIS) and a particle swarm optimization-artificial neural network model are applied to PV power output forecasting, while a domain adaptation of deep neural networks is proposed for multistep solar irradiance forecasting. Machine learning approaches are also presented for PV power prediction based on available environmental parameters. Additionally, PV plant operation failure modes and module deterioration diagnosis are studied using complex network and image analysis. After a preface, the book includes 16 carefully selected articles to cover recent trends in machine learning applications to PV systems. The book is an open access tool for engineers and researchers in applying machine learning methods to PV systems. The book is also suited for students willing to further use machine learning skills on PV applications and is a valuable resource for practicing professionals in need of understanding and pursuing advanced trends in PV systems.
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