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Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data

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Data Analytics for Renewable Energy Integration (DARE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9518))

Abstract

The classification of high-dimensional time series data can be a challenging task due to the curse-of-dimensionality problem. The classification of time series is relevant in various applications, e.g., in the task of learning meta-models of feasible schedules for flexible components in the energy domain. In this paper, we introduce a classification approach that employs a cascade of classifiers based on features of overlapping time series steps. To evaluate the feasibility of the whole time series, each overlapping pattern is evaluated and the results are aggregated. We apply the approach to the problem of combined heat and power plant operation schedules and an artificial similarly structured data set. We identify conditions under which the cascade approach shows better results than a classic One-Class-SVM.

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Notes

  1. 1.

    Data are available for download on our department website http://www.uni-oldenburg.de/informatik/ui/forschung/themen/cascade/.

  2. 2.

    \(\gamma \in \{0.1, 1, 10, 50, 100, 150,200\}\).

  3. 3.

    \(\nu \in \{0.0001, 0.001, 0.0025, 0.005, 0.0075, 0.01, 0.025, 0.05, 0.075, 0.1, 0.2\}\).

  4. 4.

    \(\epsilon \in \{0.01, 0.05, 0.1, 0.15, 0.2\}\).

  5. 5.

    \(k_t \in \{1,5,10,15\}\).

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Acknowledgments

This work was funded by the Ministry for Science and Culture of Lower Saxony with the PhD program System Integration of Renewable Energy (SEE).

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Authors and Affiliations

  1. Department of Computing Science, University of Oldenburg, Oldenburg, Germany

    Judith Neugebauer, Oliver Kramer & Michael Sonnenschein

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  1. Judith Neugebauer
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  2. Oliver Kramer
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  3. Michael Sonnenschein
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Correspondence to Judith Neugebauer .

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Editors and Affiliations

  1. Electrical Eng. & Computer Science, Masdar Inst Science & Technology, Abu Dhabi, United Arab Emirates

    Wei Lee Woon

  2. Electrical Eng. & Computer Science, Masdar Inst. of Science &Technology, Abu Dhabi, United Arab Emirates

    Zeyar Aung

  3. MIT Sloan School of Management, CAMBRIDGE, Massachusetts, USA

    Stuart Madnick

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Neugebauer, J., Kramer, O., Sonnenschein, M. (2015). Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2015. Lecture Notes in Computer Science(), vol 9518. Springer, Cham. https://doi.org/10.1007/978-3-319-27430-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-27430-0_6

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