| 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 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/pvnet2.1/runs/kqaknmuc](https://wandb.ai/openclimatefix/pvnet2.1/runs/kqaknmuc) | |
| 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 |