Wind farm power prediction based on wavelet decomposition and chaotic time series

In this paper, a prediction model is proposed for wind farm power forecasting by combining the wavelet transform, chaotic time series and GM(1, 1) method. The wavelet transform is used to decompose wind farm power into several detail parts associated with high frequencies and an approximate part associated with low frequencies. The characteristic of each high frequencies signal is identified, if it is chaotic time series then use weighted one-rank local-region method to predict it. If not, use GM(1, 1) model to predict it. And the GM(1, 1) model is also used to predict the approximate part of the low frequencies. In the end, the final forecasted result for wind farm power is obtained by summing the predicted results of all extracted high frequencies and the approximate part. According to the predicted results, the proposed method can improve the prediction accuracy of the wind farm power.

► A useful model based on wavelet transform, chaotic time series and GM (1, 1) method is presented for wind farm power forecasting.
► In the proposed method, the wind farm power is decomposed into several components from high to low frequencies by the wavelet transform, and the characteristic of each decomposed components is identified.
► Each component is respectively predicted by weighted one-rank-region method or GM (1, 1) according to their different characteristics.
► The final forecasted result is obtained by summing the predicted results of all the decomposed components.