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

Energies (Basel), 2014-08, Vol.7 (8), p.5251-5272 [Peer Reviewed Journal]

Copyright MDPI AG 2014 ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en7085251

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  • Title:
    Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)
  • Author: Giorgi, Maria Grazia De ; Campilongo, Stefano ; Ficarella, Antonio ; Congedo, Paolo Maria
  • Subjects: Artificial Neural Network (ANN) ; Decomposition ; Learning theory ; Least squares method ; Least-Squares Support Vector Machine (LS-SVM) ; Mathematical models ; Neural networks ; Sensitivity analysis ; Support vector machines ; wavelet decomposition ; Wind power ; wind power forecasting
  • Is Part Of: Energies (Basel), 2014-08, Vol.7 (8), p.5251-5272
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1996-1073
    EISSN: 1996-1073
    DOI: 10.3390/en7085251
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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