Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine

Abstract

Accurate wind speed forecasting is critical to the exploitation and utilization of wind energy. In this paper, a novel wind speed multi-step prediction model is designed based on the SSA (Singular Spectrum Analysis), EMD (Empirical Mode Decomposition) and CNNSVM (Convolutional Support Vector Machine). In the SSA-EMD-CNNSVM model, the SSA is used to reduce the noise and extract the trend information of the original wind speed data; the EMD is used to extract the fluctuation features of the wind speed data and decompose the wind speed time series into a number of sub-layers; and the CNNSVM is used to predict each of the wind speed sub-layers. To investigate the prediction performance of the proposed model, some models are used as the comparison models, including the SVM model, CNNSVM model, EMD-BP model, EMD-RBF model and EMD-Elman model. According to the prediction results of the four experiments, it can be found that the proposed model can have significantly better performance than the seven comparison models from 1-step to 3-step wind speed predictions with the MAPE of 42.85% average performance promotion, MAE of 39.21% average performance promotion, RMSE of 39.25% average performance promotion.