A multi-head RNN framework with learned confidence, ensemble uncertainty, auxiliary predictions, distance analysis, and diagnostics produces calibrated trust scores for NOx prediction, reducing MAE from 0.202 to 0.070 on the top 10% confidence subset.
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Trust-Aware Predictive Emissions Monitoring for Gas Turbine Fleets with Limited Labelled Data
A multi-head RNN framework with learned confidence, ensemble uncertainty, auxiliary predictions, distance analysis, and diagnostics produces calibrated trust scores for NOx prediction, reducing MAE from 0.202 to 0.070 on the top 10% confidence subset.