Journal
RENEWABLE ENERGY
Volume 143, Issue -, Pages 842-854Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2019.05.039
Keywords
Wind speed forecasting; Singular spectrum analysis; Convolutional gated recurrent unit network; Support vector regression; Time series; Deep learning
Funding
- National Natural Science Foundation of China, China [61873283]
- Changsha Science & Technology Project [KQ1707017]
- Shenghua Yu-ying Talents Program of the Central South University, China
- innovation driven project of the Central South University [2019CX005]
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Wind speed forecasting can effectively improve the safety and reliability of wind energy generation system. In this study, a novel hybrid short-term wind speed forecasting model is proposed based on the SSA (Singular Spectrum Analysis) method, CNN (Convolutional Neural Network) method, GRU (Gated Recurrent Unit) method and SVR (Support Vector Regression) method. In the proposed SSA-CNNGRU-SVR model, the SSA is used to decompose the original wind speed series into a number of components as: one trend component and several detail components; the CNNGRU is used to predict the trend component, while the SVR is used to predict the detail components. To investigate the prediction performance of the proposed model, several models are used as the benchmark models, including the ARIMA model, PM model, GRU model, LSTM model, CNNGRU model, hybrid SSA-SVR model and hybrid SSA-CNNGRU model. The experimental results show that: in the proposed model, the CNNGRU can have good prediction performance in the main trend component forecasting, the SVR can have good prediction performance in the detail components forecasting, and the proposed model can obtain good results in wind speed forecasting. (C) 2019 Elsevier Ltd. All rights reserved.
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