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
- 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) - Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data [4.4121133971424165]
This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory.
Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional, were developed to forecast power outage probabilities.
Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.
arXiv Detail & Related papers (2024-04-03T23:38:31Z) - 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) - 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) - Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers.
We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid.
To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
arXiv Detail & Related papers (2020-03-02T20:32:10Z)
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.