Identification of medical devices using machine learning on distribution feeder data for informing power outage response
Machine Learning
2022-11-16 v1 Signal Processing
Abstract
Power outages caused by extreme weather events due to climate change have doubled in the United States in the last two decades. Outages pose severe health risks to over 4.4 million individuals dependent on in-home medical devices. Data on the number of such individuals residing in a given area is limited. This study proposes a load disaggregation model to predict the number of medical devices behind an electric distribution feeder. This data can be used to inform planning and response. The proposed solution serves as a measure for climate change adaptation.
Keywords
Cite
@article{arxiv.2211.08310,
title = {Identification of medical devices using machine learning on distribution feeder data for informing power outage response},
author = {Paraskevi Kourtza and Maitreyee Marathe and Anuj Shetty and Diego Kiedanski},
journal= {arXiv preprint arXiv:2211.08310},
year = {2022}
}
Comments
To appear in the Tackling Climate Change with Machine Learning workshop at NeurIPS 2022 (Proposals Track) 6 pages, 3 figures