Identification of medical devices using machine learning on distribution feeder data for informing power outage response (Proposals Track)
Paraskevi Kourtza (University of Edinburgh); Maitreyee Marathe (University of Wisconsin-Madison); Anuj Shetty (Stanford University); Diego Kiedanski (Yale University)
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 to power outages and other emergencies. The proposed solution serves as a measure for climate change adaptation.