Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of “green” machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests.
Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation.
"Sinopsis" puede pertenecer a otra edición de este libro.
Dr. V. Indragandhi obtained a PhD from Anna University, Chennai, and is currently employed by VIT as a Professor at the School of Electrical Engineering. She has engaged in teaching and research work for the past 15 years, with a focus on power electronics and renewable energy systems. She has published articles in high-impact factor journals, holds 4 patents to her name, and is a prolific book author/editor for Wiley, Elsevier, and MDPI. She has successfully organized many international conferences and workshops, partnering with leading universities around the world. Recently, she has been engaged as co-PI on a joint research project with Teesside University, funded by the UK Royal Academy of Engineering.
Dr. R. Elakkiya is an Assistant Professor in the Department of Computer Science, Birla Institute of Technology & Science, Pilani, Dubai Campus. She received her PhD from Anna University, Chennai, in 2018. She secured the University First Rank and was awarded the Gold Medal during master’s in software engineering from CEG Campus, Anna University, Chennai. She won the iDEX - DISC 4 challenge and received the grant award from DIO, DRDO in 2021 and Young Achiever Award from INSc in 2019. She had received many extra-mural funded projects from various government and non-government agencies and served as Machine Learning and Data Analytics Consultant and delivered many products to different industry verticals. She is Member of the Association of Computing Machinery and Lifetime Member of International Association of Engineers.
Dr V. Subramaniyaswamy is currently working as a Professor in the School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. In total, he has 18 years of experience in academia. He has published papers in reputed international journals and conferences and filed multiple patents. His technical competencies lie in recommender systems, Artificial Intelligence, the Internet of Things, reinforcement learning, big data analytics, and cognitive analytics. He has edited Electric Motor Drives and their Applications, with Simulation Practice (Elsevier: 2022, ISBN: 9780323911627), among other books.
"Sobre este título" puede pertenecer a otra edición de este libro.
GRATIS gastos de envío desde Alemania a España
Destinos, gastos y plazos de envíoEUR 11,56 gastos de envío desde Reino Unido a España
Destinos, gastos y plazos de envíoLibrería: Buchpark, Trebbin, Alemania
Condición: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 43027229/3
Cantidad disponible: 1 disponibles
Librería: Revaluation Books, Exeter, Reino Unido
Paperback. Condición: Brand New. 400 pages. 9.00x6.00x9.00 inches. In Stock. This item is printed on demand. Nº de ref. del artículo: __0443289514
Cantidad disponible: 2 disponibles
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
Condición: new. Questo è un articolo print on demand. Nº de ref. del artículo: HFZ7WBZR5H
Cantidad disponible: Más de 20 disponibles
Librería: CitiRetail, Stevenage, Reino Unido
Paperback. Condición: new. Paperback. Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of green machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests.Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9780443289514
Cantidad disponible: 1 disponibles
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Condición: New. Nº de ref. del artículo: 47844470-n
Cantidad disponible: Más de 20 disponibles
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Condición: New. In. Nº de ref. del artículo: ria9780443289514_new
Cantidad disponible: Más de 20 disponibles
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Condición: New. Nº de ref. del artículo: 47844470-n
Cantidad disponible: Más de 20 disponibles
Librería: Grand Eagle Retail, Mason, OH, Estados Unidos de America
Paperback. Condición: new. Paperback. Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of green machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests.Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9780443289514
Cantidad disponible: 1 disponibles
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 47844470
Cantidad disponible: Más de 20 disponibles
Librería: GreatBookPricesUK, Woodford Green, Reino Unido
Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 47844470
Cantidad disponible: Más de 20 disponibles