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href=\"https://www.fz-juelich.de/en/iek/iek-3\"\u003e\u003cimg src=\"https://www.fz-juelich.de/static/media/Logo.2ceb35fc.svg\" alt=\"Forschungszentrum Juelich Logo\" width=\"230px\"\u003e\u003c/a\u003e\n\n# tsam - Time Series Aggregation Module\ntsam is a python package which uses different machine learning algorithms for the aggregation of time series. The data aggregation can be performed in two freely combinable dimensions: By representing the time series by a user-defined number of typical periods or by decreasing the temporal resolution.\ntsam was originally designed for reducing the computational load for large-scale energy system optimization models by aggregating their input data, but is applicable for all types of time series, e.g., weather data, load data, both simultaneously or other arbitrary groups of time series.\n\nThe documentation of the tsam code can be found [**here**](https://tsam.readthedocs.io/en/latest/index.html).\n\n## Features\n* flexible handling of multidimensional time-series via the pandas module\n* different aggregation methods implemented (averaging, k-means, exact k-medoids, hierarchical, k-maxoids, k-medoids with contiguity), which are based on scikit-learn, or self-programmed with pyomo\n* hypertuning of aggregation parameters to find the optimal combination of the number of segments inside a period and the number of typical periods\n* novel representation methods, keeping statistical attributes, such as the distribution\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## Installation\n\nTo avoid dependency conflicts, it is recommended that you install Tsam in its own environment. You can use either [uv](https://docs.astral.sh/uv/)  or [conda/mamba](https://conda-forge.org/download/) ) to manage environments and installations. Before proceeding, you must install either UV or Conda/Mamba, or both.\n\n**Quick Install with uv**\n\n```bash\nuv venv tsam_env\nuv pip install tsam\n```\n\nOr from conda-forge:\n\n```bash\nconda create -n tsam_env -c conda-forge tsam\n```\n\nconda and mamba can be used interchangeably\n\n### Development Installation\n\n```bash\ngit clone https://github.com/FZJ-IEK3-VSA/tsam.git\ncd tsam\n```\n\n# Using uv (recommended)\n```bash\nuv venv\nsource .venv/bin/activate  # On Windows: .venv\\Scripts\\activate\nuv pip install -e \".[develop]\"\n```\n\n# Using conda-forge\n\n```bash\nconda env create -n tsam_env --file=environment.yml\nconda activate tsam_env\npip install -e . --no-deps\n```\n\n# Set up pre-commit hooks\n```bash\npre-commit install\n```\n\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for detailed development guidelines.\n\n### MILP Solver for k-medoids\n\n[HiGHS](https://github.com/ERGO-Code/HiGHS) is installed by default. For better performance on large problems, commercial solvers (Gurobi, CPLEX) are recommended if you have a license\n\n\n## Examples\n\n### Basic workflow\n\nA small example how tsam can be used is described as follows:\n```python\nimport pandas as pd\nimport tsam\n```\n\n\nRead in the time series data set with pandas\n```python\nraw = pd.read_csv('testdata.csv', index_col=0, parse_dates=True)\n```\n\nRun the aggregation - specify the number of typical periods and configure clustering/segmentation options:\n```python\nfrom tsam import aggregate, ClusterConfig, SegmentConfig\n\nresult = tsam.aggregate(\n    raw,\n    n_clusters=8,\n    period_duration='24h',  # or 24, '1d'\n    cluster=ClusterConfig(\n        method='hierarchical',\n        representation='distribution_minmax',\n    ),\n    segments=SegmentConfig(n_segments=8),\n)\n```\n\nAccess the results:\n```python\n# Get the typical periods DataFrame\ncluster_representatives = result.cluster_representatives\n\n# Check accuracy metrics\nprint(f\"RMSE: {result.accuracy.rmse.mean():.4f}\")\n\n# Reconstruct the original time series from typical periods\nreconstructed = result.reconstructed\n\n# Save results\ncluster_representatives.to_csv('cluster_representatives.csv')\n```\n\n### Legacy API\n\nFor backward compatibility, the class-based API of TSAM Version 2 is still available.