Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications reveals the potential of innovative optimization algorithms to support sustainability in modern energy systems. This book provides a multidisciplinary foundation for the reader, with Part I breaking down fundamentals including the challenges to be addressed in renewable energy systems and detailed methodologies including swarm-, physics-, and human-based algorithms, before introducing the Barnacles Mating Optimizer and Evolutionary Mating Algorithm themselves. Part II drills deeper into examples, case studies, and applications for energy systems, offering comparative analysis with alternative tools, and providing complimentary MATLAB code using the latest Toolbox. A sandbox for readers to learn, skill-build, and develop in, ‘Intelligent Energy Systems using BMO and EMA’ provides an indispensable guide to these cutting-edge AI tools for new and experienced readers.
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. Preface - p. ix 7. Acknowledgments - p. x 8. Part I: Modern energy system challenges: fundamental methodologies, opportunities, and solutions - p. P1 9. 1. Challenges of renewable energy systems and the artificial intelligence opportunity - p. 3 10. 2. Fundamentals of evolutionary-based algorithms - p. 25 11. 3. Fundamentals of swarm-based algorithms - p. 41 12. 4. Fundamentals of physics and human-based algorithms - p. 59 13. 5. Fundamentals of the Barnacles Mating Optimizer - p. 77 14. 6. The Evolutionary Mating Algorithm: principles and applications - p. 95 15. 7. Deep learning approaches - p. 109 16. Part II: Applications for renewable energy systems - p. P142 17. 8. State of charge estimation in electric vehicles using deep learning feed-forward neural networks - p. 143 18. 9. Hybrid of metaheuristic learning with deep learning in battery management of electric vehicles - p. 169 19. 10. Optimal reactive power dispatch using the Barnacle Mating Optimizer - p. 203 20. 11. Optimal power flow solutions enhanced by the Evolutionary Mating Algorithm - p. 235 21. 12. Renewable energy power forecasting enhanced by hybrid Barnacle Mating Optimizer/Evolutionary Mating Algorithm- Deep Learning - p. 269 22. Index - p. 329
Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications reveals the potential of innovative optimization algorithms to support sustainability in modern energy systems. This book provides a multidisciplinary foundation for the reader, with Part I breaking down fundamentals including the challenges to be addressed in renewable energy systems and detailed methodologies including swarm-, physics-, and human-based algorithms, before introducing the Barnacles Mating Optimizer and Evolutionary Mating Algorithm themselves. Part II drills deeper into examples, case studies, and applications for energy systems, offering comparative analysis with alternative tools, and providing complimentary MATLAB code using the latest Toolbox. A sandbox for readers to learn, skill-build, and develop in, ‘Intelligent Energy Systems using BMO and EMA’ provides an indispensable guide to these cutting-edge AI tools for new and experienced readers.
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. Preface - p. ix 7. Acknowledgments - p. x 8. Part I: Modern energy system challenges: fundamental methodologies, opportunities, and solutions - p. P1 9. 1. Challenges of renewable energy systems and the artificial intelligence opportunity - p. 3 10. 2. Fundamentals of evolutionary-based algorithms - p. 25 11. 3. Fundamentals of swarm-based algorithms - p. 41 12. 4. Fundamentals of physics and human-based algorithms - p. 59 13. 5. Fundamentals of the Barnacles Mating Optimizer - p. 77 14. 6. The Evolutionary Mating Algorithm: principles and applications - p. 95 15. 7. Deep learning approaches - p. 109 16. Part II: Applications for renewable energy systems - p. P142 17. 8. State of charge estimation in electric vehicles using deep learning feed-forward neural networks - p. 143 18. 9. Hybrid of metaheuristic learning with deep learning in battery management of electric vehicles - p. 169 19. 10. Optimal reactive power dispatch using the Barnacle Mating Optimizer - p. 203 20. 11. Optimal power flow solutions enhanced by the Evolutionary Mating Algorithm - p. 235 21. 12. Renewable energy power forecasting enhanced by hybrid Barnacle Mating Optimizer/Evolutionary Mating Algorithm- Deep Learning - p. 269 22. Index - p. 329
