(1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)
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.
High-Tech Research and Development Program of China;National Natural Science Foundation of China;Natural Science Foundation of Guangdong Province
|||WANG Yan, WANG Zhen, HUANG Minxiang, et al. Ultra-short-term Wind Power Prediction Based on OS-ELM and Bootstrap Method[J]. Automation of Electric Power Systems,2014,38(6):14-19. DOI:10.7500/AEPS20130830010|