A Short-Term Wind Power Prediction Method Based on Two-Stage Feature Selection and Improved NRBO-BiLSTM

YUAN Bo; ZHOU Xia; DAI Jianfeng; ZHANG Tengfei

doi:10.16516/j.ceec.2025-224

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A Short-Term Wind Power Prediction Method Based on Two-Stage Feature Selection and Improved NRBO-BiLSTM

更新时间：2026-10-14

- A Short-Term Wind Power Prediction Method Based on Two-Stage Feature Selection and Improved NRBO-BiLSTM
- Southern Energy Construction Vol. 13, Issue 3, Pages: 138-151(2026)
- 作者机构：
  
  南京邮电大学自动化学院/人工智能学院,江苏,南京,210023
- 作者简介：
- 基金信息：
- DOI：10.16516/j.ceec.2025-224
  CLC： TK89;TM614;TP18
- Published：2026
- 稿件说明：
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袁博, 周霞, 戴剑丰, 等. 基于两阶段特征选择和改进NRBO-BiLSTM的短期风电功率预测方法[J]. 南方能源建设, 2026, 13(3): 138-151.

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袁博, 周霞, 戴剑丰, 等. 基于两阶段特征选择和改进NRBO-BiLSTM的短期风电功率预测方法[J]. 南方能源建设, 2026, 13(3): 138-151. DOI： 10.16516/j.ceec.2025-224.

. DOI： 10.16516/j.ceec.2025-224.

摘要

目的

针对风电功率预测中特征因素太多、关键特征与功率间映射关系难以有效挖掘和预测精度不高的问题，提出一种考虑两阶段特征选择的短期风电功率预测方法。

方法

首先，采用基于互信息（Mutual Information，MI）的最大相关-最小冗余判据（minimal Redundancy Maximal Relevance，mRMR）对风电功率预测数据中的风速、风向等特征进行相关性分析，通过最大化特征与风电功率之间的相关性并最小化特征间的冗余度，实现特征的初步筛选；接着，利用随机森林（Random Forest，RF）算法中的袋外（Out-of-Bag，OOB）重要性评价方法对筛选后的特征进行重要性评价，进行特征的二次筛选以获取最优特征子集；然后，对牛顿-拉夫逊优化（Newton-Raphson Based Optimizer，NRBO）算法的自适应参数进行改进，并利用改进后的NRBO优化算法对双向长短期记忆（Bidirectional Long Short-Term Memory，BiLSTM）网络的相关参数进行优化；最后将最优特征子集输入到NRBO-BiLSTM风电功率预测模型中进行短期风电功率预测。

结果

算例分析表明，所提两阶段特征选择方法能有效剔除特征间冗余及低重要性特征，最优特征子集显著提升了模型性能；改进NRBO算法优化效果显著，预测精度显著优于对比模型。

结论

文章成功地将两阶段特征选择与改进的NRBO算法相结合，所提模型能够精准捕捉风电功率的波动规律，有效提升了短期风电功率的预测精度。

Abstract

Objective

To address the challenges of excessive feature factors

difficulties in effectively mining the mapping relationship between key features and power

and low prediction accuracy in wind power forecasting

this paper proposes a short-term

wind power prediction method that incorporates two-stage feature selection.

Method

First

the maximum relevance minimum redundancy (mRMR) criterion based on mutual information (MI) was used to analyze the correlation among features such as wind speed and direction in wind power prediction data. Initial feature screening was achieved by maximizing the correlation between features and wind power while minimizing inter-feature redundancy. Next

the out-of-bag (OOB) feature importance evaluation method within the random forest (RF) algorithm was employed to assess the significance of the pre-screened features

and a secondary screening was performed to obtain the optimal feature subset. Then

the adaptive parameters of the Newton-Raphson based optimizer (NRBO) were improved

and the enhanced NRBO algorithm was used to optimize the parameters of a bidirectional long short-term memory network (BiLSTM). Finally

the optimal feature subset was input into the NRBO-BiLSTM model to predict short-term wind power.

Result

The case study analysis shows that the proposed two-stage feature selection method can effectively eliminate the redundancy among features and low-importance features

and the optimal feature subset significantly improves the model performance; The improved NRBO algorithm has a remarkable optimization effect

and its prediction accuracy is significantly better than that of the comparison models.

Conclusion

This study successfully combines two-stage feature selection with an improved NRBO optimization algorithm. The proposed model can accurately capture the fluctuation patterns of wind power and effectively improve the prediction accuracy of short-term wind power.

关键词

Keywords

references

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