基于改进SVR-SSA-BiLSTM误差修正的短期风电功率混合预测模型研究

王莘然; 胡皓; 吴子辰; 顾彬; 葛伟

doi:doi:10.19912/j.0254-0096.tynxb.2024-1039

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基于改进SVR-SSA-BiLSTM误差修正的短期风电功率混合预测模型研究

更新时间：2025-12-17

- 基于改进SVR-SSA-BiLSTM误差修正的短期风电功率混合预测模型研究
- Issue 10, Pages: 689-695(2025)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1039
  CLC： P311
- Published Online：31 October 2025，
  
  Published：31 October 2025
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王莘然, 胡皓, 吴子辰, et al. 基于改进SVR-SSA-BiLSTM误差修正的短期风电功率混合预测模型研究[J]. 2025, (10): 689-695.
DOI：

王莘然, 胡皓, 吴子辰, et al. 基于改进SVR-SSA-BiLSTM误差修正的短期风电功率混合预测模型研究[J]. 2025, (10): 689-695. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1039.

摘要

针对现有预测模型在应对风电功率波动剧烈情况下预测精度和误差处理方面存在的不足

提出一种基于误差修正的混合风电功率预测方法

结合基于时变滤波器的经验模态分解TVFEMD、支持向量回归SVR和双向长短时记忆神经网络BiLSTM。首先

通过应用TVFEMD将原始风电功率数据分解为本征模态函数IMFs

达到消除其复杂性和不确定性的目的;然后

采用改进的网格搜索算法和交叉验证算法（GridSearchCV）对支持向量回归模型进行优化

并使用该模型对分解后的IMFs进行预测;其次

利用一种改进的麻雀搜素算法（SSA）优化BiLSTM网络构建误差修正模型

对支持向量回归的预测误差进行预测

并将预测结果与支持向量回归的预测结果叠加

从而得到更准确的最终预测结果。这种方法不仅提高风电功率预测的准确性

也为风电功率发电等领域提供更可靠的依据。通过与其他优化方法下构建的预测模型结果进行对比

证明所构建模型的预测精度和稳定性得到明显提升。

Abstract

In response to the lack of predictive accuracy and error processing of the existing predictive model when dealing with the severe fluctuations of wind power

this work proposes a mixed wind power prediction method based on error -based modification

combining the experience modus of the time -based filter to decompose TVFEMD

TVFEMD

and Support vector return to SVR and two -way long -term memory neural network BiLSTM. First of all

the original wind power data is decomposed into this modulus function IMFS by applying TVFEMD to achieve the purpose of eliminating its complexity and uncertainty. Then

the improved grid search algorithm and cross -verification algorithm （GridsearchCV） optimize the support vector regression model

and use the model to predict the decomposed IMFS. Secondly

use a improved sparrow search algorithm （SSA） to optimize the BiLSTM network construction error correction model

predict the prediction error of support vector regression

and superimpose the prediction results with those of support vector regression to obtain a more accurate final prediction result. This method not only improves the accuracy of wind power prediction

but also provides a more reliable basis for the fields of wind power power generation. Compared with the results of the prediction model constructed under other optimization methods

this model has higher predition accuracy and stability.

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