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  1. README.md +54 -0
  2. config.json +80 -0
  3. data_config.yaml +451 -0
  4. pytorch_model.bin +3 -0
README.md CHANGED
@@ -1,3 +1,57 @@
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  ---
 
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  license: mit
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language: en
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  license: mit
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+ library_name: pytorch
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  ---
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+
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+
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+
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+
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+
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+
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+ # PVNet2
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+
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+ ## Model Description
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+
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+ <!-- Provide a longer summary of what this model is/does. -->
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+ This model class uses satellite data, numericl weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in [this google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing).
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+
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+ - **Developed by:** openclimatefix
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+ - **Model type:** Fusion model
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+ - **Language(s) (NLP):** en
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+ - **License:** mit
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+
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+
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+ # Training Details
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+
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+ ## Data
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+
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+ <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+
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+ The model is trained on data from 2019-2022 and validated on data from 2022-2023. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details.
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+
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+
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+ ### Preprocessing
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+
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+ Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [2].
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+
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+
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+ ## Results
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+
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+ The training logs for the current model can be found here:
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+ - [https://wandb.ai/openclimatefix/pvnet2.1/runs/g9einxvs](https://wandb.ai/openclimatefix/pvnet2.1/runs/g9einxvs)
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+
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+
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+ The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1).
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+
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+ Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing)
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+
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+
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+ ### Hardware
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+
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+ Trained on a single NVIDIA Tesla T4
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+
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+ ### Software
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+
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+ - [1] https://github.com/openclimatefix/PVNet
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+ - [2] https://github.com/openclimatefix/ocf_datapipes
config.json ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_target_": "pvnet.models.multimodal.multimodal.Model",
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+ "output_quantiles": [
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+ 0.02,
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+ 0.1,
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+ 0.25,
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+ 0.5,
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+ 0.75,
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+ 0.9,
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+ 0.98
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+ ],
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+ "nwp_encoders_dict": {
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+ "ukv": {
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+ "_target_": "pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet",
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+ "_partial_": true,
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+ "in_channels": 11,
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+ "out_features": 256,
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+ "number_of_conv3d_layers": 6,
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+ "conv3d_channels": 32,
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+ "image_size_pixels": 24
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+ },
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+ "ecmwf": {
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+ "_target_": "pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet",
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+ "_partial_": true,
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+ "in_channels": 12,
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+ "out_features": 256,
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+ "number_of_conv3d_layers": 4,
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+ "conv3d_channels": 32,
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+ "image_size_pixels": 12
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+ }
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+ },
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+ "sat_encoder": {
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+ "_target_": "pvnet.models.multimodal.encoders.encoders3d.ResConv3DNet2",
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+ "_partial_": true,
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+ "in_channels": 11,
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+ "out_features": 256,
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+ "image_size_pixels": 24,
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+ "hidden_channels": 32,
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+ "n_res_blocks": 3,
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+ "res_block_layers": 3,
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+ "batch_norm": true,
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+ "dropout_frac": 0.0
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+ },
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+ "add_image_embedding_channel": false,
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+ "pv_encoder": null,
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+ "output_network": {
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+ "_target_": "pvnet.models.multimodal.linear_networks.networks.ResFCNet2",
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+ "_partial_": true,
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+ "fc_hidden_features": 128,
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+ "n_res_blocks": 6,
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+ "res_block_layers": 2,
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+ "dropout_frac": 0.0
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+ },
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+ "embedding_dim": 16,
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+ "include_sun": true,
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+ "include_gsp_yield_history": false,
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+ "forecast_minutes": 480,
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+ "history_minutes": 120,
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+ "min_sat_delay_minutes": 0,
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+ "sat_history_minutes": 90,
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+ "pv_history_minutes": 180,
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+ "nwp_history_minutes": {
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+ "ukv": 120,
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+ "ecmwf": 120
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+ },
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+ "nwp_forecast_minutes": {
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+ "ukv": 480,
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+ "ecmwf": 480
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+ },
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+ "optimizer": {
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+ "_target_": "pvnet.optimizers.EmbAdamWReduceLROnPlateau",
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+ "lr": 0.0001,
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+ "weight_decay": 0.25,
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+ "amsgrad": true,
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+ "patience": 5,
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+ "factor": 0.1,
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+ "threshold": 0.002
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+ },
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+ "adapt_batches": true
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+ }
data_config.yaml ADDED
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+ general:
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+ description: Config for training the saved PVNet model
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+ name: PVNet current
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+ input_data:
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+ default_forecast_minutes: 480
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+ default_history_minutes: 120
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+ gsp:
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+ dropout_fraction: 0
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+ dropout_timedeltas_minutes: null
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+ forecast_minutes: 480
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+ gsp_zarr_path: PLACEHOLDER.zarr
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+ history_minutes: 120
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+ time_resolution_minutes: 30
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+ nwp:
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+ ecmwf:
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+ dropout_fraction: 1.0
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+ dropout_timedeltas_minutes:
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+ - -360
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+ forecast_minutes: 480
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+ history_minutes: 120
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+ max_staleness_minutes: null
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+ nwp_channels:
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+ - t2m
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+ - dswrf
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+ - dlwrf
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+ - hcc
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+ - mcc
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+ - lcc
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+ - tcc
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+ - sde
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+ - sr
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+ - duvrs
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+ - u10
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+ - v10
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+ nwp_image_size_pixels_height: 24
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+ nwp_image_size_pixels_width: 24
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+ nwp_provider: ecmwf
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+ nwp_zarr_path: PLACEHOLDER.zarr
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+ time_resolution_minutes: 60
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+ ukv:
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+ dropout_fraction: 1.0
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+ dropout_timedeltas_minutes:
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+ - -180
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+ forecast_minutes: 480
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+ history_minutes: 120
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+ max_staleness_minutes: null
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+ nwp_channels:
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+ - t
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+ - dswrf
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+ - dlwrf
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+ - hcc
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+ - mcc
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+ - lcc
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+ - sde
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+ - r
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+ - vis
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+ - si10
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+ - prate
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+ nwp_image_size_pixels_height: 48
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+ nwp_image_size_pixels_width: 48
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+ nwp_provider: ukv
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+ nwp_zarr_path: PLACEHOLDER.zarr
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+ time_resolution_minutes: 60
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+ pv:
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+ dropout_fraction: 0
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+ dropout_timedeltas_minutes: null
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+ forecast_minutes: 0
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+ history_minutes: 120
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+ pv_files_groups:
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+ - label: solar_sheffield_passiv
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+ pv_filename: PLACEHOLDER.netcdf
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+ pv_metadata_filename: PLACEHOLDER.csv
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+ pv_ml_ids:
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+ - 154
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+ system_dropout_fraction_max: 0
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+ system_dropout_fraction_min: 0
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+ time_resolution_minutes: 5
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+ satellite:
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+ dropout_fraction: 0.2
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+ dropout_timedeltas_minutes:
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+ - -5
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+ - -10
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+ - -15
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+ forecast_minutes: 0
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+ history_minutes: 90
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+ live_delay_minutes: 0
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+ satellite_channels:
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+ - IR_120
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+ satellite_image_size_pixels_height: 48
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+ satellite_image_size_pixels_width: 48
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+ satellite_zarr_path: PLACEHOLDER.zarr
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+ time_resolution_minutes: 5
pytorch_model.bin ADDED
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