An AI reliability playbook for VPs of Operations responsible for grid signal anomaly detection.
Anomaly detection on the grid is an unusually hard machine learning problem.
Signal quality is the underrated half of the problem.
Alert calibration is the other half.
We spend the first 25 percent of every anomaly detection program on the data pipeline. Sensor health, missing data handling, time alignment across systems, label quality.
We make the trade-off explicit. The operations team chooses where on the precision-recall curve they want to live.
Operators tag every alert as true positive, false positive, or 'don't know yet.' The tagged data is the most valuable training signal in the system. We make tagging easy - one click in the existing operator console.
We spend the first 25 percent of every anomaly detection program on the data pipeline.
We make the trade-off explicit.
Operators tag every alert as true positive, false positive, or 'don't know yet.' The tagged data is the most valuable training signal in the system.
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