adamd1985/Temporal_Convolution_Networks_for_Energy_Demand_Forecast

Temporal Convolution Neural Networks applied to the Electricity Demand Forecast for Malta's grid.

Temporal Convolution Networks For Energy Demand Forecast: Malta Case Study

Code for the experiments to predict the energy demands in a grid, with datasets from Malta for this usecase.

Install Common Environment

We created a python 3.10 env in conda:

conda env create -f environment.yml

but python venv is also possible:

venv create aml --python=python3.10
venv activate aml

Dependencies available in: requirements.txt: yes | pip install -r requirements.txt

Tensorflow Conda Installation

If you want to install your Tensorflow, install it from conda like this:

conda config --add channels conda-forge
conda create -n tf tensorflow
conda activate tf

or create it with our environment.yml:

conda env create -n tf -f environment.yml
conda activate tf

Datasets

All CVSs should are available in the folder './raw_data'

ENV configurations

A default .env was provided. Use your own and add it to .gitignore.

Reference

This is a tensorflow variation of the architecture presented in the paper Deep Learning for Time Series Forecasting: The Electric Load Case paper. Mind that the code has been changed a bit, thus you may notice some differences with the models described in the paper:

@article{gasparin2019deep,
  title={Deep Learning for Time Series Forecasting: The Electric Load Case},
  author={Gasparin, Alberto and Lukovic, Slobodan and Alippi, Cesare},
  journal={arXiv preprint arXiv:1907.09207},
  year={2019}
}