Predicting Power Outage During Extreme Weather with EAGLE-I and NWS Datasets

Conference · Tue Aug 01 00:00:00 EDT 2023

DOI:https://doi.org/10.1109/IRI58017.2023.00042· OSTI ID:1997746

Lee, Sangkeun (Matt) ^[1]; Choi, Jong Youl ^[1]; Jung, Gs ^[1]; Tabassum, Anika ^[1]; Stenvig, Nils ^[1]; Chinthavali, Supriya ^[1]

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1. ORNL

Extreme weather events, such as hurricanes, severe thunderstorms, and floods can significantly disrupt power grid systems, leading to electrical outages that result in inconvenience, economic losses, and life-threatening situations. There is a growing need for a robust and precise predictive model to forecast power outages, which will help prioritize emergency response before, during, and after extreme weather events. In this paper, we introduce machine-learning models that predict power outage risk at the state level during and after extreme weather events. We jointly utilized two publicly available datasets: the U.S. historical power outage data collected by the Environment for Analysis of Geo-Located Energy Information (EAGLE-I™) system, and the National Weather Service historical weather alert data sets. We highlight our initial result and discuss future work aimed at enhancing the model's robustness and accuracy for real-world applications.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE; USDOE Office of Electricity Delivery and Energy Reliability (OE)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1997746
• Country of Publication: United States
• Language: English