Short-term Wind Power Output Prediction Based on FeatureSimilarity Day and CNN-BiLSTM-Attention Model

LUAN Fuming; ZHANG Heng; CHEN Haiping

doi:10.19805/j.cnki.jcspe.2026.240699

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Short-term Wind Power Output Prediction Based on FeatureSimilarity Day and CNN-BiLSTM-Attention Model

更新时间：2026-10-10

- Short-term Wind Power Output Prediction Based on FeatureSimilarity Day and CNN-BiLSTM-Attention Model
- Chinese Journal of Power Engineering Vol. 46, Issue 2, Pages: 80-88(2026)
- 作者机构：
  
  1. 华北电力大学能源动力与机械工程学院,北京,102206
  2. 中国大唐集团科学技术研究总院有限公司华北电力试验研究院,北京,100040
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.240699
  CLC：
- Online First：10 February 2026，
  
  Published：10 February 2026
- 稿件说明：
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栾福明,张衡,陈海平. 基于特征相似日和CNN-BiLSTM-Attention模型的风电短期出力预测动力工程学报, 2026, 46(2): 80-88 https://doi.

org/10.19805/j.cnki.jcspe.2026.240699
栾福明,张衡,陈海平. 基于特征相似日和CNN-BiLSTM-Attention模型的风电短期出力预测动力工程学报, 2026, 46(2): 80-88 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240699.

org/10.19805/j.cnki.jcspe.2026.240699 DOI：

摘要

短期风电功率预测对电力系统的实时调度至关重要

可靠的风电预测不仅能够保障电力系统的安全运行

还能提升电网的运行效率。为了获得更加准确、可靠的风电功率预测结果

针对风电功率数据的非线性和时序性特征

提出了一种将特征相似日和CNN-BiLSTM-Attention相结合的短期风电功率预测方法。首先

充分考虑气象因素对风电输出功率数据的影响

利用 Spearman相关系数筛选出与风电输出功率最相关的气象因子作为模型的输入参数。其次

采用高斯混合模型(GMM)对风电数据进行聚类分析

通过手肘法确定最佳的聚类簇数

并结合特征相似度和余弦相似熵方法

确定待测日与历史数据中最相关的聚类类型。最后

使用 CNN-BiLSTM-Attention模型进行训练

深度挖掘风电功率的时序特征

获得更加精准的风电功率预测结果。以新疆地区的实际风电功率数据为例进行了仿真分析

验证结果表明该方法的预测精度较高

能够为电力系统的规划与稳定运行提供有力支持。

Abstract

Short-term wind power forecasting is crucial for real-time dispatch of power systems. Reliable wind power forecasts not only ensure the safe operation of power system but also enhance the operational efficiency of power grid.To obtain more accurate and reliable wind power forecasting results

considering the nonlinear and time-series characteristics of wind power data

a short-term wind power forecasting method based on feature similarity day combined with CNN-BiLSTM-Attention was proposed. Firstly

the impact of meteorological factors on wind power output was fully considered

and the Spearman correlation coefficient was used to select the meteorological factors most correlated with wind power output as model input parameters. Next

the Gaussian mixture model (GMM) was used to conduct cluster analysis on the wind power data. The elbow method was used to determine the optimal number of clusters

and in combination with the feature similarity and cosine similarity entropy methods

the most relevant cluster type for the day under test was determined among the historical data. Finally

the CNN-BiLSTM-Attention model was used for training to deeply extract the temporal features of wind power and obtain more accurate wind power prediction results. Taking the actual wind power data of Xinjiang region as an example

a simulation analysis was conducted. The verification results show that the prediction accuracy of the method is high

and it can provide strong support for the planning and stable operation of the power system.

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references

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