Outing Power Outages: Real-time and Predictive Socio-demographic Analytics for New York City

• URL: http://arxiv.org/abs/2202.11066v1
• Date: Tue, 22 Feb 2022 17:51:00 GMT
• Title: Outing Power Outages: Real-time and Predictive Socio-demographic Analytics for New York City
• Authors: Samuel Eckstrom, Graham Murphy, Eileen Ye, Samrat Acharya, Robert Mieth, Yury Dvorkin
• Abstract summary: We describe a tool that was designed to acquire and collect data on electric power outages in New York City since July 2020. The electrical outages are then displayed on a front-end application, which is publicly available. We use the collected outage data to analyze these outages and their socio-economic impacts on electricity vulnerable population groups.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrical outages continue to occur despite technological innovations and improvements to electric power distribution infrastructure. In this paper, we describe a tool that was designed to acquire and collect data on electric power outages in New York City since July 2020. The electrical outages are then displayed on a front-end application, which is publicly available. We use the collected outage data to analyze these outages and their socio-economic impacts on electricity vulnerable population groups. We determined that there was a slightly negative linear relationship between income and number of outages. Finally, a Markov Influence Graph was created to better understand the spatial and temporal relationships between outages.

Related papers

• Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning [7.883500674930901]
  We collected 179 million power outage records at 15-minute intervals across 3022 US contiguous counties from 2014 to 2023. We developed a power system vulnerability assessment framework based on three dimensions (intensity, frequency, and duration) and applied interpretable machine learning models (XGBoost and SHAP) to compute Power System Vulnerability Index (PSVI) at the county level. We identified 318 counties across 45 states as hotspots for high power system vulnerability, particularly in the West Coast (California and Washington), the East Coast (Florida and the Northeast area), the Great Lakes megalopolis (Chicago-Detroit metropolitan areas), and the
  arXiv  Detail & Related papers  (2024-10-11T06:37:05Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning [51.02352381270177]
  Split Federated Learning (SFL) has recently emerged as a promising distributed learning technology. The choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy. This article provides a comprehensive overview of the SFL process and thoroughly analyze energy consumption and privacy.
  arXiv  Detail & Related papers  (2023-11-15T23:23:42Z)
• Tracking electricity losses and their perceived causes using nighttime light and social media [0.0]
  This study shows how satellite imagery, social media, and information extraction can monitor blackouts and their perceived causes. Night-time light data (in March 2019 for Caracas, Venezuela) is used to indicate blackout regions. Twitter data is used to determine sentiment and topic trends, while statistical analysis and topic modeling delved into public perceptions regarding blackout causes.
  arXiv  Detail & Related papers  (2023-10-18T21:44:39Z)
• Price-Aware Deep Learning for Electricity Markets [58.3214356145985]
  We propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.
  arXiv  Detail & Related papers  (2023-08-02T21:16:05Z)
• 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)
• Assess and Summarize: Improve Outage Understanding with Large Language Models [45.39343325427484]
  We present and empirically validate a novel approach (dubbed Oasis) to help the engineers in this task. Oasis is able to automatically assess the impact scope of outages as well as to produce human-readable summarization. Results show that Oasis can effectively and efficiently summarize outages, and lead Microsoft to deploy its first prototype.
  arXiv  Detail & Related papers  (2023-05-29T13:36:19Z)
• Predicting Levels of Household Electricity Consumption in Low-Access Settings [0.05727060643816256]
  We train a Convolutional Neural Network (CNN) over pre-electrification daytime satellite imagery with a sample of utility bills. We show that competitive accuracies can be achieved at the building level, addressing the challenge of consumption variability. Results are already helping inform site selection and distribution-level planning, through granular predictions at the level of individual structures in Kenya.
  arXiv  Detail & Related papers  (2021-12-15T21:42:36Z)
• The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets [69.68068088508505]
  We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h. We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm. We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
  arXiv  Detail & Related papers  (2021-03-07T11:28:54Z)
• From Data to Knowledge to Action: Enabling the Smart Grid [0.11726720776908521]
  "The Grid" is a relic based in many respects on century-old technology. Many people are pinning their hopes on the "smart grid" Initial plans for the smart grid suggest it will make extensive use of existing information technology.
  arXiv  Detail & Related papers  (2020-07-31T19:43:48Z)
• Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data [2.1294627833637576]
  We develop a data-centric approach enabling to analyse which activities, function, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of electricity consumed at slow charging infrastructure.
  arXiv  Detail & Related papers  (2020-06-02T14:44:52Z)

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.