A toolbox to generate a de-clustered, application-independent, semi-artificial load profile benchmark set that exposes a broad feature variety and low discrepancy within feature space.
The final declustered data is available at
https://doi.org/10.5281/zenodo.10213145
The associated research paper is available at
https://doi.org/10.1186/s42162-024-00304-8
This component deals with the generation and retrieval of initial time series datasets. It is designed to serve as the starting point for any time series analysis within the module, ensuring the availability of standardized and pre-processed data.
The submodule dedicated to manifold creation focuses on transforming time series datasets through a series of mathematical and signal processing techniques.
This aspect involves the calculation and analysis of a wide range of features from time series data. The submodule utilizes various established toolboxes and custom implementations to extract meaningful features from time series.
To manage the complexity of high-dimensional data, this functionality simplifies datasets by reducing their dimensionality. The process involves retaining critical information and features from the original datasets, ensuring the essence of the data is preserved while making it more manageable for further processing and analysis.
This component enhances the representation of data in feature space. It focuses on declustering and optimizing datasets, which is vital for reducing redundancy in data representation and improving the efficiency of data handling and analysis.
The module provides tools for exploring and visualizing data distributions, correlations, and other analytical aspects of time series data.
init: Generation and retrieval of initial time series data.manifold: Data transformation through recombination and signal processing.features: Feature calculation from time series data.decluster: Optimization of feature space distribution.analyze: Analysis and visualization of feature distribution.dimred: Dimensionality reduction in feature space.io: Import/export functionalities for time series data.util: General utility functions for data manipulation.
Python 3.8. More details in Installation
There is no setup script, pip or conda package prepared. Part of the reason is
that the toolbox is still in pre-alpha stage (I didn't even dare to provide a
version number 0.1), part is missing knowledge about how to deploy it properly and
another part is that the some packages used for this one are in conflict.
The packages that are in conflict are:
kats, tsfresh, tsfel, pycatch24
The main issue seems to be a broken or too restrictive dependency tree of kats
(as of Aug 23, for a start, see
facebookresearch/Kats#308), making it hard to install
on it's own and harder to get to work with the other ones. I didn't manage to
create a setup without dependency conflicts, however, the following installation
procedure seems to work without errors in use:
mamba create python=3.8 -n estss
mamba activate estss
mamba install numpy pandas convertdate lunarcalendar holidays=0.23 tqdm pystan=2.19.1.1 fbprophet=0.7.1 packaging=21.3 kats=0.2.0 pycatch22 tsfresh ipython -c conda-forge
pip install tsfel
So, clone the repository, add it to the Python Path, e.g. add to .bashrc
export PYTHONPATH=$PYTHONPATH:$HOME/estss
setup the venv with mamba as described above and you should be good to go.
The pipeline from initial time series to the published declustered sets is as follows:
import pickle
from estss import *
# Load initial time series data
init = get_init_ts()
# Compute and save manifolded time series
mani_neg, mani_posneg, mani_info = compute_manifold_ts()
mani_neg.to_pickle('data/manifold_ts_only_neg.pkl')
mani_posneg.to_pickle('data/manifold_ts_only_posneg.pkl')
# Compute and save features
feat_neg, feat_posneg = compute_features()
feat_neg.to_pickle('data/manifold_feat_only_neg.pkl')
feat_posneg.to_pickle('data/manifold_feat_only_posneg.pkl')
# Compute and save declustered sets
sets = compute_declustered_sets()
with open('data/declustered_sets.pkl', 'wb') as file:
pickle.dump(sets, file)
# Export data as CSV
io.to_csv()
For detailed information and usage instructions, refer to the docstrings within each submodule and function.
Contributions are welcome! If you have suggestions for improvements or want to contribute code, please feel free to create an issue or submit a pull request.
This software is licensed under GPLv3, excluding later versions.
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
For details see $ESTSS/LICENSE.
GPLv3 explicitely allows a commercial usage without any royalty or further implications. However, any contact to discuss possible cooperations is appreciated.
estss - Energy System Time Series Suite
Copyright (C) 2023
Sebastian Günther
sebastian.guenther@ifes.uni-hannover.de
Leibniz Universität Hannover
Institut für Elektrische Energiesysteme
Fachgebiet für Elektrische Energiespeichersysteme
Leibniz University Hannover
Institute of Electric Power Systems
Electric Energy Storage Systems Section