Computer Vision and Machine Intelligence for Renewable Energy Systems

1st Edition - September 20, 2024
Imprint: Elsevier
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 for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
About the series
About the series editors
List of contributors
Part I: Fundamentals of computer vision and machine learning for renewable energy systems
Chapter 1. An overview of renewable energy sources: technologies, applications and role of artificial intelligence
Abstract
1.1 Introduction
1.2 Types of renewable energy sources
1.3 Role of artificial intelligence and machine learning in renewable energy system
1.4 Application of renewable energy sources
1.5 Advantages and disadvantages of renewable energy resources
1.6 Discussion
1.7 Conclusion
References
Chapter 2. Artificial intelligence for renewable energy strategies and techniques
Abstract
2.1 Introduction
2.2 Artificial intelligence in resource evaluation and forecasting
2.3 Advanced artificial intelligence techniques for renewable energy optimization
2.4 Integrated framework: artificial intelligence integration in renewable energy strategies
2.5 Artificial intelligence–based predictive models for grid management
2.6 Practical applications and challenges of implementing artificial intelligence in renewable energy
2.7 Conclusion: illuminating the future of energy with artificial intelligence
References
Chapter 3. Computer vision-based regression techniques for renewable energy: predicting energy output and performance
Abstract
3.1 Introduction
3.2 Literature review
3.3 Methods
3.4 Results
3.5 Discussion
3.6 Conclusion and future work
References
Chapter 4. Utilization of computer vision and machine learning for solar power prediction
Abstract
4.1 Introduction
4.2 Solar power prediction overview
4.3 Computer vision in solar power prediction
4.4 Machine learning models for solar power prediction
4.5 Integration of computer vision and machine learning
4.6 Real-world applications and case studies
4.7 Discussion
4.8 Conclusions and future work
References
Further reading
Chapter 5. Exploring data-driven multivariate statistical models for the prediction of solar energy
Abstract
5.1 Introduction
5.2 Related prior work
5.3 Solar energy forecasting framework
5.4 Results and analysis
5.5 Conclusion
References
Chapter 6. Solar energy generation and power prediction through computer vision and machine intelligence
Abstract
6.1 Introduction
6.2 Computer vision and machine intelligence for solar power prediction
6.3 Fundamentals and need of solar power prediction
6.4 Data-centric methodologies for solar energy production
6.5 Transfer learning perspective in solar energy production
6.6 Case study for implementing multistep forecasting for PV ramp-rate control
6.7 Future perspectives
6.8 Conclusions
Abbreviations
References
Part II: Computer vision techniques for renewable energy systems
Chapter 7. A machine intelligence model based on random forest for data related renewable energy from wind farms in Brazil
Abstract
7.1 Introduction
7.2 Literature Review
7.3 Methods
7.4 Results
7.5 Discussion
7.6 Conclusions
References
Chapter 8. Bioenergy prediction using computer vision and machine intelligence: modeling and optimization of bioenergy production
Abstract
8.1 Introduction to bioenergy
8.2 Literature review
8.3 Overview of computer vision in the energy sector
8.4 Advanced deep learning techniques for bioenergy prediction
8.5 Emerging technologies in bioenergy production
8.6 Case studies and experimental results: real-world applications of bioenergy prediction
8.7 Discussion
8.8 Future research directions
8.9 Limitations of the study
8.10 Future suggestions
8.11 Conclusion
References
Chapter 9. Artificial intelligence and machine intelligence: modeling and optimization of bioenergy production
Abstract
9.1 Introduction
9.2 Biomass resource prediction
9.3 Biomass conversion processes
9.4 Biofuel property prediction
9.5 Bioenergy end-use systems
9.6 Generating data for design and optimization
9.7 Technical and interdisciplinary considerations
9.8 Conclusion
References
Chapter 10. Advancing bioenergy: leveraging artificial intelligence for efficient production and optimization
Abstract
10.1 Introduction
10.2 Overview of artificial intelligence and its use in bioenergy prediction
10.3 AI tools to forecast the properties of biomass feedstock
10.4 Prediction and optimization of biochemical conversion technologies using artificial intelligence
10.5 Prediction and optimization of thermochemical conversion technologies using artificial intelligence
10.6 Conclusion
Acknowledgment
References
