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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Dr. Baniotopoulos is Professor at the Leibniz University Hanover, Germany, Professor em at the University of Birmingham, UK, Professor hc at the Jordan University of Science and Technology, Jordan, and Professor em at Aristotle University of Thessaloniki, Greece. He has coordinated teaching and research on Structural Engineering topics, with a focus on Wind Energy structures and sustainable energy systems, for more than 40 years. As research project leader, he successfully carried out a plethora of research projects on relevant topics funded by the EU, and national and international Organisations.
Dr. Marino is an associate professor of Solid and Structural Mechanics at the University of Florence, Italy. His research interests focus on Computational Mechanics, Mechanics of Materials, and the Structural Dynamics of Offshore Wind Energy Systems. He currently serves as an associate editor of the Journal of Offshore Mechanics and Arctic Engineering (ASME).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. N° de réf. du vendeur 3258971119
Quantité disponible : Plus de 20 disponibles
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 276 pp. Englisch. N° de réf. du vendeur 9783032288141
Quantité disponible : 2 disponible(s)
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Hardcover. Etat : new. Hardcover. 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 datadriven 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 performancebased on highquality monitoring and inspection datais 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, AIbased 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. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N° de réf. du vendeur 9783032288141
Quantité disponible : 1 disponible(s)
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Hardcover. Etat : new. Hardcover. 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 datadriven 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 performancebased on highquality monitoring and inspection datais 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, AIbased 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783032288141
Quantité disponible : 1 disponible(s)
Vendeur : California Books, Miami, FL, Etats-Unis
Etat : New. N° de réf. du vendeur I-9783032288141
Quantité disponible : Plus de 20 disponibles
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783032288141
Quantité disponible : 1 disponible(s)
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 datadriven 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 performancebased on highquality monitoring and inspection datais 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, AIbased 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 292 pp. Englisch. N° de réf. du vendeur 9783032288141
Quantité disponible : 1 disponible(s)
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Hardcover. Etat : new. Hardcover. 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 datadriven 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 performancebased on highquality monitoring and inspection datais 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, AIbased 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9783032288141
Quantité disponible : 1 disponible(s)
Vendeur : Revaluation Books, Exeter, Royaume-Uni
Hardcover. Etat : Brand New. 289 pages. 6.14x0.69x9.21 inches. In Stock. N° de réf. du vendeur x-3032288142
Quantité disponible : 2 disponible(s)