\n```python\nimport tsam.timeseriesaggregation as tsam_legacy\n\naggregation = tsam_legacy.TimeSeriesAggregation(\n    raw,\n    noTypicalPeriods=8,\n    hoursPerPeriod=24,\n    segmentation=True,\n    noSegments=8,\n    representationMethod=\"distributionAndMinMaxRepresentation\",\n    clusterMethod='hierarchical'\n)\ncluster_representatives = aggregation.createTypicalPeriods()\n```\n\n### Detailed examples\nDetailed examples can be found at:/docs/source/examples_notebooks/\n\nA [**quickstart example**](/docs/source/examples_notebooks/quickstart.ipynb) shows the capabilities of tsam as a Jupyter notebook.\n\nA [**second example**](/docs/source/examples_notebooks/optimization_input.ipynb) shows in more detail how to access the relevant aggregation results required for parameterizing 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) 2017-2025 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\nThe core developer team sits in the [Institute of Energy and Climate Research - Techno-Economic Energy Systems Analysis (IEK-3)](https://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html) belonging to the [Forschungszentrum Jülich](https://www.fz-juelich.de/).\n\n## Citing and further reading\n\nIf you want to use tsam in a published work, **please kindly cite** our latest journal articles:\n* Hoffmann et al. (2022):\\\n[**The Pareto-Optimal Temporal Aggregation of Energy System Models**](https://www.sciencedirect.com/science/article/abs/pii/S0306261922004342)\n\n\nIf you are further interested in the impact of time series aggregation on the cost-optimal results on different energy system use cases, you can find a publication which validates the methods and describes their cababilites via the following [**link**](https://www.sciencedirect.com/science/article/pii/S0960148117309783). A second publication introduces a method how to model state variables (e.g. the state of charge of energy storage components) between the aggregated typical periods which can be found [**here**](https://www.sciencedirect.com/science/article/pii/S0306261918300242). Finally yet importantly the potential of time series aggregation to simplify mixed integer linear problems is investigated [**here**](https://www.mdpi.com/1996-1073/12/14/2825).\n\nThe publications about time series aggregation for energy system optimization models published alongside the development of tsam are listed below:\n* Hoffmann et al. (2021):\\\n[**The Pareto-Optimal Temporal Aggregation of Energy System Models**](https://www.sciencedirect.com/science/article/abs/pii/S0306261922004342)\\\n(open access manuscript to be found [**here**](https://arxiv.org/abs/1710.07593))\n* Hoffmann et al. (2021):\\\n[**Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models**](https://www.sciencedirect.com/science/article/abs/pii/S0306261921011545)\n* Hoffmann et al. (2020):\\\n[**A Review on Time Series Aggregation Methods for Energy System Models**](https://www.mdpi.com/1996-1073/13/3/641)\n* Kannengießer et al. (2019):\\\n[**Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System**](https://www.mdpi.com/1996-1073/12/14/2825)\n* Kotzur et al. (2018):\\\n[**Time series aggregation for energy system design: Modeling seasonal storage**](https://www.sciencedirect.com/science/article/pii/S0306261918300242)\\\n(open access manuscript to be found [**here**](https://arxiv.org/abs/1710.07593))\n* Kotzur et al. (2018):\\\n[**Impact of different time series aggregation methods on optimal energy system design**](https://www.sciencedirect.com/science/article/abs/pii/S0960148117309783)\\\n(open access manuscript to be found [**here**](https://arxiv.org/abs/1708.00420))\n\n\n\n## Acknowledgement\n\nThis work is 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/) and the program [\"Energy System Design\"](https://www.esd.kit.edu/index.php) and within the [BMWi/BMWk](https://www.bmwk.de/Navigation/DE/Home/home.html) funded project [**METIS**](http://www.metis-platform.net/).\n\n\u003ca href=\"https://www.helmholtz.de/en/\"\u003e\u003cimg src=\"https://www.helmholtz.de/fileadmin/user_upload/05_aktuelles/Marke_Design/logos/HG_LOGO_S_ENG_RGB.jpg\" alt=\"Helmholtz Logo\" width=\"200px\" style=\"float:right\"\u003e\u003c/a\u003e\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["energy-system-modeling","renewable-energy","geospatial-analysis","climate-change","energy-systems","optimisation","renewables","raster-data","geospatial-data","general-purpose"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/76445","html_url":"https://ost.ecosyste.ms/projects/76445"}