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. 2025 Jun 25;20(6):e0326752.
doi: 10.1371/journal.pone.0326752. eCollection 2025.

Leveraging multi-modal data for early prediction of severity in forced transmission outages with hierarchical spatiotemporal multiplex networks

Rafaa Aljurbua  1   2 , Jumanah Alshehri  1   3 , Shelly Gupta  1 , Abdulrahman Alharbi  1   4 , Zoran Obradovic  1
Affiliations

Leveraging multi-modal data for early prediction of severity in forced transmission outages with hierarchical spatiotemporal multiplex networks

Rafaa Aljurbua et al. PLoS One. .

Abstract

Extended power transmission outages caused by weather events can significantly impact the economy, infrastructure, and residents' quality of life in affected regions. One of the challenges is providing early, accurate warnings for these disruptions. To address this challenge, we introduce HMN-RTS, a hierarchical multiplex network designed to predict the duration of a forced transmission outage by leveraging a multi-modal approach. We investigate outage duration prediction over two years at the county level, focusing on the states of the Pacific Northwest region, including Idaho, California, Montana, Washington, and Oregon. The multiplex network layers collect diverse data sources, including information about power outages, weather data, weather forecasts, lightning, land cover, transmission lines, and social media. Our findings demonstrate that this approach enhances the accuracy of predicting power outage duration. The HMN-RTS model improves 3 hours ahead outage predictions, achieving a macro F1 score of 0.79 compared to the best alternative of 0.73 for a five-class classification. The HMN-RTS model provides valuable predictions of outage duration across multiple time horizons and seasons, enabling grid operators to implement timely outage mitigation strategies. Overall, the results underscore the HMN-RTS model's capability to deliver early and practical risk assessments.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The HMN-RTS framework, a hierarchical spatiotemporal multiplex network, is designed for multi-modal prediction of power outage duration.
Fig 2
Fig 2. The normalized confusion matrix for the LSTM model illustrates its performance in predicting outage durations.
Fig 3
Fig 3. The normalized confusion matrix for the GRUs model illustrates its performance in predicting outage durations.
Fig 4
Fig 4. The normalized confusion matrix for the RTMNO model illustrates its performance in predicting outage durations.
Fig 5
Fig 5. The normalized confusion matrix for the HON-RTS model illustrates its performance in predicting outage durations.
Fig 6
Fig 6. The normalized confusion matrix for the HMN-RTS model illustrates its performance in predicting outage durations.
Fig 7
Fig 7. The performance of the HMN-RTS model in the early detection of outages is evaluated using the macro F1 score for a five-class problem, as detailed in Table 1.
The X-axis in the corresponding figure shows the number of hours before the power outage at which predictions are made.
Fig 8
Fig 8. Percentage of power outages by season during 2021–2022.
See this image and copyright information in PMC

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