Reduction of Energy Time Series: Unterschied zwischen den Versionen

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(Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Lucas Krauß |email=uneif@student.kit.edu |vortragstyp=Bachelorarbeit |betreuer=Edouard Fouché |termin=Institutsseminar/2018-04-20 |ku…“)
 
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|kurzfassung=Data Reduction is known as the process of compressing large amounts of data down to its most relevant parts and is an important sub-field of Data Mining.
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Energy time series (ETS) generally feature many components and are gathered at a high temporal resolution.
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Hence, it is required to reduce the data in order to allow analysis or further processing of the time series.
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However, existing data reduction methods do not account for  energy-related characteristics of ETS and thus may lead to unsatisfying results.
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In this work, we present a range of state-of-the art approaches for time series reduction (TSR) in the context of energy time series.
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The aim is to identify representative time slices from the multivariate energy time series without any prior knowledge about the inherent structure of the data.
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We rely on unsupervised approaches, i.e., clustering algorithms, to derive these representatives.
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For validation purpose, we apply the proposed reduction methods in two distinct approaches.
 
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Version vom 16. April 2018, 19:51 Uhr

Vortragende(r) Lucas Krauß
Vortragstyp Bachelorarbeit
Betreuer(in) Edouard Fouché
Termin Fr 20. April 2018
Vortragsmodus
Kurzfassung Data Reduction is known as the process of compressing large amounts of data down to its most relevant parts and is an important sub-field of Data Mining.

Energy time series (ETS) generally feature many components and are gathered at a high temporal resolution. Hence, it is required to reduce the data in order to allow analysis or further processing of the time series. However, existing data reduction methods do not account for energy-related characteristics of ETS and thus may lead to unsatisfying results.

In this work, we present a range of state-of-the art approaches for time series reduction (TSR) in the context of energy time series. The aim is to identify representative time slices from the multivariate energy time series without any prior knowledge about the inherent structure of the data. We rely on unsupervised approaches, i.e., clustering algorithms, to derive these representatives. For validation purpose, we apply the proposed reduction methods in two distinct approaches.