Green Machine Learning and Big Data for Smart Grids
Practices and Applications
- 1st Edition - November 1, 2024
- Editors: V. Indragandhi, R. Elakkiya, V. Subramaniyaswamy
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 8 9 5 1 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 8 9 5 2 - 1
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 innova… Read more
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Request a sales quoteGreen 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.
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.
1. Introduction to Green Machine and Machine Learning in Smart Grids
2. Characteristics and Essential Technologies of Green Machine Learning in the Energy Sector
3. Smart Grid Stability Prediction through Big Data Analytics
4. Descriptive, Predictive, Prescriptive and Diagnostic Analytical Models for Managing Power Systems
5. Integrating Green Machine Learning and Big Data Framework for Renewable Energy Grids
6. Green Machine Learning with Big Data for Grid Operations
7. Big Data Green Machine Learning for Smart Metering
8. Analysis and Real-time Implementation of Power Line Disturbances Test in Smart Grids
9. Analysis and Implementation of Power Optimizer Using Sliding Mode Control enabled String Inverter for Renewable Applications
10. Smart Edge Devices for Electric Grid Computing
11. Combined Flyback Converter and Forward Converter Based Active Cell Balancing in Lithium-Ion Battery Cell for Smart Electric Vehicle Application
12. Predictive Modelling in Asset and Workforce Management
13. Sustainability Consideration of Smart Grid with Big Data Analytics in Social, Economic, Technical and Policy Aspects
14. Real-Time of Big Data and Analytics in Smart Grid and Energy Management Applications
15. Challenges and Future Directions
2. Characteristics and Essential Technologies of Green Machine Learning in the Energy Sector
3. Smart Grid Stability Prediction through Big Data Analytics
4. Descriptive, Predictive, Prescriptive and Diagnostic Analytical Models for Managing Power Systems
5. Integrating Green Machine Learning and Big Data Framework for Renewable Energy Grids
6. Green Machine Learning with Big Data for Grid Operations
7. Big Data Green Machine Learning for Smart Metering
8. Analysis and Real-time Implementation of Power Line Disturbances Test in Smart Grids
9. Analysis and Implementation of Power Optimizer Using Sliding Mode Control enabled String Inverter for Renewable Applications
10. Smart Edge Devices for Electric Grid Computing
11. Combined Flyback Converter and Forward Converter Based Active Cell Balancing in Lithium-Ion Battery Cell for Smart Electric Vehicle Application
12. Predictive Modelling in Asset and Workforce Management
13. Sustainability Consideration of Smart Grid with Big Data Analytics in Social, Economic, Technical and Policy Aspects
14. Real-Time of Big Data and Analytics in Smart Grid and Energy Management Applications
15. Challenges and Future Directions
- No. of pages: 400
- Language: English
- Edition: 1
- Published: November 1, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780443289514
VI
V. Indragandhi
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.
Affiliations and expertise
Associate Professor, School of Electrical Engineering, VIT University, IndiaRE
R. Elakkiya
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.
Affiliations and expertise
Assistant Professor, Department of Computer Science, Birla Institute of Technology & Science, DubaiVS
V. Subramaniyaswamy
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 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).
Affiliations and expertise
Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.