Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions

Due to the rapid development of solar energy and photovoltaic industries in China, it is crucial to provide the reliable and accurate solar radiation predictions. In this work, three commonly used machine learning models for predicting global and diffuse solar radiation were assessed in eight Chinese cities, representing different geoclimatic and pollutant conditions. According to the results; regarding the nRMSE, nMAE, nMBE and R values, coastal locations (such as Shanghai, Guangzhou, etc.) obtained higher values than inland locations (such as Lanzhou and Wuhan). Moreover, the SVM (support vector machine) highly outperformed the other models in all locations, regardless of whether the study area was arid, semiarid, semihumid or humid, followed by GLMNET (generalized linear modeling) and RF (random forest). In addition, when assessing the SVM in different locations under different climatic and pollution conditions, it was indicated that the accuracy of solar radiation prediction was closely related to the weather and pollution condition levels. In general, the global solar radiation prediction error was in line with the weather condition levels. The prediction error increased as the weather level increased. However, the relationship between the pollution condition levels and the global solar radiation prediction showed a non-linear relationship. Moreover, for the prediction results of diffuse solar radiation, its variation law with different weather and pollution condition levels was almost different from that of global solar radiation. The maximum high error occurrence probability of global solar radiation and diffuse solar radiation appeared at pollution levels 5 and 1, respectively. Overall, the SVM model demonstrated its reliability in radiation prediction under slight pollution and stable weather conditions. This is crucial in locations with scarce meteorological data and can be used to optimize the selection of geographical locations for photovoltaic power station construction. (C) 2022 The Authors. Published by Elsevier Ltd.