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

TL;DR

This study uses machine learning to analyze how socioeconomic factors influence hurricane-related power outages in Florida, revealing social disparities affect system resilience and outage occurrence.

Contribution

It introduces a machine learning approach to quantify the importance of socioeconomic factors in hurricane power system performance, highlighting social disparities in outage impacts.

Findings

Socioeconomic variables significantly influence outage occurrence.

Social disparities are linked to power system resilience.

Machine learning effectively quantifies factor importance.

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.