基于变换域分析和XGBoost算法的超短期风电功率预测模型

Ultra-short-term Wind Power Prediction Model Based on Transform Domain Analysis and XGBoost Algorithm

为应对传统超短期风电功率预测方法在数据潜在关系挖掘和模型收敛速度等方面存在的问题,提出了一种基于变换域分析和极端梯度提升回归树算法(extreme gradient boosting, XGBoost)的超短期风电功率预测方法。首先,通过时间滑动窗口和风电功率指标进行数据构建和低级特征提取。然后,结合快速傅里叶变换(fastFourier transform, FFT)和哈尔小波变换构成的多层次变换域分析方法对风电数据进行分解,充分考虑频域信息在特征学习中的重要性。最后,建立包含FFT、哈尔小波变换和XGBoost算法组合的超短期风电功率预测模型。实验结果表明,采用的多层次变换域分析方法能够充分挖掘原始特征之间的潜在关系,深入捕捉数据的时序关联性,而且XGBoost算法可以有效提升模型的预测性能,与其他预测模型相比,所提方法在不同数据集上均展现出较高的预测精度和较强的特征提取能力。

To cope with the problems of traditional ultra-short-term wind power prediction methods in terms of data po-tential relationship mining and model convergence speed,an ultra-short-term wind power prediction method based on transform domain analysis and extreme gradient boosting(XGBoost)is proposed.First,time-sliding windows and wind power indicators are utilized to perform data construction and low-level feature extraction.Then,the wind power data are decomposed by the multilevel transform domain analysis method which is composed of fast Fourier transform(FFT)and Haal wavelet transform,and the importance of frequency domain information in feature learning is fully considered.Fi-nally,an ultra-short-term wind power prediction model is established with FFT,Haal wavelet transform,and the XGBoost algorithm.The experimental results show that the multilevel transform domain analysis method can be adopted to fully explore the potential relationship between the original features and capture the temporal correlation of the data in depth.The XGBoost algorithm can be adopted to effectively improve the prediction performance of the model.The proposed method shows high prediction accuracy and excellent feature extraction ability on different data sets compared to other prediction models.