基于 VMD-ORELM-EC 的超短期风速组合预测模型

为提高超短期风速预测的精度,文章提出一种基于变分模态分解(variational mode decomposition, VMD)、离群鲁棒极限学习机(outlier-robust extreme learning machine, ORELM)和误差修正(error correction, EC)的超短期风速组合预测模型VMD-ORELM-EC。首先利用VMD将原始风速序列分解,并对每个分解子序列分别建立ORELM模型,将各子模型预测结果相加得到模型初步预测序列;然后将原始风速序列与初步预测序列相减得到模型的误差序列,并对误差序列进行VMD分解,对分解得到的误差子序列建立ORELM模型,从而得到误差预测序列;最后将模型的初步预测序列与误差预测序列组合得到最终的风速预测序列。利用该文提出的预测模型对北京测风塔实测的风速数据进行分析,结果表明模型可以有效挖掘风速序列特性,在超短期风速预测上具有较高的预测性能。

In this paper, based on the variational mode decomposition (VMD), outlier-robust extreme learning machine (ORELM) and error correction (EC), a combined wind speed prediction model (VMD-ORELM-EC) is proposed to improve the accuracy of ultra-short-term wind speed prediction. Firstly, the original wind speed series are decomposed by the VMD, and the obtained decomposition sub-series are used to build the ORELM sub-models. The prediction results of each sub-model are calculated to obtain the preliminary prediction series. Then, by subtracting the preliminary prediction series from the original wind speed series, the error series of the model can be determined. Accordingly, by employing the VMD and the ORELM, the error prediction series can be obtained. Finally, the preliminary prediction series are combined with the error prediction series to determine the final wind speed prediction series. The proposed VMD-ORELM-EC model is further employed to analyze the field-measured wind speed data obtained from the Beijing anemometer tower. The results show that the model can effectively exploit the characteristics of wind speed series and has high prediction performance in ultra-short-term wind speed prediction.