Computer Vision and Machine Intelligence for Renewable Energy Systems
- 1st Edition - October 1, 2024
- Editors: Ashutosh Kumar Dubey, Abhishek Kumar, Umesh Chandra Pati, Fausto Pedro Garcia Marquez, Vicente García-Díaz, Arun Lal Srivastav
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 8 9 4 7 - 7
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 8 9 4 8 - 4
Computer Vision and Machine Intelligence in Renewable Energy Systems, the first release in Elsevier's cutting-edge new series, Advances in Intelligent Energy Systems, offers a prac… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteComputer Vision and Machine Intelligence in Renewable Energy Systems, the first release in Elsevier's cutting-edge new series, Advances in Intelligent Energy Systems, offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration. The book equips readers with a variety of essential tools and applications, outlining the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence and breaking down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures.
Other sections offer case studies and applications to a wide range of renewable energy source and the future possibilities of the technology. This book provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.
Other sections offer case studies and applications to a wide range of renewable energy source and the future possibilities of the technology. This book provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.
Part I: Fundamentals of Computer Vision and Machine Learning for Renewable Energy Systems
1. Introduction to Computer Vision and AI for Renewable Energy: Challenges and Opportunities
2. Overview of Renewable Energy Sources: Technologies and Applications
3. Image Acquisition and Processing Techniques for Renewable Energy: From Sensors to Images
4. AI for Renewable Energy: Strategies and Techniques
5. AI for Renewable Energy: Fundamentals and Applications
Part II: Computer Vision Techniques for Renewable Energy Systems
6. Recurrent Neural Networks for Renewable Energy: Modeling and Optimization
7. Generative Adversarial Networks for Renewable Energy: Synthesizing and Enhancing Data
8. Transfer Learning for Renewable Energy: Fine-tuning and Domain Adaptation
9. Semantic Segmentation for Renewable Energy: Segmentation and Classification of Renewable Energy Images
10. Instance Segmentation for Renewable Energy: Accurate Detection and Segmentation of Renewable Energy Assets
11. Classification Techniques for Renewable Energy: Identifying Renewable Energy Sources and Features
12. Computer Vision-based Regression Techniques for Renewable Energy: Predicting Energy Output and Performance
13. Anomaly Detection for Renewable Energy: Identifying and Diagnosing Faults and Anomalies in Renewable Energy Systems
14. Predictive Maintenance for Renewable Energy: Proactive Maintenance and Asset Management Strategies
15. Optimization of Renewable Energy Systems using Computer Vision: Multi-objective Optimization and Decision-making
Part III: Renewable Energy Sources and Computer Vision Opportunities
16. Wind Power Prediction using Computer Vision and Machine Intelligence: Modeling and Forecasting Wind Energy Production
17. Solar Power Prediction using Computer Vision and Machine Intelligence: Predicting and Optimizing Solar Energy Generation
18. Wave Energy Prediction using Computer Vision and Machine Intelligence: Modeling and Simulation of Wave Energy Converters
19. Tidal Energy Prediction using Computer Vision and Machine Intelligence: Analysis and Optimization of Tidal Energy Systems
20. Bioenergy Prediction using Computer Vision and Machine Intelligence: Modeling and Optimization of Bioenergy Production
21. Energy Storage using Computer Vision: Control and Optimization of Energy Storage Systems
Part IV: Future Directions
22. Future Directions of Computer Vision and AI for Renewable Energy: Trends and Challenges in Renewable Energy Research and Applications
1. Introduction to Computer Vision and AI for Renewable Energy: Challenges and Opportunities
2. Overview of Renewable Energy Sources: Technologies and Applications
3. Image Acquisition and Processing Techniques for Renewable Energy: From Sensors to Images
4. AI for Renewable Energy: Strategies and Techniques
5. AI for Renewable Energy: Fundamentals and Applications
Part II: Computer Vision Techniques for Renewable Energy Systems
6. Recurrent Neural Networks for Renewable Energy: Modeling and Optimization
