Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks

[Display omitted] ► Three new methods are proposed to predict wind speed for the wind power system. ► The Wavelet Packet-BFGS is better than the Wavelet Packet-ARIMA-BFGS. ► The Wavelet Packet-BFGS is better than the Wavelet-BFGS. ► They are compared to the Neuro-Fuzzy, ANFIS, RBF neural network and PM. Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BFGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BFGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them.