Feature Selection with a Grouping Genetic Algorithm – Extreme Learning Machine Approach for Wind Power Prediction

Abstract

This paper proposes a hybrid algorithm for feature selection in a Wind Power prediction problem, based on a Grouping Genetic Algorithm-Extreme Learning Machine (GGA-ELM) approach. The proposed approach follows the classical wrapper method where a global search algorithm looks for the best set of features which minimize the output of a given predictors. In this case a GGA searches for several subsets of features and the ELM provides the fitness of the algorithm. Moreover, we propose to use variables from atmospheric reanalysis data as predictive inputs for the system, which opens the possibility of hybridizing numerical weather models with Machine Learning (ML) techniques for wind power prediction in real systems. The ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts has been the one used in this paper. Specifically, after the process of feature selection, we have tested the ELM and Gaussian Processes (GPR) to solve the problem. Experimental evaluation of the prediction system in real data from three wind farms in Spain has been carried out, obtaining excellent prediction results when the ELM is applied after the feature selection but not enough in the case of the GPR algorithm.