Artificial Intelligence Empowered Smart Energy Systems
Wiley-IEEE Press (Verlag)
978-1-394-25361-6 (ISBN)
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In Artificial Intelligence Empowered Smart Energy Systems, the editors along with a team of distinguished researchers deliver an original and comprehensive discussion of artificial intelligence enabled smart energy systems. The book offers a deep dive into AI's integration with energy, examining critical topics like renewable energy forecasting, load monitoring, fault diagnosis, resilience-oriented optimization, and efficiency-driven control.
The contributors discuss the real-world applications of AI in smart energy systems, showing you AI's transformative effects on energy landscapes. It provides practical solutions and strategies to address complicated problems in energy systems.
The book also includes:
A thorough introduction to cybersecurity, privacy, and virtual power plants
Comprehensive demonstrations of the effective leveraging of AI technologies in energy systems
Practical discussions of the potential of AI to create sustainable, efficient, and resilient energy systems
Detailed case studies and real-world examples of AI's implementation in smart energy systems
Perfect for researchers, data scientists, and policymakers, Artificial Intelligence Empowered Smart Energy Systems will also benefit graduate and senior undergraduate students in both the tech and energy industries.
Qiang Yang, PhD, is a Full Professor at the College of Electrical Engineering at Zhejiang University, China. He is a Senior Member of the IEEE and a Distinguished Member of the China Computer Federation. His primary research interests include smart energy systems, learning-based optimization and control. Gang Huang, PhD, is an Assistant Professor at the College of Electrical Engineering at Zhejiang University, China. He is a Senior Member of the IEEE and a Senior Member of the China Computer Federation. His primary research interests include artificial intelligence for power and energy systems.
Chapter 1 Machine Learning-based Applications for Cyberattack and Defense in Smart Energy Systems
Chapter 2 Enhancing Cybersecurity in Power Communication Networks: An Approach to Resilient CPPS through Communication Channel Expansion and Optimal Defense Resource Allocation
Chapter 3 Multi-Objective Real-time Control of Operating Condition Using Deep Reinforcement Learning
Chapter 4 Smart Generation Control based on Multi-agent
Chapter 5 Power System Fault Diagnosis Method under Disaster Weather Based on Random Self-regulating Algorithm
Chapter 6 Statistical Machine Learning Model for Production Simulation of Power Systems with a High Proportion of Photovoltaics1
Chapter 7 Dynamic Reconfiguration of PV-TEG Hybrid Systems via Improved Whale Optimization Algorithm
Chapter 8 Coordinating Transactive Energy and Carbon Emission Trading among Multi-Energy Virtual Power Plants for Distributed Learning
Chapter 9 Cluster-Based Heuristic Algorithm for Collection System Topology Generation of A Large-Scale Offshore Wind Farm
Chapter 10 Transmission Line Multi-fitting Detection Method Based on Implicit Space Knowledge Fusion
| Erscheint lt. Verlag | 12.5.2026 |
|---|---|
| Reihe/Serie | IEEE Press Series on Power and Energy Systems |
| Sprache | englisch |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik ► Elektrotechnik / Energietechnik | |
| ISBN-10 | 1-394-25361-3 / 1394253613 |
| ISBN-13 | 978-1-394-25361-6 / 9781394253616 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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