Green Machine Learning and Big Data for Smart Grids

Practices and Applications
Herausgeber: Indragandhi, V.; Subramaniyaswamy, V.; Elakkiya, R.

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Elsevier Health Sciences
• Seitenzahl: 400
• Erscheinungstermin: 1. November 2024
• Englisch
• ISBN-13: 9780443289514
• ISBN-10: 0443289514
• Artikelnr.: 70253716

Inhaltsangabe

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