Real-Time Prediction of the Duration of Distribution System Outages
This addresses the problem of outage management for utility companies, but it is incremental as it combines existing methods like neural networks and NLP for a specific application.
The paper tackles predicting unplanned power outage durations by training neural networks on historical records, using environmental factors for initial predictions and updating them with NLP on field reports, showing improved performance with text analysis in experiments over 15 years of data.
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.