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Artificial Intelligence Empowered Smart Energy Systems
About this book
An illuminating and up-to-date exploration of the latest advances in AI-empowered smart energy systems
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.
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Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- List of Contributors
- About the Editors
- Foreword
- Preface
- Acknowledgments
- 1 Machine Learning-Based Applications for Cyberattack and Defense in Smart Energy Systems
- 2 Enhancing Cybersecurity in Power Communication Networks: An Approach to Resilient CPPS Through Channel Expansion and Defense Resource Allocation
- 3 Multi-Objective Real-Time Control of Operating Conditions Using Deep Reinforcement Learning
- 4 Smart Generation Control Based on Multi-Agents
- 5 Power System Fault Diagnosis Method Under Disaster Weather Based on Random Self-Regulating Algorithm
- 6 Statistical Machine Learning Model for Production Simulation of Power Systems with a High Proportion of Photovoltaics
- 7 Dynamic Reconfiguration of PV-TEG Hybrid Systems via Improved Whale Optimization Algorithm
- 8 Coordinating Transactive Energy and Carbon Emission Trading Among Multi-Energy Virtual Power Plants for Distributed Learning
- 9 Cluster-Based Heuristic Algorithm for Collection System Topology Generation of a Large-Scale Offshore Wind Farm
- 10 Transmission Line Multi-Fitting Detection Method Based on Implicit Space Knowledge Fusion
- Index
- IEEE Press Series on Power and Energy Systems
- End User License Agreement
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