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Intelligent Management of Energy Systems provides a holistic overview of the opportunities for utilising artificial intelligence to optimize modern energy systems, including system dynamics, components, and business perspectives. This book begins with an overview of energy system dynamics, followed by detailed step-by-step breakdowns of critical artificial intelligence and machine learning techniques, as well as methods for assessing greenhouse-gas emissions. Further chapters spotlight microgrids, high-voltage testing for essential components such as circuit breakers and transformers, and perspectives from business and social disciplines. Designed for students, engineers, and researchers,
Intelligent Management of Energy Systems combines clear, foundational methodologies, technical implementation, and business perspectives: a robust toolkit for AI utilization in energy system optimization.
List of contents
1. Energy System Dynamics
2. Artificial Intelligence and Machine Learning Techniques
3. Energy and GHG Trends Analysis with Ensemble Models
4. Intelligent Energy Management in Microgrids
5. AI-Assisted Community Microgrid Energy Management
6. Artificial Intelligence in High Voltage Testing: Circuit Breakers
7. Artificial Intelligence in High Voltage Testing: Transformers
8. A Business Model for AI-based Smart Energy Systems
9. Public Perceptions and Ethical Considerations concerning AI-based Smart Energy Systems
About the author
Dr. Md Shafiullah is currently working as a faculty member in the Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) at King Fahd University of Petroleum & Minerals (KFUPM). He received a Ph.D. in Electrical Engineering (Electrical Power & Energy Systems) from KFUPM in 2018. Prior to that, he received the B.Sc. and M.Sc. degrees in Electrical & Electronic Engineering (EEE) from Bangladesh University of Engineering & Technology (BUET) in 2009 and 2013, respectively. He demonstrated his research contributions in 70+ scientific articles (peer-reviewed journals, international conference proceedings, and book chapters). His research interest includes power system fault diagnosis, grid integration of renewable energy resources, power system stability and quality analysis, and machine learning techniques. He received the best research paper awards in two different IEEE flagship conferences (ICEEICT 2014 in Bangladesh and CAIDA 2021 in Saudi Arabia).
