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Energies 2014, 7(8), 5251-5272; doi:10.3390/en7085251

Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)

Department of Engineering for Innovation, University of Salento, via Monteroni, Lecce I-73100, Italy
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Received: 5 May 2014 / Revised: 5 August 2014 / Accepted: 5 August 2014 / Published: 14 August 2014
(This article belongs to the Special Issue Wind Turbines 2014)
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Abstract

A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition. View Full-Text
Keywords: wind power forecasting; Least-Squares Support Vector Machine (LS-SVM); Artificial Neural Network (ANN); wavelet decomposition wind power forecasting; Least-Squares Support Vector Machine (LS-SVM); Artificial Neural Network (ANN); wavelet decomposition
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

De Giorgi, M.G.; Campilongo, S.; Ficarella, A.; Congedo, P.M. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Energies 2014, 7, 5251-5272.

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