As extreme weather events such as hurricanes, severe thunderstorms, and floods grow in frequency and intensity, the disruption of power grid systems poses significant challenges, including widespread electrical outages, economic losses, and threats to public safety. This paper presents a forward-looking approach that leverages geographical graph-based machine learning models to predict county-level maximum power outages during such events. By capturing the intricate interdependencies within power system networks, our approach aims to provide precise and actionable predictions that can optimize emergency response efforts and enhance grid resilience. Through the integration of real-world data, including hurricane advisories and power outage records, we have trained and benchmarked multiple machine learning models, demonstrating the feasibility and potential of this method. While our initial results are promising, this paper also charts a course for advancing these models, addressing the remaining challenges, and ultimately transforming how we anticipate and respond to the impacts of extreme weather on power systems.

Extreme weather events, such as hurricanes, tornadoes, and floods, have caused significant damage to power grid systems, affecting critical infrastructures like substations, transmission lines, and generation plants. This damage often results in widespread power outages in disaster-affected areas, disrupting essential services such as healthcare, transportation, and national security. Annually, these power outage events due to extreme weather lead to economic losses ranging from 25 to 70 billion in the United States [1] ). Analyzing and understanding grid resiliency—particularly the dynamics of power outages under various weather events— is crucial for effective resource planning and maintaining reliable grid operations during such events. In this study, we conducted a preliminary analysis to model grid system resiliency under different extreme weather events across various states. Leveraging state-of-the-art deep learning time-series forecasting models, we aim to address the following research questions: • (R1) Can machine learning models predict grid resiliency in advance for a given weather event at a specific location? • (R2) Is it feasible to develop a generalized model to understand resiliency during extreme weather events? • (R3) Is historical data sufficient to model and predict resiliency for future extreme weather events?