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Intelligent Learning Approaches for Renewable and Sustainable Energy

By: (Edited by) Alexander Micallef , (Edited by) Denmark) Guerrero Josep M. , (Edited by) Pankaj Gupta , (Edited by) India) Kandari Delhi IGDTUW Ritu in Renewable Energy

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Ksh 24,850.00

Format: Paperback or Softback

ISBN-10: 0443158061

ISBN-13: 9780443158063

Publisher: Elsevier - Health Sciences Division

Imprint: Elsevier - Health Sciences Division

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Feb 27th, 2024

Print length: 314 Pages

Weight: 510 grams

Dimensions (height x width x thickness): 15.10 x 22.80 x 1.90 cms

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Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy. Sections introduce intelligent learning approaches and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. Other sections provide detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, optimization, and more. This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence.
Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy. Sections introduce intelligent learning approaches and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. Other sections provide detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, optimization, and more.

This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence.

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