CAJ | 학술논문

提出一种经验模态分解、样本熵和支持向量机相结合的短期风速组合预测方法。首先利用经验模态分解将原始风速序列逐级分解成若干个规律性更强的子序列，以减小不同特征尺度序列间的相互影响，提高预测精度。接着计算各风速子序列的样本熵，将复杂度相近的序列归类形成一个新序列，以减少所需建立的预测模型的数量。然后对经 EMD-SE 处理后得到的新的风速子序列分别建立支持向量机预测模型，并采用遗传算法实现各模型参数的自动选取和寻优，最后将各序列的预测结果叠加得到风速预测结果。算例研究表明，该方法充分挖掘了风速序列的特性，能快速地对风速变化作出响应，预测的均方根误差和百分比误差分别比单纯采用支持向量机法降低了5.1%和5.4%，有效地提高了短期风速预测的准确度。
제출일충경험모태분해、양본적화지지향량궤상결합적단기풍속조합예측방법。수선이용경험모태분해장원시풍속서렬축급분해성약간개규률성경강적자서렬，이감소불동특정척도서렬간적상호영향，제고예측정도。접착계산각풍속자서렬적양본적，장복잡도상근적서렬귀류형성일개신서렬，이감소소수건립적예측모형적수량。연후대경 EMD-SE 처리후득도적신적풍속자서렬분별건립지지향량궤예측모형，병채용유전산법실현각모형삼수적자동선취화심우，최후장각서렬적예측결과첩가득도풍속예측결과。산례연구표명，해방법충분알굴료풍속서렬적특성，능쾌속지대풍속변화작출향응，예측적균방근오차화백분비오차분별비단순채용지지향량궤법강저료5.1%화5.4%，유효지제고료단기풍속예측적준학도。
This paper put forward a short-term wind speed forecasting method combined with em-pirical mode decomposition (EMD),sample entropy (SE)and support vector machine (SVM). Firstly,EMD was used to change the original wind speed sequence into several more regular sub-sequences step by step to minimize the mutual influence between different sequences and improve the prediction precision.Then calculating the SE of each wind speed sequence,cluster sequences of similar complexity was formed a new sequence,which can reduce the number of forecast model required.SVM prediction models was set up respectively for the new wind speed sequences opera-ted by EMD-SE,and then automatic selection and optimization of model parameters can be real-ized by using genetic algorithm(GA).Case study results showed that the method fully exploited the characteristics of the wind speed sequence,and can respond to the change of wind speed quickly.The RMSE and MAPE of prediction were reduced by 5.1 percent and 5.4 percent com-pared with using SVM separately,and the precision of short-term wind speed forecasting was im-proved.