AutoDL for Load Forecasting

This is the code for the paper: Automated Deep Learning for Load Forecasting.

The data used is stored in dataset/data.csv. The target is called conso_rte, the other variables are anonymized.

The main file is main.py. You can run it with several arguments:

• filename: the data to use for your experimentations (here dataset/data.csv)
• config: name of the configuration dictionary related to the dataset (ie: filename) refered in exp_configs in utils/config.py
• target: the target name (here conso_rte)
• mh: the algorithm to use, autopytorch, random search or evolutionary algorithm
• save_dir: where to save your experiments results
• seed: general seed for your experiments

You can run the code with AutoPytorch without mpi4py but you must have it for EnergyDragon. A small example can be found small_test.ipynb. The code to recreate the paper figures can be found plots_paper.ipynb.

The dragon folder contains the code from the DRAGON package that we are using for EnergyDragon.

The dataset folder contains the data used for our experiments as well as the saved outputs from our baseline.

The utils folder contains our code. In the utils/config.py you can find our configurations for our use case: training procedure, features, etc. To incorporate the CNN/MLP model within the SSEA algorithm, please set 'models' = True in the steady_state_config variable in the config.py.

The experiments have be ran using a bash environment. Two bash files for AutoPytorch script_autopytorch.sh and for the Dragon optimization script_dragon.sh can be found in the repository. Yo will need an MPI environment to use the second one.

Installation

The code works with Python version 3.9.18.

• Create a conda (or mamba) environment: conda create -n new_env python=3.9.18
• Activate the environment: conda activate new_env
• Install the zellij package using pip (be carefull to use the pip from your conda environment): pip install git+https://github.com/ThomasFirmin/zellij.git@dag
• Install matplotlib: conda install matplotlib
• Install AutoPytorch with pip: pip install autoPyTorch
• Install Ipykernel (for the notebooks): conda install -c anaconda ipykernel
• Add your environment as a jupyter kernel: python -m ipykernel install --user --name=new_env (If not already installed by also the previous installation: conda install -c anaconda python-graphviz)

Norway use case

The plots for the Norway use case can be recreated using the plots_norway.ipynb notebook. The necessary data file should be included in the dataset/save_norway folder.