APS will present Grid Explorer, an AI-powered analytics platform designed to unify feeder-head SCADA measurements and AMI data into a single operational view of the distribution grid. As utilities face increasing pressure from electrification, distributed energy resources, and data center growth, Grid Explorer helps engineers better understand feeder behavior, identify anomalies, and forecast future infrastructure needs. The session will cover how APS combines high-frequency PI System data with transformer-level AMI aggregation and machine learning to improve visibility, forecasting, and operational planning.

Session Takeaways:
• How to combine SCADA and AMI data into feeder analytics
• Applications of machine learning for anomaly detection
• Strategies for improving feeder visibility and forecasting
• Planning approaches for EV and DER growth