Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm

Part I: Modern Energy System Challenges: Fundamental Methodologies, Opportunities, and Solutions<br>1. Challenges of renewable energy systems and the artificial intelligence opportunity<br>2. Fundamentals of swarm-based algorithms<br>3. Fundamentals of evolution-based algorithms<br>4. Fundamentals of physics- and human-based algorithms<br>5. Fundamentals of the Barnacles Mating Optimizer<br>6. The Evolutionary Mating Algorithm: principles and applications<br>7. Deep learning approaches<br>7.i. Supervised learning with feedforward neural networks (FFNN)<br>7.ii. Other deep learning families<br><br>Part II: Applications for Renewable Energy Systems<br>8. State of charge (SOC) estimation in electric vehicles using deep learning feedforward neural networks<br>9. Hybrid of metaheuristic learning with deep learning in battery management of electric vehicles<br>10. Optimal reactive power dispatch using the Barnacle Mating Optimizer<br>11. Optimal power flow solutions enhanced by the Evolutionary Mating Algorithm<br>12. Renewable energy power forecasting, enhanced by hybrid Barnacle Mating Optimizer-Evolutionary Mating Algorithm deep learning<br>12.i. Solar power<br>12.ii. Wind power