Overview

Key Capabilities

ProLoaF comes with the following scripts as entry points

• preprocess.py: Preprocess your input data for use with ProLoaF.

• train.py: Train an RNN model for load forecasting based on provided data.

• evaluate.py: Evaluate a previously trained model.

• baselines.py: Train and compare performance with typical statsmodels.

Dependency Diagrams

Example Workflow

• Add the data a model should be trained on to the data/ folder

• Preprocess the data using prep.py or custom scripts if needed.

• Specify all required and desired settings and values in config.json in the targets/ folder

• Train a model using python3 ./src/train.py -s <path_to_config_in_targets_folder>

• Evaluate the model using evaluate.py in the same fashion.

Test Data

This repository contains the following submodules:

• proloaf-data: Example gefcom2017 and open power system data for testing purposes.

Where should I go next?

• Getting Started: Get started with your own project
• Source Code Documentation: Dive in here if you’re looking for a detailed reference guide.