Spatio-Temporal Conformal Prediction for Power Outage Data

With the global climate change, extreme weather events like hurricanes, winter storms, and tornadoes have increasingly led to widespread electric power outages across the United States [14]. For instance, during March 2018, the northeastern U.S. was battered by three consecutive winter storms within a span of 14 days. This series of events caused power outages that left over 2.75 million customers without electricity in the New England region, resulting in economic losses of approximately $4 billion, including $2.9 billion in insured damages [8]. Such severe weather-related incidents often leave millions without power for extended periods, resulting in significant economic disruption [19] and, tragically, sometimes even loss of life [25]. Given the considerable impact of extreme weather on power systems since the early 2000s, regulatory bodies in the U.S. have called on the energy sector to enhance the resilience of power grids through various hardening measures [1]. Consequently, accurately assessing the resilience of power grids is crucial not only for estimating potential damage from extreme weather but also for informing short-term disaster response strategies, long-term resilience planning, and shaping energy policy.