EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur.
Given the absence of a universally optimal streaming method in unsupervised settings, we compare streaming methods with state-of-the-art TSAD models deployed online on a real nuclear power plant dataset.
This work also evaluates Automated Anomaly Detection in a streaming context.
Results show higher consistency for online TSAD and strong robustness from ensembling strategies.