Applications of Deep Machine Learning in Future Energy Systems

Applications of Deep Machine Learning in Future Energy Systems

Khooban, Mohammad-Hassan

171,60 €(IVA inc.)

Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation before laying out current AI approaches and limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply. An essential tool for the management, control, and modelling of future energy systems, this book maps a practical path towards AI capable of supporting sustainable energy. Clarifies the current state and future trends of energy system machine learning and the pitfalls facing our transitioning systemsProvides guidance on 3rd-generation AI tools for meeting the challenges of modeling and control in modern energy systemsIncludes case studies and practical examples of potential applications to inspire and inform researchers and industry developers INDICE: 1. Introduction2. Artificial intelligence and Machine learning in Future Energy Systems (State-of-Art, future development)3. Advanced Control of Power Electronics-based AI4. Charging EV Market-based Deep Machine Learning5. Deep Frequency Control of Power Grids Under Cyber Attacks6. Application of AI in P2X Technology7. Design of Next-Generation of 5G Data Center Power Supply based on AI8. Smart EV Battery Charger Based on Deep Machine Learning9. Uncertainty-Aware Management of Smart Grids Using Cloud-Based Prediction Interval

  • ISBN: 978-0-443-21432-5
  • Editorial: Elsevier
  • Encuadernacion: Rústica
  • Páginas: 250
  • Fecha Publicación: 26/07/2024
  • Nº Volúmenes: 1
  • Idioma: Inglés