Abstract
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.
Highlights
Feature selection can be considered one of the main preprocessing steps of machine learning [1]
Prediction Results: Before versus after Applying the Feature Selection Techniques In Table 6, the prediction errors (MAE and MASE) of the individual machine learning techniques are compared on the basis of before supplying selected feature subsets and after supplying selected feature subsets on them
The graphical representation clearly shows that the result is improved in terms of mean absolute percent error (MAPE) after applying selected feature subsets
Summary
Feature selection can be considered one of the main preprocessing steps of machine learning [1]. Without the intervention of irrelevant, redundant, and noisy features, learning algorithms can centrally point on most essential features of data and build simpler but more precise data models. Feature selection can help build a simpler and more common model and get a better insight into the fundamental perception of the task [3,4,5]. The feature selection aspect is fairly significant for the reason that with the same training data, it may happen that an individual regression algorithm can perform better with different feature subsets. The success of machine learning on a particular task is affected by many factors. Among those factors first and foremost is the representation and quality of the instance data [6]. It is not important to include every piece of information from the raw data source for modelling
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