%0 Journal Article
%A MA Junyan
%A YUAN Yiping
%A CHAI Tong
%A ZHAO Qin
%T Short Term Wind Speed Prediction of Wind Turbine Hubs Based on Combined Neural Network
%D 2021
%R 10.3969/j.issn.1004-132X.2021.17.009
%J China Mechanical Engineering
%P 2082-2089
%V 32
%N 17
%X The wind speed less than 6 hours at the wind turbine hubs was predicted by comprehensively considering the historical wind speed rules and wind speed changes in recent numerical weather forecasts. In order to improve the accuracy of short-term wind speed prediction under the condition of sudden changes of wind speeds， a new combined neural network was proposed. According to the characteristics of different frequency subsequences obtained by signal decomposition， the deep convolution neural network and the gated cyclic recursive unit were used to predict the low-frequency subsequence， and Elman recurrent neural network was established to predict the high-frequency sequence. The downscaling numerical weather prediction wind speed was used to identify inflection points， and the generalized autoregressive conditional heteroscedasticity model was used to modify the non inflection point of wind speed. Finally， the actual wind speed data of a wind farm in Xinjiang were used for the experiments. The mean absolute errors， mean square errors and mean absolute percentage errors were used to calculate the deterministic prediction accuracy， and the coverage rate and interval width were used to calculate the uncertainty prediction accuracy， which verified the effectiveness of the proposed algorithm.
%U http://www.cmemo.org.cn/EN/10.3969/j.issn.1004-132X.2021.17.009