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1. Socioeconomic vulnerability and electric power restoration timelines in Florida: the case of Hurricane Irma

   https://doi.org/10.1007/s11069-018-3413-x  

   Mitsova, Diana; Esnard, Ann-Margaret; Sapat, Alka; Lai, Betty S. (July 2018, Natural Hazards)

   Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, com- munities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the rela- tionship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities. 

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2. Cut-n-Reveal: Time Series Segmentations with Explanations

   https://doi.org/10.1145/3394118  

   Muralidhar, Nikhil; Tabassum, Anika; Chen, Liangzhe; Chinthavali, Supriya; Ramakrishnan, Naren; Prakash, B. Aditya (September 2020, ACM Transactions on Intelligent Systems and Technology)

   null (Ed.)

   Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of electric power in many regions, which causes breakdowns in essential public services. Understanding power outages and how they evolve during a hurricane provides insights on how to reduce outages in the future, and how to improve the robustness of the underlying critical infrastructure systems. In this article, we propose a novel scalable segmentation with explanations framework to help experts understand such datasets. Our method, CnR (Cut-n-Reveal), first finds a segmentation of the outage sequences based on the temporal variations of the power outage failure process so as to capture major pattern changes. This temporal segmentation procedure is capable of accounting for both the spatial and temporal correlations of the underlying power outage process. We then propose a novel explanation optimization formulation to find an intuitive explanation of the segmentation such that the explanation highlights the culprit time series of the change in each segment. Through extensive experiments, we show that our method consistently outperforms competitors in multiple real datasets with ground truth. We further study real county-level power outage data from several recent hurricanes (Matthew, Harvey, Irma) and show that CnR recovers important, non-trivial, and actionable patterns for domain experts, whereas baselines typically do not give meaningful results. 

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3. Grid Restoration After Extreme Weather Events

   https://doi.org/10.1109/ISGTEUROPE56780.2023.10407350  

   Ekisheva, Svetlana; Pratt, Donna K.; Kachadurian, Maria; Martin, William G.; Norris, Jack; Dobson, Ian (October 2023, Innovative Smart Grid Technologies Conference Europe)

   The North American Electric Reliability Corporation (NERC) tracks the restoration of the North American transmission system after events which test the grid resilience and reliability. Quantifying and analyzing these historical events is a foundation for studying and maintaining resilience. After showing that the largest recent events are dominated by extreme weather events, the paper analyzes these events by extracting the restore process for each event and defining, calculating, and discussing various metrics that quantify the restoration. The metrics include a duration metric of time to substantial restoration. In 2021, Hurricane Ida was the largest resilience event in the North American system. A case study of Hurricane Ida analyzes the generator outages and restoration as well as the transmission system outages and restoration. 

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4. Spatiotemporal Multiplex Network Model for Predicting Forced Outage Severity in Distribution Grids

   Aljurbua, R; Baembitov, R; Petridis, C; Saranovic, D; Kezunovic, M; Obradovic, Z (June 2025, Springer Verlag)

   Iliadis, L; Maglogiannis, I; Kyriacou, E; Jayne, C (Ed.)

   Weather-related power disruptions present significant challenges to public infrastructure, societal well-being, and the distribution grid. Predicting outage durations in distribution grids is another challenge compared to transmission line outage durations due to distribution networks’ complexity and finer granularity. While forecasting forced power outages is crucial, accurately estimating their duration is essential for timely response and mitigation measures. This study introduces the Spatiotemporal Multiplex Network (SMN-WVF), a methodology designed to predict power outage durations across varying lead times, tackling the difficulties posed by small, high-complexity spaces within distribution grids. SMN-WVF employs multiplex networks that incorporate multi-modal data across both time and space, including layers such as power outages, weather conditions, weather forecasts, vegetation, and distances between substations. We demonstrate the importance of incorporating additional layers of data sources as they are shown to help the model’s predictions through gradual improvement in the macro F1 score performance. 

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5. Early Prediction of Power Outage Duration Through Hierarchical Spatiotemporal Multiplex Networks

   https://doi.org/10.1007/978-3-031-82435-7_26  

   Aljurbua, Rafaa; Alshehri, Jumanah; Gupta, Shelly; Alharbi, Abdulrahman; Obradovic, Zoran (January 2025, Springer Nature Switzerland)

   Cherifi, H; Donduran, M; Rocha, LM; Cherifi, C; Varol, O (Ed.)

   Long power outages caused by weather can have a big impact on the economy, infrastructure, and quality of life in affected areas. It’s hard to provide early and accurate warnings for these disruptions because severe weather often leads to missing weather recordings, making it difficult to make learning-based predictions. To address this challenge, we have developed HMN-RTS, a hierarchical multiplex network that classifies disruption severity by temporal learning from integrated weather recordings and social media posts. This new framework’s multiplex network layers gather information about power outages, weather, lighting, land cover, transmission lines, and social media comments. Our study shows that this method effectively improves the accuracy of predicting the duration of weather-related outages. The HMN-RTS model improves 3 h ahead outage severity prediction, resulting in a 0.76 macro F1-score vs 0.51 for the best alternative for a five-class problem formulation. The HMN-RTS model provides useful predictions of outage duration 6 h ahead, enabling grid operators to implement outage mitigation strategies promptly. The results highlight the HMN-RTS’s ability to offer early, reliable, and efficient risk assessment. 

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