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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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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 \(t=i\).

References

  1. Greaves, B., Collins, J., Parkes, J., Tindal, A.: Temporal forecast uncertainty for ramp events. Wind Eng. 33(4), 309–319 (2009)

    CrossRef  Google Scholar 

  2. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector. Mach. Learn. 46(1–3), 389–442 (2002)

    CrossRef  MATH  Google Scholar 

  3. Heinermann, J., Kramer, O.: Precise wind power prediction with SVM ensemble regression. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 797–804. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  4. Hunter, J.D.: Matplotlib: a 2d graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007)

    CrossRef  Google Scholar 

  5. Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: open source scientific tools for Python (2001). Accessed 15 July 2014

    Google Scholar 

  6. Kamath, C.: Understanding wind ramp events through analysis of historical data. In: Proceedings of the IEEE PES Transmission and Distribution Conference and Exposition, pp. 1–6 (2010)

    Google Scholar 

  7. Kramer, O., Gieseke, F., Satzger, B.: Wind energy prediction and monitoring with neural computation. Neurocomputing 109, 84–93 (2013)

    CrossRef  Google Scholar 

  8. Kramer, O., Treiber, N.A., Sonnenschein, M.: Wind power ramp event prediction with support vector machines. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 37–48. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  9. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MATH  MathSciNet  Google Scholar 

  10. Poloczek, J., Treiber, N.A., Kramer, O.: KNN regression as geo-imputation method for spatio-temporal wind data. In: de la Puerta, J.G., et al. (eds.) International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. AISC, vol. 299, pp. 185–193. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  11. Potter, C.W., Lew, D., McCaa, J., Cheng, S., Eichelberger, S., Grimit, E.: Creating the dataset for the western wind and solar integration study (USA). In: 7th International Workshop on Large Scale Integration of Wind Power and on Transmission Networks for Offshore Wind Farms, (2008)

    Google Scholar 

  12. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    CrossRef  Google Scholar 

  13. Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)

    CrossRef  Google Scholar 

  14. Treiber, N.A., Heinermann, J., Kramer, O.: Aggregation of features for wind energy prediction with support vector regression and nearest neighbors. In: European Conference on Machine Learning (ECML), Workshop DARE (2013)

    Google Scholar 

  15. Treiber, N.A., Kramer, O.: Evolutionary turbine selection for wind power predictions. In: Lutz, C., Thielscher, M. (eds.) KI 2014. LNCS, vol. 8736, pp. 267–272. Springer, Heidelberg (2014)

    Google Scholar 

  16. Vanderplas, J., Connolly, A., Ivezić, Ž, Gray, A.: Introduction to astroml: machine learning for astrophysics. In: Conference on Intelligent Data Understanding (CIDU), pp. 47–54 (2012)

    Google Scholar 

  17. van der Walt, S., Colbert, S.C., Varoquaux, G.: The numpy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13(2), 22–30 (2011)

    CrossRef  Google Scholar 

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Acknowledgments

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

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Correspondence to Oliver Kramer .

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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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  • DOI: https://doi.org/10.1007/978-3-319-13290-7_8

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