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.