Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications [Book News]

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