Elsevier Artificial Intelligence in Energy Systems (Wind Energy Engineering)

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