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Time series aggregation module (tsam) to create typical periods

Project description

PyPI Build Status Version

Forschungszentrum Juelich Logo

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.

If you want to use tsam in a published work, please kindly cite following publication which validates the methods and describes their cababilites. The manuscript is found here.

A second publication introduces a method how to model states (e.g. state of charge of storage) between the aggregated typical periods. The manuscript is found here.

Features

  • 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

Installation

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.

Examples

Basic workflow

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

	typPeriods.to_csv('typperiods.csv')

Detailed examples

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.

The example time series are based on a department publication and the test reference years of the DWD.

License

MIT License

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

About Us

Abteilung VSA

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 Jülich. 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’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

Within the BMWi funded project METIS we extend the methodology together with the RWTH-Aachen (Prof. Aaron Praktiknjo), the EDOM Team at FAU (PD Lars Schewe) and the Jülich Supercomputing Centre.

METIS Team

Acknowledgement

This work was supported by the Helmholtz Association under the Joint Initiative "Energy System 2050 A Contribution of the Research Field Energy".

Helmholtz Logo

DOI

Project details


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