Green Machine Learning and Big Data for Smart Grids

Dr. V. Indragandhi is a Professor in the School of Electrical Engineering, Department of Energy and Power Electronics, at Vellore Institute of Technology (VIT), Vellore. Her areas of specialization include power electronics, advanced semiconductor devices, energy storage, artificial intelligence, and electric vehicles. 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.

• 1. Introduction to Green Machine and Machine Learning in Smart Grids2. Characteristics and Essential Technologies of Green Machine Learning in the Energy Sector3. Smart Grid Stability Prediction through Big Data Analytics4. Descriptive, Predictive, Prescriptive and Diagnostic Analytical Models for Managing Power Systems5. Integrating Green Machine Learning and Big Data Framework for Renewable Energy Grids6. Green Machine Learning with Big Data for Grid Operations7. Big Data Green Machine Learning for Smart Metering8. Analysis and Real-time Implementation of Power Line Disturbances Test in Smart Grids9. Analysis and Implementation of Power Optimizer Using Sliding Mode Control enabled String Inverter for Renewable Applications10. Smart Edge Devices for Electric Grid Computing11. Combined Flyback Converter and Forward Converter Based Active Cell Balancing in Lithium-Ion Battery Cell for Smart Electric Vehicle Application12. Predictive Modelling in Asset and Workforce Management13. Sustainability Consideration of Smart Grid with Big Data Analytics in Social, Economic, Technical and Policy Aspects14. Real-Time of Big Data and Analytics in Smart Grid and Energy Management Applications15. Challenges and Future Directions