A novel framework for wind speed prediction based on recurrent neural networks and support vector machine

作者：Yu, Chuanjin; Li, Yongle^*; Bao, Yulong; Tang, Haojun; Zhai, Guanghao

来源：Energy Conversion and Management, 2018, 178: 137-145.

DOI：10.1016/j.enconman.2018.10.008

• Wind speed forecasting
• Standard recurrent neural network (RNN)
• Long short term memory neural networks (LSTM)
• Gated recurrent unit neural networks (GRU)
• Support vector machine (SVM)

In this paper, a novel framework for wind speed forecasting is proposed. In the new prediction framework, wavelet transform is firstly adopted to decompose original wind speed history into several sub-series. Then, for low-frequency sub-series, recurrent neural networks are used to extract deeper features, which are fed into suitable machine learning methods for predicting, while others are still predicted by normal methods. Meanwhile, three new hybrid models are established, where support vector machine is taken as the predictor, and the standard recurrent neural network and its variant version: long short term memory neural networks and gated recurrent unit neural networks are employed to extract the deeper features. Four experiments from the real world are conducted through the proposed methods as well as normal algorithms. The results demonstrate that the three new proposed hybrid models based on the novel framework yield more accurate predictions.

• 出版日期2018-12-15
• 单位西南交通大学