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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Artificial intelligence and machine learning in future energy systems (state-of-the-art, future development) - p. 3 3. Chapter 3 Digital twins−assisted design of next-generation DC microgrid - p. 31 4. Chapter 4 Intelligent charging station recommendations for electric vehicles in the charging market: a fuzzy−deep learning approach - p. 63 5. Chapter 5 Deep frequency control of power grids under cyber attacks - p. 93 6. Chapter 6 Application of Q-learning in stabilization of multicarrier energy systems - p. 127 7. Chapter 7 Design of next-generation of 5G data center power supply based on artificial intelligence - p. 155 8. Chapter 8 Smart EV battery charger based on deep machine learning - p. 171 9. Chapter 9 Machine learning in talkative power technology - p. 197 10. Chapter 10 Advanced control of power electronics−based machine learning - p. 239 11. Chapter 11 Multilevel energy management and optimal control system in smart cities based on deep machine learning - p. 265 12. Index - p. 315
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.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Artificial intelligence and machine learning in future energy systems (state-of-the-art, future development) - p. 3 3. Chapter 3 Digital twins−assisted design of next-generation DC microgrid - p. 31 4. Chapter 4 Intelligent charging station recommendations for electric vehicles in the charging market: a fuzzy−deep learning approach - p. 63 5. Chapter 5 Deep frequency control of power grids under cyber attacks - p. 93 6. Chapter 6 Application of Q-learning in stabilization of multicarrier energy systems - p. 127 7. Chapter 7 Design of next-generation of 5G data center power supply based on artificial intelligence - p. 155 8. Chapter 8 Smart EV battery charger based on deep machine learning - p. 171 9. Chapter 9 Machine learning in talkative power technology - p. 197 10. Chapter 10 Advanced control of power electronics−based machine learning - p. 239 11. Chapter 11 Multilevel energy management and optimal control system in smart cities based on deep machine learning - p. 265 12. Index - p. 315
