Summary of Deep Learning-based Weather-related Power Outage Prediction with Socio-economic and Power Infrastructure Data, by Xuesong Wang et al.
Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data
by Xuesong Wang, Nina Fatehi, Caisheng Wang, Masoud H. Nazari
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a deep learning-based method for predicting hourly power outage probabilities within specific geographic areas served by a utility company. Two models, conditional and unconditional Multi-Layer Perceptron (MLP), are developed to forecast power outages based on various input features from publicly available sources, including weather data, infrastructure maps, demographic statistics, and power outage records. The models predict power outage probability for each census tract given a one-hour-ahead weather forecast, considering both the weather prediction and location characteristics. The paper highlights the importance of socio-economic factors in improving the accuracy of power outage predictions at the census tract level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a way to use artificial intelligence to predict when there might be power outages in different parts of an area served by a utility company. They developed two types of computer models, called MLPs, that look at weather forecasts and other information to figure out which areas are most likely to have power outages. The researchers found that understanding the demographics and social factors of each neighborhood helps make their predictions more accurate. |
Keywords
* Artificial intelligence * Deep learning * Probability
