000			05039nam a22003737a 4500
003			OSt
005			20260730153238.0
008			260730b |||||||| |||| 00| 0 eng d
020			_a 9780443289477 [paperback]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
245			_aComputer vision and machine intelligence for renewable energy systems / _cvolume editors & series editors by Ashtosh Kumar Dubey, Arun Lal Srivastav, Abhishek Kumar, Umesh Chandra Pati, Fausto Pedro García Márques, Vicente García-Díaz.
260			_aUnited States : _bElsevier Inc., _cc2025.
300			_axviii, 369 pages : _billustrations (black and white) ; _c28 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
505			_aContents: List of contributors — 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: levering 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 technique 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-tunning and domain adaptation — Part III. Renewable energy sources and computer vision opportunities — 18. Future directions of computer vision and AI for renewable energy: trends and challenges in renewable energy research and applications.
520			_a"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" —Provided by the publisher
521			_aAdult
541			_xOrtega, Eric _yCollege of Computer Studies _zComputer Science
546			_aText in English
650			_aComputer.
650			_aRenewable energy,
650			_aArtificial Intelligence.
700			_aDubey, Ashtosh Kumar, _eeditor.
700			_aSrivastav, Arun Lal, _eeditor.
700			_aKumar, Abhisnek, _eeditor.
700			_aPati, Umesh Chandra, _eeditor.
700			_aMárques, Fausto Pedro García, _eeditor.
700			_aGarcía-Díaz, Vicente, _eeditor.
942			_2ddc _cBK
998			_cJanna [new] _d07/30/2026
999			_c15571 _d15571