Artificial Intelligence Empowered Smart Energy Systems

by Qiang Yang, Gang Huang

Overview

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.

Product Details

ISBN-13:	9781394253616
Publisher:	Wiley
Publication date:	01/15/2026
Series:	IEEE Press Series on Power and Energy Systems
Pages:	240
Product dimensions:	6.50(w) x 1.50(h) x 9.50(d)

About the Author

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.

Table of Contents

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