Closed peterdudfield closed 11 months ago
I would probably start with removing the sde
from training, and then probably more lag features? I think XGBoost models don't need the data to be normalized, so not sure that's necessary, although I guess if the units are different between CEDA and live MetOffice it probably makes sense to do that first.
Bonus one is to add mcc
and hcc
to nwp variables
if the units are different between CEDA and live MetOffice it probably makes sense to do that first
Yeah, it's pretty essential that the data the model sees at inference time is exactly the same as the data it sees at training time :slightly_smiling_face: so I agree that sounds like the priority!
And I agree with @jacobbieker that I don't think XGBoost models require the data to be normalised (because it chops real-valued inputs up into bins).
Does the model also get historical NWP data? If not, I think that might help a bit: i.e. if the model gets lagged GSP data for n timesteps in the past, then it might be useful to give the model NWP data for those same timesteps so the model can see the difference between the expected forecast (given the NWP) and what actually happened in the recent past. But maybe the model is already doing that?
Thanks @JackKelly and @jacobbieker , i re-ordered above, do you that order is about right?
Lgtm!
Looks great!
Thanks, @dantravers you happy with this?
Looks reasonable to me! I'd be curious to see if this does well, so could be higher? Use historic NWP data, not just forecasts But seems sensible. Thanks for asking the open question!
sde
from the training - https://github.com/openclimatefix/gradboost_pv/pull/48hcct
from the training - https://github.com/openclimatefix/gradboost_pv/pull/51mcc
andhcc
to nwp variables - https://github.com/openclimatefix/gradboost_pv/pull/52n_estimators
- https://github.com/openclimatefix/gradboost_pv/pull/53smoother
to resultsWould be interested to think what people think I should do first? @JackKelly @jacobbieker @dantravers