Computer Vision and Machine Intelligence for Renewable Energy Systems

Erscheinungsjahr 2024
ISBN: 978-0-443-28947-7
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 216 mm x 276 mm, Gewicht: 450 g

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.

Autoren/Hrsg.

Dubey, Ashutosh Kumar

Kumar, Abhishek

Garcia Marquez, Fausto Pedro

García-Díaz, Vicente

Srivastav, Arun Lal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik

Inhaltsverzeichnis

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

Über Autor(innen)

Kumar, Abhishek
Dr. Abhishek Kumar is a professor and post-doctorate fellow in computer science at Ingenium Research Group, based at Universidad De Castilla-La Mancha in Spain. He has been teaching in academia for more than 8 years, and published more than 50 articles in reputed, peer reviewed national and international journals, books, and conferences. His research area includes artificial intelligence, image processing, computer vision, data mining, and machine learning.

García-Díaz, Vicente
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.

Garcia Marquez, Fausto Pedro
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.

Srivastav, Arun Lal
Dr. Arun Lal Srivastav is working as Assistant Professor at Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India. He has obtained his PhD from the Indian Institute of Technology (BHU), Varanasi, India on the adsorption of nitrate and fluoride from water. Also, he has done post-doctoral research at National Chung Hsing University, Taiwan. He is currently involved in the teaching of Environmental Science, Environmental Engineering, Disaster Management and Design Thinking to the undergraduate engineering students. His research interests include water quality surveillance, climate change, water treatment, river ecosystem, soil health maintenance, engineering education, phytoremediation and waste management. He has published > 86 research papers in various prestigious journals (Elsevier, Springer, IWA, Taylor & Francis etc.) including some book chapters and conferences. He is also the editor of 20 books with Elsevier, Springer, NOVA and Wiley. Additionally, he is also one of the series editors of Elsevier and Nova publisher on energy sustainability and e-waste management, respectively. Further, in his credit, 12 patents have been granted by the Government of India on multidisciplinary topics. He is also working on 04 Government sponsored projects (worth ~16 million INR) on phytoremediation, adsorption, capacity building, organic farming, leachate treatment, agro-waste management etc.

Dubey, Ashutosh Kumar
Ashutosh Kumar Dubey is an associate Professor in the Department of Computer Science and Engineering at Chitkara University, Himachal Pradesh, India. Ashutosh is also a Postdoctoral Fellow of the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Ciudad Real, Spain. He has more than 14 years of teaching experience. His research areas are Data Mining, Health Informatics, Optimization, Machine Learning, Cloud Computing, Artificial Intelligence and Object-Oriented Programming.