NDLI: Radial basis function neural network based short-term wind power forecasting with Grubbs test

Content Provider	IEEE Xplore Digital Library
Author	Xiaomei Wu Fushuan Wen Binzhuo Hong Xiangang Peng Jiansheng Huang
Copyright Year	2011
Description	Author affiliation: School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China (Xiangang Peng) \|\| School of Electrical Engineering, South China University of Technology, Guangzhou 510640, China (Xiaomei Wu) \|\| Faculty of Automation, Guangdong University of Technology Guangzhou 510006, China (Fushuan Wen; Binzhuo Hong) \|\| School of Electrical Engineering, University of Western Sydney, Sydney, Australia (Jiansheng Huang)
Abstract	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.
Starting Page	1879
Ending Page	1882
File Size	646950
Page Count	4
File Format	PDF
ISBN	9781457703645
e-ISBN	9781457703652
DOI	10.1109/DRPT.2011.5994206
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind power generation Wind speed Forecasting Predictive models Wind forecasting Wind farms Power systems Artificial Neural Network (ANN) Wind Power Short-term Forecast Grubbs Test Radial Basis Function (RBF)
Content Type	Text
Resource Type	Article

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