摘要
A novel prediction network composed of some chaotic operators is proposed to predict the wind speed series.Training samples are constructed by the theory of phase space reconstruction.Genetic algorithm is adopted to optimize the control parameters of chaotic operators to change the dynamic characteristic of the network to approach to the predicted system.In this way,the dynamic prediction of wind speed series can be completed.The wind acceleration series can also be predicted by the same network.And the prediction results of both series can be fused by Kalman Filter to get the optimal estimation prediction result of the wind speed series,which is superior to the result obtained by each single method.Simulation results show that the prediction network has less computation cost than BP neural network,and it has better prediction performance than BP neural network and autoregressive integrated moving average model.Kalman Filter can improve the prediction performance further.
A novel prediction network composed of some chaotic operators is proposed to predict the wind speed series. Training samples are constructed by the theory of phase space reconstruction. Genetic algorithm is adopted to optimize the control parameters of chaotic operators to change the dynamic characteristic of the network to approach to the predicted system. In this way, the dy- namic prediction of wind speed series can be completed. The wind acceleration series can also be predicted by the same net- work. And the prediction results of both series can be fused by Kalman Filter to get the optimal estimation prediction result of the wind speed series, which is superior to the result obtained by each single method. Simulation results show that the predic- tion network has less computation cost than BP neural network, and it has better prediction performance than BP neural net- work and autoregressive integrated moving average model. Kalman Filter can improve the prediction performance further.
基金
supported by the National Natural Science Foundation of China(Grant No.61203302)
the Natural Science Foundation of Tianjin(Grant No.11JCYBJC07000)
