Adel Mellit - Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modeling, Control, Optimization, Forecasting and Fault Diagnosis

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Adel Mellit is Professor at the Faculty of Sciences and Technology, Jijel University, Algeria. He received his MS and PhD in electronics from the University of Sciences and Technology Houari Boumediene (USTHB), Algiers in 2002 and 2006, respectively. His research interests focus on the application of artificial intelligence techniques in photovoltaic systems and microgrids (control, fault diagnosis, optimization, and real-time applications). Dr. Adel Mellit has authored or coauthored more than 150 papers in international peer-reviewed journals (mostly with Elsevier), papers in conference proceedings (mostly with the IEEE) mainly on photovoltaic systems, six book chapters, and two books. He is the director of the Renewable Energy Laboratory at the Jijel University, Algeria, and is an associate member at the ICTP Trieste, Italy. He is serving on the editorial board of the Renewable Energy and is Editor of the IEEE Journal of Photovoltaic and of Energy (Elsevier Ltd). https://orcid.org/0000-0001-5458-3502.

Soteris Kalogirou is Professor at the Department of Mechanical Engineering and Materials Sciences and Engineering of the Cyprus University of Technology, Limassol, Cyprus. He is currently the Dean of the School of Engineering and Technology. In addition to PhD, he holds the title of DSc. He is a fellow of the European Academy of Sciences and a founding member of the Cyprus Academy of Sciences, Letters, and Arts. For more than 35 years, he is actively involved in research in the area of solar energy. He is also involved in research related to the use of artificial intelligence for the modeling, performance prediction, and fault finding of renewable energy systems. He has a large number of publications in books, book chapters, international scientific journals, and refereed conference proceedings. He is the editor-in-chief of Renewable Energy and deputy editor-in-chief of Energy. He is the editor of the book Artificial Intelligence in Energy and Renewable Energy Systems, published by Nova Science Inc.; the coeditor of the book Soft Computing in Green and Renewable Energy Systems, published by Springer; the editor of the book McEvoys Handbook of Photovoltaics, published by Academic Press of Elsevier; and the author of the books Solar Energy Engineering: Processes and Systems and Thermal Solar Desalination: Methods and Systems, published by Academic Press of Elsevier. https://orcid.org/0000-0002-4497-0602.

This book is the result of many years of collaboration between two researchers in the field of artificial intelligence (AI) techniques and solar energy systems. The idea behind writing the book is to give to readers the first book on the application of AI techniques, including machine learning, and deep learning in photovoltaic systems.

Recently, the use and the application of AI techniques in renewable energy systems (RESs) have increased considerably, showing substantial advances in solving some complex problems. However, in some cases, the application of AI techniques is considered a magic solution by some researchers, which is not correct. Therefore, the main objective of writing this book is to demonstrate to the readers a comprehensive way and detailed methods on how, when, and why to use and apply AI techniques in solving various photovoltaic problems, including solar radiation forecasting, optimization and management, control and maximization of output power, fault diagnosis, power forecasting, power sharing, and smart monitoring systems using the Internet of things (IoT) technique.

The book provides readers with a comprehensive overview of the application of AI techniques in photovoltaic systems. We believe that this comprehensive illustration of the AI techniques and their application in photovoltaic systems will be a valuable key guidance for scientists, researchers, and industry sectors working in this area, to apply these techniques effectively. The book can also be used as advanced background reading by students who are studying postgraduate courses and who are motivated and interested in the application of AI techniques in photovoltaics. In addition, readers will be able to get an idea about different photovoltaic configurations, including grid-connected, hybrid, and micro-grids. Understating the main challenging issues in the implementation of AI in photovoltaic applications is another objective.

The most valuable features and content in the book are (1) understanding how to apply AI techniques; (2) showing the advantages of AI to solve some challenging problems in photovoltaics (autonomous, grid-connected, hybrid, and micro-grids), such as fault detection, identification, and localization of faults; advanced management and control process; output power forecasting; and smart monitoring systems; (3) showing the most important aspects for the implementation of intelligent methods in real time using some reconfigurable circuits such as field programmable gate arrays (FPGAs), microcontrollers (Arduino), and Minco-processor (Raspberry); and (4) designing smart prototypes based on embedded technologies.