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

This guidebook explores the best practices and potential for green data analytics in generating innovative solutions for renewable energy integration in the power grid.

The 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 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 way

- Includes real-world, practical applications and case studies for replication and innovative solution development

- Introduces readers with a range of expertise to best practices and the latest technological advances