How important are socioeconomic factors for hurricane performance of
power systems? An analysis of disparities through machine learning
- URL: http://arxiv.org/abs/2208.09063v1
- Date: Thu, 18 Aug 2022 20:59:39 GMT
- Title: How important are socioeconomic factors for hurricane performance of
power systems? An analysis of disparities through machine learning
- Authors: Alexys Herleym Rodr\'iguez Avellaneda, Abdollah Shafieezadeh, Alper
Yilmaz
- Abstract summary: This paper investigates whether socioeconomic factors are important for the hurricane performance of the electric power system in Florida.
The study shows that socioeconomic variables are considerably important for the system performance model.
- Score: 0.4297070083645048
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Analysis of Premature Death Rates in Texas Counties: The Impact of Air Quality, Socioeconomic Factors, and COPD Prevalence [0.0]
We analyze the impact of air quality (PM2.5 levels), socioeconomic factors (median household income), and health conditions (COPD prevalence) through statistical analysis and modeling techniques.
Results reveal COPD prevalence as a strong predictor of premature death rates, with higher prevalence associated with a substantial increase in years of potential life lost.
arXiv Detail & Related papers (2024-12-27T18:12:04Z) - Integrated GIS- and network-based framework for assessing urban critical infrastructure accessibility and resilience: the case of Hurricane Michael [0.0]
This study presents a framework for assessing urban critical infrastructure resilience during extreme events, such as hurricanes.
The approach combines GIS and network analysis with open remote sensing data of the aftermath, vector data on infrastructure, and socio-demographic attributes of populations in affected areas.
arXiv Detail & Related papers (2024-12-18T11:07:27Z) - Correlating Power Outage Spread with Infrastructure Interdependencies During Hurricanes [5.2878398959711985]
This study investigates the spread of power outages during hurricanes by analyzing the correlation between the network of critical infrastructure and outage propagation.
Our analysis reveals a consistent positive correlation between the extent of critical infrastructure components accessible within a certain number of steps.
This insight suggests that understanding the interconnectedness among critical infrastructure elements is key to identifying areas indirectly affected by extreme weather events.
arXiv Detail & Related papers (2024-07-13T18:05:07Z) - Climate Change Impact on Agricultural Land Suitability: An Interpretable
Machine Learning-Based Eurasia Case Study [94.07737890568644]
As of 2021, approximately 828 million people worldwide are experiencing hunger and malnutrition.
Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages.
Our study focuses on Central Eurasia, a region burdened with economic and social challenges.
arXiv Detail & Related papers (2023-10-24T15:15:28Z) - Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch.
In this paper, we collected large-scale load datasets and released a new renewable energy dataset.
We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
arXiv Detail & Related papers (2023-07-14T06:50:02Z) - Causal Effect Estimation with Global Probabilistic Forecasting: A Case
Study of the Impact of Covid-19 Lockdowns on Energy Demand [2.126171264016785]
It is necessary to analyse the uncertainty of external intervention impacts on electricity demand.
This paper uses a deep learning approach to estimate the causal impact distribution of an intervention.
We consider the impact of Covid-19 lockdowns on energy usage as a case study.
arXiv Detail & Related papers (2022-09-19T09:39:29Z) - Equitable Community Resilience: The Case of Winter Storm Uri in Texas [0.0]
This research investigated aspects of equity related to community resilience in the aftermath of Winter Storm Uri in Texas.
Satellite imagery was used to examine data at a much higher geographical resolution focusing on census tracts in the city of Houston.
Results revealed statistically significant negative associations between counties' percentage of non-Hispanic whites and median household income with the ratio of outages.
arXiv Detail & Related papers (2022-01-17T22:54:07Z) - Investigating Underlying Drivers of Variability in Residential Energy
Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns.
This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
arXiv Detail & Related papers (2021-02-16T16:56:27Z) - Predictive Analytics for Water Asset Management: Machine Learning and
Survival Analysis [55.41644538483948]
We study a statistical and machine learning framework for the prediction of water pipe failures.
We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain.
The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
arXiv Detail & Related papers (2020-07-02T19:08:36Z) - Magnify Your Population: Statistical Downscaling to Augment the Spatial
Resolution of Socioeconomic Census Data [48.7576911714538]
We present a new statistical downscaling approach to derive fine-scale estimates of key socioeconomic attributes.
For each selected socioeconomic variable, a Random Forest model is trained on the source Census units and then used to generate fine-scale gridded predictions.
As a case study, we apply this method to Census data in the United States, downscaling the selected socioeconomic variables available at the block group level, to a grid of 300 spatial resolution.
arXiv Detail & Related papers (2020-06-23T16:52:18Z) - Sub-Seasonal Climate Forecasting via Machine Learning: Challenges,
Analysis, and Advances [44.28969320556008]
Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales.
In this paper, we study a variety of machine learning (ML) approaches for SSF over the US mainland.
arXiv Detail & Related papers (2020-06-14T18:39:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.