Chapter 11. Image acquisition and processing techniques for crucial component of renewable energy technologies: mapping of rare earth element-bearing peralkaline granites
Abstract
11.1 Introduction
11.2 Literature review
11.3 Materials and methods
11.4 Results and discussion
11.5 Conclusion
Data availability
References
Chapter 12. Energy storage using computer vision: control and optimization of energy storage
Abstract
12.1 Introduction
12.2 Background
12.3 Fundamentals of energy storage systems
12.4 Integration of computer vision in energy storage
12.5 Challenges and Considerations
12.6 Current energy storage status
12.7 Discussion on government initiatives
12.8 Conclusion
References
Chapter 13. Classification techniques for renewable energy: identifying renewable energy sources and features
Abstract
13.1 Historical background of energy
13.2 Introduction
13.3 Classification of energy
13.4 Sources of energy
13.5 Technology-based classification criteria
13.6 Storage technology of renewable energy
13.7 Parameters for different renewable energy sources
13.8 Role of renewable energy in sustainable development
13.9 Importance of renewable energy
13.10 Challenges of renewable energy techniques
13.11 Identifying techniques for renewable energy sources and features
13.12 The environmental impacts of renewable energy sources
13.13 Reducing overall energy demand and complementing renewable energy adoption
13.14 Interconnection of renewable energy with other sectors
13.15 Emerging technologies in renewable energy and potential challenges
13.16 A global overview, including regional analysis of renewable energy
13.17 Comparative results using graphs and tables to demonstrate the utilization of computer vision and machine learning for the classification
13.18 Conclusions
13.19 Discussion
13.20 Future directions
References
Chapter 14. Machine learning in renewable energy: classification techniques for identifying sources and features
Abstract
14.1 Introduction
14.2 Literature review
14.3 Procedures for classification
14.4 Identification and characterization of features
14.5 Case studies and applications
14.6 Challenges and prospects for the future
14.7 Conclusion
References
Chapter 15. Advancing the frontier: hybrid renewable energy technologies for sustainable power generation
Abstract
15.1 Introduction
15.2 Conventional methods used for the development of RE resources
15.3 Renewable energy technologies
15.4 Challenges
15.5 Conclusion
References
Chapter 16. Transfer learning for renewable energy: fine-tuning and domain adaptation
Abstract
16.1 Introduction
16.2 Renewable energy sources
16.3 Artificial intelligence
16.4 Deep learning in renewable energy through sample articles
16.5 Materials and methods
16.6 Discussion and conclusions
References
Part III: Renewable energy sources and computer vision opportunities
Chapter 17. Exploring the artificial intelligence in renewable energy: a bibliometric study using R Studio and VOSviewer
Abstract
17.1 Introduction
17.2 Literature review
17.3 Methodology
17.4 Findings
17.5 Discussion
17.6 Conclusion
References
Chapter 18. Future directions of computer vision and AI for renewable energy: trends and challenges in renewable energy research and applications
Abstract
18.1 Introduction
18.2 Computer vision and AI in renewable energy
18.3 Recent advances in computer vision for renewable energy
18.4 Artificial intelligence applications in renewable energy
18.5 Future trends in computer vision and AI for renewable energy
18.6 Challenges and limitations
18.7 Conclusion and future directions
References
Index

Ashutosh Kumar Dubey

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.

Affiliations and expertise

Department of Computer Science and Engineering, Institute of Engineering and Technology, Chitkara University, India

Abhishek Kumar

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.

Affiliations and expertise

Professor, Post-doctorate Fellow, Ingenium Research Group, Universidad De Castilla-La Mancha, Spain

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, India

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, Spain

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, Spain

Arun Lal Srivastav

Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.

Affiliations and expertise

Chitkara University, Himachal Pradesh, Solan, India

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Physical Sciences & Engineering

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Computer Vision and Machine Intelligence for Renewable Energy Systems

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Ashutosh Kumar Dubey

Abhishek Kumar

Umesh Chandra Pati

Fausto Pedro Garcia Marquez

Vicente García-Díaz

Arun Lal Srivastav

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