Texas Power Outage Prediction Dataset with NWS/EAGLE-I Geographical and Lag Features (Lag 1, 12, 24)

作者：Jangjae Lee, Stephanie German Paal, Sangkeun Lee, Supriya Chinthavali · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.21340776 · 研究领域：Environmental science、Meteorology、Computer science

This dataset supports the development and evaluation of machine-learning models for predicting county-level power outages caused by multiple types of extreme weather events in Texas. It contains hourly, county-level data covering the period from 2014 to 2023 and integrates power outage records with meteorological, geographic, vegetation, transportation infrastructure, and extreme weather event information. The dataset includes historical variables at three temporal lag intervals: 1 hour (lag 1), 12 hours (lag 12), and 24 hours (lag 24). Separate data configurations are provided for each lag interval, enabling the evaluation of short-term and longer-term power outage prediction horizons. The lagged variables include historical power outage counts, weather conditions, and vegetation information, where applicable. The primary target variable is the number of customers experiencing power outages in each county. Power outage data were obtained from the Environment for the Analysis of Geo-Located Energy Information (EAGLE-I) dataset. Meteorological variables were derived from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) application programming interface (API) and include hourly weather indicators such as temperature, precipitation, wind speed, humidity, and atmospheric conditions. Additional variables include county-level population and geographic characteristics, the Leaf Area Index (LAI) derived from Moderate Resolution...