Handbook of Artificial Intelligence Techniques in Photovoltaic Systems

Modeling, Control, Optimization, Forecasting and Fault Diagnosis

Adel Mellit, Soteris Kalogirou

• Publisher: Academic Press
• Year: 2022
• ISBN:
  9780128206423
• Topic: Technology & Engineering
• Subtopic: Renewable Power Resources
• Index: Technology & Engineering

Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modelling, Control, Optimization, Forecasting and Fault Diagnosis provides readers with a comprehensive and detailed overview of the role of artificial intelligence in PV systems. Covering up-to-date research and methods on how, when and why to use and apply AI techniques in solving most photovoltaic problems, this book will serve as a complete reference in applying intelligent techniques and algorithms to increase PV system efficiency. Sections cover problem-solving data for challenges, including optimization, advanced control, output power forecasting, fault detection identification and localization, and more.Supported by the use of MATLAB and Simulink examples, this comprehensive illustration of AI-techniques and their applications in photovoltaic systems will provide valuable guidance for scientists and researchers working in this area.

• Includes intelligent methods in real-time using reconfigurable circuits FPGAs, DSPs and MCs
• Discusses the newest trends in AI forecasting, optimization and control applications
• Features MATLAB and Simulink examples highlighted throughout

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Authors’ biographies
• Preface
• Acknowledgments
• 1: Solar radiation and photovoltaic systems: Modeling and simulation
• 2: Artificial intelligence techniques: Machine learning and deep learning algorithms
• 3: Forecasting of solar radiation using machine learning and deep learning algorithms
• 4: Forecasting of photovoltaic output power using machine learning and deep learning algorithms
• 5: Optimization of photovoltaic systems based on artificial intelligence techniques
• 6: Machine learning and deep learning algorithms for fault diagnosis of photovoltaic systems
• 7: Control and optimal management of grid-connected photovoltaic systems and micro-grids using artificial intelligence and metaheuristic techniques
• 8: Internet of things (IoT) and embedded systems for photovoltaic systems
• Appendices
• Index