Short‐term wind speed forecasting using the wavelet decomposition and AdaBoost technique in wind farm of East China. Issue 11 (1st August 2016)

• Record Type: Journal Article
• Authors: Shao, Haijian; Deng, Xing; Cui, Fang
• Is Part Of: IET generation, transmission & distribution. Volume 10:Issue 11(2016)
• Page Start: 2585
• Page End: 2592
• Publication Date: 2016-08-01
• Subjects: wind power plants -- learning (artificial intelligence) -- load forecasting -- neural nets -- power engineering computing; wind farm -- wavelet decomposition -- AdaBoost technique -- East China -- reliable short‐term wind speed forecasting -- wind speed distribution -- meteorological interactions -- AdaBoost neural network -- type‐FD77 wind turbine; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2015.0911
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The accurate and reliable short‐term wind speed forecasting can benefit the stability of the grid operation. However, it is a challenging issue to generate consistently accurate forecasts due to the complex and stochastic nature of wind speed distribution in meteorological interactions. In this study, a novel solution using AdaBoost neural network in combination with wavelet decomposition is proposed to solve the defects of the lower accuracy and enhance the model robustness. Based on the real data provided by sampling device weak wind turbine (type‐FD77) in a wind farm plant of East China, the experimental evaluation demonstrates that the proposed strategy can significantly enhance model robustness and effectively improve the prediction accuracy.