Demystifying AI/ML for Proactive Network Operations

Electric utilities are inundated with operational data, obscuring key insights needed for proactive network management. This session demystifies a core AI/ML concept by focusing on a powerful statistical tool: Spearman's Rank Correlation. Ideal for the complex, non-linear relationships found in grid systems, Spearman’s can identify leading indicators of failure often missed by conventional analysis.

The session progresses logically from the essential probability concepts that underpin the algorithm to the core mechanics of Spearman's Correlation. It culminates in a real-world case study based on simulated network data, demonstrating how this theory is applied to automatically correlate disparate network KPIs with physical equipment events. Attendees will leave with a practical understanding of how to apply this AI/ML technique to enhance grid reliability and operational intelligence. The session is designed for utility ICT engineers, network operations specialists, and technical managers seeking actionable AI/ML knowledge.