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Ingest numerical weather prediction data (NWP) #3
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todo: finish get_nwp_for_datetime() (just under PyTorch dataset heading) |
Largely done now. Need to finish off NWPDataSource. If it's too slow to load then maybe just pre-load the temperature data for each PV system. But that's not very flexible. Probably better to find a good way to load in a separate thread |
Resample to 5-minutely and interpolate, whilst making sure the resulting target_datetimes are correct. Need to make sure there's always data available at the start and end for the interpolation. Maybe try resampling to 5 minutely as soon as we load the data. But prob won't work. So prob need to resample after creating a single target_datetime index for the NWPs. Also need to throw an error if there isn't data for a particular datetime. And/or filter datetimes before training. |
Done with resampling just using xarray's built-in resampling :) |
Actually, no, using the 'simple approach' is an order of magnitude slower (1.8 seconds vs about 180 ms) and produces discontinuities between different NWP inits. I'll interpolate manually... |
…introduces discontinuities between NWP inits. #4
New approach for getting NWPs is way faster (2 ms)! |
If the pesky |
OK! The code runs past |
Code appears to be running now; but is super-slow. (with 1 worker, it's now 15 seconds per iteration. It used to be more like 40 iterations per second!) Still need to standardise and convert to float32 Ideas for speeding up in openclimatefix/predict_pv_yield_2#18 |
…Ps are still a little slow though (#24). About to try resampling 'step' in load_single_chunk()
Use temperature at surface, precipitation, irradiance, cloud fraction, accumulated snow cover.
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