Green Machine Learning and Big Data for Smart Grids: Practices and Applications

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 INDICE: 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

• ISBN: 978-0-443-28951-4
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 400
• Fecha Publicación: 01/11/2024
• Nº Volúmenes: 1
• Idioma: Inglés