Green Machine Learning and Big Data for Smart Grids

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. 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 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).

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