TY - GEN
T1 - Prediction of wind power generation based on chaotic phase space reconstruction models
AU - Lei, Dong
AU - Lijie, Wang
AU - Shi, Hu
AU - Shuang, Gao
AU - Xiaozhong, Liao
PY - 2007
Y1 - 2007
N2 - The development of wind generation has rapidly progressed over the last decade, but it must be integrated into power grids and electric utility systems. However, it cannot be dispatched like conventional generators because the pover generated by the wind changes rapidly because of the continuous fluctuation of wind speed and direction. So it is very important to predict the wind power generation. This paper discusses why the wind power generation can be predicted in short-term, and how to setup the construction of an ANN (Artificial Neural Network) prediction model of wind power based on chaotic time series. The analysis of modeling with low dimensions nonlinear dynamics indicates that time series of wind power generation have chaotic characteristics, and wind power can be predicted in short-term. Phase space reconstruction method can be used for ANN model design. The data from the wind farm located in the Saihanba China are used for this study.
AB - The development of wind generation has rapidly progressed over the last decade, but it must be integrated into power grids and electric utility systems. However, it cannot be dispatched like conventional generators because the pover generated by the wind changes rapidly because of the continuous fluctuation of wind speed and direction. So it is very important to predict the wind power generation. This paper discusses why the wind power generation can be predicted in short-term, and how to setup the construction of an ANN (Artificial Neural Network) prediction model of wind power based on chaotic time series. The analysis of modeling with low dimensions nonlinear dynamics indicates that time series of wind power generation have chaotic characteristics, and wind power can be predicted in short-term. Phase space reconstruction method can be used for ANN model design. The data from the wind farm located in the Saihanba China are used for this study.
KW - Chaotic dynamic system
KW - Forecast
KW - Neural network and wind power prediction
UR - https://www.scopus.com/pages/publications/49949103713
U2 - 10.1109/PEDS.2007.4487786
DO - 10.1109/PEDS.2007.4487786
M3 - Conference contribution
AN - SCOPUS:49949103713
SN - 1424406455
SN - 9781424406456
T3 - Proceedings of the International Conference on Power Electronics and Drive Systems
SP - 744
EP - 748
BT - 7th International Conference on Power Electronics and Drive Systems, PEDS 2007
T2 - 7th International Conference on Power Electronics and Drive Systems, PEDS 2007
Y2 - 27 November 2007 through 30 November 2007
ER -