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A statistical comparison of feature selectiontechniques for solar energy forecastingbased on geographical data

EL Motaki Salou, El Fengour Abdelhak

Computer Assisted Methods in Engineering and Science

2021

Vol. 28, no. 2

105--118

In recent years, solar energy forecasting has been increasingly embraced as a sustainablelow-energy solution to environmental awareness. It is a subject of interest to the scientificcommunity, and machine learning techniques have proven to be a powerful means toconstruct an automatic learning model for an accurate prediction. Along with the variousmachine learning and data mining utilities applied to solar energy prediction, the processof feature selection is becoming an ultimate requirement for improving model buildingefficiency. In this paper, we consider the feature selection (FS) approach potential. Weprovide a detailed taxonomy of various feature selection techniques and examine theirusability and ability to deal with a solar energy forecasting problem, given meteorologicaland geographical data. We focus on filter-based, wrapper-based, and embedded-basedfeature selection methods. We use the reduced number of selected features, stability, andregression accuracy and compare feature selection techniques. Moreover, the experimentalresults demonstrate how the feature selection methods studied can considerably improvethe prediction process and how the selected features vary by method, depending on thegiven data constraints.