PVNet summation

This project is used for training a model to sum the GSP predictions of PVNet into a national estimate.

Using this model to sum the GSP predictions rather than doing a simple sum increases the accuracy of the national predictions and can be configured to produce estimates of the uncertainty range of the national estimate. See the PVNet repo for more details and our paper.

Setup / Installation

git clone https://github.com/openclimatefix/PVNet_summation
cd PVNet_summation
pip install .

Additional development dependencies

pip install ".[dev]"

Getting started with running PVNet summation

In order to run PVNet summation, we assume that you are already set up with PVNet and have met all the requirements there.

Before running any code, copy the example configuration to a configs directory:

cp -r configs.example configs

You will be making local amendments to these configs.

Datasets

The datasets required are the same as documented in PVNet. The only addition is that you will need PVLive data for the national sum i.e. GSP ID 0.

Generating pre-made concurrent batches of data for PVNet

It is required that you preprepare batches using the save_concurrent_batches.py script from PVNet. This saves the batches as required by the PVNet model to make predictions for all GSPs for a single forecast init time. Seen the PVNet package for more details on this.

Set up and config example for batch creation

The concurrent batches created in the step above will be augmented with a few additional pieces of data required for the summation model. Within your copy of PVNet_summation/configs make sure you have replaced all of the items marked with PLACEHOLDER

Training PVNet_summation

How PVNet_summation is run is determined by the extensive configuration in the config files. The configs stored in PVNet/configs.example should work with batches created using the steps and batch creation config mentioned above.

Make sure to update the following config files before training your model:

1. In configs/datamodule/default.yaml:
   • update batch_dir to point to the directory you stored your concurrent batches in during batch creation.
   • update gsp_zarr_path to point to the PVLive data containing the national estimate
2. In configs/model/default.yaml:
   • update the PVNet model for which you are training a summation model for. A new summation model should be trained for each PVNet model
   • update the hyperparameters and structure of the summation model
3. In configs/trainer/default.yaml:
   • set accelerator: 0 if running on a system without a supported GPU
4. In configs.config.yaml:
   • It is recommended that you set presave_pvnet_outputs to True. This means that the concurrent batches that you create will only be run through the PVNet model once before training and their outputs saved, rather than being run on the fly on each batch throughout training. This can speed up training significantly.

Assuming you have updated the configs, you should now be able to run:

python run.py

Testing

You can use python -m pytest tests to run tests