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Energies 2018, 11(3), 526; https://doi.org/10.3390/en11030526

Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory

1
Departamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
2
Departamento de Ingeniería Eléctrica, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
3
Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
*
Authors to whom correspondence should be addressed.
Received: 5 February 2018 / Revised: 22 February 2018 / Accepted: 24 February 2018 / Published: 28 February 2018
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting)
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Abstract

Wind power generation has presented an important development around the world. However, its integration into electrical systems presents numerous challenges due to the variable nature of the wind. Therefore, to maintain an economical and reliable electricity supply, it is necessary to accurately predict wind generation. The Wind Power Prediction Tool (WPPT) has been proposed to solve this task using the power curve associated with a wind farm. Recurrent Neural Networks (RNNs) model complex non-linear relationships without requiring explicit mathematical expressions that relate the variables involved. In particular, two types of RNN, Long Short-Term Memory (LSTM) and Echo State Network (ESN), have shown good results in time series forecasting. In this work, we present an LSTM+ESN architecture that combines the characteristics of both networks. An architecture similar to an ESN is proposed, but using LSTM blocks as units in the hidden layer. The training process of this network has two key stages: (i) the hidden layer is trained with a descending gradient method online using one epoch; (ii) the output layer is adjusted with a regularized regression. In particular, the case is proposed where Step (i) is used as a target for the input signal, in order to extract characteristics automatically as the autoencoder approach; and in the second stage (ii), a quantile regression is used in order to obtain a robust estimate of the expected target. The experimental results show that LSTM+ESN using the autoencoder and quantile regression outperforms the WPPT model in all global metrics used. View Full-Text
Keywords: wind power forecasting; long short-term memory; echo state network; recurrent neural networks; time series; data science wind power forecasting; long short-term memory; echo state network; recurrent neural networks; time series; data science
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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López, E.; Valle, C.; Allende, H.; Gil, E.; Madsen, H. Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory. Energies 2018, 11, 526.

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