{"product_id":"9781119761693","title":"Artificial Intelligence for Renewable Energy Systems","description":"\u003ch3\u003eArtificial Intelligence and Soft Computing for Industrial Transformation\u003c\/h3\u003e\u003ch1\u003eArtificial Intelligence for Renewable Energy Systems\u003c\/h1\u003e\u003ch3\u003eAjay Kumar Vyas | S. Balamurugan | Kamal Kant Hiran | Harsh S. Dhiman\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cb\u003eARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS\u003c\/b\u003e \u003cp\u003e\u003cb\u003eRenewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eDue to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e Audience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information \u0026amp; communication technology.\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e \u003cp\u003e\u003cb\u003e Ajay Kumar Vyas, PhD\u003c\/b\u003e is an assistant professor at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has authored several research papers in peer-reviewed international journals and conferences, three books, and two Indian patents.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e S. Balamurugan, PhD\u003c\/b\u003e SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals\/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e Kamal Kant Hiran, PhD\u003c\/b\u003e is an assistant professor at the School of Engineering, Sir Padampat Singhania University (SPSU), Udaipur, Rajasthan, India, as well as a research fellow at the Aalborg University, Copenhagen, Denmark. He has published more than 35 scientific research papers in SCI\/Scopus\/Web of Science and IEEE Transactions Journal, conferences, two Indian patents, one Australian patent granted, and nine books. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003e Harsh S. Dhiman, PhD\u003c\/b\u003e is an assistant professor in the Department of Electrical Engineering at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has published 12 SCI-indexed journal articles and two books, and his research interests include hybrid operation of wind farms, hybrid wind forecasting techniques, and anomaly detection in wind turbines. \u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e02 March 2022\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eWiley\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eWiley-Scrivener\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9781119761693\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\u003e272\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight (oz): \u003c\/td\u003e\n\u003ctd\u003e16.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":44380593094796,"sku":"9781119761693","price":198.86,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781119761693_c8a6e723-eb40-43e5-a897-0702a50981f5.jpg?v=1780105739","url":"https:\/\/lateknightbooks.com\/products\/9781119761693","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}