Computer Vision and Machine Intelligence for Renewable Energy Systems

Herausgeber: Dubey, Ashutosh Kumar; Srivastav, Arun Lal; García-Díaz, Vicente; Garcia Marquez, Fausto Pedro; Pati, Umesh Chandra; Kumar, Abhishek

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 388
• Erscheinungstermin: 25. September 2024
• Englisch
• Gewicht: 450g
• ISBN-13: 9780443289477
• ISBN-10: 0443289476
• Artikelnr.: 70071174

Inhaltsangabe

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