---
language: en
library_name: pytorch
license: mit
---
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# PVNet2

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## Model Description

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This model class uses satellite data, and numerical weather predictions to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1].

- **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 2019-2022 and validated on data from 2022-2023. It uses NWP data from ECMWF IFS model, and the UK Met Office UKV model. It uses satellite data from the EUMETSAT MSG SEVIRI instrument.

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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The model is trained on data from 2019-2021 and validated on data from 2022. It uses NWP data from ECMWF IFS model, and the UK Met Office UKV model. It uses also uses inputs from OCF's cloudcasting model



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### Preprocessing

Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/pvnet_uk` Dataset [2].


## Results

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The training logs for the current model can be found here:


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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_summation==1.1.0.post0+git.3b6f1115.dirty
 - ocf-data-sampler==0.5.26.post2+git.90ee263d.dirty