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README.md

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 ---
+language: en
 license: mit
+library_name: pytorch
 ---
+
+
+
+
+
+
+# PVNet2
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is/does. -->
+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).
+
+- **Developed by:** openclimatefix
+- **Model type:** Fusion model
+- **Language(s) (NLP):** en
+- **License:** mit
+
+
+# Training Details
+
+## Data
+
+<!-- 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. -->
+
+The model is trained on data from 2017-2020 and validated on data from 2021. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details.
+
+
+## Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+### Preprocessing
+
+Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [2].
+
+
+## Results 
+
+The training logs for the current model can be found [here on wandb](#https://wandb.ai/openclimatefix/pvnet2.1/runs/None).
+
+The training logs for all model runs of PVNet2 can be found [here](#https://wandb.ai/openclimatefix/pvnet2.1).
+
+Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing)
+
+
+### Hardware
+
+Trained on a single NVIDIA Tesla T4
+
+### Software
+
+- [1] https://github.com/openclimatefix/PVNet
+- [2] https://github.com/openclimatefix/ocf_datapipes
+

config.json

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-{"_target_": "pvnet.models.multimodal.multimodal.Model", "image_encoder": {"_target_": "pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet", "_partial_": true}, "encoder_out_features": 256, "encoder_kwargs": {"number_of_conv3d_layers": 6, "conv3d_channels": 32}, "include_sat": true, "include_nwp": true, "add_image_embedding_channel": true, "sat_image_size_pixels": 24, "nwp_image_size_pixels": 24, "number_sat_channels": 11, "number_nwp_channels": 2, "output_network": {"_target_": "pvnet.models.multimodal.linear_networks.networks.ResFCNet2", "_partial_": true}, "output_network_kwargs": {"fc_hidden_features": 128, "n_res_blocks": 6, "res_block_layers": 2, "dropout_frac": 0.0}, "embedding_dim": 16, "include_sun": true, "include_gsp_yield_history": false, "forecast_minutes": 480, "history_minutes": 120, "sat_history_minutes": 90, "nwp_history_minutes": 120, "nwp_forecast_minutes": 480, "optimizer": {"_target_": "pvnet.optimizers.AdamWReduceLROnPlateau", "lr": 0.0001, "weight_decay": 0.25, "amsgrad": true, "patience": 5, "factor": 0.1, "threshold": 0.002}}
+{"_target_": "pvnet.models.multimodal.multimodal.Model", "image_encoder": {"_target_": "pvnet.models.multimodal.encoders.encoders3d.DefaultPVNet", "_partial_": true}, "encoder_out_features": 256, "encoder_kwargs": {"number_of_conv3d_layers": 6, "conv3d_channels": 32}, "include_sat": true, "include_nwp": true, "add_image_embedding_channel": true, "sat_image_size_pixels": 24, "nwp_image_size_pixels": 24, "number_sat_channels": 11, "number_nwp_channels": 2, "output_network": {"_target_": "pvnet.models.multimodal.linear_networks.networks.ResFCNet2", "_partial_": true}, "output_network_kwargs": {"fc_hidden_features": 128, "n_res_blocks": 6, "res_block_layers": 2, "dropout_frac": 0.0}, "embedding_dim": 16, "include_sun": true, "include_gsp_yield_history": false, "forecast_minutes": 480, "history_minutes": 120, "sat_history_minutes": 90, "nwp_history_minutes": 120, "nwp_forecast_minutes": 480, "optimizer": {"_target_": "pvnet.optimizers.AdamWReduceLROnPlateau", "lr": 0.0001, "weight_decay": 0.25, "amsgrad": true, "patience": 5, "factor": 0.1, "threshold": 0.002}, "min_sat_delay_minutes": 30}

pytorch_model.bin

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