# Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data

arXiv: https://arxiv.org/abs/2404.03115 · HTML: https://mlsanity.com/paper/2404.03115/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Real-Time Prediction of the Duration of Distribution System Outages](https://mlsanity.com/paper/1804.01189/) (2018)
- [Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks](https://mlsanity.com/paper/1911.07690/) (2019)
- [Outing Power Outages: Real-time and Predictive Socio-demographic Analytics for New York City](https://mlsanity.com/paper/2202.11066/) (2022)

## Built on by (most cited first, 2 of 2)

- [Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors](https://mlsanity.com/paper/2512.22699/) (2025)
- [Data Leakage Inflates Generalizability of Power Outage Prediction Models](https://mlsanity.com/paper/2608.24665/) (2026)

## Abstract (arXiv)

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.