Short Term Wind Power Forecasting Using Hilbert Huang Transform and Artificial Neural Network
Capacity and output power forecasting have great significance in seamless integration of renewable energy to the grid.However,the uncertainty of wind power and intermittence of wind energy are the main factors which affect forecasting precision.The wind power output data can be treated as a signal stream which has characteristics for possible wind capacity forecasting.Hilbert-Huang Transforms (HHT) and Hilbert spectral analysis have been applied extensively to analysis nonlinear and non-stationary stochastic signal.The time series of wind power output has been transformed into certain signals with different frequencies.Each signal is taken as input data joining with wind speed data to establish Artificial Neural Network (ANN) forecasting model.The models are combined together to obtain the final results on potential wind power output.This paper proposes HHT-ANN model for wind power forecasting.A case study of a wind farm in Texas,U.S shows that the MRE of proposed method is lower than the traditional ANN approach.
wind power output forecasting:Hilbert spectral analysis:HHT:ANN
Jie Shi Wei-Jen Lee Yongqian Liu Yongping Yang Peng Wang
North China Electric Power University 2 Beinong Road,Changping District Beijing,China Fellow,IEEE University of Texas at Arlington 701 S.Nedderman Arlington,TX U.S.A. North China Electric Power University 2 Beinong Road,Changping Beijing,China
国际会议
威海
英文
162-167
2011-07-06（万方平台首次上网日期，不代表论文的发表时间）