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.
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Adel Mellit is a Professor at the Faculty of Sciences and Technology, University of Jijel, Algeria, and a Senior Associate Member at the International Centre of Theoretical Physics (ICTP), Trieste, Italy. He was previously the Director of the Renewable Energy Laboratory at University of Jijel. Prof. Mellit’s research focuses on the application of AI techniques, including deep learning and TinyML, in solar photovoltaic systems. He has authored or co-authored over 200 papers in international peer reviewed journals and numerous papers in conference proceedings, and has acted as editor of various conference proceedings. He is an Editor of the IEEE Journal of Photovoltaics, Subject Editor of the Energy journal (Elsevier) and an editorial board member of the Renewable Energy journal (Elsevier). He co-authored the Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modelling, Control, Optimization, Forecasting, and Fault Diagnosis, published by Elsevier under the Academic Press imprint in 2022.
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 of how, when and why to use and apply AI techniques in solving most photovoltaic problems.
The book serves as a complete reference in applying intelligent techniques and algorithms to increase the PV systems efficiency. Each chapter solves a specific problem and can stand alone independently, this detailed book contains problem-solving data for challenges including optimization, advanced control, output power forecasting, fault detection identification and localization, and real time implementation of designed intelligent models (into FPGAs, DSPs or MCs) and smart monitoring systems. Supported by the use of MATLAB and Simulink examples, 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.
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