融合残差与VMD-ELM-LSTM的短期风速预测

张琰妮; 史加荣; 李津; 云斯宁

doi:10.19912/j.0254-0096.tynxb.2022-0738

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (9) : 340-347. DOI: 10.19912/j.0254-0096.tynxb.2022-0738

融合残差与VMD-ELM-LSTM的短期风速预测

张琰妮¹, 史加荣¹, 李津¹, 云斯宁²

作者信息 +

SHORT-TERM WIND SPEED PREDICTION BASED ON RESIDUAL AND VMD-ELM-LSTM

Zhang Yanni¹, Shi Jiarong¹, Li Jin¹, Yun Sining²

Author information +

文章历史 +

摘要

准确可靠的风速预测有利于维护电力系统的安全运行。为提高预测精度,本文提出一种融合残差与变分模态分解（VMD）、极限学习机（ELM）、长短时记忆（LSTM）的短期风速预测模型。首先,VMD算法将风速序列分解为若干个子序列以降低原始数据复杂度。接着将ELM作为初始预测引擎,用来提取各风速子序列特征。然后,对所有预测子序列进行重构,得到初步预测结果。为进一步挖掘原始风速序列中的不平稳特征,采用LSTM对初步预测结果的残差进行建模。最后,集成预测的残差与初步结果,得到最终的预测值。在真实风电场数据上开展实验,并将预测结果与其他模型对比。实验结果表明,所提模型能显著提升风速序列的预测性能。

Abstract

Reliable and accurate wind speed prediction is beneficial to maintain the safe operation of power system. In order to improve the prediction accuracy, a short-term wind speed forecasting model is proposed based on residual, variational mode decomposition （VMD）, extreme learning machine （ELM） and long short-term memory（LSTM）. Firstly, the VMD algorithm is used to decompose the wind speed sequence into several sub-sequences to reduce the complexity of the original data. Secondly, the ELM network is employed as the initial prediction engine to extract the features of each wind speed sub-sequence. Then, all the sub-sequences are reconstructed to obtain the preliminary prediction results. To further mine the unstable characteristics of the raw wind speed time series, the LSTM is utilized for modelling the residuals of the preliminary prediction results. Finally, the resulting prediction wind speeds are obtained by integrating the predicted residuals and the preliminary results. Experiments are carried out on a real wind farm dataset and the predicted results are compared with other models. Experimental results show that the proposed model can significantly improve the prediction performance.

导出引用

张琰妮, 史加荣, 李津, 云斯宁. 融合残差与VMD-ELM-LSTM的短期风速预测[J]. 太阳能学报. 2023, 44(9): 340-347 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0738

Zhang Yanni, Shi Jiarong, Li Jin, Yun Sining. SHORT-TERM WIND SPEED PREDICTION BASED ON RESIDUAL AND VMD-ELM-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(9): 340-347 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0738

中图分类号： TM614

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