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.
1. Introduction
2. Artificial intelligence and Machine learning in Future Energy Systems (State-of-Art, future development)
3. Advanced Control of Power Electronics-based AI
4. Charging EV Market-based Deep Machine Learning
5. Deep Frequency Control of Power Grids Under Cyber Attacks
6. Application of AI in P2X Technology
7. Design of Next-Generation of 5G Data Center Power Supply based on AI
8. Smart EV Battery Charger Based on Deep Machine Learning
9. Uncertainty-Aware Management of Smart Grids Using Cloud-Based Prediction Interval
Dr. Mohammad-Hassan Khooban is an Assistant Professor in the Department of Engineering and the Director of the Power Circuits and Systems Research Group at Aarhus University in Denmark. He has authored or co-authored more than 220 publications in peer-reviewed journals (mostly IEEE) and international conferences, written three book chapters, and holds one patent. He has been involved in six national and international projects. He was identified in 2019, 2020, and 2021 by Stanford University as one of the worlds top 2% researchers in engineering. He was also ranked 16th in the list of top 30 Electronics and Electrical Engineering Scientists in Denmark in 2022. His research interests include the application of advanced control, and optimization of artificial intelligence-inspired techniques in power electronics and systems.
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