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1. Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

   https://doi.org/10.3390/en15061958  

   Owolabi, Olukunle O.; Sunter, Deborah A. (March 2022, Energies)

   Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO. 

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2. 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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3. “Optimized Asset Management in Distribution Systems Based on Predictive Risk Analysis

   T. Dokic, M. Kezunovic (January 2018, Energies)

   The paper introduces an optimal maintenance scheduler based on predictive assessment of risk of outage and equipment failure in distribution networks. The variety of severe weather conditions are observed and their impact on the network components is quantified. The equipment deterioration and failure rates are observed continuously across the space and time using heterogeneous data. The risk of weather-related outages for each component is generated in real-time, and can be extracted at multiple temporal and spatial scales depending on the application of interest. The optimal maintenance scheduling that minimizes the system risk while maintaining the economic investment limits is developed. The benefits of the framework are presented using a distribution network asset management example. 

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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. Hazard Resistance-Based Spatiotemporal Risk Analysis for Distribution Network Outages During Hurricanes

   https://doi.org/10.1109/TPWRS.2024.3469168  

   Xu, Luo; Lin, Ning; Xi, Dazhi; Feng, Kairui; Poor, H Vincent (September 2024, IEEE Transactions on Power Systems)

   In recent decades, blackouts have shown an increasing prevalence of power outages due to extreme weather events such as hurricanes. Precisely assessing the spatiotemporal outages in distribution networks, the most vulnerable part of power systems, is critical to enhancing power system resilience. The Sequential Monte Carlo (SMC) simulation method is widely used for spatiotemporal risk analysis of power systems during extreme weather hazards. However, it is found here that the SMC method can lead to large errors as it repeatedly samples the failure probability from the time-invariant fragility functions of system components in time-series analysis, particularly overestimating damages under evolving hazards with high-frequency sampling. To address this issue, a novel hazard resistance-based spatiotemporal risk analysis (HRSRA) method is proposed. This method converts the failure probability of a component into a hazard resistance and uses it as a time-invariant value in time-series analysis. The proposed HRSRA provides an adaptive framework for incorporating high-spatiotemporal-resolution meteorology models into power outage simulations. By leveraging the geographic information system data of the power system and a physics-based hurricane wind field model, the superiority of the proposed method is validated using real-world time-series power outage data from Puerto Rico, including data collected during Hurricane Fiona in 2022. 

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