Wind speed forecasting based on wavelet packet decomposition and artificial neural networks trained by crisscross optimization algorithm

Wind speed forecasting is of great significance for wind farm management and safe integration into electric power grid. As wind speed is characterized by high autocorrelation and inherent volatility, it is difficult to predict with a single model. The aim of this study is to develop a new hybrid model for predicting the short wind speed at 1 h intervals up to 5 h based on wavelet packet decomposition, crisscross optimization algorithm and artificial neural networks. In the data pre-processing phase, the wavelet packet technique is used to decompose the original wind speed series into subseries. For each transformed components with different frequency sub-bands, the back-propagation neural network optimized by crisscross optimization algorithm is employed to predict the multi-step ahead wind speed. The eventual predicted results are obtained through aggregate calculation. To validate the effectiveness of the proposed approach, two wind speed series collected from a wind observation station located in the Netherlands are used to do the multi-step wind speed forecasting. To reduce the statistical errors, all forecasting methods are executed 50 times independently. The results of this study show that: (1) the proposed crisscross optimization algorithm has significant advantage over the back-propagation algorithm and particle swarm optimization in addressing the prematurity problems when applied to train the neural network. (2) Compared with the previous hybrid models used in this study, the proposed hybrid model consistently has the minimum mean absolute percentage error regardless of one-step, three step or five-step prediction. (C) 2016 Elsevier Ltd. All rights reserved.