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Intelligent Learning Approaches for Renewable and Sustainable Energy
- 1st Edition - February 21, 2024
- Editors: Josep M. Guerrero, Pankaj Gupta, Ritu Kandari, Alexander Micallef
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 8 0 6 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 5 8 0 7 - 0
Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adapti… Read more
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Request a sales quoteIntelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy. Sections introduce intelligent learning approaches and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. Other sections provide detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, optimization, and more.
This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence.
This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence.
- Explores cutting-edge intelligent techniques and their implications for future energy systems development
- Opens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and more
- Includes a range of case studies that provide insights into the challenges and solutions in real-world applications
Academic: Researchers, scientists, and advanced students working in sustainable energy and integration of renewable energy sources into utility grids, as well as across energy systems, energy engineering, machine learning, and artificial intelligence. Industry: Energy engineers, R&D professionals, and industry personnel working in the field of renewable and sustainable energy.
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Section I: Introduction to intelligent learning approaches for renewable and sustainable energy
- Section II: Applications of intelligence learning approaches for renewable and sustainable energy
- Section III: Intelligent learning methods for optimizing integrated energy systems
- Section I: Introduction to intelligent learningapproaches for renewable and sustainable energy
- Chapter one. Transforming the grid: AI, ML, renewable, storage, EVs, and prosumers
- Abstract
- 1.1 Introduction
- 1.2 Artificial intelligence and machine learning in the modern grid
- 1.3 Status of RES and storage systems in the modern grid
- 1.4 Case study: application of AI in power electronics driven RES
- References
- Chapter two. A new artificial intelligence-based demand side management method for EV charging stations
- Abstract
- 2.1 Introduction
- 2.2 Problem description
- 2.3 Proposed method
- 2.4 Conclusion
- References
- Chapter three. Modeling stochastic renewable energy processes by combining the Monte Carlo method and mixture density networks
- Abstract
- 3.1 Introduction to stochastic phenomena in renewable energies
- 3.2 Monte Carlo method (MCM)
- 3.3 Mixture density networks
- 3.4 Case study
- 3.5 Concluding remarks
- Acknowledgments
- References
- Chapter four. Profitability and performance improvement of smart photovoltaic/energy storage microgrid by integration of solar production forecasting tool
- Abstract
- 4.1 Introduction
- 4.2 Forecasting of solar radiation and PV production
- 4.3 Application of the predictive methods to a Mediterranean site
- 4.4 Energy Management System in a photovoltaic microgrid with battery storage
- 4.5 Linear programming and Mixed-integer linear programming (MILP)
- 4.6 Nonlinear programming
- 4.7 Dynamic programming
- 4.8 Model predictive control (MPC)
- 4.9 Rules-based control (RBC)
- 4.10 RBC without PV production forecasting
- 4.11 RBC with PV production forecasting
- 4.12 Results
- 4.13 Conclusion
- References
- Section II: Applications of intelligencelearning approaches for renewable andsustainable energy
- Chapter five. Intelligent learning models for renewable energy forecasting
- Abstract
- 5.1 Introduction
- 5.2 Cases of study
- 5.3 Forecasting/modeling techniques
- 5.4 Approaches and results over cases studies
- 5.5 Conclusion
- Acknowledgments
- References
- Chapter Six. Intelligent learning approach for multienergy load forecasting
- Abstract
- 6.1 Introduction
- 6.2 Multivariate load characteristics
- 6.3 Research status of multienergy coincidence prediction
- 6.4 Prediction process of an artificial intelligence method
- 6.5 Standard prediction models and their mechanisms
- 6.6 Example analysis
- 6.7 Conclusions and outlook
- References
- Chapter Seven. Intelligent learning approaches for demand-side controller for BIPV-integrated buildings
- Abstract
- 7.1 Introduction
- 7.2 Literature review
- 7.3 Challenges
- 7.4 Case study
- References
- Section III: Intelligent learning methods foroptimizing integrated energy systems
- Chapter eight. Intelligent learning methods for optimizing integrated energy systems (Predictive and prescriptive approaches to optimizing integrated energy systems that take into account uncertainty)
- Abstract
- 8.1 Introduction
- 8.2 Literature on intelligent learning methods
- 8.3 Problem formulation
- 8.4 Solution methodology
- 8.5 Case study
- 8.6 Conclusion
- Appendix
- References
- Chapter Nine. Intelligent power quality disturbance detection methods in virtual power plants: state-of-the-art
- Abstract
- 9.1 Introduction
- 9.2 Integration criteria and power quality
- 9.3 Power quality disturbances
- 9.4 Planning parameters in VPP systems
- 9.5 Electrical infrastructure and physical evaluation For Gökçeada VPP
- 9.6 Results and conclusion
- Acknowledgments
- References
- Index
- No. of pages: 350
- Language: English
- Edition: 1
- Published: February 21, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443158063
- eBook ISBN: 9780443158070
JG
Josep M. Guerrero
Josep M. Guerrero is a full professor with AAU Energy, Aalborg University, Denmark. He is the director of the Center for Research on Microgrids (CROM). He has published more than 800 journal articles in the fields of microgrids and renewable energy systems, which have been cited more than 80,000 times. His research interests focus on different microgrid aspects, including hierarchical and cooperative control, and energy management systems.
