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

  • Saloua El Motaki University Sidi Mohamed Ben Abdellah
  • Abdelhak El Fengour University Ibn Tofail/University of Castilla-La Mancha

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.

Keywords

feature selection, filter method, wrapped method, embedded method, solar energy forecasting, regression performance, smart environment,

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Published
Jun 21, 2021
How to Cite
EL MOTAKI, Saloua; EL FENGOUR, Abdelhak. A Statistical Comparison of Feature Selection Techniques for Solar Energy Forecasting Based on Geographical Data. Computer Assisted Methods in Engineering and Science, [S.l.], v. 28, n. 2, p. 105–118, june 2021. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/324>. Date accessed: 16 sep. 2021. doi: http://dx.doi.org/10.24423/cames.324.
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