基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测

ULTRA-SHORT-TERM WIND SPEED FORECASTING BASED ON OPTIMALCOMBINATIONMODELOF SECONDARYDECOMPOSITIONAND CROW SEARCH ALGORITHM

针对风速的波动性和随机性等特点,提出一种基于二次分解和乌鸦搜索算法优化组合模型的超短期风速预测方法。该方法的基本思路是构造基于变分模态分解、样本熵和奇异谱分析的二次分解的方法,将原始风速序列分解为不同的子序列,并对这些子序列分别建立预测模型,最后重构。对变分模态分解的子序列建立基于长短时记忆网络的深度学习模型预测,而残差序列进行二次分解后的子序列建立乌鸦搜索算法优化的组合预测模型预测。最后,对子序列进行重构并得到最终的预测结果。使用实际的风速观测资料开展模拟实验,结果表明:在3个风电场中,所提模型与其他模型相比平均相对误差分别提升了30.07%、37.56%和37.40%,验证了混合模型在超短期风速预测中的有效性和稳定性,以及在不同数据集上的泛化性能。

Aiming at the characteristics of wind speed series such as volatility and randomness,this study develops an ultra-short-term wind speed forecasting method based on an optimal combination model of secondary decomposition and crow search algorithm.The basic idea is to construct a secondary decomposition process based on variational mode decomposition,sample entropy and singular spectrum analysis,which is applied to decompose the original wind speed series into a set of subcomponents.After this,establish a prediction model for each subcomponent.Specifically,for the subcomponents of variational mode decomposition,a deep learning model based on long short-term memory network is applied,and for the subcomponents of residual sequence after secondary decomposition,a combination forecasting model optimized by crow search algorithm is designed.The final prediction is obtained by reconstructing the predictions of all subcomponents.Using real observation datasets of wind speed for model simulation,the developed method decreases MAPE by 19.76%,24.91%,and 26.36%compared with several other models,signifying the effectiveness and stability of the developed method for ultra-short-term wind speed forecasting as well as a good generalization performance on different datasets.