2027 Event Agenda

Advanced Metering Infrastructure (AMI) has long provided utilities with unprecedented visibility into grid conditions through outage notifications and alarm signals. While power outage alarms are widely used to enhance outage management processes, a significant portion of available data is filtered out to ensure accurate dispatch. Historically, this discarded data, along with other alarm types such as sag/swell and momentary events, has been underutilized due to uncertainty around its operational value and the lack of actionable context.

This session explores how utilities can unlock the latent value within AMI data streams to move from reactive outage response toward predictive operations. Through real-world examples, we will demonstrate how patterns in meter alarms can serve as early indicators of equipment and system degradation—including insulator and cut-out failures, underground cable defects, transformer issues, and vegetation interference. In many cases, these precursors occur without customer awareness, yet provide critical insight into emerging reliability risks.

We will discuss the evolution of outage prediction efforts, including early challenges with analytical limitations and the need for high-confidence predictions to drive operational decisions. The presentation highlights how advancements in artificial intelligence and data analytics are enabling more reliable prediction models, capable of identifying potential failures with lead times ranging from hours to days. Attendees will gain insight into model development considerations, confidence thresholds, and the integration of AMI data with enterprise systems such as SCADA, OMS, and ADMS.

Furthermore, the session examines the growing role of grid-edge intelligence, including waveform capture capabilities in next-generation meters, and the ongoing industry debate around centralized analytics versus distributed sensing. We will share perspectives on the strengths and limitations of meter-based analysis, particularly in the context of behind-the-meter influences.

Designed for engineers and strategic decision-makers alike, this presentation provides a practical framework for transforming AMI data into actionable intelligence, improving reliability outcomes, and advancing the transition to predictive, data-driven grid operations.