A Statistical Comparison of Feature Selection Techniques for Solar Energy Forecasting Based on Geographical Data

Abstract

In recent years, solar energy forecasting has been increasingly embraced as a sustainable low-energy solution to environmental awareness. It is a subject of interest to the scientific community, and machine learning techniques have proven to be a powerful means to construct an automatic learning model for an accurate prediction. Along with the various machine learning and data mining utilities applied to solar energy prediction, the process of feature selection is becoming an ultimate requirement for improving model building efficiency. In this paper, we consider the feature selection (FS) approach potential. We provide a detailed taxonomy of various feature selection techniques and examine their usability and ability to deal with a solar energy forecasting problem, given meteorological and geographical data. We focus on filter-based, wrapper-based, and embedded-based feature selection methods. We use the reduced number of selected features, stability, and regression accuracy and compare feature selection techniques. Moreover, the experimental results demonstrate how the feature selection methods studied can considerably improve the prediction process and how the selected features vary by method, depending on the given data constraints.