Non-Fiction Books:

Green Machine Learning and Big Data for Smart Grids

Practices and Applications
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$494.00
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Description

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 utilising 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. Part of the cutting-edge series ‘Advances in Intelligent Energy Systems’, ‘Green Machine Learning and Big Data for Smart Grids’ provides researchers, students, and industry practitioners with an understanding of the complex interactions and opportunities between data science and sustainable energy systems.

Author Biography:

V. Indragandhi is an Associate Professor at the School of Electrical Engineering of VIT University, India. She has been teaching and researching for the past 10 years in the area of Power Electronics and Renewable Energy Systems. She has authored almost 200 research articles in leading peer-reviewed international journals, and filed 3 patents. She has previously edited two books with Elsevier focused on electronics engineering and simulation. R. Elakkiya is an Assistant Professor in the Department of Computer Science, at Birla Institute of Technology and Science, Dubai. She has acted as a machine learning and data analytics consultant, delivering many solutions to a variety of industries. During the COVID-19 pandemic, she developed an Artificial Intelligence-based screening tool for preliminary screening and deployed it as an open-source tool in three Government Hospitals in Tamilnadu, India. She holds three patents, has published two books, and has authored more than 50 research articles in reputable international journals on topics including AI enhancement of conductor reliability and optimization algorithms for machine learning. V. Subramaniyaswamy is currently working as a Professor in the School of Computing, SASTRA Deemed University, India. In total, he has 18 years of experience in academia. He has published more than 120 papers in reputed international journals and conferences, and filed 5 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 two books, including Electric Motor Drives and their Applications, with Simulation Practice (Elsevier: 2022, ISBN: 9780323911627).
Release date NZ
November 1st, 2024
Audience
  • Professional & Vocational
Contributors
  • Edited by R Elakkiya
  • Edited by V. Indragandhi
  • Edited by V. Subramaniyaswamy
Pages
400
ISBN-13
9780443289514
Product ID
38739360

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