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Ordinal Multi-class Architecture for Predicting Wind Power Ramp Events Based on Reservoir Computing

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Abstract

Wind power ramp events (WPREs) are strong increases or decreases of wind speed in a short period of time. Predicting WPREs in wind farms is of vital importance given that they can produce damages in the turbines, and, in any case, they suddenly affect the wind farm production. In contrast to previous binary definitions of the prediction problem (ramp vs non-ramp), a three-class prediction model is used in this paper, proposing a novel discretization function, able to detect the nature of WPREs: negative ramp, non-ramp and positive ramp events. Moreover, the natural order of these labels is exploited to obtain better results in the prediction of these events. The independent variables used for prediction include, in this case, past wind speed values and meteorological data obtained from physical models (reanalysis data). Reanalysis will be also used for recovering missing data from the measuring stations in the wind farm. The proposed prediction methodology is based on Reservoir Computing and an over-sampling process for alleviating the high degree of unbalance in the dataset (non-ramp events are much more frequent than ramps). Three elements are combined in the prediction method: a recurrent neural network layer, a nonlinear kernel mapping and an ordinal logistic regression,to exploit the information provided by the order of the classes). Preprocessing is based on a variation of the standard synthetic minority over-sampling technique, which is applied to the reservoir activations (since the direct application over the input variables would damage its temporal structure). The performance of the method is analysed by comparing it against other state-of-the-art classifiers, such as Support Vector Machines, nominal logistic regression, an autoregressive ordinal neural network, or the use of leaky integrator neurons instead of the standard sigmoidal units. From the results obtained, the benefits of the kernel mapping and the ordinal model are clear, and, in general, the performance obtained with the Reservoir Computing approach is shown to be very robust in the detection of ramps.

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Acknowledgements

This work has been subsidized by the TIN2017-85887-C2-1-P, TIN2017-85887-C2-2-P, TIN2017-90567-REDT, TIN2014-54583-C2-1-R and TIN2014-54583-C2-2-R projects of the Spanish Ministry of Economy and Competitiveness (MINECO) and FEDER funds. Manuel Dorado-Moreno’s research has been subsidised by the FPU Predoctoral Program (Spanish Ministry of Education and Science), grant reference FPU15/00647. The authors acknowledge NVIDIA Corporation for the grant of computational resources through the GPU Grant Program

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Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, Universidad de Córdoba, Córdoba, Spain

    M. Dorado-Moreno, P. A. Gutiérrez & C. Hervás-Martínez

  2. Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, Spain

    L. Cornejo-Bueno & S. Salcedo-Sanz

  3. Department of Energy Resource, Iberdrola, Madrid, Spain

    L. Prieto

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  1. M. Dorado-Moreno
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  2. P. A. Gutiérrez
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  3. L. Cornejo-Bueno
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  5. S. Salcedo-Sanz
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  6. C. Hervás-Martínez
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Correspondence to M. Dorado-Moreno.

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Dorado-Moreno, M., Gutiérrez, P.A., Cornejo-Bueno, L. et al. Ordinal Multi-class Architecture for Predicting Wind Power Ramp Events Based on Reservoir Computing. Neural Process Lett 52, 57–74 (2020). https://doi.org/10.1007/s11063-018-9922-5

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