Identification of medical devices using machine learning on distribution feeder data for informing power outage response
This addresses health risks for over 4.4 million individuals dependent on medical devices during outages, but it is incremental as it applies existing methods to a new domain-specific data problem.
The study tackled the problem of identifying medical device users during power outages by developing a load disaggregation model to predict the number of such devices on distribution feeders, enabling better planning and response for climate change adaptation.
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.