Computer Vision and Machine Intelligence for Renewable Energy Systems

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, Universidadde Castilla-La Mancha, Ciudad Real, Spain. Abhishek Kumar is Assistant Director and Professor in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. He holds a Ph.D. in Computer Science from the University of Madras and is currently a Post-Doctoral Fellow with the Ingenium Research Group, Universidad de Castilla-La Mancha, Ciudad Real, Spain. He received his M.Tech in Computer Science and Engineering and B.Tech in Information Technology from Rajasthan Technical University, Kota, India. He has over thirteen years of academic teaching experience. His research interests include artificial intelligence, computer vision, image processing, data mining, machine learning, and renewable energy systems. He has authored and edited several books with leading international publishers and serves as a reviewer for reputed journals. 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 Professor and Associate Dean (Research)Department of Applied Sciences and EngineeringTula's Institute Dehradun – 248011, Uttarakhand, India

• Part I Fundamentals of computer vision and machine learning for renewable energy systems1. An overview of renewable energy sources: technologies, applications and role of artificial intelligence2. Artificial intelligence for renewable energy strategies and techniques3. Computer vision-based regression techniques for renewable energy: predicting energy output and performance4. Utilization of computer vision and machine learning for solar power prediction5. Exploring data-driven multivariate statistical models for the prediction of solar energy6. Solar energy generation and power prediction through computer vision and machine intelligencePart II Computer vision techniques for renewable energy systems7. A machine intelligence model based on random forest for data-related renewable energy from wind farms in Brazil8. Bioenergy prediction using computer vision and machine intelligence: modeling and optimization of bioenergy production9. Artificial intelligence and machine intelligence: modeling and optimization of bioenergy production10. Advancing bioenergy: leveraging artificial intelligence for efficient production and optimization11. Image acquisition and processing techniques for crucial component of renewable energy technologies: mapping of rare earth element-bearing peralkaline granites12. Energy storage using computer vision: control and optimization of energy storage13. Classification techniques for renewable energy: identifying renewable energy sources and features14. Machine learning in renewable energy: classification techniques for identifying sources and features15. Advancing the frontier: hybrid renewable energy technologies for sustainable power generation16. Transfer learning for renewable energy: fine-tuning and domain adaptationPart III Renewable energy sources and computer vision opportunities17. Exploring the artificial intelligence in renewable energy: a bibliometric study using R Studio and VOSviewer18. Future directions of computer vision and AI for renewable energy: trends and challenges in renewable energy research and applications