Short-term wind speed forecasting model based on ARMA-LSSVM and wavelet transform

doi:10.11930/j.issn.1004-9649.2012.4.78.3

Abstract

Abstract: A wind speed forecasting with high accuracy can effectively reduce or avoid the adverse effect of wind farm on power grids, meanwhile it can enhance the competitive ability of wind power in electricity market. A short-term wind speed forecasting method based on auto-regressive moving average （ARMA） model and least square support vector machine （LS-SWM） model was proposed. By using wavelet transform method, the historical load data was decomposed into series with different frequency characteristics. The low frequency components were predicted by LS-SVM model, while the high frequency components were predicted by ARMA model. The final forecasting results were obtained with wavelet reconstruction. Research results show that the prediction accuracy is different from each method. The mean square error of the proposed hybrid forecast model is 11.5%, better than the results by single forecasting methods.

Key words: short-term wind speed forecasting, wavelet transform, time series, least square support vector machine

CLC Number:

TM614

ZHAO Hui¹, LI Bin¹, LI Biao², YUE You-jun¹. Short-term wind speed forecasting model based on ARMA-LSSVM and wavelet transform[J]. Electric Power, 2012, 45(4): 78-81.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2012.4.78.3

https://www.electricpower.com.cn/EN/Y2012/V45/I4/78

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