- By:
- Lee, Sangkeun M; Choi, Jong Youl ; Jung, Gang Seob ; Tabassum, Anika ; Stenvig, Nils M; Chinthavali, Supriya
- Book Title:
- Proceedings of IEEE 24th International Conference on Information Reuse and Integration for Data Science
- Publication Date:
- November 9, 2023
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 24th IEEE International Conference on Information Reuse and Integration for Data Science (IRI)
- Conference Location:
- Bellevue, Washington, United States of America
- Conference Sponsor:
- IEEE
Abstract
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.
