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ClustForOpt is a julia implementation of unsupervised machine learning methods for finding representative periods for energy systems optimization problems. By reducing the number of time steps used in the optimization model, using representative periods leads to significant reductions in computational complexity.

The package has three main purposes:

  1. Provide a simple process of finding representative periods for time-series input data, with implementations of the most commonly used clustering methods and extreme value selection methods.
  2. Provide an interface between representative period data and optimization problem by having representative period data stored in a generalized type system.
  3. Provide a generalized import feature for time series, where variable names, attributes, and node names are automatically stored and can then be used in the definition of sets of the optimization problem later.

An example energy systems optimization problem that uses ClustForOpt for its input data is the package CapacityExpansion, which implements a scalable generation and transmission capacity expansion problem.

The ClustForOpt package follows the clustering framework presented in Teichgraeber and Brandt, 2019. The package is actively developed, and new features are continuously added. For a reproducible version of the methods and data of the original paper by Teichgraeber and Brandt, 2019, please refer to v0.1 (including shape based methods such as k-shape and dynamic time warping barycenter averaging).

This package is developed by Holger Teichgraeber @holgerteichgraeber and Elias Kuepper @YoungFaithful.


This package runs under julia v1.0 and higher. Install using:

import Pkg


Documentation (Stable): Please refer to this documentation for details on how to use ClustForOpt the current version of ClustForOpt. This is the documentation of the default version of the package.

Documentation (Development): If you like to try the development version of ClustForOpt, please refer to this documentation.

See NEWS for significant breaking changes when updating from one version of ClustForOpt to another.

Citing ClustForOpt

If you find ClustForOpt useful in your work, we kindly request that you cite the following paper (link):

  author = {Holger Teichgraeber and Adam Brandt},
  title = {Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison},
  journal = {Applied Energy},
  volume = {239},
  pages = {1283–1293},
  year = {2019},
  doi = {},

Quick Start Guide

This quick start guide introduces the main concepts of using ClustForOpt. For more detail on the different functionalities that ClustForOpt provides, please refer to the subsequent chapters of the documentation or the examples in the examples folder, specifically workflow_introduction.jl.

Generally, the workflow consists of three steps:

  • load data
  • find representative periods (clustering + extreme period selection)
  • optimization

Example Workflow

After ClustForOpt is installed, you can use it by saying:

using ClustForOpt

The first step is to load the data. The following example loads hourly wind, solar, and demand data for Germany (1 region) for one year.

ts_input_data = load_timeseries_data(:CEP_GER1)

The output ts_input_data is a ClustData data struct that contains the data and additional information about the data. # a dictionary with the data.["wind-germany"] # the wind data (choose solar, el_demand as other options in this example)
ts_input_data.K # number of periods

The second step is to cluster the data into representative periods. Here, we use k-means clustering and get 5 representative periods.

clust_res = run_clust(ts_input_data;method="kmeans",n_clust=5)
ts_clust_data = clust_res.clust_data

The ts_clust_data is a ClustData data struct, this time with clustered data (i.e. less representative periods). # the clustered data["wind-germany"] # the wind data. Note the dimensions compared to ts_input_data
ts_clust_data.K # number of periods

The clustered input data can be used as input to an optimization problem. The optimization problem formulated in the package CapacityExpansion can be used with the data clustered in this example.