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This repository has been archived by the owner on Sep 7, 2023. It is now read-only.
One network which gets PV metadata when it's available. When it's not available, somehow mask those inputs. Set to -1? Or have a separate 'mask' input?
Two networks: One which predicts distribution of PV yield, without knowing any PV metadata. A second network which takes that PV distribution, and refines it when metadata is available
The text was updated successfully, but these errors were encountered:
optimistically speaking, approach 1 "should" be handled by dropout on the input layer. As long as the missing values are set to the same as the training-time dropout uses (presumably zeroes). But that's pure optimism
Two approaches:
One network which gets PV metadata when it's available. When it's not available, somehow mask those inputs. Set to -1? Or have a separate 'mask' input?
Two networks: One which predicts distribution of PV yield, without knowing any PV metadata. A second network which takes that PV distribution, and refines it when metadata is available
The text was updated successfully, but these errors were encountered: