R. Elakkiya – författare
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2 produkter
2 produkter
1 967 kr
Skickas inom 10-15 vardagar
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.Packages core concepts of green machine learning and smart grids in a clear, understandable wayIncludes real-world, practical applications and case studies for replication and innovative solution developmentIntroduces readers with a range of expertise to best practices and the latest technological advances
Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure
Häftad, Engelska, 2026
1 906 kr
Kommande
Artificial Intelligence and Data Science in Electric Vehicle Technology and Infrastructure offers a comprehensive exploration of how AI and data science are revolutionizing the electric vehicle (EV) industry. It guides readers through the basic concepts of EV technology and explains how machine learning and blockchain optimize battery management, predictive maintenance, and secure fault detection. The book highlights cutting-edge techniques like sensor fusion and computer vision for autonomous driving, alongside real-time analytics and edge computing for low-latency AI applications. It also covers intelligent charging infrastructure, route optimization, and renewable energy integration and shares insights into cybersecurity, business models, and demand forecasting, complemented by practical case studies.This book is a useful resource for researchers, scientists, advanced students, software engineers, data scientists, R&D professionals, and other industrial personnel working at the intersection of computer science, electrical engineering, artificial intelligence, data science, and machine learning with an interest in advancing AI and ML applications in electric vehicle technologies.Demonstrates how AI algorithms improve battery management, energy use, and vehicle performance to tackle EV reliability and efficiency issuesExplains how predictive analytics leverage data science and machine learning to prevent vehicle malfunctions, minimizing downtime and reducing maintenance costsShowcases the development of smart charging infrastructure that utilizes data analysis to optimize energy distribution and significantly cut charging timesDiscusses the role of AI and data science in advancing autonomous driving capabilities, enhancing safety and operational efficiency in transportationHighlights innovative, data-driven solutions for sustainable energy, aiding in reducing carbon emissions and promoting environmentally friendly EV technologies