TY - GEN
T1 - Short-term wind power prediction with signal decomposition
AU - Wang, Lijie
AU - Dong, Lei
AU - Gao, Shuang
AU - Liao, Xiaozhong
PY - 2011
Y1 - 2011
N2 - Wind power is widely used to replace conventional power plant and reduce carbon emission. However, the variability and intermittency of wind makes the wind power output uncertain, which will bring great challenges to the electricity dispatch and the system reliability. So it is very important to predict the wind power generation. Two different signal decomposition methods are introduced into the prediction of wind power generation in this paper. One is wavelet transform (WT), and another is empirical mode decomposition (EMD). Both of them are good at decreasing the non-stationary behavior of the signal. ANN with the capacity of nonlinear mapping is used to model the decomposed time series. The prediction models WT-ANN and EMD-ANN are compared each other and a combined model based on them is tested. The wind power data from the Saihanba wind farm of China is used for this study.
AB - Wind power is widely used to replace conventional power plant and reduce carbon emission. However, the variability and intermittency of wind makes the wind power output uncertain, which will bring great challenges to the electricity dispatch and the system reliability. So it is very important to predict the wind power generation. Two different signal decomposition methods are introduced into the prediction of wind power generation in this paper. One is wavelet transform (WT), and another is empirical mode decomposition (EMD). Both of them are good at decreasing the non-stationary behavior of the signal. ANN with the capacity of nonlinear mapping is used to model the decomposed time series. The prediction models WT-ANN and EMD-ANN are compared each other and a combined model based on them is tested. The wind power data from the Saihanba wind farm of China is used for this study.
KW - combined model
KW - empirical mode decomposition
KW - wavelet transform
KW - wind power prediction
UR - http://www.scopus.com/inward/record.url?scp=79959888347&partnerID=8YFLogxK
U2 - 10.1109/ICEICE.2011.5776981
DO - 10.1109/ICEICE.2011.5776981
M3 - Conference contribution
AN - SCOPUS:79959888347
SN - 9781424480395
T3 - 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings
SP - 2569
EP - 2573
BT - 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011 - Proceedings
T2 - 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011
Y2 - 15 April 2011 through 17 April 2011
ER -