Researchers built an AI model that gets better at predicting when your power comes back the more storm updates roll in.
Most outage-restoration predictions are treated as a one-shot guess: a utility looks at a storm, makes an estimate, and mostly sticks with it. Researchers instead built a Longitudinal Tabular Transformer that treats every update, crew assignment, dispatch, damage assessment, partial restoration, as a new data point and re-predicts the restoration time each time. Tested on 242,928 storm-related outages across six utilities, covering 10 million individual revisions, the model cut customer-weighted error by a median of 36.9 percent compared to the estimates utilities actually published during those storms, and by 11.3 percent against the best existing machine-learning baseline. It also beat the incumbent utility estimates on customer-satisfaction impact and root-mean-squared error at all six companies, not just on average.
This matters because a restoration estimate is not just a number on an outage map. It is how a family decides whether to throw out the food in the fridge, whether a medical-device user needs to evacuate, and whether it is worth driving to a hotel. Utilities have historically been criticized for restoration estimates that are vague or wrong for exactly this reason, so an approach that gets measurably more accurate as real information accumulates is a meaningful operational fix, not a flashy one.
The catch is right there in the data: the model's errors are largest at the very first revision, when there is no history to learn from, which is precisely the moment anxious customers are refreshing the outage map hardest. Improving on that first guess, not just on later ones, is the harder problem still left on the table.