Description

Code (Version 1.1.1) utilized to generate the results in the publication “Prosumer Response Estimation using SINDyc in conjunction with Markov-chain Monte-Carlo Sampling”:

Banis, F.; Madsen, H.; Poulsen, N.K.; Guericke, D. Prosumer Response Estimation Using SINDyc in Conjunction with Markov-Chain Monte-Carlo Sampling. Energies 2020, 13, 3183.

This code is citable through the following BibTeX entry:

@software{banis_frederik_2020_3911952, author = {Banis, Frederik}, title = {SINDyc And MCMC Framework}, month = jun, year = 2020, publisher = {Zenodo}, version = {1.1.1}, doi = {10.5281/zenodo.3911952}, url = {https://doi.org/10.5281/zenodo.3911952} }

See https://zenodo.org/record/3911952#.XvmavjWxWCh.

Prerequisites

• Tested on a UNIX-based system.

python

Tested with version 3.7.7.

numpy

https://pypi.org/project/numpy/. Tested with version 1.18.5.

pandas

https://pypi.org/project/pandas/. Tested with version 1.0.5.

control

https://pypi.org/project/control/. Tested with version 0.8.2.

pystan

https://pypi.org/project/pystan/. Tested with version 2.19.1.1.

pySINDy

https://github.com/luckystarufo/pySINDy/.

Recent package version, untested

https://github.com/dynamicslab/.

Installation

• Install python and pip for your system and install the packages listed in the previous Section Prerequisites via pip.
• Create the ./Illustrations folder in this directory, if not already present. Plots will be saved in this directory.

License

This code is licensed under the MIT license. See the License statement in this directory.

Miscellaneous

Formatted using black (https://pypi.org/project/black/). Versioned using semver (https://semver.org/).