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This repository has been archived by the owner on Sep 7, 2023. It is now read-only.
With some probability (30%?), zero-out the output from the PV system ID encoding. And, separately, zero-out different elements of the PV metadata.
Then, during inference, just feed the network zeros for any missing metadata, or zero-out the ID encoding if we haven't seen this PV system during training.
With luck, this should clearly fatten-out the probability distribution of the predicted PV yield.
The text was updated successfully, but these errors were encountered:
During training:
With some probability (30%?), zero-out the output from the PV system ID encoding. And, separately, zero-out different elements of the PV metadata.
Then, during inference, just feed the network zeros for any missing metadata, or zero-out the ID encoding if we haven't seen this PV system during training.
With luck, this should clearly fatten-out the probability distribution of the predicted PV yield.
The text was updated successfully, but these errors were encountered: