Time series aggregation module (tsam) to create typical periods
tsam - Time Series Aggregation Module
tsam 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.
- flexible read-in and handling of multidimensional time-series via the pandas module
- different aggregation methods implemented (averaging, k-means, exact k-medoids, hierarchical), which are based on scikit-learn or pyomo
- flexible integration of extreme periods as own cluster centers
- weighting for the case of multidimensional time-series to represent their relevance
Directly install via pip as follows:
pip install tsam
Alternatively, clone a local copy of the repository to your computer
git clone https://github.com/FZJ-IEK3-VSA/tsam.git
Then install tsam via pip as follow
cd tsam pip install .
Or install directly via python as
python setup.py install
In 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.
A small example how tsam can be used is decribed as follows
import pandas as pd import tsam.timeseriesaggregation as tsam
Read in the time series data set with pandas
raw = pd.read_csv('testdata.csv', index_col = 0)
Initialize an aggregation object and define the number of typical periods, the length of a single period and the aggregation method
aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 8, hoursPerPeriod = 24, clusterMethod = 'hierarchical')
Run the aggregation to typical periods
typPeriods = aggregation.createTypicalPeriods()
Store the results as .csv file
A first example shows the capabilites of tsam as jupyter notebook.
A second example shows in more detail how to access the relevant aggregation results required for paramtrizing e.g. an optimization.
Copyright (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)
You should have received a copy of the MIT License along with this program. If not, see https://opensource.org/licenses/MIT
We are the Techno-Economic Energy Systems Analysis department at the Institute of Energy and Climate Research: Electrochemical Process Engineering (IEK-3) belonging to the Forschungszentrum JÃ¼lich. 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 Governmentâ€™s 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.
Contributions and Users
This work was supported by the Helmholtz Association under the Joint Initiative "Energy System 2050 A Contribution of the Research Field Energy".
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