A Hybrid Model With Error Correction for Wind Speed Forecasting

In recent times wind energy generation has stood out due its integration with traditional electricity grids. Many investigations addressed wind speed forecasting since it presents high volatile and intermittent behavior. Due to this, such a source shows accuracy challenges in relation to its prediction. In this work, a hybrid model based on error correction is proposed, combining the linear Autoregressive and Moving average (ARMA) model and the Multilayer Perceptron (MLP). The approaches was applied in two databases referring to the Brazilian northeast a prominent region in wind energy. The results reveal that the proposed hybrid model showed good results in comparison to linear and neural-based methods.