A Novel Framework Based on the Stacking Ensemble Machine Learning (SEML) Method: Application in Wind Speed Modeling

Abstract

Wind speed (WS) is an important factor in wind power generation. Because of this, drastic changes in the WS make it challenging to analyze accurately. Therefore, this study proposed a novel framework based on the stacking ensemble machine learning (SEML) method. The application of a novel framework for WS modeling was developed at sixteen stations in Iran. The SEML method consists of two levels. In particular, eleven machine learning (ML) algorithms in six categories neuron based (artificial neural network (ANN), general regression neural network (GRNN), and radial basis function neural network (RBFNN)), kernel based (least squares support vector machine-grid search (LSSVM-GS)), tree based (M5 model tree (M5), gradient boosted regression (GBR), and least squares boost (LSBoost)), curve based (multivariate adaptive regression splines (MARS)), regression based (multiple linear regression (MLR) and multiple nonlinear regression (MNLR)), and hybrid algorithm based (LSSVM-Harris hawks optimization (LSSVM-HHO)) were selected as the base algorithms in level 1 of the SEML method. In addition, LSBoost was used as a meta-algorithm in level 2 of the SEML method. For this purpose, the output of the base algorithms was used as the input for the LSBoost. A comparison of the results showed that using the SEML method in WS modeling greatly affected the performance of the base algorithms. The highest correlation coefficient (R) in the WS modeling at the sixteen stations using the SEML method was 0.89. The SEML method increased the WS modeling accuracy by >43%.