论文标题
社会经济因素对电力系统的飓风表现有多重要？通过机器学习对差异的分析
How important are socioeconomic factors for hurricane performance of power systems? An analysis of disparities through machine learning
论文作者
论文摘要
本文研究了社会经济因素是否对于佛罗里达电力系统的飓风表现很重要。使用随机森林分类器进行调查，其准确性平均降低（MDA），以衡量一组因素的重要性，包括危害强度，最大影响的恢复时间以及受影响人群的社会经济特征。这项研究的数据集（在县规模上）包括来自美国5年社区调查（ACS）的社会经济变量以及风速，以及包括Alberto和Michael在内的五次飓风数据，2018年，2019年的Dorian，以及2020年的Dorian，ETA和Isaias。研究表明，该社会经济变量非常重要。这表明在发生停电的发生中可能存在社会差异，这直接影响了社区的弹性，因此需要立即关注。
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.
