基于VMD和改进BiLSTM的短期风电功率预测

朱菊萍; 魏霞; 谢丽蓉; 杨家梁

doi:10.19912/j.0254-0096.tynxb.2023-0032

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (6) : 422-428. DOI: 10.19912/j.0254-0096.tynxb.2023-0032

基于VMD和改进BiLSTM的短期风电功率预测

朱菊萍, 魏霞, 谢丽蓉, 杨家梁

作者信息 +

SHORT-TERM WIND POWER PEEDICTION BASED ON VMD AND IMPROVED BiLSTM

Zhu Juping, Wei Xia, Xie Lirong, Yang Jialiang

Author information +

文章历史 +

摘要

精准的短期风电功率预测对电力系统稳定运行至关重要。为提高短期预测精确度,提出一种基于变分模态分解（VMD）-样本熵（SE）和利用注意力（attention）机制改进双向长短期记忆网络（BiLSTM）以及误差修正的组合预测模型。首先,采用VMD将原始功率数据分解为若干个相对平稳的子序列,重构样本熵值相似分量以降低复杂性;然后,引入Attention对BiLSTM的隐含层状态输出分配相应的权重以突出重要影响的输入特征,同时采用极限梯度提升（XGBoost）对误差进行修正,从而进一步提高预测精确度;最后,将初步预测值和修正预测值相加得到最终结果。采用风电场实际数据进行验证,结果表明,所提组合模型的平均绝对误差（MAE）下降至1.6565,与其他模型相比精度提升25.8%~56.5%,具有较好的预测效果。

Abstract

Accurate short-term wind power forecasting is critical to stable power system operation. To improve the short-term prediction accuracy, a combined prediction model based on variational mode decomposition （VMD）, sample entropy （SE） and improved bidirectional long short-term memory （BiLSTM） with error correction using Attention mechanism is proposed. Firstly, VMD is used to decompose the original power data into several relatively smooth subsequences and reconstruct the sample entropy similar components to reduce the complexity. Then, attention is introduced to assign corresponding weights to the state outputs of the implicit layer of BiLSTM to highlight the important influential input features, and extreme gradient boosting （XGBoost） is used to correct the error so as to further improve the prediction accuracy. Finally, the final result is obtained by adding the preliminary prediction and the revised prediction. The actual data of wind farms are used for verification, and the results show that the mean absolute error （MAE） of the proposed combined model decreases to 1.6565, and the accuracy is improved by 25.8%-56.5% compared with other models, which has a better prediction effect.

导出引用

朱菊萍, 魏霞, 谢丽蓉, 杨家梁. 基于VMD和改进BiLSTM的短期风电功率预测[J]. 太阳能学报. 2024, 45(6): 422-428 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0032

Zhu Juping, Wei Xia, Xie Lirong, Yang Jialiang. SHORT-TERM WIND POWER PEEDICTION BASED ON VMD AND IMPROVED BiLSTM[J]. Acta Energiae Solaris Sinica. 2024, 45(6): 422-428 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0032

中图分类号： TM614

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