The increasing frequency and intensity of storms pose significant challenges to electric utilities, emphasizing the need for technological solutions to optimize storm response. Accurate storm outage and damage prediction is crucial for mitigating the adverse impacts of power disruptions, enhancing grid resilience, and ensuring timely restoration of services. Emerging in-field and AI technology allows utilities to proactively allocate resources, optimize crew deployments, and communicate more effectively with customers and stakeholders. However, challenges remain, including data quality and availability, integration with existing systems, upstream process improvement, and change management. 

Exelon has teamed up with the University of Connecticut (UConn) to develop an outage prediction model for its area. As part of the partnership, UConn is developing four machine learning-based models for rain/windstorms, tropical storms, snow/ice storms, and thunderstorms. Panelists from Exelon, Oncor, and UConn will discuss their use of AI to enhance the tools and procedures for managing storms within their utilities. They will also address the existing challenges in securing stakeholder acceptance and trust and implementing these solutions in an operational environment.