CAJ | 학술논문

提出了一种基于在线序贯极限学习机(OS-ELM)的超短期风电功率预测方法。利用 OS-ELM学习速度快、泛化能力强的优点，将批处理和逐次迭代相结合，不断更新训练数据和网络结构，实现了对数值天气预报风速的快速实时修正和风电机组输出功率的快速预测。随后，采用计算机自助(Bootstrap)法构造伪样本，给出了预测功率的置信区间评估。实例和研究结果表明，该预测方法与反向传播(BP)网络、支持向量机(SVM)方法相比，在计算时间上更能满足在线应用需求，而且预测精度相当，有较好的应用前景。
제출료일충기우재선서관겁한학습궤(OS-ELM)적초단기풍전공솔예측방법。이용 OS-ELM학습속도쾌、범화능력강적우점，장비처리화축차질대상결합，불단경신훈련수거화망락결구，실현료대수치천기예보풍속적쾌속실시수정화풍전궤조수출공솔적쾌속예측。수후，채용계산궤자조(Bootstrap)법구조위양본，급출료예측공솔적치신구간평고。실례화연구결과표명，해예측방법여반향전파(BP)망락、지지향량궤(SVM)방법상비，재계산시간상경능만족재선응용수구，이차예측정도상당，유교호적응용전경。
An ultra-short-term wind power prediction method based on an online sequential extreme learning machine (OS-ELM) is proposed.Firstly,the OS-ELM is utilized to correct the predicted wind speed sequence so as to amend and improve the accuracy of predicted wind speed.Then,by combining batch processing with successive iteration,real-time prediction of wind turbine power output is accomplished with the help of the advantages of OS-ELM”s fast learning speed and strong generalization ability.Finally,a Bootstrap method is adopted to estimate the predicted intervals by resampling data.Analysis results show that,compared with the back propagation (BP) network and support vector machine (SVM) method,this prediction method can better meet the demand of online application and has good application prospects,while its forecasting accuracy is comparable to BP network and SVM method. <br> This work is supported by National High Technology Research and Development Program of China (863 Program) (No.2011AA050204)and National Natural Science Foundation of China(No.51277160).