7. Generative Adversarial Networks for Renewable Energy: Synthesizing and Enhancing Data
8. Transfer Learning for Renewable Energy: Fine-tuning and Domain Adaptation
9. Semantic Segmentation for Renewable Energy: Segmentation and Classification of Renewable Energy Images
10. Instance Segmentation for Renewable Energy: Accurate Detection and Segmentation of Renewable Energy Assets
11. Classification Techniques for Renewable Energy: Identifying Renewable Energy Sources and Features
12. Computer Vision-based Regression Techniques for Renewable Energy: Predicting Energy Output and Performance
13. Anomaly Detection for Renewable Energy: Identifying and Diagnosing Faults and Anomalies in Renewable Energy Systems
14. Predictive Maintenance for Renewable Energy: Proactive Maintenance and Asset Management Strategies
15. Optimization of Renewable Energy Systems using Computer Vision: Multi-objective Optimization and Decision-making
Part III: Renewable Energy Sources and Computer Vision Opportunities
16. Wind Power Prediction using Computer Vision and Machine Intelligence: Modeling and Forecasting Wind Energy Production
17. Solar Power Prediction using Computer Vision and Machine Intelligence: Predicting and Optimizing Solar Energy Generation
18. Wave Energy Prediction using Computer Vision and Machine Intelligence: Modeling and Simulation of Wave Energy Converters
19. Tidal Energy Prediction using Computer Vision and Machine Intelligence: Analysis and Optimization of Tidal Energy Systems
20. Bioenergy Prediction using Computer Vision and Machine Intelligence: Modeling and Optimization of Bioenergy Production
21. Energy Storage using Computer Vision: Control and Optimization of Energy Storage Systems
Part IV: Future Directions
22. Future Directions of Computer Vision and AI for Renewable Energy: Trends and Challenges in Renewable Energy Research and Applications
- No. of pages: 300
- Language: English
- Edition: 1
- Published: October 1, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443289477
AD
Ashutosh Kumar Dubey
Ashutosh Kumar Dubey is an Associate Professor in the Department of Computer Science and Engineering at the Institute of Engineering and Technology of Chitkara University, India. 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.
Affiliations and expertise
Department of Computer Science and Engineering, Institute of Engineering and Technology, Chitkara University, IndiaAK
Abhishek Kumar
Dr. Abhishek Kumar is a 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.
Affiliations and expertise
Post-doctorate Fellow, Ingenium Research Group, Universidad De Castilla-La Mancha, SpainUP
Umesh Chandra Pati
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.
Affiliations and expertise
Professor, National Institute of Technology, IndiaFG
Fausto Pedro Garcia Marquez
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.
Affiliations and expertise
Professor, Universidad De Castilla-La Mancha, SpainVG
Vicente García-Díaz
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.
Affiliations and expertise
Associate Professor, Department of Computer Science, University of Oviedo, SpainAS
Arun Lal Srivastav
Dr. Arun Lal Srivastav is an Associate Professor at Chitkara University,
Himachal Pradesh (India). He received PhD from the Indian Institute of
Technology (BHU), Varanasi, India. Also, he has done postdoctoral research at
National Chung Hsing University, Taiwan. He is currently involved in teaching
environmental science, waste management, environmental engineering, disaster
management, and design thinking to undergraduate engineering students. His
research interests include water quality surveillance, climate change, river ecosystem,
soil health maintenance, engineering education, phytoremediation, and waste
management. He has published 86 SCI/SCOPUS research publications in different
prestigious journals, books, and conferences. He has edited many books with
prestigious publishers including Elsevier, Springer, Nova, IGI Global, and Wiley.
Moreover, he is also working on four government-sponsored projects (worth
approximately 16 million INR) on phytoremediation, adsorption, capacity building,
organic farming, leachate treatment, agro-waste management, and so on.
Also, 12 Indian patents have been granted in his credit on multidisciplinary
topics.
Affiliations and expertise
Associate Professor, Chitkara University, Himachal Pradesh, India