The effectiveness of feature selection method in solar power prediction

This paper empirically shows that the effect of applying selected feature subsets on machine learning techniques significantly improves the accuracy for solar power prediction. Experiments are performed using five well-known wrapper feature selection methods to obtain the solar power prediction accuracy of machine learning techniques with selected feature subsets. For all the experiments, the machine learning techniques, namely, least median square (LMS), multilayer perceptron (MLP), and support vector machine (SVM), are used. Afterwards, these results are compared with the solar power prediction accuracy of those same machine leaning techniques (i.e., LMS, MLP, and SVM) but without applying feature selection methods (WAFS). Experiments are carried out using reliable and real life historical meteorological data. The comparison between the results clearly shows that LMS, MLP, and SVM provide better prediction accuracy (i.e., reduced MAE and MASE) with selected feature subsets than without selected feature subsets. Experimental results of this paper facilitate to make a concrete verdict that providing more attention and effort towards the feature subset selection aspect (e.g., selected feature subsets on prediction accuracy which is investigated in this paper) can significantly contribute to improve the accuracy of solar power prediction.