# Real-Time Prediction of the Duration of Distribution System Outages

arXiv: https://arxiv.org/abs/1804.01189 · HTML: https://mlsanity.com/paper/1804.01189/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data](https://mlsanity.com/paper/2404.03115/) (2024)
- [A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction](https://mlsanity.com/paper/2509.16743/) (2025)
- [Graph Attention Network for Predicting Duration of Large-Scale Power Outages Induced by Natural Disasters](https://mlsanity.com/paper/2511.10898/) (2025)
- [End-to-End Topology-Aware Machine Learning for Power System Reliability Assessment](https://mlsanity.com/paper/2205.14792/) (2022)
- [Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors](https://mlsanity.com/paper/2512.22699/) (2025)

## Abstract (arXiv)

This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors. The initial duration prediction is made based on environmental factors, and it is updated based on incoming field reports using natural language processing to automatically analyze the text. Experiments using 15 years of outage records show good initial results and improved performance leveraging text. Case studies show that the language processing identifies phrases that point to outage causes and repair steps.