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Support Vector Machine Enhanced Markov Model for Short TermWind Power Forecast

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Spatio-Temporal Data Analytics for Wind Energy Integration

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

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Abstract

Wind ramps introduce significant uncertainty in wind power generation. Reliable system operation, however, requires accurate detection and forecast of wind ramps, especially at high wind generation penetration levels. In this chapter, a support vector machine (SVM) enhanced Markov model for short-term wind power forecast is developed, taking into account not only wind ramps but also the diurnal non-stationarity and the seasonality of wind farm generation. Specifically, using the historical data of the wind turbine power outputs recorded at an actual wind farm, multiple finite-state Markov chains that take into account the diurnal non-stationarity and the seasonality of wind generation are first developed to model the “normal” fluctuations of wind generation. To deal with the wind ramp dynamics, an SVM is then employed, based on one key observation from the measurement data that wind ramps often occur with specific patterns. Next, the forecast by the SVM is integrated cohesively into the finite-state Markov chain. Based on the SVM enhanced Markov model, both (short-term) distributional forecasts and point forecasts are then derived.

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Notes

  1. 1.

    Examples of the designed state space and the corresponding transition matrix can be found in Chap. 2.

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Authors and Affiliations

  1. Electrical Computer & Energy Engineering Ira A. Fulton School of Engineering, Arizona State University, Tempe, Arizona, USA

    Lei Yang, Junshan Zhang & Vijay Vittal

  2. Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas, USA

    Miao He

Authors
  1. Lei Yang
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  2. Miao He
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  3. Junshan Zhang
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  4. Vijay Vittal
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Correspondence to Lei Yang .

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Yang, L., He, M., Zhang, J., Vittal, V. (2014). Support Vector Machine Enhanced Markov Model for Short TermWind Power Forecast. In: Spatio-Temporal Data Analytics for Wind Energy Integration. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-12319-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-12319-6_3

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  • Online ISBN: 978-3-319-12319-6

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