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International Workshop on Data Analytics for Renewable Energy Integration

DARE 2014: Data Analytics for Renewable Energy Integration pp 97–107Cite as

A Framework for Data Mining in Wind Power Time Series

Part of the Lecture Notes in Computer Science book series (LNAI,volume 8817)

Abstract

Wind energy is playing an increasingly important part for ecologically friendly power supply. The fast growing infrastructure of wind turbines can be seen as large sensor system that screens the wind energy at a high temporal and spatial resolution. The resulting databases consist of huge amounts of wind energy time series data that can be used for prediction, controlling, and planning purposes. In this work, we describe WindML, a Python-based framework for wind energy related machine learning approaches. The main objective of WindML is the continuous development of tools that address important challenges induced by the growing wind energy information infrastructures. Various examples that demonstrate typical use cases are introduced and related research questions are discussed. The different modules of WindML reach from standard machine learning algorithms to advanced techniques for handling missing data and monitoring high-dimensional time series.

Keywords

  • Wind Turbine
  • Wind Power
  • Support Vector Regression
  • Wind Energy
  • Smart Grid

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-13290-7_8
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Notes

  1. 1.

    The source code is publicly available on http://www.windml.org.

  2. 2.

    Similarly, Greaves et al.  [1] define a ramp event for a wind farm as a change of energy output of more than \(\theta = 50\) of the installed capacity within a time span of four hours or less.

  3. 3.

    We define a pattern as \(\mathbf {x}_i = (p_1(t),\ldots ,p_d(t))^T\) with turbines \(1,\ldots ,d\) at time \(t=i\).

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Acknowledgments

We thank the National Renewable Energy Laboratory for providing the Western Wind Data Set [11].

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Authors and Affiliations

  1. Computational Intelligence Group, Department of Computer Science, University of Oldenburg, Oldenburg, Germany

    Oliver Kramer, Justin Heinermann, Jendrik Poloczek & Nils André Treiber

  2. Image Group, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Fabian Gieseke

Authors
  1. Oliver Kramer
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  2. Fabian Gieseke
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  3. Justin Heinermann
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  4. Jendrik Poloczek
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  5. Nils André Treiber
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Corresponding author

Correspondence to Oliver Kramer .

Editor information

Editors and Affiliations

  1. Masdar Inst. of Science and Technology, Abu Dhabi, Utd.Arab.Emir.

    Wei Lee Woon

  2. Masdar Inst. of Science and Technology, Abu Dhabi, Utd.Arab.Emir.

    Zeyar Aung

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

    Stuart Madnick

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Kramer, O., Gieseke, F., Heinermann, J., Poloczek, J., Treiber, N.A. (2014). A Framework for Data Mining in Wind Power Time Series. In: Woon, W., Aung, Z., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2014. Lecture Notes in Computer Science(), vol 8817. Springer, Cham. https://doi.org/10.1007/978-3-319-13290-7_8

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