利用随机方法、小波和梯度提升决策树进行短期风速预测的集合方法

Wind Pub Date : 2024-02-04 DOI:10.3390/wind4010003
K. S. Sivhugwana, E. Ranganai
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引用次数: 0

摘要

考虑到风力发电量与风速变量的立方成正比,而风速变量具有高度随机性,因此产生了复杂的电网管理任务。短期风速预测对于负荷调度计划和负荷增减决策至关重要。风速的混沌间歇性通常表现为固有的线性和非线性模式,以及非稳态行为;因此,通常很难使用单一的线性或非线性模型来准确有效地预测风速。在本研究中,小波变换 (WT)、自回归综合移动平均 (ARIMA)、极梯度提升树 (XGBoost) 和支持向量回归 (SVR) 被结合起来,用于预测从三个南部非洲大学辐射测量网络 (SAURAN) 站获得的高分辨率短期风速:里奇特斯维尔德(RVD)、中央技术大学(CUT)和比勒陀利亚大学(UPR)。这种混合模型被称为 WT-ARIMA-XGBoost-SVR。在拟议的混合模型中,ARIMA 部分用于捕捉线性,而 XGBoost 则使用残差的小波分解子序列作为输入特征来捕捉非线性。最后,SVR 模型调和了线性和非线性预测。我们评估了 WT-ARIMA-XGBoost-SVR 与 ARIMA 和其他两种混合模型的功效,前者用轻梯度提升机(LGB)组件替代 XGBoost,形成 WT-ARIMA-LGB-SVR 混合模型,后者用随机梯度提升机(SGB)替代 XGBoost,形成 WT-ARIMA-SGB-SVR 混合模型。根据平均绝对误差 (MAE)、平均绝对百分比误差 (MAPE)、均方根误差 (RMSE)、判定系数 (R2) 和预测区间归一化平均宽度 (PINAW),所提出的混合模型为所有三个数据集提供了更准确、更可靠、不确定性更小的预测。这项研究对于提高风速预测的可靠性以确保制定有效的风能管理策略至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Ensemble Approach to Short-Term Wind Speed Predictions Using Stochastic Methods, Wavelets and Gradient Boosting Decision Trees
Considering that wind power is proportional to the cube of the wind speed variable, which is highly random, complex power grid management tasks have arisen as a result. Wind speed prediction in the short term is crucial for load dispatch planning and load increment/decrement decisions. The chaotic intermittency of speed is often characterised by inherent linear and nonlinear patterns, as well as nonstationary behaviour; thus, it is generally difficult to predict it accurately and efficiently using a single linear or nonlinear model. In this study, wavelet transform (WT), autoregressive integrated moving average (ARIMA), extreme gradient boosting trees (XGBoost), and support vector regression (SVR) are combined to predict high-resolution short-term wind speeds obtained from three Southern African Universities Radiometric Network (SAURAN) stations: Richtersveld (RVD); Central University of Technology (CUT); and University of Pretoria (UPR). This hybrid model is termed WT-ARIMA-XGBoost-SVR. In the proposed hybrid, the ARIMA component is employed to capture linearity, while XGBoost captures nonlinearity using the wavelet decomposed subseries from the residuals as input features. Finally, the SVR model reconciles linear and nonlinear predictions. We evaluated the WT-ARIMA-XGBoost-SVR’s efficacy against ARIMA and two other hybrid models that substitute XGBoost with a light gradient boosting machine (LGB) component to form a WT-ARIMA-LGB-SVR hybrid model and a stochastic gradient boosting machine (SGB) to form a WT-ARIMA-SGB-SVR hybrid model. Based on mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and prediction interval normalised average width (PINAW), the proposed hybrid model provided more accurate and reliable predictions with less uncertainty for all three datasets. This study is critical for improving wind speed prediction reliability to ensure the development of effective wind power management strategies.
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