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"description": "\u00ef\u00bb\u00bf[](https://pypi.python.org/pypi/tsam) [](https://travis-ci.com/FZJ-IEK3-VSA/tsam) [](https://pypi.python.org/pypi/tsam)\n\n![\"Forschungszentrum](\"https://www.fz-juelich.de/SharedDocs/Bilder/IBG/IBG-3/DE/Plant-soil-atmosphere%20exchange%20processes/INPLAMINT%20(BONARES)/Bild3.jpg?__blob=poster\")
\n\n# tsam - Time Series Aggregation Module\ntsam is a python package which uses different machine learning algorithms for the aggregation of typical periods. It is applicable for all type of time series, eather weather data, load data or both simultaneously. The module is able to significantly reduce input time series for energy system models, and therefore the model's complexity and computational time. \n\n\nIf you want to use tsam in a published work, please [**kindly cite following publication**](https://www.sciencedirect.com/science/article/pii/S0960148117309783) which validates the methods and describes their cababilites. The manuscript is found [**here**](https://arxiv.org/abs/1708.00420). \n\nA [**second publication**](https://www.sciencedirect.com/science/article/pii/S0306261918300242) introduces a method how to model states (e.g. state of charge of storage) between the aggregated typical periods. The manuscript is found [**here**](https://arxiv.org/abs/1710.07593).\n\n## Features\n* flexible read-in and handling of multidimensional time-series via the pandas module\n* different aggregation methods implemented (averaging, k-means, exact k-medoids, hierarchical), which are based on scikit-learn or pyomo\n* flexible integration of extreme periods as own cluster centers\n* weighting for the case of multidimensional time-series to represent their relevance\n\n\n## Installation\nDirectly install via pip as follows:\n\n\tpip install tsam\n\nAlternatively, clone a local copy of the repository to your computer\n\n\tgit clone https://github.com/FZJ-IEK3-VSA/tsam.git\n\nThen install tsam via pip as follow\n\n\tcd tsam\n\tpip install . \n\nOr install directly via python as \n\n\tpython setup.py install\n\nIn order to use the k-medoids clustering, make sure that you have installed a MILP solver. As default solver GLPK is used. Nevertheless, in case you have access to a license we recommend commercial solvers (e.g. Gurobi or CPLEX) since they have a better performance.\n\n\n## Examples\n\n### Basic workflow\n\nA small example how tsam can be used is decribed as follows\n```python\n\timport pandas as pd\n\timport tsam.timeseriesaggregation as tsam\n```\n\n\nRead in the time series data set with pandas\n```python\n\traw = pd.read_csv('testdata.csv', index_col = 0)\n```\n\nInitialize an aggregation object and define the number of typical periods, the length of a single period and the aggregation method\n```python\n\taggregation = tsam.TimeSeriesAggregation(raw, \n\t\t\t\t\t\tnoTypicalPeriods = 8, \n\t\t\t\t\t\thoursPerPeriod = 24, \n\t\t\t\t\t\tclusterMethod = 'hierarchical')\n```\n\nRun the aggregation to typical periods\n```python\n\ttypPeriods = aggregation.createTypicalPeriods()\n```\n\nStore the results as .csv file\n\n```python\n\ttypPeriods.to_csv('typperiods.csv')\n```\n\n### Detailed examples\n\nA [**first example**](/examples/aggregation_example.ipynb) shows the capabilites of tsam as jupyter notebook. \n\nA [**second example**](/examples/aggregation_optiinput.ipynb) shows in more detail how to access the relevant aggregation results required for paramtrizing e.g. an optimization.\n\nThe example time series are based on a department [publication](https://www.mdpi.com/1996-1073/10/3/361) and the [test reference years of the DWD](https://www.dwd.de/DE/leistungen/testreferenzjahre/testreferenzjahre.html).\n\n## License\n\nMIT License\n\nCopyright (C) 2016-2019 Leander Kotzur (FZJ IEK-3), Maximilian Hoffmann (FZJ IEK-3), Peter Markewitz (FZJ IEK-3), Martin Robinius (FZJ IEK-3), Detlef Stolten (FZJ IEK-3)\n\nYou should have received a copy of the MIT License along with this program.\nIf not, see https://opensource.org/licenses/MIT\n\n## About Us \n![\"Abteilung](\"https://www.fz-juelich.de/SharedDocs/Bilder/IEK/IEK-3/Abteilungen2015/VSA_DepartmentPicture_2019-02-04_459x244_2480x1317.jpg?__blob=normal\")
\n\nWe are the [Techno-Economic Energy Systems Analysis](https://www.fz-juelich.de/iek/iek-3/EN/Forschung/_Process-and-System-Analysis/_node.html) department at the [Institute of Energy and Climate Research: Electrochemical Process Engineering (IEK-3)](https://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html) belonging to the [Forschungszentrum J\u00c3\u00bclich](https://www.fz-juelich.de/). Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government\u00e2\u20ac\u2122s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks.\n\n## Contributions and Users\n\nWithin the BMWi funded project [**METIS**](http://www.metis-platform.net/) we extend the methodology together with the RWTH-Aachen ([**Prof. Aaron Praktiknjo**](https://www.wiwi.rwth-aachen.de/cms/Wirtschaftswissenschaften/Die-Fakultaet/Institute-und-Lehrstuehle/Professoren/~jgfr/Praktiknjo-Aaron/?allou=1&lidx=1)), the EDOM Team at FAU ([**PD Lars Schewe**](https://www.mso.math.fau.de/de/edom/team/schewe-lars/dr-lars-schewe/)) and the [**J\u00c3\u00bclich Supercomputing Centre**](https://www.fz-juelich.de/ias/jsc/DE/Home/home_node.html).\n\n![\"METIS](\"http://www.metis-platform.net/metis-platform/DE/_Documents/Pictures/projectTeamAtKickOffMeeting_640x338.jpg?__blob=normal\")
\n\n\n## Acknowledgement\n\nThis work was supported by the Helmholtz Association under the Joint Initiative [\"Energy System 2050 A Contribution of the Research Field Energy\"](https://www.helmholtz.de/en/research/energy/energy_system_2050/).\n\n![\"Helmholtz](\"https://www.helmholtz.de/fileadmin/user_upload/05_aktuelles/Marke_Design/logos/HG_LOGO_S_ENG_RGB.jpg\")
\n\n\n[](https://doi.org/10.5281/zenodo.597956)\n\n\n",
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