Investigating the impact of feature selection on the prediction of solar radiation in different locations in Saudi Arabia☆
Introduction
Solar energy is linked with the variability of different types of weather, topography, and geography. Such variability is considered to be one of the main obstacles that impacts energy availability and, thus, prevents the optimal exploitation of solar energy, especially in the short term. To best exploit solar energy, a reliable prediction model is crucial. For any location, a reliable source of solar radiation data is a requirement in many associated applications [1]. The prediction process is affected by the uncertainty about future weather conditions, which are linked to a large number of meteorological and solar radiation variables in different time horizons. Historical solar radiation and meteorological records can be used to build automated prediction models that do not require experts knowledge. Therefore, the prediction process, especially for short-term, is tough task for researchers and operators. Accordingly, many models of solar radiation have been presented in the literature. Two of the main tracks taken to develop prediction models are numerical weather prediction (NWP) and artificial intelligence (AI) models (see [[2], [3], [4]]). In order to obtain reliable prediction models, there is a clear need to reduce the large number of meteorological and solar radiation variables into a representative set that enhance the accuracy of the prediction model and reduce the data redundancy before applying the prediction model.
This paper focuses on the investigation of the impact of the feature selection process on the prediction of solar radiation. The proposed methodology uses four different feature selection algorithms to determine the most important variables. A neural network predictor is then used to predict the next-day solar radiation for eight locations in Saudi Arabia. The background for this study is discussed in Section 2. The methodology, the results, and the analysis are described in Sections 3 and 4. Finally, the conclusions and future work are highlighted in Section 5.
Section snippets
Background
The objective of this study is to investigate the impacts of using feature selection methods prior to predicting next-day solar radiation. In this section, we review the problem considerations and the methods applied in this study.
Methodology
The experiment is made up of four main stages: preparing the data sets, selecting the features, building the neural networks predictor, and testing the new data set using the predictor model. Testing process uses new data sets that are different spatially and temporally from the training data. The experiment stages are illustrated in Fig. 2 where neural network models are built with and without the four feature selection algorithms. The experiment is carried out in the MATLAB environment using
Results and discussion
The experiment was run 50 times using a Matlab environment. The average, maximum, minimum, and standard deviations of the training and testing errors have been calculated using different error metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean bias error (MBE). The errors metrics are defined as follows:
where f(x) is the predicted value, and is the actual value. Estimation error
Conclusion and future works
In this article, the use of different feature selection algorithms and neural networks to predict daily solar radiation in eight locations in Saudi Arabia has been presented. According to the experimental results for feature selection, the use of four feature selection algorithms produced the best prediction behavior for all locations compared with algorithms that used all the features. The improvements in RMSE were found to be between 19.9% and 52.1% compared with the prediction values when
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This study is part of a funded research project by King Abdulaziz City for Science and Technology (KACST) (Grant Number: 13-ENES2373-10). In addition, the author thanks King Abdullah City for Atomic and Renewable Energy (KACARE) for providing data.