Pioneering Approaches in Intelligence, Optimization, and Viability for Renewable Energy Development brings together key advances that are driving growth and development across the renewable energy sector. Innovative areas covered include machine learning, cutting-edge optimization techniques, artificial intelligence, environmental protection, economic viability, integration of blockchain technology, forecasting and predictive analytics, resilience and reliability, and energy storage systems. This book will be of interest to all those looking to understand and implement advanced techniques for renewable energy development, including researchers, advanced students, faculty, engineers, R&D, other industry professionals, and policy makers.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Chapter 1 Machine learning in the renewable energy sector - p. 1 8. Chapter 2 Optimization techniques in the renewable energy sector - p. 35 9. Chapter 3 Role of artificial intelligence in the renewable energy sector - p. 57 10. Chapter 4 Environmental protection with the renewable energy sector - p. 71 11. Chapter 5 Economic viability of the renewable energy sectors in developing countries - p. 115 12. Chapter 6 Recent trends in renewable energy management with integrated AI and blockchain - p. 157 13. Chapter 7 Forecasting and predictive analytics for renewable energy integration using machine learning and deep learning techniques - p. 169 14. Chapter 8 Resilience and reliability in renewable energy systems - p. 201 15. Chapter 9 Recent innovations in energy storage system solutions for renewable integration - p. 233 16. Chapter 10 Future trends and emerging technologies in the renewable energy sector - p. 263 17. Index - p. 331
Pioneering Approaches in Intelligence, Optimization, and Viability for Renewable Energy Development brings together key advances that are driving growth and development across the renewable energy sector. Innovative areas covered include machine learning, cutting-edge optimization techniques, artificial intelligence, environmental protection, economic viability, integration of blockchain technology, forecasting and predictive analytics, resilience and reliability, and energy storage systems. This book will be of interest to all those looking to understand and implement advanced techniques for renewable energy development, including researchers, advanced students, faculty, engineers, R&D, other industry professionals, and policy makers.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Chapter 1 Machine learning in the renewable energy sector - p. 1 8. Chapter 2 Optimization techniques in the renewable energy sector - p. 35 9. Chapter 3 Role of artificial intelligence in the renewable energy sector - p. 57 10. Chapter 4 Environmental protection with the renewable energy sector - p. 71 11. Chapter 5 Economic viability of the renewable energy sectors in developing countries - p. 115 12. Chapter 6 Recent trends in renewable energy management with integrated AI and blockchain - p. 157 13. Chapter 7 Forecasting and predictive analytics for renewable energy integration using machine learning and deep learning techniques - p. 169 14. Chapter 8 Resilience and reliability in renewable energy systems - p. 201 15. Chapter 9 Recent innovations in energy storage system solutions for renewable integration - p. 233 16. Chapter 10 Future trends and emerging technologies in the renewable energy sector - p. 263 17. Index - p. 331
