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.

Key Features

Explores cutting-edge intelligent techniques and their implications for future energy systems development

Opens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and more

Includes a range of case studies that provide insights into the challenges and solutions in real-world applications