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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Table of Contents
Section I: Introduction to intelligent learning approaches for renewable and sustainable energy1. Artificial Intelligence-based sustainability in energy
2. Machine-learning-based sustainability in energy
3. Transforming the grid: AI, ML, Renewable, Storage, EVs, and Prosumers
4. Role of intelligent techniques in large-scale integration of renewable energy
5. Variability of renewable energy generation and flexibility initiatives
Section II: Applications of intelligence learning approaches for renewable and sustainable energy
6. Intelligent learning models for renewable energy forecasting
7. Intelligent learning models for balancing supply and demand
8. Intelligent learning analysis for a flexibility energy approach towards renewable energy integration
9. Intelligent learning analysis for energy management
10. Intelligent learning approaches for demand-side controller for BIPVs integrated buildings
11. Intelligent learning approaches for single and multi-objective optimization methodology
12. Intelligent learning approaches for optimization of integrated energy systems
