Explores the application of random matrix theory to smart grid data analytics, providing a novel approach to analyzing complex smart grid data.
Presents a comprehensive overview of smart grid technologies, big data analytics, and random matrix theory, making it accessible to readers from diverse backgrounds.
Develops novel algorithms and methodologies for smart grid data analytics based on random matrix theory, addressing challenges such as dimensionality reduction, anomaly detection, and forecasting.
Provides case studies and real-world examples to demonstrate the effectiveness of the proposed approaches in improving smart grid performance and reliability.
* Contributes to the advancement of smart grid research and development by offering a powerful tool for data-driven decision-making and optimization.