Computer Vision and Machine Intelligence for Renewable Energy Systems
Published on 20. September 2024
300 pages
978-0-443-28948-4 (ISBN)
Description
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.
- Provides a sorely needed primer on the opportunities of computer vision techniques for renewable energy systems
- Builds knowledge and tools in a systematic manner, from fundamentals to advanced applications
- Includes dedicated chapters with case studies and applications for each sustainable energy source
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Ashutosh Kumar Dubey | Abhishek Kumar | Umesh Chandra Pati
Computer Vision and Machine Intelligence for Renewable Energy Systems
Book
09/2024
Elsevier
€178.50
Shipment within 15-20 days
Content
Part I Fundamentals of computer vision and machine learning for renewable energy systems1. An overview of renewable energy sources: technologies, applications and role of artificial intelligence2. Artificial intelligence for renewable energy strategies and techniques3. Computer vision-based regression techniques for renewable energy: predicting energy output and performance4. Utilization of computer vision and machine learning for solar power prediction5. Exploring data-driven multivariate statistical models for the prediction of solar energy6. Solar energy generation and power prediction through computer vision and machine intelligencePart II Computer vision techniques for renewable energy systems7. A machine intelligence model based on random forest for data-related renewable energy from wind farms in Brazil8. Bioenergy prediction using computer vision and machine intelligence: modeling and optimization of bioenergy production9. Artificial intelligence and machine intelligence: modeling and optimization of bioenergy production10. Advancing bioenergy: leveraging artificial intelligence for efficient production and optimization11. Image acquisition and processing techniques for crucial component of renewable energy technologies: mapping of rare earth element-bearing peralkaline granites12. Energy storage using computer vision: control and optimization of energy storage13. Classification techniques for renewable energy: identifying renewable energy sources and features14. Machine learning in renewable energy: classification techniques for identifying sources and features15. Advancing the frontier: hybrid renewable energy technologies for sustainable power generation16. Transfer learning for renewable energy: fine-tuning and domain adaptationPart III Renewable energy sources and computer vision opportunities17. Exploring the artificial intelligence in renewable energy: a bibliometric study using R Studio and VOSviewer18. Future directions of computer vision and AI for renewable energy: trends and challenges in renewable energy research and applications
