About the Book
Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.
This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered.
The very first book in Elsevier’s cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.
Table of Contents:
Part I Fundamentals of computer vision and machine learning for renewable energy systems
1. An overview of renewable energy sources: technologies, applications and role of artificial intelligence
2. Artificial intelligence for renewable energy strategies and techniques
3. Computer vision-based regression techniques for renewable energy: predicting energy output and performance
4. Utilization of computer vision and machine learning for solar power prediction
5. Exploring data-driven multivariate statistical models for the prediction of solar energy
6. Solar energy generation and power prediction through computer vision and machine intelligence
Part II Computer vision techniques for renewable energy systems
7. A machine intelligence model based on random forest for data-related renewable energy from wind farms in Brazil
8. Bioenergy prediction using computer vision and machine intelligence: modeling and optimization of bioenergy production
9. Artificial intelligence and machine intelligence: modeling and optimization of bioenergy production
10. Advancing bioenergy: leveraging artificial intelligence for efficient production and optimization
11. Image acquisition and processing techniques for crucial component of renewable energy technologies: mapping of rare earth element-bearing peralkaline granites
12. Energy storage using computer vision: control and optimization of energy storage
13. Classification techniques for renewable energy: identifying renewable energy sources and features
14. Machine learning in renewable energy: classification techniques for identifying sources and features
15. Advancing the frontier: hybrid renewable energy technologies for sustainable power generation
16. Transfer learning for renewable energy: fine-tuning and domain adaptation
Part III Renewable energy sources and computer vision opportunities
17. Exploring the artificial intelligence in renewable energy: a bibliometric study using R Studio and VOSviewer
18. Future directions of computer vision and AI for renewable energy: trends and challenges in renewable energy research and applications
About the Author :
Ashutosh Kumar Dubey is an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Himachal Pradesh, India. He is also a Postdoctoral Fellow of the Ingenium Research Group Lab, Universidad
de Castilla-La Mancha, Ciudad Real, Spain. Dr. Abhishek Kumar is Assistant Director and Associate Professor in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. He completed his PhD in computer science at the University of Madras (India), and previously worked as a post-doctorate fellow in computer science at Ingenium Research Group, based at the Universidad de Castilla-La Mancha in Spain. He has been teaching in academia for more than 13 years, and has over 160 publications in peer reviewed national and international journals, books, and conferences. His research area includes artificial intelligence, renewable energy applications, image processing, computer vision, data mining, and machine learning.
Umesh Chandra Pati is a Professor in the Department of Electronics and Communication Engineering at the National Institute of Technology, India. He has authored/edited two books and published over 100 articles in peer-reviewed international journals and conference proceedings. He has also guest-edited special issues of Cognitive Neurodynamics and International Journal of Signal and Imaging System Engineering. Dr. Pati has filed 2 Indian patents. Besides other sponsored projects, he is currently associated with a high value IMPRINT project “Intelligent Surveillance Data Retriever (ISDR) for Smart City Applications, an initiative of the Ministries of Education, and Housing and Urban Affairs in the Government of India. His current areas of research include Computer Vision, Artificial Intelligence, the Internet of Things (IoT), Industrial Automation, and Instrumentation Systems.
Professor Fausto works as Professor at Universidad De Castilla-La Mancha, Spain. Honorary Senior Research Fellow at Birmingham University, UK, Lecturer at the Postgraduate European Institute. He has published more than 150 papers and is author and editor of 31 books (Elsevier, Springer, Pearson, Mc-GrawHill, Intech, IGI, Marcombo, AlfaOmega). He is Editor of 5 Int. Journals, Committee Member more than 40 Int. Conferences. He has been Principal Investigator in 4 European Projects, 6 National Projects, and more than 150 projects for Universities, Companies, etc. His main interests are: Artificial Intelligence, Maintenance, Management, Renewable Energy, Transport, Advanced Analytics, Data Science. He is an expert in the European Union in AI4People (EISMD), and ESF and Director of www.ingeniumgroup.eu. Dr. Vicente García-Díaz is a Software Engineer and has a PhD in Computer Science. He is an Associate Professor in the Department of Computer Science at the University of Oviedo. He is also part of the editorial and advisory board of several journals and has been editor of several special issues in books and journals. He has supervised 80+ academic projects and published 80+ research papers in journals, conferences and books. His research interests include decision support systems, Domain-Specific languages and eLearning. Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.
