Investigating the impact of feature selection on the prediction of solar radiation in different locations in Saudi Arabia

Predictions about the future amount of solar radiation is an important factor that affects the planning and operating of solar energy projects. However, it is a difficult task due to the existence of a high level of uncertainty that is associated with unknown future weather conditions. Accordingly, the prediction process, especially for the short term, requires more attention to unveil hidden relationships and interactions between related variables. Since a large number of parameters affect the estimation and prediction processes, there is a need to apply an effective and efficient feature selection of the input feature space. In this paper, an investigation of different feature selection methods is carried out in order to predict the daily amounts of solar radiation in different locations in Saudi Arabia using a neural networks (NN) predictor. First, the selection of the most important variables is carried out using four different algorithms: ReliefF algorithm, Monte Carlo uninformative variable elimination algorithm (MCUVE), random-frog algorithm, and Laplacian score algorithm (LS). Then, a computational intelligence model of a multi-layer neural network is used as the predictor. The predictor aims to predict the next-day global horizontal irradiance using selected meteorological and solar radiation observations. The experimentation and results of the feature selection methods and the prediction process are described. The results showed the importance of using feature selection methods in order to obtain a reliable prediction of the amount of solar radiation compared with using all the features available. (C) 2018 Elsevier B.V. All rights reserved.