Handbook of Artificial Intelligence Techniques in Photovoltaic Systems

Modeling, Control, Optimization, Forecasting and Fault Diagnosis

1st Edition - June 23, 2022
Authors: Adel Mellit, Soteris Kalogirou
Language: English
Paperback ISBN:
9 7 8 - 0 - 1 2 - 8 2 0 6 4 1 - 6
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 2 0 6 4 2 - 3

Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modelling, Control, Optimization, Forecasting and Fault Diagnosis provides readers with a compre… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Authors’ biographies
Preface
Acknowledgments
1: Solar radiation and photovoltaic systems: Modeling and simulation
Abstract
1.1: Introduction
1.2: Solar radiation
1.3: Photovoltaics
1.4: Main issues of photovoltaic systems
1.5: Summary
References
2: Artificial intelligence techniques: Machine learning and deep learning algorithms
Abstract
2.1: Introduction
2.2: Artificial intelligence
2.3: Machine learning
2.4: Ensemble learning
2.5: Deep learning
2.6: Advantages and disadvantages of ML, EL, and DL algorithms
2.7: Summary
References
3: Forecasting of solar radiation using machine learning and deep learning algorithms
Abstract
3.1: Introduction
3.2: Solar radiation forecasting based on data-driven methods
3.3: Datasets description and preparation
3.4: Application of machine learning and deep learning algorithms for forecasting of solar radiation
3.5: Summary
References
4: Forecasting of photovoltaic output power using machine learning and deep learning algorithms
Abstract
4.1: Introduction
4.2: Forecasting of photovoltaic power based on data-driven methods
4.3: Datasets description and preparation
4.4: Machine learning for forecasting of photovoltaic output power
4.5: Neural networks for forecasting of photovoltaic output power from meteorological parameters and historical power
4.6: Ensemble learning for forecasting of photovoltaic power for one-step ahead
4.7: Deep learning for forecasting of photovoltaic power (one-step and multistep ahead)
4.8: Uncertainty quantification and interval prediction
4.9: Summary
References
5: Optimization of photovoltaic systems based on artificial intelligence techniques
Abstract
5.1: Introduction
5.2: Maximum power point tracking methods
5.3: Photovoltaic array reconfiguration methods
5.4: Maximum power point tracking using artificial intelligence techniques
5.5: PV module reconfiguration based on dynamic techniques and AI techniques
5.6: Summary
References
6: Machine learning and deep learning algorithms for fault diagnosis of photovoltaic systems
Abstract
6.1: Introduction
6.2: Type of faults in photovoltaic arrays
6.3: Protection devices for photovoltaic systems
6.4: Fault detection and diagnosis methods
6.5: Datasets description and preparation
6.6: Feature selection and extraction
6.7: Fault detection in photovoltaic arrays
6.8: Fault classification of photovoltaic arrays
6.9: Summary
References
7: Control and optimal management of grid-connected photovoltaic systems and micro-grids using artificial intelligence and metaheuristic techniques
Abstract
7.1: Introduction
7.2: Control of grid-connected and hybrid photovoltaic systems
7.3: Application of AI techniques for management and control of hybrid micro-grids
7.4: Energy management of micro-grids
7.5: Power sharing in micro-grids
7.6: Summary
References
8: Internet of things (IoT) and embedded systems for photovoltaic systems
Abstract
8.1: Introduction
8.2: Programmable electronic boards and IDE
8.3: Internet of things (IoT)
8.4: Literature review of the application of FPGA, Arduino, and Raspberry Pi for photovoltaic systems
8.5: Real-time applications
8.6: Summary
References
Appendices
Appendix A: PV module modeling
Appendix B: Machine learning and deep learning functions
Appendix C: Error metrics
Appendix D: Confusion matrix
Appendix E: Evaluation error metrics in Python
Appendix F: K-fold cross validation
Appendix G
Appendix H: XSG models
Appendix I
Index

Adel Mellit

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 Technologies (USTHB), Algiers in 2002 and 2006, respectively. His research interests Q1 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 170 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

Affiliations and expertise

Professor of Electronics, Faculty of Sciences and Technology, Jijel University, Algeria

Soteris Kalogirou

Professor Soteris Kalogirou obtained his PhD and DSc from the University of Glamorgan, UK. For more than 30 years, he has been actively involved in research of solar energy and in flat plate and concentrating collectors, solar water heating, solar steam generating systems, desalination, photovoltaics and absorption cooling in particular. He has many books and book contributions and has published large number papers in international scientific journals and refereed conference proceedings. He is Editor-in-Chief of Renewable Energy and Deputy Editor-in-Chief of Energy, as well as Editorial Board Member of another eleven journals. He is the author of the book Solar Energy Engineering: Processes and Systems and Thermal Solar Desalination, both published by Academic Press of Elsevier. He has been a member of World Renewable Energy Network (WREN), American Society of Heating Refrigeration and Air-conditioning Engineers (ASHRAE), Institute of Refrigeration (IoR) and International Solar Energy Society (ISES).

Affiliations and expertise

Professor, Department of Mechanical Engineering and Materials Sciences and Engineering, Cyprus University of Technology, Limassol, Cyprus

Life sciences

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