---
language: en
library_name: pytorch
license: mit
---
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Do not remove elements like the above surrounded by two curly braces and do not add any more of them. These entries are required by the PVNet library and are automaticall infilled when the model is uploaded to huggingface
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# WindNet with GenCast

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## Model Description

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This model uses ECMWF deterministc NWP (HRES) it forecasts up to 48 hours ahead at 15 min intervals and is trained on regional wind power data in NW India. More information can be found in the model repo [1]. The model repo also includes links to our workshop paper on this model and some experimental notes.
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- **Developed by:** openclimatefix
- **Model type:** Fusion model
- **Language(s) (NLP):** en
- **License:** mit

# Training Details

## Data

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The model is trained on data from 2020-2022 and validated on data from 2023. This model uses ECMWF deterministc NWP (HRES).

See the data_config.yaml file for more information on the channels and window-size used for each input data source.
-->

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### Preprocessing

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Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/site` Dataset [2].
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## Results

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The training logs for this model commit can be found here:
 - [https://wandb.ai/openclimatefix/gdm-gen/runs/mvgv1iv3](https://wandb.ai/openclimatefix/gdm-gen/runs/mvgv1iv3)


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### Hardware
Trained on a single NVIDIA Tesla T4

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### Software

This model was trained using the following Open Climate Fix packages:

- [1] https://github.com/openclimatefix/PVNet
- [2] https://github.com/openclimatefix/ocf-data-sampler

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The versions of these packages can be found below:
 - pvnet==5.2.3
 - ocf-data-sampler==0.6.2


---
**Migration Note**: This model was migrated on 2025-12-19 to pvnet version 5.3.1.post0+git.3d1628c1.dirty