The aim of the project is to build an open source PV forecast that is free and easy to use.
Open Climate Fix also provide a commercial PV forecast, please get in touch at quartz.support@openclimatefix.org

The current model uses GFS NWPs to predict the solar generation at a site


```python
from quartz_solar_forecast.forecast import run_forecast
from quartz_solar_forecast.pydantic_models import PVSite

# make input data
site = PVSite(latitude=51.75, longitude=-1.25, capacity_kwp=1.25)
ts = datetime(2023, 11, 1, 0, 0, 0)

# run model
predications_df = run_forecast(site=site, ts='2023-11-01')
```

Which gives the following prediction

![predictions.png](predictions.png)

## Model

The model is a gradient boosted tree model and uses 9 NWP variables.
It is trained on 25,000 PV sites with over 5 years of PV history, which is available [here](https://huggingface.co/datasets/openclimatefix/uk_pv).
The training of this model is handled in [pv-site-prediction](https://github.com/openclimatefix/pv-site-prediction)
TODO - we need to benchmark this forecast. 

## Known restrictions

- The model is trained on [UK MetOffice](https://www.metoffice.gov.uk/services/data/met-office-weather-datahub) NWPs, but when running inference we use [GFS](https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast) data from [Open-meteo](https://open-meteo.com/). The differences between GFS and UK MetOffice, could led to some odd behaviours.
- It looks like the GFS data on Open-Meteo is only available for free for the last 3 months. 

## Abbreviations

- NWP: Numerical Weather Predictions
- GFS: Global Forecast System
- PV: Photovoltaic
- 

## Contribution

We welcome other models