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Link to original content: https://doi.org/10.48550/arXiv.2208.09063
[2208.09063] How important are socioeconomic factors for hurricane performance of power systems? An analysis of disparities through machine learning
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Computer Science > Machine Learning

arXiv:2208.09063 (cs)
[Submitted on 18 Aug 2022]

Title:How important are socioeconomic factors for hurricane performance of power systems? An analysis of disparities through machine learning

Authors:Alexys Herleym Rodríguez Avellaneda, Abdollah Shafieezadeh, Alper Yilmaz
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Abstract:This paper investigates whether socioeconomic factors are important for the hurricane performance of the electric power system in Florida. The investigation is performed using the Random Forest classifier with Mean Decrease of Accuracy (MDA) for measuring the importance of a set of factors that include hazard intensity, time to recovery from maximum impact, and socioeconomic characteristics of the affected population. The data set (at county scale) for this study includes socioeconomic variables from the 5-year American Community Survey (ACS), as well as wind velocities, and outage data of five hurricanes including Alberto and Michael in 2018, Dorian in 2019, and Eta and Isaias in 2020. The study shows that socioeconomic variables are considerably important for the system performance model. This indicates that social disparities may exist in the occurrence of power outages, which directly impact the resilience of communities and thus require immediate attention.
Comments: 6 pages, 5 figures, 4 tables, 2022 IEEE International Conference on Power Systems Technology (PowerCon)
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2208.09063 [cs.LG]
  (or arXiv:2208.09063v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2208.09063

Submission history

From: Alexys Herleym Rodríguez Avellaneda [view email]
[v1] Thu, 18 Aug 2022 20:59:39 UTC (533 KB)
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