Artificial Intelligence in Energy Systems

Erscheinungsjahr 2026
ISBN: 978-0-443-44552-1
Verlag: Elsevier Science

Buch, Englisch, 350 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

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.

Autoren/Hrsg.

Tian, Zhuang

Zhou, Daming

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien
• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik

Inhaltsverzeichnis

1. Artificial Intelligence in Wind Energy Systems and Future Energy Ecology. Frontiers and Synergies
2. Adaptive Control Methods of Intelligent Control Theory in Wind Energy Systems
3. Data-Driven Intelligent Forecasting and Optimization Scheduling of Wind Energy Resources
4. Intelligent Fault Diagnosis and Strategies for Hydrogen Energy Systems
5. Intelligent Energy Management and Fault Prevention in Multi-Energy Complementary Microgrids
6. Collaborative Optimization and Management Strategies in Distributed Multi-Energy Systems
7. AI-Based Multi-Energy Grid Management and Load Control Strategies
8. Intelligent Scheduling and Optimization Technologies in Multi-Energy Grid Systems
9. Coordinated Scheduling and Fault Recovery Mechanisms in Dynamic Power Grids
10. Conclusion. The Vision and Challenges of Intelligent Energy Futures

Über Autor(innen)

Tian, Zhuang
Dr. Zhuang Tian is a researcher based at Northwestern Polytechnical University, China, where he is a member of the research team working on energy system modeling, control, and energy management, covering multiple areas such as wind energy and fuel cells. Dr. Tian has published numerous articles in reputed international journals and has been involved in several book publications.

Zhou, Daming
Prof. Daming Zhou is a Full Professor at Northwestern Polytechnical University, China, and a recipient of China’s National Youth Talent Program. His primary research areas include energy system modeling, control, and energy management, with over 10 years of research in wind energy, fuel cells, and other renewable energy technologies, currently as research team leader. Prof. Zhou has published more than 40 high-impact papers, with over 1,800 citations, and has authored four monographs. He holds eight patents.