NukeWorker Outage Schedule Scorecard

The Challenge

Nuclear utilities publish outage schedules months in advance, but the reality rarely matches the plan. Refueling outages routinely extend days or weeks beyond the published end date. Start dates shift. And when an unplanned shutdown occurs, there's no published estimate at all — workers and stakeholders are left guessing.

We set out to answer three questions: When will the next outage start? How long will a scheduled outage actually take? And when a forced outage happens, how long will it last?

Our Two-Stage Approach

Our predictions use a two-stage pipeline that combines traditional cycle analysis with machine learning. The first stage analyzes historical refueling patterns to generate a baseline forecast. The second stage applies a trained machine learning model to correct the baseline using 28 contextual features — dramatically improving accuracy.

What the Model Learns

Our machine learning model is trained on 960 historical predictions spanning 2010–2026, learning from 28 features across five categories:

The single most predictive feature is cycle length — the number of days since the last refueling outage. Owner fleet sequencing (how an operator coordinates outages across multiple units) is the second most important factor for start date prediction.

Rigorous Backtesting

Every model earns its place. We validate by hiding one year of data at a time, asking the model to predict that year using only past information, then rotating through every year from 2015 to 2026. This ensures the model is always tested on data it has never seen.

Across 12 years of backtested predictions, our model predicts outage start dates within an average of 10 days of the actual date. Accuracy is consistent across the entire fleet, with the strongest results at operators who manage complex multi-unit outage schedules.

Key Insights

The fleet is not homogeneous. Owner operating practices, reactor design, containment type, and geographic region all create measurable differences in outage timing. An Entergy unit behaves differently from a Constellation unit — and the model learns these patterns.

Fleet coordination matters. When an operator runs multiple units, they stagger outages in a sequence. The gap between outages within an owner's fleet is the second most important predictor of start date accuracy.

Major maintenance leaves fingerprints. Steam generator replacements, vessel head inspections, and other extended campaigns happen on multi-year cycles. The model tracks time since the last major work to anticipate when the next extended outage is due.

Continuous Improvement

Our models aren't static. As new outage data comes in, the system recalculates predictions and accuracy scores automatically. The machine learning model is periodically retrained on the latest data, and the leaderboards on this page reflect live, up-to-date accuracy measurements — not cherry-picked results.

Every unit's accuracy score is computed by comparing what we predicted against what actually happened, weighted toward recent performance. If a unit's behavior changes, our model adapts.