AI 中文总结
研究人口普查区极端事件期间停电预测及敏感性，开发两阶段障碍模型，整合客户停电、天气、社会经济指标、脆弱性指数及植被覆盖率等异构数据流，用底特律地区高分辨率数据集验证模型。
AI 中文摘要
本文开发了一个两阶段障碍模型，用于预测人口普查区层面的停电发生情况和严重程度。该框架随后用于评估极端事件期间停电对社会经济、人口和环境因素的敏感性。在人口普查区层面整合了五个异构数据流：15分钟客户停电数据、OpenMeteo每小时天气记录、美国社区调查（ACS）社会经济指标、疾病控制中心（CDC）社会脆弱性指数（SVI）和地理信息系统（GIS）得出的植被覆盖率。使用覆盖底特律地区290个人口普查区、超过14个月、时间分辨率为15分钟的高分辨率停电数据集对该框架进行了验证。
英文摘要
This paper develops a two-stage hurdle model for predicting power outage occurrence and severity at the census-tract level. The proposed framework is then used to assess the sensitivity of power outage to socioeconomic, demographic, and environmental factors during extreme events. Five heterogeneous data streams are integrated at the census tract level: 15-minute customer outage data, OpenMeteo hourly weather records, American Community Survey (ACS) socioeconomic indicators, Centers for Disease Control (CDC) social vulnerability indices (SVI), and Geographic Information System (GIS) derived vegetation coverage. The proposed framework is validated using a high-resolution power outage dataset covering 290 census tracts in the Detroit area over a period exceeding 14 months, with a temporal resolution of 15 minutes.
CommentsThe has been accepted for presentation at the 58th North American Power Symposium (NAPS 58)
