Predicting Power Outage During Extreme Weather with EAGLE-I and NWS Datasets
- 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 Office of Electricity (OE)
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1997746
- Resource Relation:
- Conference: 24th IEEE International Conference on Information Reuse and Integration for Data Science (IRI) - Bellevue, Washington, United States of America - 8/4/2023 8:00:00 AM-8/6/2023 8:00:00 AM
- Country of Publication:
- United States
- Language:
- English
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