Applications of Deep Machine Learning in Future Energy Systems
eBook - PDF

Applications of Deep Machine Learning in Future Energy Systems

  1. 250 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Applications of Deep Machine Learning in Future Energy Systems

About this book

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 the current AI approaches and our outstanding 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, Applications of Deep Machine Learning 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 systems - Provides guidance on 3rd-generation AI tools for meeting the challenges of modeling and control in modern energy systems - Includes case studies and practical examples of potential applications to inspire and inform researchers and industry developers

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Yes, you can access Applications of Deep Machine Learning in Future Energy Systems by Mohammad-Hassan Khooban in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Electrical Engineering & Telecommunications. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Front Cover
  2. Applications of Deep Machine Learning in Future Energy Systems
  3. Copyright Page
  4. Contents
  5. List of contributors
  6. Preface
  7. 1 Introduction
  8. 2 Artificial intelligence and machine learning in future energy systems (state-of-the-art, future development)
  9. 3 Digital twins−assisted design of next-generation DC microgrid
  10. 4 Intelligent charging station recommendations for electric vehicles in the charging market: a fuzzy−deep learning approach
  11. 5 Deep frequency control of power grids under cyber attacks
  12. 6 Application of Q-learning in stabilization of multicarrier energy systems
  13. 7 Design of next-generation of 5G data center power supply based on artificial intelligence
  14. 8 Smart EV battery charger based on deep machine learning
  15. 9 Machine learning in talkative power technology
  16. 10 Advanced control of power electronics−based machine learning
  17. 11 Multilevel energy management and optimal control system in smart cities based on deep machine learning
  18. Index
  19. Back Cover