Artificial Intelligence in Energy Systems

Zhuang Tian, Daming Zhou

• Publisher: Academic Press
• Year: 2026
• eBook ISBN:
  9780443445538
• Topic: Physical Sciences
• Subtopic: Energy

Al-Driven Intelligent Optimization and Synergistic Integration of Multi-Energy Systems introduces advanced artificial intelligence methods in the operations, management, and performance of renewable energy systems, focusing on wind energy, solar energy, and hydrogen systems. The book addresses key problems such as low accuracy and efficiency in traditional wind power system modeling and control, challenges in wind energy resource prediction and intelligent scheduling, intelligent fault management of wind and hydrogen energy systems, complex scheduling and energy management challenges of multi-energy complementary systems when connected to the grid, and scheduling and reliability challenges in multi-energy system grid connection. Real-world applications and case studies are used throughout to help readers integrate academic research with practical engineering applications, to enhance energy system design and management. This latest volume in the Elsevier Wind Energy Engineering Series is of interest to all those who are interested in the integration of AI in the operation of complex energy systems, including researchers, students, faculty, engineers, practitioners, and policy makers. - Introduces artificial intelligence (AI) methods in the operation, management, and optimization of renewable energy systems - Details comprehensive and systematic knowledge on the latest developments and future trends in modern energy systems - Focuses on the integration of wind energy, solar energy, and hydrogen in power grids - Provides systematic theoretical and practical guidance for practitioners to improve the efficiency and the reliability of renewable energy systems - Includes recent technology developments, real-world applications and case studies in the design and management of modern energy systems

Table of contents

• Title of Book
• Chapter One: AI-based multi-energy grid management and load control strategies
• Chapter Two: Synergistic AI-driven coordination and predictive energy management in hybrid multi-energy microgrids using the deep synergistic coordination optimization framework
• Chapter Three: Nature-inspired frameworks for wind energy system optimization
• Chapter Four: Deep reinforcement learning-based optimal multi-energy dispatch in a droop-controlled islanded microgrid
• Chapter Five: Intelligent optimization-based hierarchical predictive dispatch for renewable microgrids with hybrid energy storage
• Chapter Six: Advanced nonlinear control strategies for efficient energy management in resilient DC microgrids
• Chapter Seven: Advanced optimization techniques for smart home energy management
• Chapter Eight: Design and simulation of an optimal microgrid for a commercial building in India: advancing towards net-zero emissions with high renewable energy penetration
• Chapter Nine: Artificial intelligence in wind turbine condition monitoring: frontiers and challenges
• Chapter Ten: Data-driven aging prediction and intelligent optimization for hydrogen fuel cell systems
• Conclusion and future perspectives
• Index