# Identification of medical devices using machine learning on distribution feeder data for informing power outage response

arXiv: https://arxiv.org/abs/2211.08310 · HTML: https://mlsanity.com/paper/2211.08310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Structured Dictionary Learning for Energy Disaggregation](https://mlsanity.com/paper/1907.06581/) (2019)
- [Real-Time Energy Disaggregation of a Distribution Feeder's Demand Using Online Learning](https://mlsanity.com/paper/1701.04389/) (2017)
- [End-to-end NILM System Using High Frequency Data and Neural Networks](https://mlsanity.com/paper/2004.13905/) (2020)

## Abstract (arXiv)

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.