ICWE16 took place August 27-31, 2023 and was a coming together of the wind engineering community. It was an important meeting, for colleagues new to the discipline, experts, researchers, and students from all over the world. The conference included (for the first time) Mini-symposia and Special Sessions mainly devoted to the focus of specialized and interdisciplinary topics.

ICWE16 took place August 27-31, 2023 and was a coming together of the wind engineering community. It was an important meeting, for colleagues new to the discipline, experts, researchers, and students from all over the world. The conference included (for the first time) Mini-symposia and Special Sessions mainly devoted to the focus of specialized and interdisciplinary topics.

This book explores current trends in the use of artificial intelligence to advance wind energy. Alongside fundamental concepts of wind dynamics and energy generation, it presents emerging technologies such as LiDAR for assessing aeolian potential, as well as Machine Learning and Digital Twin approaches applied to the operation and maintenance of wind energy systems. Recent advances in improving the accuracy of wind resource assessment and wind flow characterization are also discussed.This book further examines how knowledge of offshore wind structure dynamics supports the development of data‑driven predictive models. In particular, it highlights advances in the design and optimized maintenance of wind energy converters enabled by Machine Learning. Today, timely prediction of system response and performance—based on high‑quality monitoring and inspection data—is a key game changer. Digital Twin concepts are therefore employed to bridge the gap between numerical models and physical assets, integrating measurements that are difficult to obtain using traditional tools. From this perspective, AI‑based Digital Twin prototypes offer a promising solution to optimize and control wind energy systems by integrating monitoring, inspection, and Machine Learning data, providing new insights into the condition of wind energy infrastructure.

ICWE16 took place August 27-31, 2023 and was a coming together of the wind engineering community. It was an important meeting, for colleagues new to the discipline, experts, researchers, and students from all over the world. The conference included (for the first time) Mini-symposia and Special Sessions mainly devoted to the focus of specialized and interdisciplinary topics.

ICWE16 took place August 27-31, 2023 and was a coming together of the wind engineering community. It was an important meeting, for colleagues new to the discipline, experts, researchers, and students from all over the world. The conference included (for the first time) Mini-symposia and Special Sessions mainly devoted to the focus of specialized and interdisciplinary topics.

This book explores current trends in the use of artificial intelligence to advance wind energy. Alongside fundamental concepts of wind dynamics and energy generation, it presents emerging technologies such as LiDAR for assessing aeolian potential, as well as Machine Learning and Digital Twin approaches applied to the operation and maintenance of wind energy systems. Recent advances in improving the accuracy of wind resource assessment and wind flow characterization are also discussed.This book further examines how knowledge of offshore wind structure dynamics supports the development of data‑driven predictive models. In particular, it highlights advances in the design and optimized maintenance of wind energy converters enabled by Machine Learning. Today, timely prediction of system response and performance—based on high‑quality monitoring and inspection data—is a key game changer. Digital Twin concepts are therefore employed to bridge the gap between numerical models and physical assets, integrating measurements that are difficult to obtain using traditional tools. From this perspective, AI‑based Digital Twin prototypes offer a promising solution to optimize and control wind energy systems by integrating monitoring, inspection, and Machine Learning data, providing new insights into the condition of wind energy infrastructure.