Day-ahead European Electricity Price Forecasting KIT

Instructions for users:

1- Ensure you have all the required dependencies installed on your local device. 2- Copy and paste the whole repository on your local device. 3- Start using the data_download_kit (.ipynb) to download the data for your specific zone. Remember to use your own API key that could be requested from the Entso-e transparency platform. 4- For large-scale predictions of historical prices, use prediction_kit (.ipynb).

Instructions for developers:

1- Deepforkit is developed based on functional programming (instead of object-oriented programming) to allow for easier adjustments. 2- The data.py file is devoted to data-related modules like data downloading. 3- The models.py file is devoted to the model architecture suitable for hyperparameter optimization. 4- The hyperparameters.py file is devoted to hyperparameter optimization and feature selection tasks. 5- The prediction.py file is devoted to prediction modules.

Note: All functions have doc strings, and by following the .py files based on the order above, you can understand the underlying logic of Deepforkit.