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Erschienen in:
Imitative Learning for Online Planning in Microgrids Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data

verfasst von : Judith Neugebauer, Oliver Kramer, Michael Sonnenschein

Erschienen in: Data Analytics for Renewable Energy Integration

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Evaluation of Forecasting Methods for Very Small-Scale Networks
Nächstes Kapitel Correlation Analysis for Determining the Potential of Home Energy Management Systems in Germany
Fußnoten
2
\(\gamma \in \{0.1, 1, 10, 50, 100, 150,200\}\).
 
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
\(\epsilon \in \{0.01, 0.05, 0.1, 0.15, 0.2\}\).
 
5
\(k_t \in \{1,5,10,15\}\).
 
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Metadaten
Titel
Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data
verfasst von
Judith Neugebauer
Oliver Kramer
Michael Sonnenschein
Copyright-Jahr
2015
Buch
Data Analytics for Renewable Energy Integration

Print ISBN: 978-3-319-27429-4

Electronic ISBN: 978-3-319-27430-0

Copyright-Jahr: 2015

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