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Imitative Learning for Online Planning in Microgrids Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data

Authors : Judith Neugebauer, Oliver Kramer, Michael Sonnenschein

Published in: Data Analytics for Renewable Energy Integration

Publisher: 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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previous chapter Evaluation of Forecasting Methods for Very Small-Scale Networks
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Footnotes
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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Metadata
Title
Classification Cascades of Overlapping Feature Ensembles for Energy Time Series Data
Authors
Judith Neugebauer
Oliver Kramer
Michael Sonnenschein
Copyright Year
2015
Book
Data Analytics for Renewable Energy Integration

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

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

Copyright Year: 2015

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

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