| Abstrakt: |
Accurate wind speed prediction has become more and more popular. Herein, an enhanced multi‐scale nonlinear ensemble model is proposed to predict short‐term wind speed. First, advanced data preprocessing technology is used to extract these feature sequences. Second, each feature sequence is predicted by bidirectional long short‐term memory. In addition, the gated recurrent unit (GRU) realizes nonlinear ensemble and integrates the prediction information. Finally, GRU with white noise test is utilized to correct the residual to retain the possible effective information in the residual. The performance of the model is evaluated using the daily and hourly wind speeds of two wind farms in the Hexi Corridor region. The experimental results show that the proposed model has the best prediction accuracy among all relevant models. Taking Zhangye wind farm as an example, in the daily wind speed dataset, mean absolute percentage error, square sum error, theil inequality coefficient and the index of agreement of the model are 0.6416, 5.4234, 0.0151, and 0.9979, respectively. In the hourly dataset, they are 0.1037, 0.0042, 0.0003, and 1.0000, respectively. In the wind speed data of the Guazhou wind farm, the hybrid model proposed in this research also performs best. [ABSTRACT FROM AUTHOR] |
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