论文标题
使用机器学习在配电馈线数据上识别医疗设备，以告知停电响应
Identification of medical devices using machine learning on distribution feeder data for informing power outage response
论文作者
论文摘要
在过去的二十年中，由于气候变化导致的极端天气事件引起的停电增加了一倍。依赖于家庭医疗设备的超过440万人对中断构成严重的健康风险。有关居住在给定区域的此类人数的数据有限。这项研究提出了一个负载分解模型，以预测电源馈线背后的医疗设备数量。这些数据可用于告知计划和响应。提出的解决方案是气候变化适应的量度。
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.
