Radial Basis Function Neural Network Based Short-term Wind Power Forecasting with Grubbs Test
Accurate prediction on wind power generation plays an important role in power system dispatching and wind farm operation.The Radial Basis Function (RBF) neural network,owing to its superior performance of linear/nonlinear algorithm with respect to fast convergence and accurate prediction,is very suitable for wind power forecasting.Based on the historical data from a wind farm composed of wind speed,environmental temperature,and power generation,the authors develop a short-term wind power prediction model for one-hour-ahead forecasting using a RBF neural network.Due to the existence of incorrect values in the original data,the Grubbs test is conducted to preprocess the samples.In the case study,the forecasting results are compared with the actual wind power outputs.The simulation shows that the presented method could provide accurate and stable forecasting.
Wind Power:Short-term Forecast:Grubbs Test Radial Basis Function (RBF):Artificial Neural Network (ANN)
Xiaomei WU Fushuan WEN Binzhuo HONG Xiangang PENG Jiansheng HUANG
School of Electrical Engineering South China University of Technology Guangzhou 510640,China; Facult School of Electrical Engineering Zhejiang University Hangzhou 310027,China Faculty of Automation Guangdong University of Technology Guangzhou 510006,China School of Electrical Engineering University of Western Sydney Sydney,Australia
国际会议
威海
英文
1879-1882
2011-07-06（万方平台首次上网日期，不代表论文的发表时间）