TimeSeriesClustering: An extensible framework in Julia

Teichgraeber, Holger; Kuepper, Lucas Elias; Brandt, Adam R.

doi:10.21105/joss.01573

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…To apply the k-means and hierarchical clustering methods their implementation in the TimeSeriesClustering.jl package was used [23]. For details see the supplementary material.…”

Section: Methods For Obtaining Reduced Time-seriesmentioning

confidence: 99%

Adequacy of time-series reduction for renewable energy systems

Göke,

Kendziorski

2021

Preprint

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To manage computational complexity, models of macro-energy systems commonly deploy reduced sets of time-series data. This paper evaluates the adequacy of time-series reduction when modelling energy systems with fully renewable generation and a consequent dependency on storage. Analysis includes various methods to derive reduced time-series and to implement them into models, either as time-slices, also referred to as representative days, or continuous timesteps. All methods are tested with regard to unmet demand and accuracy of estimated system costs using a simple capacity expansion model of the power sector within a renewable energy system. Some methods achieve little unmet demand, but instead their results regarding storage are biased and favour seasonal at the expense of short-term storage. We conclude that renewable energy systems limit the adequacy of time-series reduction and future research should focus on alternative methods to reduce computational complexity.

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“…To apply the k-means and hierarchical clustering methods their implementation in the TimeSeriesClustering.jl package was used [23]. For details see the supplementary material.…”

Section: Methods For Obtaining Reduced Time-seriesmentioning

confidence: 99%

Adequacy of time-series reduction for renewable energy systems

Göke,

Kendziorski

2021

Preprint

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“…Teichgraeber, H. and Brandt, A.R., ( 2019), [11], conclusively describe a framework for approaching clustering methods, and comparing different forms of grouping between cluster members obtain support for solutions in energy system optimization problems.…”

Section: Research From the Specialized Literaturementioning

confidence: 99%

The Complex System of Mining and Energy Resources: Premises for Clusterization

et al. 2023

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The article shows that when dealing with many enterprises in mining areas, it is necessary to search for methods of efficiency, ways of maximizing the utility of their operation in accordance with the requirements for the consumption of useful mineral substances, primary energy resources and energy and in relation to their production potential. Clustering in the complex system of mining and energy resources is an approach that refers to geographical concentrations of interconnected companies and institutions in a certain field (mining and energy), related industries and entities for competition. Mainly, the authors deal with issues related to conceptual approaches related to clustering, anthropogenic impacts and mining/energy clustering, the delimitation of “clusters - links - infrastructure” alignments for the promotion of the mining and energy industry / economy in a collaborative regime. The conclusions are useful to the developers of strategies, tactics and programs in the field of the new industry and mining/energy economies in Romania and in other reference countries studied by the authors from the perspective of the article’s theme.

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