PVNet_summation 1.1.0

PVNet_summation

PVNet summation

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

Using the summation 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 a trained PVNet model already available either locally or pushed to HuggingFace.

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.

Training PVNet_summation

How PVNet_summation is run is determined by the extensive configuration in the config files. The configs stored in configs.example.

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

1. At the very start of training we loop over all of the input samples and make predictions for them using PVNet. These predictions are saved to disk and will be loaded in the training loop for more efficient training. In configs/config.yaml update sample_save_dir to set where the predictions will be saved to.

2. In configs/datamodule/default.yaml:

• Update pvnet_model.model_id and pvnet_model.revision to point to the Huggingface commit or local directory where the exported PVNet model is.
• Update configuration to point to a data configuration compatible with the PVNet model whose outputs will be fed into the summation model.
• Set train_period and val_period to control the time ranges of the train and val period
• Optionally set max_num_train_samples and max_num_val_samples to limit the number of possible train and validation example which will be used.

3. In configs/model/default.yaml:
   • Update the hyperparameters and structure of the summation model
4. In configs/trainer/default.yaml:
   • Set accelerator: 0 if running on a system without a supported GPU

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