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Computer Vision and Machine Intelligence for Renewable Energy Systems

Language EnglishEnglish
Book Paperback
Book Computer Vision and Machine Intelligence for Renewable Energy Systems Ashutosh Kumar Dubey
Libristo code: 45112309
Publishers Elsevier Inc, October 2024
Computer Vision and Machine Intelligence in Renewable Energy Systems offers a practical, systemic gu... Full description
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Computer Vision and Machine Intelligence in 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 modelling, 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 in 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 systemsBuilds knowledge and tools in a systematic manner, from fundamentals to advanced applicationsIncludes dedicated chapters with case studies and applications for each sustainable energy source

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About the book

Full name Computer Vision and Machine Intelligence for Renewable Energy Systems
Language English
Binding Book - Paperback
Date of issue 2024
Number of pages 300
EAN 9780443289477
ISBN 0443289476
Libristo code 45112309
Publishers Elsevier Inc
Weight 897
Dimensions 152 x 229
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