Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed…mehr
Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing.
Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.
Sanjeevikumar Padmanaban is a Full Professor in Electrical Power Engineering with the Department of Electrical Engineering, Information Technology, and Cybernetics of the University of South-Eastern Norway, Norway. He has over a decade of academic and teaching experience, including Associate/Assistant Professorships at the University of Johannesburg, South Africa (2016-2018), Aalborg University, Denmark (2018-2021) and the CTIF Global Capsule Laboratory at Aarhus University, Denmark (2021-present). Prof. Padmanaban received a lifetime achievement award from Marquis Who's Who - USA 2017 for contributing to power electronics and renewable energy research, and was listed among the world's top 2% of scientists by Stanford University, USA in 2019.Dr Jens Bo Holm-Nielsen is Associate Professor and Head of Center for Bioenergy and Green Engineering, Aalborg University, Aalborg, Denmark
Inhaltsangabe
1. Introduction: Artificial intelligence and Smart Power Systems 2. Integrated Architecture of Machine Learning and Smart Power System 3. Challenges and issues in Power Systems 4. Load shedding and related techniques to solve the power crisis 5. ML in distributed energy resources and prosumers market 6. ML-based electricity demand prediction 7. Applying ML to determine the power outage 8. Predictive and Prescriptive analytics for component fault detection 9. Balancing demand and supply of electricity with machine learning 10. Preventive care of grid hardware with anomaly detection 11. AI-based Smart feeder monitoring system 12. Algorithms for buss loss and reliability indices calculations 13. ML-based security solutions to protect smart power systems 14. Cyber-attacks ,security data detection, and critical loads in the power systems 15. Integration of AI/ML into the energy sector: Case Studies
1. Introduction: Artificial intelligence and Smart Power Systems 2. Integrated Architecture of Machine Learning and Smart Power System 3. Challenges and issues in Power Systems 4. Load shedding and related techniques to solve the power crisis 5. ML in distributed energy resources and prosumers market 6. ML-based electricity demand prediction 7. Applying ML to determine the power outage 8. Predictive and Prescriptive analytics for component fault detection 9. Balancing demand and supply of electricity with machine learning 10. Preventive care of grid hardware with anomaly detection 11. AI-based Smart feeder monitoring system 12. Algorithms for buss loss and reliability indices calculations 13. ML-based security solutions to protect smart power systems 14. Cyber-attacks ,security data detection, and critical loads in the power systems 15. Integration of AI/ML into the energy sector: Case Studies
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