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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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# PVNet2 |
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## Model Description |
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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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- **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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# Training Details |
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## Data |
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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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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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### Preprocessing |
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Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [2]. |
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## Results |
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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/v4xgpar9](https://wandb.ai/openclimatefix/pvnet2.1/runs/v4xgpar9) |
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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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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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### Hardware |
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Trained on a single NVIDIA Tesla T4 |
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### Software |
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- [1] https://github.com/openclimatefix/PVNet |
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- [2] https://github.com/openclimatefix/ocf_datapipes |
