Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature

Increasing air pollutants attenuate surface solar radiation, and thus can be influential variables for solar radiation prediction. In this study, six air pollutants of PM2.5, PM10, SO2, NO2, CO and O-3 as well as air quality index (AQI) were chosen for analyzing their single and integrated effects on daily global and diffuse solar radiation (R-s and R-d) prediction. Seven single air pollution parameters, 15 combinations of two parameters and 20 combinations of three parameters were considered using Support Vector Machine (SVM) based on sunshine duration or air temperature. Daily meteorological and air pollution data between January 2014 and December 2015 from China's capital city of Beijing were used to train SVM models and data from January 2016 to December 2016 for testing. Results show that AQI was the most relevant air pollution parameter for both Rs and Rd prediction, followed by O-3 for Rs and by PM2.5 for Rd with slight difference as that of AQI. The combination of PM10 and O-3 and the combination of PM2.5 and O-3 were the most influential combination of two air pollution inputs for Rs and Rd prediction, respectively. The combination of PM2.5, PM10 and O3 was the most optimal combination of three air pollution inputs for both daily Rs and Rd prediction. Compared with SVM models without considering air pollution, the accuracy of SVM models with the most influential combinations of one, two and three air pollution inputs was improved by 13.9%, 19.8% and 22.2% in terms of RMSE for sunshinebased R-s, respectively. The corresponding values were 15.2%, 22.0% and 22.8% for temperature-based R-s, 16.1%, 21.5% and 24.5% for sunshine-based R-d, and 16.8%, 22.0% and 23.3% for temperature-based R-d. The results demonstrate the importance of appropriate selection of air pollution inputs to improve the accuracy of R-s and R-d prediction in air-polluted regions.