Abstract
A new compound model based on wavelet packet decomposition (WPD) and quantum particle swarm optimization algorithm (QPSO) tuning least squares support vector machine (LSSVM), namely WPD-QPSO-LSSVM, is developed in this study for forecasting short-term wind speed. In the developed model, WPD is firstly applied to preprocess the raw volatile wind speed data test samples to obtain relatively stable different components. Then, LSSVMs are utilized to predict short-term wind speed by these stable subseries components after the input variables are reconstructed by partial autocorrelation function (PACF), and the final short term wind speed forecasting results can be obtained by aggregation of each prediction of different components. In the end, the actual historical wind speed data are applied to evaluate the forecasting performance of the proposed WPD-QPSO-LSSVM model. Compared with the recent developed methods, the proposed compound WPD-QPSO-LSSVM approach can effectively improve the forecasting accuracy.
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