Abstract
In this work, a hybrid system for wind power ramps events prediction in wind farms is proposed. The system is based on modelling the prediction problem as a binary classification problem from atmospheric reanalysis data inputs. On the other hand, a hybrid neuro-evolutive algorithm is proposed, which combines Artificial Neuronal Networks such as Extreme Learning Machines, with evolutionary algorithms to optimize the trained models. The phenomenon under study occurs with a very low probability, for this reason the problem is so unbalanced, and it is necessary to resort to techniques focused on obtain good results by means of a reduction of the samples from the majority class, as the SMOTE approach. A feature selection is performed by the evolutionary algorithm in order to choose the best trained model. Finally, this model is evaluated by a test set and its accuracy performance is given. The accuracy obtained in the results is quite good in terms of classification performance.
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Acknowledgements
This work has been partially supported by Comunidad de Madrid, under project number S2013/ICE-2933, and by project TIN2014-54583-C2-2-R of the Spanish Ministerial Commission of Science and Technology (MICYT). The authors acknowledge support by DAMA network TIM2015-70308-REDT.
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Cornejo-Bueno, L., Aybar-Ruiz, A., Camacho-Gómez, C., Prieto, L., Barea-Ropero, A., Salcedo-Sanz, S. (2017). A Hybrid Neuro-Evolutionary Algorithm for Wind Power Ramp Events Detection. 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_64
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