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Computer Vision and Machine Intelligence for Renewable Energy Systems
Paperback
Series: Advances in Intelligent Energy Systems
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ISBN10: 0443289476
ISBN13: 9780443289477
Publisher: Elsevier
Published: Sep 25 2024
Pages: 388
Weight: 1.98
Height: 0.80 Width: 8.50 Depth: 11.00
Language: English
ISBN13: 9780443289477
Publisher: Elsevier
Published: Sep 25 2024
Pages: 388
Weight: 1.98
Height: 0.80 Width: 8.50 Depth: 11.00
Language: English
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.
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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