---
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 day-ahead (36 hour) 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/13POUvP8thrNekW0C-qF4hG3hLGfPOjwZe17M7Q6l85Y/edit?usp=sharing).

- **Developed by:** openclimatefix
- **Model type:** Fusion model
- **Language(s) (NLP):** en
- **License:** mit

## Results

The training logs for the current model can be found here:
 - [https://wandb.ai/openclimatefix/pvnet2.1/runs/[]](https://wandb.ai/openclimatefix/pvnet_day_ahead_36_hours/workspace?)


# 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 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.


### Preprocessing

Data is prepared with the `ocf_datapipes.training.pvnet` datapipe [2].


## Results

The training logs for the current model can be found [here](https://wandb.ai/openclimatefix/pvnet_day_ahead_36_hours/workspace?)



The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1).


### Hardware

Trained on a single NVIDIA Tesla T4

### Software

- [1] https://github.com/openclimatefix/PVNet
- [2] https://github.com/openclimatefix/ocf_datapipes