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. The book begins by introducing the
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. The second section of the book provides detailed coverage
of intelligent learning techniques as applied to key areas of
renewable and sustainable energy, including forecasting, supply and
demand, integration, energy management, and optimization, supported
by case studies, figures, schematics, and references. 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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