{"product_id":"9783032288141","title":"Advances in Wind Energy in the Era of Artificial Intelligence","description":"\u003ch3\u003eCISM International Centre for Mechanical Sciences\u003c\/h3\u003e\u003ch1\u003eAdvances in Wind Energy in the Era of Artificial Intelligence\u003c\/h1\u003e\u003ch3\u003eCharalampos Baniotopoulos | Enzo Marino\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eTechnology \u0026amp; Engineering \/ Power Resources \/ Alternative \u0026amp; Renewable\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan lang=\"EN-GB\" style=\"mso-ansi-language: EN-GB; mso-fareast-language: EN-IN;\"\u003eThis 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan lang=\"EN-GB\" style=\"mso-ansi-language: EN-GB; mso-fareast-language: EN-IN;\"\u003eThis 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan lang=\"EN-GB\" style=\"font-size: 12.0pt; mso-ascii-font-family: Calibri; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-font-family: Calibri; mso-bidi-font-family: Calibri; color: #002060; mso-ansi-language: EN-GB;\"\u003eDr. 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\" style=\"mso-margin-top-alt: auto; mso-margin-bottom-alt: auto; line-height: normal;\"\u003e\u003cspan lang=\"EN-GB\" style=\"font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; color: #002060; mso-ansi-language: EN-GB;\"\u003eDr. 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).\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp class=\"MsoNormal\"\u003e\u003cspan style=\"color: #002060;\"\u003e \u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e03 September 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Switzerland\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9783032288141\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003eHardback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e344\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Switzerland","offers":[{"title":"Default Title","offer_id":49471338086540,"sku":"9783032288141","price":179.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783032288141.jpg?v=1780593344","url":"https:\/\/lateknightbooks.com\/products\/9783032288141","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}