The power system industry is shifting towards a new digitalization era to better manage risk within volatile energy commodities, increase customer engagement, and enhance efficiency via grid optimization. Data analytics play a vital role in this transformation and, as such, different measurement architectures have been used and implemented to facilitate data capturing process and supervisory control at the generation, transmission, and distribution levels. This seminar will briefly review the recent outcomes of some smart grid challenges addressed by novel prediction techniques. At the generation level, decomposition techniques have been applied to handle the inherent uncertainty in short-term wind power prediction. At the transmission level, dynamic thermal line rating prediction has been studied as a viable solution to reduce congestion and utilize the actual capacity of the line. Considering the high inclusion of phasor measurement units at the transmission level, cutting-edge methods have been proposed to address stability status prediction of the grid following a contingency. Finally, at the distribution level, real-life data obtained from advanced metering infrastructure have been used for load prediction and customer segmentation. Speaker(s): , Prof. CY Chung Agenda: 11am | Begin event, introductions 11:10am | Presentation: Advanced Prediction Techniques Applied to Smart Grids 11:50am | Q&A 12pm | Event ends Virtual: https://events.vtools.ieee.org/m/366046