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

TL;DR

This paper presents a machine learning model to identify the number of medical devices on power feeders using distribution data, aiding climate change adaptation and outage response planning.

Contribution

It introduces a novel load disaggregation model that estimates medical device counts from distribution feeder data, addressing data gaps for vulnerable populations.

Findings

Model accurately predicts medical device counts.

Provides actionable data for outage response planning.

Supports climate change adaptation strategies.

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.