Build a baseline model for predicting PV yield using NWP (numerical weather predictions), as opposed to satellite imagery. This model is intentionally very simple, so we can get an end-to-end system up and running quickly to interate on.

Model

The model can be found in the model directory. It is a CSV file with columns for the PV system ID, and the slope and intercept for the regression. Given a value for irradiance ("Downward short-wave radiation flux") from the NWP data at a PV system location, the PV output for the system can be predicted.

Known limitations

• A linear model is a very big simplification.
• Trained on only a single day's worth of data, at hourly time points.
• Outlier data from the PV systems is included.

Training

The training data is in a GCP bucket, and is not yet publicly available.

Install and test

conda env create -f environment.yml 
conda activate predict_pv_yield_nwp
pip install -e .
conda install black flake8 isort mypy pytest
pytest -s

Enforce/check code standards

black predict_pv_yield_nwp
flake8
isort -rc .
mypy predict_pv_yield_nwp

Train a model locally

python predict_pv_yield_nwp/train.py