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tsam 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. tsam 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.
The documentation of the tsam code can be found here.
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 HiGHS 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 length of a single period, the number of typical periods, the number of segments in each period, the aggregation method and the representation method - here duration/distribution representation which contains the minimum and maximum value of the original time series
aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 8, hoursPerPeriod = 24, segmentation = True, noSegments = 8, representationMethod = "distributionAndMinMaxRepresentation", distributionPeriodWise = False 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) 2016-2022 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
The core developer team sits in the Institute of Energy and Climate Research - Techno-Economic Energy Systems Analysis (IEK-3) belonging to the Forschungszentrum Jülich.
If you want to use tsam in a published work, please kindly cite our latest journal articles:
If 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. 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. Finally yet importantly the potential of time series aggregation to simplify mixed integer linear problems is investigated here.
The publications about time series aggregation for energy system optimization models published alongside the development of tsam are listed below:
This work is supported by the Helmholtz Association under the Joint Initiative "Energy System 2050 A Contribution of the Research Field Energy" and the program "Energy System Design" and within the BMWi/BMWk funded project METIS.