Affiliations and expertise
Full Professor, AAU Energy, Aalborg University and Director of the Center for Research on Microgrids (CROM), DenmarkPG
Pankaj Gupta
Dr. Pankaj Gupta his received his BE degree in Electrical Engineering from BIT, Durg, India, ME degree in Electrical Engineering from Delhi College of Engineering, Delhi University, and his PhD degree in Electrical Engineering from NIT, Kurukshetra, India. He was conferred with the prestigious POSOCO Power System Award 2017 for outstanding PhD research work on protection issues of grid connected distributed generation by the Power System Operation Corporation Ltd., a subsidiary of Power Grid Corporation, India, in partnership with the Foundation for Innovation and Technology Transfer, IIT, Delhi. Currently, Dr. Gupta is working with Indira Gandhi Delhi Technical University for Women, Delhi, India as Assistant Professor. His research interests include power system protection, microgrid control and protection, smart grid technologies, and islanding detection techniques.
Affiliations and expertise
Assistant Professor, Indira Gandhi Delhi Technical University for Women, Delhi, IndiaRK
Ritu Kandari
Ritu Kandari received her B.Tech. Degree in Electrical and Electronics from Guru Gobind Singh Indraprastha University, India, in 2010, and M.Tech. Degree in Digital Communication from Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, in 2012. She joined HMR Institute of Technology and Management, Delhi as an assistant professor in 2012. She is currently pursuing her PhD. Degree in renewable energy at the Department of Electronics and Communication Engineering, IGDTUW, Delhi. Kandari has co-edited 3 books with Elsevier and was also a guest associate editor of the special issue on electricity islands for the journal Renewable & Sustainable Energy Reviews.
Affiliations and expertise
Senior Research Fellow (SRF) in Renewable Energy, IGDTUW, Delhi, IndiaAM
Alexander Micallef
Dr. Alexander Micallef is a Senior Lecturer with the Department of Industrial Electrical Power Conversion at the University of Malta, Malta. He has also been a guest lecturer for the PhD course on “Power Quality and Synchronization Techniques in Microgrids” at Aalborg University since 2015. Dr. Micallef received the B.Eng. (Hons.), M. Sc. in Engineering, and PhD degrees from the University of Malta (Malta) in 2006, 2009 and 2015 respectively. He has authored or co-authored more than 20 journal and conference papers on microgrids, ship electrification, zero energy buildings, and power quality. Dr. Micallef is an Associate Editor for IEEE Access and the IET Smart Grids journal. He is a Senior Member of IEEE and is a member of the IEEE Standards Association, as well as vice-chair of the IEEE Malta Section and previously Chair of the Smart Buildings and Customer Systems Subcommittee within the IEEE PES SBLCS Technical Committee.
Affiliations and expertise
Senior Lecturer, Department of Industrial Electrical Power Conversion, University of Malta, MaltaRead Intelligent Learning Approaches for Renewable and Sustainable Energy on ScienceDirect