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Power outages caused by extreme weather events due to climate change havedoubled in the United States in the last two decades. Outages pose severehealth risks to over 4.4 million individuals dependent on in-home medicaldevices. Data on the number of such individuals residing in a given area islimited. This study proposes a load disaggregation model to predict the numberof medical devices behind an electric distribution feeder. This data can beused to inform planning and response. The proposed solution serves as a measurefor climate change adaptation.

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