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

Maria Grazia De Giorgi, Stefano Campilongo, Antonio Ficarella and Paolo Maria Congedo
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Maria Grazia De Giorgi: Department of Engineering for Innovation, University of Salento, via Monteroni, Lecce I-73100, Italy
Stefano Campilongo: Department of Engineering for Innovation, University of Salento, via Monteroni, Lecce I-73100, Italy
Antonio Ficarella: Department of Engineering for Innovation, University of Salento, via Monteroni, Lecce I-73100, Italy
Paolo Maria Congedo: Department of Engineering for Innovation, University of Salento, via Monteroni, Lecce I-73100, Italy

Energies, 2014, vol. 7, issue 8, 1-22

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.

Keywords: wind power forecasting; Least-Squares Support Vector Machine (LS-SVM); Artificial Neural Network (ANN); wavelet decomposition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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