When the power goes out, minutes matter. For customers, delays mean disrupted work, safety concerns, and financial impact. For utility providers, outage response time is a critical performance metric tied to public trust and operational efficiency. Traditionally, outage detection has depended heavily on customers manually reporting service interruptions. While effective to a degree, this approach introduces unavoidable lag between event occurrence and organizational response. In an era of advanced metering infrastructure and real time data, a reactive model is no longer sufficient.
This student research presentation proposes a more proactive framework by integrating smart meter data with real time weather information. Rather than waiting for customer calls, the system uses artificial intelligence and machine learning predictive models to detect outage events automatically. By combining meter analytics with environmental data, utilities can identify patterns that signal instability across the power grid before widespread manual reports accumulate.
The proposed workflow outlines four key stages: data collection, data processing, predictive modeling, and integration with the Outage Management System. Smart meter data and publicly available weather datasets feed into an AI driven model that evaluates anomaly signals. The predictive engine then generates actionable outputs for dispatch teams through the Outage Management System. This structured workflow transforms outage response from reactive reporting to automated detection and guided decision support.
The study evaluates effectiveness using three measurable performance indicators. Outage Detection Time measures how quickly an outage is identified. Restoration Decision Time captures how long it takes to dispatch a crew after detection. Overall Workflow Efficiency compares automated outage detections to total detections within a defined simulation period. The project establishes clear improvement targets, including a 25 percent reduction in detection time, a 20 percent decrease in restoration decision time, and an 80 percent efficiency rate in automated detection accuracy.
By benchmarking results against traditional call in reporting models, the research demonstrates how AI driven analytics can meaningfully reduce operational delays. Faster detection not only accelerates restoration but also improves resource allocation and crew scheduling. Integrating predictive intelligence into grid management strengthens system resilience, particularly during severe weather events when rapid response is essential.
For operations managers and utility leaders, this session illustrates how advanced analytics can enhance infrastructure reliability and workflow efficiency. The transition from manual reporting to predictive detection represents a shift toward data driven decision making across utility management. In doing so, the research highlights how AI can serve as a practical operational tool rather than a theoretical innovation.
Author and Affiliation
Cody Cruz, New England Institute of Technology
This presentation will be delivered in person at the SAM International Business Conference as part of the Information Systems and Operations Management track. Attendees will explore how smart meter analytics and machine learning models can reduce outage detection and restoration times while strengthening grid resilience. For more information visit www.samnational.org/conference