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.
1. Computer Vision and Machine Intelligence for Renewable Energy Systems - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. About the series - p. v 6. About the series editors - p. vii 7. Table of Contents - p. viii 8. List of contributors - p. xvii 9. Chapter 1 An overview of renewable energy sources: technologies, applications and role of artificial intelligence - p. 3 10. Chapter 2 Artificial intelligence for renewable energy strategies and techniques - p. 17 11. Chapter 3 Computer vision-based regression techniques for renewable energy: predicting energy output and performance - p. 41 12. Chapter 4 Utilization of computer vision and machine learning for solar power prediction - p. 67 13. Chapter 5 Exploring data-driven multivariate statistical models for the prediction of solar energy - p. 85 14. Chapter 6 Solar energy generation and power prediction through computer vision and machine intelligence - p. 103 15. Chapter 7 A machine intelligence model based on random forest for data related renewable energy from wind farms in Brazil - p. 127 16. Chapter 8 Bioenergy prediction using computer vision and machine intelligence: modeling and optimization of bioenergy production - p. 141 17. Chapter 9 Artificial intelligence and machine intelligence: modeling and optimization of bioenergy production - p. 163 18. Chapter 10 Advancing bioenergy: leveraging artificial intelligence for efficient production and optimization - p. 177 19. Chapter 11 Image acquisition and processing techniques for crucial component of renewable energy technologies: mapping of rare earth element-bearing peralkaline granites - p. 187 20. Chapter 12 Energy storage using computer vision: control and optimization of energy storage - p. 223 21. Chapter 13 Classification techniques for renewable energy: identifying renewable energy sources and features - p. 241 22. Chapter 14 Machine learning in renewable energy: classification techniques for identifying sources and features - p. 281 23. Chapter 15 Advancing the frontier: hybrid renewable energy technologies for sustainable power generation - p. 295 24. Chapter 16 Transfer learning for renewable energy: fine-tuning and domain adaptation - p. 305 25. Chapter 17 Exploring the artificial intelligence in renewable energy: a bibliometric study using R Studio and VOSviewer - p. 317 26. Chapter 18 Future directions of computer vision and AI for renewable energy: trends and challenges in renewable energy research and applications - p. 335 27. Index - p. 363
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.
1. Computer Vision and Machine Intelligence for Renewable Energy Systems - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright - p. iv 5. About the series - p. v 6. About the series editors - p. vii 7. Table of Contents - p. viii 8. List of contributors - p. xvii 9. Chapter 1 An overview of renewable energy sources: technologies, applications and role of artificial intelligence - p. 3 10. Chapter 2 Artificial intelligence for renewable energy strategies and techniques - p. 17 11. Chapter 3 Computer vision-based regression techniques for renewable energy: predicting energy output and performance - p. 41 12. Chapter 4 Utilization of computer vision and machine learning for solar power prediction - p. 67 13. Chapter 5 Exploring data-driven multivariate statistical models for the prediction of solar energy - p. 85 14. Chapter 6 Solar energy generation and power prediction through computer vision and machine intelligence - p. 103 15. Chapter 7 A machine intelligence model based on random forest for data related renewable energy from wind farms in Brazil - p. 127 16. Chapter 8 Bioenergy prediction using computer vision and machine intelligence: modeling and optimization of bioenergy production - p. 141 17. Chapter 9 Artificial intelligence and machine intelligence: modeling and optimization of bioenergy production - p. 163 18. Chapter 10 Advancing bioenergy: leveraging artificial intelligence for efficient production and optimization - p. 177 19. Chapter 11 Image acquisition and processing techniques for crucial component of renewable energy technologies: mapping of rare earth element-bearing peralkaline granites - p. 187 20. Chapter 12 Energy storage using computer vision: control and optimization of energy storage - p. 223 21. Chapter 13 Classification techniques for renewable energy: identifying renewable energy sources and features - p. 241 22. Chapter 14 Machine learning in renewable energy: classification techniques for identifying sources and features - p. 281 23. Chapter 15 Advancing the frontier: hybrid renewable energy technologies for sustainable power generation - p. 295 24. Chapter 16 Transfer learning for renewable energy: fine-tuning and domain adaptation - p. 305 25. Chapter 17 Exploring the artificial intelligence in renewable energy: a bibliometric study using R Studio and VOSviewer - p. 317 26. Chapter 18 Future directions of computer vision and AI for renewable energy: trends and challenges in renewable energy research and applications - p. 335 27. Index - p. 363
