An improved wavelet transform and multi-block forecast engine based on a novel training mechanism.

In this paper, a novel prediction technique is proposed to predict wind-power generation. Because of the growth of wind-generated electricity as a component of power grids, various wind-power prediction methods have been proposed recently by researchers. To achieve accurate prediction, a novel approach using a dual-tree complex wavelet transform, a new feature selection procedure, and a combinatorial prediction engine has been implemented to forecast wind-power generation. To improve feature selection to reduce diagnostic efficiency degradation caused by outliers in data-driven diagnostics, an outlier-insensitive combinatorial feature selection procedure has been used to determine candidate subgroup characteristics. Furthermore, a multi-stage forecast engine equipped with a new training mechanism for optimizing free parameters and based on the Elman neural network (ENN) is presented in this work. This training mechanism was developed using an efficient stochastic search method to attain the high learning capabilities of the proposed ENN-based forecast engine. The proposed model has been applied to real-world engineering data from Alberta, Canada, and Oklahoma, United States. The outcomes achieved by the different forecasting methods are compared, proving the effectiveness of the proposed procedure.