Short Term Wind Speed Prediction of Wind Turbine Hubs Based on Combined Neural Network

doi:10.3969/j.issn.1004-132X.2021.17.009

Abstract

Abstract: 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.

Key words: short term wind speed prediction, combination model, wind speed inflection point, error correction

摘要： 综合考虑风电机组轮毂处历史风速规律和近期数值气象预报风速变化，对时间尺度为6 h以内的风电机组轮毂处风速进行预测。为提高风速骤变情况下短期风速预测精度，提出一种新的组合神经网络。针对信号分解后的不同频率子序列特点，采用深度卷积神经网络和门控循环递归单元对趋势项子序列进行预测，用建立的Elman循环递归神经网络对细节项子序列进行预测。利用降尺度后的数值气象预报风速来判断风速骤变拐点，采用广义自回归条件异方差模型对非拐点风速预测值进行修正。最后，利用新疆某风电场实际风速数据进行实验，以平均绝对误差、均方误差及平均绝对百分比误差计算确定性预测精度，以覆盖率和区间宽度计算不确定性预测精度，结果验证了所提算法的有效性。

关键词: 短期风速预测, 组合模型, 风速拐点, 误差修正

CLC Number:

TM614

MA Junyan, YUAN Yiping, CHAI Tong, ZHAO Qin. Short Term Wind Speed Prediction of Wind Turbine Hubs Based on Combined Neural Network[J]. China Mechanical Engineering, 2021, 32(17): 2082-2089.

马军岩, 袁逸萍, 柴同, 赵琴. 基于组合神经网络的风机轮毂处短期风速预测[J]. 中国机械工程, 2021, 32(17): 2082-2089.

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