Assessing the potential of random forest method for estimating solar radiation using air pollution index

Simulations of solar radiation have become increasingly common in recent years because of the rapid global development and deployment of solar energy technologies. The effect of air pollution on solar radiation is well known. However, few studies have attempting to evaluate the potential of the air pollution index in estimating solar radiation. In this study, meteorological data, solar radiation, and air pollution index data from three sites having different air pollution index conditions are used to develop random forest models. We propose different random forest models with and without considering air pollution index data, and then compare their respective performance with that of empirical methodologies. In addition, a variable importance approach based on random forest is applied in order to assess input variables. The results show that the performance of random forest models with air pollution index data is better than that of the empirical methodologies, generating 9.1-17.0% lower values of root-mean square error in a fitted period and 2.0-17.4% lower values of root-mean-square error in a predicted period. Both the comparative results of different random forest models and variance importance indicate that applying air pollution index data is improves estimation of solar radiation. Also, although the air pollution index values varied largely from season to season, the random forest models appear more robust performances in different seasons than different models. The findings can act as a guide in selecting used variables to estimate daily solar radiation and improve the accuracy of solar radiation estimation. (C) 2016 Elsevier Ltd. All rights reserved.