Fine-tuning the pre-trained Timer model on a single wind turbine's SCADA data yields the best prediction accuracy across the whole wind farm, while the model does not always beat simpler baselines on larger datasets.
Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning
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Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data
Fine-tuning the pre-trained Timer model on a single wind turbine's SCADA data yields the best prediction accuracy across the whole wind farm, while the model does not always beat simpler baselines on larger datasets.