Abstract
Wind power ramp events (WPREs) are strong increases or decreases of wind speed in a short period of time. Predicting WPREs is of vital importance given that they can damage the turbines in a wind farm. In contrast to previous binary approaches (ramp versus non-ramp), a three-class prediction is proposed in this paper by considering: negative ramp, non-ramp and positive ramp, where the natural order of the events is clear. The independent variables used for prediction include past ramp function values and meteorological data obtained from physical models (reanalysis data). The proposed methodology is based on reservoir computing and an over-sampling process for alleviating the high degree of unbalance of the dataset (non-ramp events are much more frequent than ramps). The reservoir computing model is a modified echo state network composed by: a recurrent neural network layer, a nonlinear kernel mapping and an ordinal logistic regression, in such a way that the order of the classes can be exploited. The standard synthetic minority oversampling technique (SMOTE) is applied to the reservoir activations, given that the direct application over the input variables would damage its temporal structure. The performance of this proposal is compared to the original dataset (without over-sampling) and to nominal logistic regression, and the results obtained with the oversampled dataset and ordinal logistic regression are found to be more robust.
This work has been subsidized by the TIN2014-54583-C2-1-R, TIN2014-54583-C2-2-R and TIN2015-70308-REDT projects of the Spanish Ministerial Commission of Science and Technology (MINECO, Spain) and FEDER funds (EU). Manuel Dorado-Moreno’s research has been subsidized by the FPU Predoctoral Program of the Spanish Ministry of Education, Culture and Sport, grant reference FPU15/00647.
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Dorado-Moreno, M., Cornejo-Bueno, L., Gutiérrez, P.A., Prieto, L., Salcedo-Sanz, S., Hervás-Martínez, C. (2017). Combining Reservoir Computing and Over-Sampling for Ordinal Wind Power Ramp Prediction. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_61
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