期刊
ENERGY CONVERSION AND MANAGEMENT
卷 180, 期 -, 页码 196-205出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2018.11.006
关键词
Wind speed forecasting; Singular spectrum analysis; Convolutional neural network; Support vector machine; Time series; Deep learning
资金
- National Natural Science Foundation of China [61873283]
- Changsha Science & Technology Project [KQ1707017]
- Shenghua Yu-ying Talents Program of the Central South University
- innovation driven project of the Central South University [502501002]
- engineering college 'double first-rate' supporting project of the Central South University [2018zzts163]
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.
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